GRec_TF_Pretrain.py

import tensorflow as tf
import data_loader_recsys
import generator_peterrec_non as generator_recsys
import utils
import shutil
import time
import math
import eval
import numpy as np
import argparse
import collections
import random


def shuffleseq(train_set,padtoken):
    shuffle_seqtrain = []
    for i in range(len(train_set)):
        # print x_train[i]
        seq = train_set[i][1:]
        lenseq = len(seq)
        # split=np.split(padtoken)
        copyseq=list(seq)
        padcount = copyseq.count(padtoken)  #the number of padding elements
        copyseq = copyseq[padcount:] # the remaining elements
        # copyseq=seq
        shuffle_indices = np.random.permutation(np.arange(len(copyseq)))
        # list to array
        copyseq= np.array(copyseq)
        copyseq_shuffle=copyseq[shuffle_indices]

        padtoken_list=[padtoken]*padcount
        # array to list, + means concat in list and  real plus in array
        seq=list(train_set[i][0:1])+padtoken_list+list(copyseq_shuffle)
        shuffle_seqtrain.append(seq)


    x_train = np.array(shuffle_seqtrain)  # list to ndarray
    print "shuffling is done!"
    return x_train


def generatesubsequence(train_set):
    # create subsession only for training
    subseqtrain = []
    for i in range(len(train_set)):
        # print x_train[i]
        seq = train_set[i]
        lenseq = len(seq)
        # session lens=100 shortest subsession=5 realvalue+95 0
        for j in range(lenseq - 2):
            subseqend = seq[:len(seq) - j]
            subseqbeg = [0] * j
            subseq = np.append(subseqbeg, subseqend)
            subseqtrain.append(subseq)
    x_train = np.array(subseqtrain)  # list to ndarray
    del subseqtrain
    # Randomly shuffle data
    np.random.seed(10)
    shuffle_train = np.random.permutation(np.arange(len(x_train)))
    x_train = x_train[shuffle_train]
    print "generating subsessions is done!"
    return x_train

MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
                                          ["index", "label"])
def create_masked_lm_predictions_frombatch(item_batch, masked_lm_prob,
                                 max_predictions_per_seq, items, rng,item_size):
    rng = random.Random()
    output_tokens_batch=[]
    maskedpositions_batch=[]
    maskedlabels_batch=[]
    masked_lm_weights_batch=[]
    for line_list in range(item_batch.shape[0]):

        # output_tokens, masked_lm_positions, masked_lm_labels=create_masked_lm_predictions(item_batch[line_list],masked_lm_prob,max_predictions_per_seq,items,rng,item_size)
        output_tokens, masked_lm_positions, masked_lm_labels = create_masked_lm_predictions(item_batch[line_list],
                                                                                            masked_lm_prob,
                                                                                            max_predictions_per_seq,
                                                                                            items, rng, item_size)
        # print output_tokens
        output_tokens_batch.append(output_tokens)
        maskedpositions_batch.append(masked_lm_positions)
        maskedlabels_batch.append(masked_lm_labels)


        masked_lm_weights = [1.0] * len(masked_lm_labels)

        # note you can not change here since it should be consistent with 'num_to_predict' in create_masked_lm_predictions
        num_to_predict = min(max_predictions_per_seq,
                              max(1, int(round(len(item_batch[line_list]) * masked_lm_prob))))

        while len(masked_lm_weights) < num_to_predict:
            masked_lm_weights.append(0.0)
        masked_lm_weights_batch.append(masked_lm_weights)

    return output_tokens_batch,maskedpositions_batch,maskedlabels_batch,masked_lm_weights_batch

def create_masked_predictions_frombatch(item_batch):
    output_tokens_batch = []
    maskedpositions_batch = []
    maskedlabels_batch = []
    for line_list in range(item_batch.shape[0]):
        output_tokens,masked_lm_positions,masked_lm_labels=create_endmask(item_batch[line_list])
        output_tokens_batch.append(output_tokens)
        maskedpositions_batch.append(masked_lm_positions)
        maskedlabels_batch.append(masked_lm_labels)
    return output_tokens_batch,maskedpositions_batch,maskedlabels_batch




def create_endmask(tokens):
    masked_lm_positions = []
    masked_lm_labels = []
    lens=len(tokens)
    masked_token = 0
    dutokens=list(tokens)
    dutokens[-1]=masked_token

    masked_lm_positions.append(lens-1)
    masked_lm_labels.append(tokens[-1])
    return dutokens,masked_lm_positions,masked_lm_labels



def create_masked_lm_predictions(tokens, masked_lm_prob,
                                 max_predictions_per_seq, vocab_words, rng,item_size):
  cand_indexes = []
  for (i, token) in enumerate(tokens):
    if token == "[CLS]" or token == "[SEP]":
      continue
    cand_indexes.append(i)

  rng.shuffle(cand_indexes)

  output_tokens = list(tokens)

  num_to_predict = min(max_predictions_per_seq,
                       max(1, int(round(len(tokens) * masked_lm_prob))))

  masked_lms = []
  covered_indexes = set()
  for index in cand_indexes:
    if len(masked_lms) >= num_to_predict:
      break
    if index in covered_indexes:
      continue
    covered_indexes.add(index)

    masked_token = None
    if rng.random() < 0.8:
      masked_token=0  #item_size is "[MASK]"   0 represents '<unk>'
    else:
      # 10% of the time, keep original
      if rng.random() < 0.5:
        masked_token = tokens[index]
      # 10% of the time, replace with random word
      else:
        masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]

    output_tokens[index] = masked_token

    masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))

  masked_lms = sorted(masked_lms, key=lambda x: x.index)

  masked_lm_positions = []
  masked_lm_labels = []
  for p in masked_lms:
    masked_lm_positions.append(p.index)
    masked_lm_labels.append(p.label)

  return (output_tokens, masked_lm_positions, masked_lm_labels)


#Using GRec Encoder for pretraining
#Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation. WWW2020. F.Yuan. et al
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--top_k', type=int, default=5,
                        help='Sample from top k predictions')
    parser.add_argument('--beta1', type=float, default=0.9,
                        help='hyperpara-Adam')
    parser.add_argument('--datapath', type=str, default='Data/Session/history_sequences_20181014_fajie_transfer_pretrain_small.csv',
                        help='data path')
    parser.add_argument('--eval_iter', type=int, default=2,
                        help='Sample generator output evry x steps')
    parser.add_argument('--save_para_every', type=int, default=2,
                        help='save model parameters every')
    parser.add_argument('--tt_percentage', type=float, default=0.1,
                        help='0.2 means 80% training 20% testing')

    parser.add_argument('--masked_lm_prob', type=float, default=0.3,
                        help='0.2 means 20% items are masked')
    parser.add_argument('--max_predictions_per_seq', type=int, default=30,
                        help='maximum number of masked tokens')
    parser.add_argument('--max_position', type=int, default=100,
                         help='maximum number of for positional embedding, it has to be larger than the sequence lens')
    parser.add_argument('--is_generatesubsession', type=bool, default=False,
                        help='whether generating a subsessions, e.g., 12345-->01234,00123,00012  It may be useful for very some very long sequences')
    parser.add_argument('--has_positionalembedding', type=bool, default=False,
                        help='whether contains positional embedding before performing cnnn')
    parser.add_argument('--padtoken', type=str, default='-1',
                        help='is the padding token in the beggining of the sequence')
    parser.add_argument('--is_shuffle', type=bool, default=False,
                        help='whether shuffle the training and testing dataset, e.g., 012345-->051324')



    args = parser.parse_args()



    dl = data_loader_recsys.Data_Loader({'model_type': 'generator', 'dir_name': args.datapath})
    all_samples = dl.item
    items = dl.item_dict#key is the original token, value is the mapped value, i.e., 0, 1,2,3...
    itemlist=items.values()
    item_size=len(items) # the first token is 'unk'
    print "len(items)",item_size

    if items.has_key(args.padtoken):
        padtoken = items[args.padtoken]  # is the padding token in the beggining of the sentence
    else:
        padtoken = item_size+1

    max_predictions_per_seq=args.max_predictions_per_seq
    masked_lm_prob=args.masked_lm_prob

    np.random.seed(10)
    shuffle_indices = np.random.permutation(np.arange(len(all_samples)))
    all_samples = all_samples[shuffle_indices]

    dev_sample_index = -1 * int(args.tt_percentage * float(len(all_samples)))
    train_set, valid_set = all_samples[:dev_sample_index], all_samples[dev_sample_index:]

    if args.is_generatesubsession:
        train_set = generatesubsequence(train_set)

    if args.is_shuffle:
        train_set = shuffleseq(train_set, padtoken)

    model_para = {
        #if you changed the parameters here, also do not forget to change paramters in nextitrec_generate.py
        'item_size': item_size,
        'dilated_channels': 64,
        'dilations': [1,4,1,4,1,4,1,4,],
        'kernel_size': 3,
        'learning_rate':0.001,
        'batch_size':2,
        'iterations':2,
        'max_position':args.max_position,#maximum number of for positional embedding, it has to be larger than the sequence lens
        'has_positionalembedding':args.has_positionalembedding,
        'is_negsample':True #False denotes no negative sampling
    }

    itemrec = generator_recsys.NextItNet_Decoder(model_para)
    itemrec.train_graph()
    optimizer = tf.train.AdamOptimizer(model_para['learning_rate'], beta1=args.beta1).minimize(itemrec.loss)
    itemrec.predict_graph(reuse=True)



    tf.add_to_collection("dilate_input", itemrec.dilate_input)
    tf.add_to_collection("context_embedding", itemrec.context_embedding)
    sess= tf.Session()
    init=tf.global_variables_initializer()
    sess.run(init)
    saver = tf.train.Saver()




    numIters = 1
    for iter in range(model_para['iterations']):
        batch_no = 0
        batch_size = model_para['batch_size']
        while (batch_no + 1) * batch_size < train_set.shape[0]:

            start = time.time()
            item_batch = train_set[batch_no * batch_size: (batch_no + 1) * batch_size, :]
            output_tokens_batch, maskedpositions_batch, maskedlabels_batch,masked_lm_weights_batch= create_masked_lm_predictions_frombatch(
                item_batch,masked_lm_prob,max_predictions_per_seq,items=itemlist,rng=None,item_size=item_size
            )
            _, loss = sess.run(
                [optimizer, itemrec.loss],
                feed_dict={
                    itemrec.itemseq_input: output_tokens_batch,
                    itemrec.masked_position: maskedpositions_batch,
                    itemrec.masked_items: maskedlabels_batch,
                    itemrec.label_weights: masked_lm_weights_batch

                })
            end = time.time()
            if numIters % args.eval_iter == 0:
                print "-------------------------------------------------------train1"
                print "LOSS: {}\tITER: {}\tBATCH_NO: {}\t STEP:{}\t total_batches:{}".format(
                    loss, iter, batch_no, numIters, train_set.shape[0] / batch_size)
                print "TIME FOR BATCH", end - start

            if numIters % args.eval_iter == 0:
                print "-------------------------------------------------------test1"
                batch_no_valid=0
                batch_size_valid=batch_size
                if (batch_no_valid + 1) * batch_size_valid < valid_set.shape[0]:
                    start = time.time()
                    item_batch = valid_set[(batch_no_valid) * batch_size_valid: (batch_no_valid + 1) * batch_size_valid, :]
                    output_tokens_batch, maskedpositions_batch, maskedlabels_batch, masked_lm_weights_batch = create_masked_lm_predictions_frombatch(
                        item_batch, masked_lm_prob, max_predictions_per_seq, items=itemlist, rng=None,
                        item_size=item_size
                    )
                    loss = sess.run(
                        [itemrec.loss],
                        feed_dict={
                            itemrec.itemseq_input: output_tokens_batch,
                            itemrec.masked_position: maskedpositions_batch,
                            itemrec.masked_items: maskedlabels_batch,
                            itemrec.label_weights: masked_lm_weights_batch

                        })
                    end = time.time()
                    print "LOSS: {}\tITER: {}\tBATCH_NO: {}\t STEP:{}\t total_batches:{}".format(
                        loss, iter, batch_no_valid, numIters, valid_set.shape[0] / batch_size_valid)
                    print "TIME FOR BATCH", end - start
            batch_no += 1

            if numIters % args.eval_iter == 0:
                batch_no_test = 0
                batch_size_test = batch_size*1
                curr_preds_5=[]
                rec_preds_5=[] #1
                ndcg_preds_5=[] #1
                curr_preds_20 = []
                rec_preds_20 = []  # 1
                ndcg_preds_20  = []  # 1
                while (batch_no_test + 1) * batch_size_test < valid_set.shape[0]:
                    if (numIters / (args.eval_iter) < 10):
                        if (batch_no_test > 20):
                            break
                    else:
                        if (batch_no_test > 1000):
                            break
                    item_batch = valid_set[batch_no_test * batch_size_test: (batch_no_test + 1) * batch_size_test, :]

                    output_tokens_batch,maskedpositions_batch,maskedlabels_batch=create_masked_predictions_frombatch(item_batch)
                    [probs] = sess.run(
                        [itemrec.log_probs],
                        feed_dict={
                            itemrec.itemseq_input: output_tokens_batch,
                            itemrec.masked_position: maskedpositions_batch
                        })
                    for bi in range(probs.shape[0]):
                        pred_items_5 = utils.sample_top_k(probs[bi], top_k=args.top_k)#top_k=5
                        pred_items_20 = utils.sample_top_k(probs[bi], top_k=args.top_k+15)


                        true_item=item_batch[bi][-1]
                        predictmap_5={ch : i for i, ch in enumerate(pred_items_5)}
                        pred_items_20 = {ch: i for i, ch in enumerate(pred_items_20)}

                        rank_5=predictmap_5.get(true_item)
                        rank_20 = pred_items_20.get(true_item)
                        if rank_5 ==None:
                            curr_preds_5.append(0.0)
                            rec_preds_5.append(0.0)#2
                            ndcg_preds_5.append(0.0)#2
                        else:
                            MRR_5 = 1.0/(rank_5+1)
                            Rec_5=1.0#3
                            ndcg_5 = 1.0 / math.log(rank_5 + 2, 2)  # 3
                            curr_preds_5.append(MRR_5)
                            rec_preds_5.append(Rec_5)#4
                            ndcg_preds_5.append(ndcg_5)  # 4
                        if rank_20 ==None:
                            curr_preds_20.append(0.0)
                            rec_preds_20.append(0.0)#2
                            ndcg_preds_20.append(0.0)#2
                        else:
                            MRR_20 = 1.0/(rank_20+1)
                            Rec_20=1.0#3
                            ndcg_20 = 1.0 / math.log(rank_20 + 2, 2)  # 3
                            curr_preds_20.append(MRR_20)
                            rec_preds_20.append(Rec_20)#4
                            ndcg_preds_20.append(ndcg_20)  # 4


                    batch_no_test += 1
                    print "BATCH_NO: {}".format(batch_no_test)
                    print "Accuracy mrr_5:",sum(curr_preds_5) / float(len(curr_preds_5))#5
                    print "Accuracy mrr_20:", sum(curr_preds_20) / float(len(curr_preds_20))  # 5
                    print "Accuracy hit_5:", sum(rec_preds_5) / float(len(rec_preds_5))#5
                    print "Accuracy hit_20:", sum(rec_preds_20) / float(len(rec_preds_20))  # 5
                    print "Accuracy ndcg_5:", sum(ndcg_preds_5) / float(len(ndcg_preds_5))  # 5
                    print "Accuracy ndcg_20:", sum(ndcg_preds_20) / float(len(ndcg_preds_20))  #
            numIters += 1
            if numIters % args.save_para_every == 0:
                save_path = saver.save(sess,
                                       "Data/Models/generation_model/model_nextitnet_cloze".format(iter, numIters))

if __name__ == '__main__':
    main()