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SASRec.py
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SASRec.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=60,
help='Number of max epochs.')
parser.add_argument('--dataset',
nargs='?',
default='RC15',
help='datasets: RC15, Retailrocket.')
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('--l2_emb', default=0.0, 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')
return parser.parse_args()
def evaluate(model, dataset):
eval_sessions = pd.read_pickle(
os.path.join(data_directory, 'sampled_val.df'))
eval_ids = eval_sessions.session_id.unique()
groups = eval_sessions.groupby('session_id')
batch = 100
evaluated = 0
total_clicks = 0.0
total_purchase = 0.0
total_reward = [0, 0, 0, 0]
hit_clicks = [0, 0, 0, 0]
ndcg_clicks = [0, 0, 0, 0]
hit_purchase = [0, 0, 0, 0]
ndcg_purchase = [0, 0, 0, 0]
eval_start = time.time()
while evaluated < len(eval_ids):
states, len_states, actions, rewards = [], [], [], []
for i in range(batch):
if dataset == "Retailrocket":
if evaluated == len(eval_ids):
break
id = eval_ids[evaluated]
group = groups.get_group(id)
history = []
for _, row in group.iterrows():
state = list(history)
len_states.append(state_size if len(state) >= state_size else
1 if len(state) == 0 else len(state))
state = utils.pad_history(state, state_size, item_num)
states.append(state)
action = row['item_id']
is_buy = row['is_buy']
reward = reward_buy if is_buy == 1 else reward_click
if is_buy == 1:
total_purchase += 1.0
else:
total_clicks += 1.0
actions.append(action)
rewards.append(reward)
history.append(row['item_id'])
evaluated += 1
states = np.asarray(states)
len_states = np.asarray(len_states)
prediction = model(states, len_states)
prediction = prediction.detach().cpu().numpy()
sorted_list = np.argsort(prediction)
utils.calculate_hit(sorted_list, topk, actions, rewards, reward_click,
total_reward, hit_clicks, ndcg_clicks, hit_purchase,
ndcg_purchase)
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)):
hr_click = hit_clicks[i] / total_clicks
hr_purchase = hit_purchase[i] / total_purchase
ng_click = ndcg_clicks[i] / total_clicks
ng_purchase = ndcg_purchase[i] / total_purchase
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('cumulative reward @ %d: %f' % (topk[i], total_reward[i]))
print('purchase hr and ndcg @%d : %f, %f' %
(topk[i], hr_purchase, ng_purchase))
print('clicks hr ndcg @ %d : %f, %f' % (topk[i], hr_click, ng_click))
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.output = 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)
layer_slices = []
for b_index, len_s in enumerate(len_state - 1):
last_layer_norm_slice = seqs[b_index,len_s, :]
layer_slices.append(last_layer_norm_slice)
gathered = torch.stack(layer_slices)
# 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.output(gathered)
return output
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Network parameters
args = parse_args()
dataset = args.dataset
data_directory = 'data/' + 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]
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, 'replay_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('Model : SASRec')
print('Dataset :', dataset)
print('Seed: ', args.seed)
print('Initial Evaluation.')
evaluate(SASRec, dataset)
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)
prediction = SASRec(state, len_state)
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, dataset)