bpr_postclick.py

import numpy as np
import sys, os
import argparse
from openrec import ModelTrainer
from openrec.recommenders import BPR
from openrec.utils import Dataset
from openrec.utils.evaluators import AUC 
from openrec.utils.samplers import RandomPairwiseSampler, EvaluationSampler
from stratified_pairwise_sampler import StratifiedPairwiseSampler
from dataloader import *


### training parameter ###
total_iter = 10000   # iterations for training 
batch_size = 1000      # training batch size
eval_iter = 1000        # iteration of evaluation
save_iter = eval_iter   # iteration of saving model

### embeding ### 
dim_user_embed = 100     # dimension of user embedding
dim_item_embed = 100     # dimension of item embedding


def exp(dataset, l2_reg, p_n_ratio, eval_explicit, save_log, eval_rank):
    
    if dataset == 'spotify':
        data = loadSpotify()
        
    elif dataset == 'bytedance':
        data = loadByteDance()
        
    else:
        print ("Unsupported dataset...")
        return 
    
    # save logging and model
    log_dir = "validation_logs/{}_{}_{}_{}_{}/".format(dataset, l2_reg, p_n_ratio, eval_explicit, eval_rank)
    os.popen("mkdir -p %s" % log_dir).read()
    if save_log:
        log = open(log_dir + "validation.log", "w")
        sys.stdout = log
   
    
    # prepare train, val, test sets
    train_dataset = Dataset(data['train'], data['total_users'], data['total_items'], name='Train')   
    if p_n_ratio is None:
        train_sampler = RandomPairwiseSampler(batch_size=batch_size, dataset=train_dataset, num_process=5)
    else:
        train_sampler = StratifiedPairwiseSampler(batch_size=batch_size, dataset=train_dataset, p_n_ratio=p_n_ratio, num_process=5)
        if p_n_ratio > 0.0:
            print ("Re-weighting implicit negative feedback")
        else:
            print ("Corrected negative feedback labels but not re-weighting")
        
    eval_num_neg = None if eval_explicit else 500 # num of negative samples for evaluation
    if eval_rank:
        # show evaluation metrics for click-complete and click-skip items separately
        pos_dataset = Dataset(data['pos_test'],  data['total_users'], data['total_items'], 
                              implicit_negative=not eval_explicit, name='Pos_Test', num_negatives=eval_num_neg)
        neg_dataset = Dataset(data['neg_test'],  data['total_users'], data['total_items'], 
                              implicit_negative=not eval_explicit, name='Neg_Test', num_negatives=eval_num_neg)
        pos_sampler = EvaluationSampler(batch_size=batch_size, dataset=pos_dataset)
        neg_sampler = EvaluationSampler(batch_size=batch_size, dataset=neg_dataset)
        eval_samplers = [pos_sampler, neg_sampler]
    else:
        val_dataset = Dataset(data['val'],  data['total_users'], data['total_items'], 
                              implicit_negative=not eval_explicit, name='Val', num_negatives=eval_num_neg)
        test_dataset = Dataset(data['test'],  data['total_users'], data['total_items'], 
                               implicit_negative=not eval_explicit, name='Test', num_negatives=eval_num_neg)
        val_sampler = EvaluationSampler(batch_size=batch_size, dataset=val_dataset)
        test_sampler = EvaluationSampler(batch_size=batch_size, dataset=test_dataset)
        eval_samplers = [val_sampler, test_sampler]
    
    # set evaluators
    auc_evaluator = AUC()
    evaluators = [auc_evaluator]

    
    # set model parameters
    model = BPR(l2_reg=l2_reg, 
                batch_size=batch_size, 
                total_users=train_dataset.total_users(), 
                total_items=train_dataset.total_items(), 
                dim_user_embed=dim_user_embed, 
                dim_item_embed=dim_item_embed, 
                save_model_dir=log_dir, 
                train=True, 
                serve=True)
    
    
    # set model trainer
    model_trainer = ModelTrainer(model=model)  
    model_trainer.train(total_iter=total_iter, 
                        eval_iter=eval_iter, 
                        save_iter=save_iter, 
                        train_sampler=train_sampler, 
                        eval_samplers=eval_samplers, 
                        evaluators=evaluators)



if __name__ == '__main__':
    
    parser = argparse.ArgumentParser(description='Parse parameters')
    parser.add_argument('--dataset', type=str, default='bytedance', help='dataset to use')
    parser.add_argument('--l2_reg', type=float, default=0.01, help='l2 regularization of latent factor')
    parser.add_argument('--p_n_ratio', type=float, default=None, help='pos-neg pair ratio during sampling')
    parser.add_argument('--eval_explicit', action='store_true', help='turn on to use labels to evaluate, by default treat click as positive and non-click as negative')
    parser.add_argument('--eval_rank', action='store_true', help='show ranking accuracy for pos and neg samples')
    parser.add_argument('--log', action='store_true', help='turn on for logging results to file, by default will print on screen')
    args = parser.parse_args()
    print (args)
    
    # run experiments
    exp(dataset=args.dataset, l2_reg=args.l2_reg, p_n_ratio=args.p_n_ratio, eval_explicit=args.eval_explicit, save_log=args.log, eval_rank=args.eval_rank)