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SASRec_norew.py
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SASRec_norew.py
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# Copyright (c) 2019-present, Royal Bank of Canada.
# Copyright (c) 2019-present, Wang-Cheng Kang, Julian McAuley.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#####################################################################################
# Code is based on the SASREC (https://arxiv.org/abs/1808.09781.pdf) implementation
# from https://github.com/kang205/SASRec by Michael Kelly and Julian McAuley
####################################################################################
import numpy as np
import torch
import pandas as pd
import os
import argparse
import time
import utils
def parse_args():
parser = argparse.ArgumentParser(description="SASRec.")
parser.add_argument('--epoch',
type=int,
default=1000,
help='Number of max epochs.')
parser.add_argument('--dataset',
nargs='?',
default='movielens',
help='dataset')
parser.add_argument('--batch_size',
type=int,
default=256,
help='Batch size.')
parser.add_argument('--maxlen', default=10, type=int)
parser.add_argument('--hidden_factor',
type=int,
default=32,
help='Number of hidden factors, i.e., embedding size.')
parser.add_argument('--r_click',
type=float,
default=0.2,
help='reward for the click behavior.')
parser.add_argument('--r_buy',
type=float,
default=1.0,
help='reward for the purchase behavior.')
parser.add_argument('--lr',
type=float,
default=0.01,
help='Learning rate.')
parser.add_argument('--num_heads', default=1, type=int)
parser.add_argument('--num_blocks', default=1, type=int)
parser.add_argument('--dropout_rate', default=0.1, type=float)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--eval_interval', default=2000, type=int)
parser.add_argument('--exp_id', type=str, default='SASRec_70_30')
parser.add_argument('--seq', type=int, default=30)
return parser.parse_args()
def calculate_hit_norew(sorted_list,topk,true_items):
hit_purchase = [0, 0, 0, 0]
ndcg_purchase = [0, 0, 0, 0]
for i in range(len(topk)):
rec_list = sorted_list[:, -topk[i]:]
for j in range(len(true_items)):
if true_items[j] in rec_list[j]:
rank = topk[i] - np.argwhere(rec_list[j] == true_items[j])
hit_purchase[i] += 1.0
ndcg_purchase[i] += 1.0 / np.log2(rank + 1)
return hit_purchase, ndcg_purchase
def evaluate(model, replay_buffer):
batch = 100
total_clicks = 0.0
total_purchase = 0.0
total_reward = [0, 0, 0, 0]
eval_start = time.time()
num_rows = replay_buffer.shape[0]
num_batches = int(num_rows / args.batch_size)
for j in range(num_batches):
batch = replay_buffer.sample(n=args.batch_size).to_dict()
states = list(batch['state'].values())
len_states = list(batch['len_state'].values())
actions = list(batch['action'].values())
states = np.asarray(states)
len_states = np.asarray(len_states)
logits = model(states, len_states)
prediction = model.cls_layer(logits)
prediction = prediction.detach().cpu().numpy()
sorted_list = np.argsort(prediction)
hit_purchase, ndcg_purchase = calculate_hit_norew(sorted_list, topk, actions)
print('#############################################################')
print('total clicks: %d, total purchase:%d' %
(total_clicks, total_purchase))
eval_total_time = time.time() - eval_start
for i in range(len(topk)):
try:
hr_purchase = hit_purchase[i] / args.batch_size
except:
hr_purchase = 0
try:
ng_purchase = ndcg_purchase[i] / args.batch_size
except:
ng_purchase=0
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('purchase hr and ndcg @%d : %f, %f' %
(topk[i], hr_purchase, ng_purchase))
print('#############################################################')
class SASRecnetwork(torch.nn.Module):
def __init__(self, hidden_size, learning_rate, item_num, state_size,
batch_size, device):
super(SASRecnetwork, self).__init__()
self.state_size = state_size
self.learning_rate = learning_rate
self.hidden_size = hidden_size
self.item_num = int(item_num)
self.batch_size = batch_size
self.is_training = torch.BoolTensor()
self.dev = device
self.state_embeddings = torch.nn.Embedding(self.item_num + 1,
self.hidden_size)
# Positional Encoding
self.pos_embeddings = torch.nn.Embedding(self.state_size,
self.hidden_size)
self.emb_dropout = torch.nn.Dropout(p=args.dropout_rate)
# to be Q for self-attention
self.attention_layernorms = torch.nn.ModuleList()
self.attention_layers = torch.nn.ModuleList()
self.forward_layernorms = torch.nn.ModuleList()
self.forward_layers = torch.nn.ModuleList()
self.last_layernorm = torch.nn.LayerNorm(self.hidden_size, eps=1e-8)
# Build Blocks
for _ in range(args.num_blocks):
new_attn_layernorm = torch.nn.LayerNorm(self.hidden_size, eps=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = utils.MultiheadAttention(self.hidden_size,
args.dropout_rate,
num_heads=args.num_heads,
device=self.dev,
causality=True)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = torch.nn.LayerNorm(self.hidden_size, eps=1e-8)
self.forward_layernorms.append(new_fwd_layernorm)
new_fwd_layer = utils.PointWiseFeedForward(self.hidden_size,
args.dropout_rate)
self.forward_layers.append(new_fwd_layer)
self.cls_layer = torch.nn.Linear(self.hidden_size, self.item_num)
# The input is expected to contain the unnormalized logits for each class
# We want the equivalent of tf.nn.sparse_softmax_cross_entropy_with_logits
self.loss = torch.nn.CrossEntropyLoss()
self.opt = torch.optim.Adam(self.parameters(),
lr=args.lr,
betas=(0.9, 0.999))
def forward(self, state_seq, len_state):
seqs = self.state_embeddings(torch.LongTensor(state_seq).to(self.dev))
seqs *= self.state_embeddings.embedding_dim**0.5
positions = np.tile(np.array(range(state_seq.shape[1])),
[state_seq.shape[0], 1])
seqs += self.pos_embeddings(torch.LongTensor(positions).to(self.dev))
seqs = self.emb_dropout(seqs)
timeline_mask = ~torch.BoolTensor(state_seq == self.item_num).to(
self.dev)
# broadcast in last dim
seqs *= timeline_mask.unsqueeze(-1)
for i in range(len(self.attention_layers)):
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](Q, seqs)
seqs = Q + mha_outputs
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
seqs *= timeline_mask.unsqueeze(-1)
# (U, T, C) -> (U, -1, C)
seqs = self.last_layernorm(seqs)
indices = len_state - 1
# Create a tensor containing the batch indices
batch_indices = torch.arange(seqs.size(0))
# Gather elements using advanced indexing
gathered = seqs[batch_indices, indices]
# The final output is fed to a fully connected layer with no activation func.
# We use the CrossEntropyLoss which combines a LogSoftmax and NLLLoss.
# output = self.cls_layer(gathered)
return gathered
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Network parameters
args = parse_args()
dataset = args.dataset
if dataset == 'movielens':
data_directory = 'data/non-reward/movie_lens'
data_directory += '_' + str(args.seq)
projname = 'MovieLens'
elif dataset == 'amazonfood':
data_directory = 'data/non-reward/amazon_food'
data_directory += '_' + str(args.seq)
projname = 'AmazonFood'
else:
raise ValueError('Invalid dataset.')
data_statis = pd.read_pickle(os.path.join(data_directory,
'data_statis.df'))
state_size = data_statis['state_size'][0]
item_num = data_statis['item_num'][0]
reward_click = args.r_click
reward_buy = args.r_buy
topk = [5, 10, 20]
print('#############################################################')
print('Training on dataset : ', projname, 'with sequence length ', state_size)
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
eval_interval = args.eval_interval
SASRec = SASRecnetwork(hidden_size=args.hidden_factor,
learning_rate=args.lr,
item_num=item_num,
state_size=state_size,
batch_size=args.batch_size,
device=device)
SASRec = SASRec.to(device)
replay_buffer = pd.read_pickle(
os.path.join(data_directory, 'train_replay_buffer.df'))
eval_buffer = pd.read_pickle(
os.path.join(data_directory, 'eval_buffer.df'))
total_step = 0
num_rows = replay_buffer.shape[0]
num_batches = int(num_rows / args.batch_size)
model_parameters = filter(lambda p: p.requires_grad, SASRec.parameters())
total_parameters = sum([np.prod(p.size()) for p in model_parameters])
print('Total number of parameters : ', total_parameters)
print('Initial Evaluation.')
evaluate(SASRec, replay_buffer)
for i in range(args.epoch):
for j in range(num_batches):
batch = replay_buffer.sample(n=args.batch_size).to_dict()
state = list(batch['state'].values())
len_state = list(batch['len_state'].values())
target = list(batch['action'].values())
state = np.asarray(state)
len_state = np.asarray(len_state)
action_target = torch.Tensor(np.asarray(target)).long().to(
SASRec.dev)
logits = SASRec(state, len_state)
prediction = SASRec.cls_layer(logits)
SASRec.opt.zero_grad()
loss = SASRec.loss(prediction, action_target)
loss.backward()
SASRec.opt.step()
total_step += 1
if total_step % 200 == 0:
print("the loss in %dth batch is: %f" % (total_step, loss))
if total_step % eval_interval == 0:
evaluate(SASRec, eval_buffer)