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SASRec_co.py
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SASRec_co.py
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# Copyright (c) 2020-present, Royal Bank of Canada.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
import torch
import pandas as pd
import os
import random
import argparse
import time
import utils
import torch.nn.functional as F
import torch.nn.init as init
def parse_args():
parser = argparse.ArgumentParser(description="SASRec CO.")
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('--hidden_factor',
type=int,
default=64,
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('--discount',
type=float,
default=0.5,
help='Discount factor for RL.')
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('--eval_interval', default=2000, type=int)
parser.add_argument('--switch_interval', default=30000, type=int)
parser.add_argument('--console', default=500, type=int)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--contrastive_loss', type=str, default='InfoNCE')
parser.add_argument('--aug', type=str, default='permutation')
parser.add_argument('--exp_id', type=str, default='SASRecCO')
return parser.parse_args()
def evaluate(model, total_step, dataset):
model.eval()
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]
total_qestimates_neg = [0, 0, 0, 0]
total_qestimates_mb = [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)
logits = model(states, len_states)
prediction_tensor = model.output2(logits)
prediction = prediction_tensor.detach().cpu().numpy()
sorted_list = np.argsort(prediction)
# Evaluate Q on negative samples
# Sample negative actions to evaluate the Q-function.
all_values = set(range(eval_sessions.item_id.min() + 1, eval_sessions.item_id.max()-1))
# Remove the values in the input list from the set of all possible values.
remaining_values = all_values - set(actions)
# Randomly sample from the remaining values.
negative_actions = random.sample(remaining_values, len(actions))
negative_actions = torch.Tensor(np.asarray(negative_actions)).long().to(device)
q_estimates = model.batched_index(prediction_tensor, negative_actions)
shifted_q_values = q_estimates + abs(min(q_estimates)) + 1e-8
shifted_q_values = shifted_q_values.detach().cpu().numpy()
# Evaluate Q on minibatch
pos_actions_tensor = torch.Tensor(np.asarray(actions)).long().to(device)
q_estimates_minibatch = model.batched_index(prediction_tensor, pos_actions_tensor)
shifted_q_minibatch = q_estimates_minibatch + abs(min(q_estimates_minibatch)) + 1e-8
shifted_q_minibatch = shifted_q_minibatch.detach().cpu().numpy()
utils.calculate_hit_wqestimates(sorted_list, topk, actions, rewards,
reward_click, total_reward, hit_clicks,
ndcg_clicks, hit_purchase,
ndcg_purchase, shifted_q_values,
shifted_q_minibatch,
total_qestimates_neg, total_qestimates_mb)
eval_total_time = time.time() - eval_start
print('#############################################################')
print('total clicks: %d, total purchase:%d' %
(total_clicks, total_purchase))
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('q-function estimates neg actions @ %d: %f' % (topk[i], total_qestimates_neg[i]))
print('q-function estimates mini-batch @ %d: %f' % (topk[i], total_qestimates_mb[i]))
if topk[i] == 20 and total_step >= 10:
model.best_ckpt_tracker.update_values(total_reward[i], hr_purchase,
ng_purchase, total_step)
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
best_ckpt = model.best_ckpt_tracker.get_max_values()
print('Best Ckpt Iter: ', (best_ckpt['checkpoint']))
print('Reward: {:.2f}'.format((best_ckpt['max_reward'])),
'Purchase HR and NDCG @20 %f, %f' % (
best_ckpt['max_hr_20'], best_ckpt['max_ndcg_20']))
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)
# Initialize the weights of the Embedding layers
init.normal_(self.state_embeddings.weight, mean=0.0, std=0.01)
init.normal_(self.pos_embeddings.weight, mean=0.0, std=0.01)
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)
# Initialize the weights of the MultiheadAttention and PointWiseFeedForward layers
for attn_layer, fwd_layer in zip(self.attention_layers,
self.forward_layers):
# Initialize weights of MultiheadAttention layer
init.normal_(attn_layer.Q.weight, mean=0.0, std=0.01)
init.zeros_(attn_layer.Q.bias)
init.normal_(attn_layer.K.weight, mean=0.0, std=0.01)
init.zeros_(attn_layer.K.bias)
init.normal_(attn_layer.V.weight, mean=0.0, std=0.01)
init.zeros_(attn_layer.V.bias)
# Initialize weights of PointWiseFeedForward layer
init.normal_(fwd_layer.conv1.weight, mean=0.0, std=0.01)
init.zeros_(fwd_layer.conv1.bias)
init.normal_(fwd_layer.conv2.weight, mean=0.0, std=0.01)
init.zeros_(fwd_layer.conv2.bias)
self.output1 = torch.nn.Linear(self.hidden_size, self.item_num)
self.output2 = torch.nn.Linear(self.hidden_size, self.item_num)
# Initialize the weights of the Linear layers
init.normal_(self.output1.weight, mean=0.0, std=0.01)
init.zeros_(self.output1.bias)
init.normal_(self.output2.weight, mean=0.0, std=0.01)
init.zeros_(self.output2.bias)
self.celoss1 = torch.nn.CrossEntropyLoss()
self.celoss2 = torch.nn.CrossEntropyLoss()
self.opt = torch.optim.Adam(self.parameters(),
lr=args.lr,
betas=(0.9, 0.999))
self.opt2 = torch.optim.Adam(self.parameters(),
lr=args.lr,
betas=(0.9, 0.999))
self.best_ckpt_tracker = utils.Tracker()
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)
# There is no equivalent of tf.gather_nd in pytorch afaik
# For now we do it in a loop.
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)
model_output = torch.stack(layer_slices)
return model_output
def double_qlearning(self, q_vals_state, actions, rewards, discount, q_vals_next_state,
q_vals_next_state_selector):
""" Double-Q operator.
Args:
q_vals_state: Tensor holding Q-values for s in a batch of transitions,
shape `[B x num_actions]`.
actions: Tensor holding action indices, shape `[B]`.
rewards: Tensor holding rewards, shape `[B]`.
discount: Tensor holding pcontinue values, shape `[B]`.
q_vals_next_state: Tensor of Q-values for s' in a batch of transitions,
used to estimate the value of the best action, shape `[B x num_actions]`.
q_vals_next_state_selector: Tensor of Q-values for s' in a batch of
transitions used to estimate the best action, shape `[B x num_actions]`.
"""
with torch.no_grad():
# Build target and select head to update.
best_action = torch.argmax(q_vals_next_state_selector, 1)
double_q_bootstrapped = self.batched_index(q_vals_next_state, best_action)
target = rewards + discount * double_q_bootstrapped
qa_state = self.batched_index(q_vals_state, actions)
# Temporal difference error and loss.
# Loss is MSE scaled by 0.5, so the gradient is equal to the TD error.
td_error = target - qa_state
loss = 0.5 * torch.square(td_error)
return loss, td_error, best_action
def batched_index(self, values, indices):
"""Equivalent to `values[:, indices]` or tf.gather`.
"""
one_hot_indices = torch.nn.functional.one_hot(indices, num_classes=self.item_num)
sum_vals = torch.sum(values * one_hot_indices, dim=-1)
return sum_vals
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]
pad_item = item_num
reward_click = args.r_click
reward_buy = args.r_buy
topk = [5, 10, 20]
console = args.console
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
eval_interval = args.eval_interval
switch_interval = args.switch_interval
# InfoNCE, InstanceInfoNCE, Hierarchical
contrastive_objective = args.contrastive_loss
augmentation = args.aug
SASRec1 = 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)
SASRec2 = 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)
SASRec1 = SASRec1.to(device)
SASRec2 = SASRec2.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, SASRec1.parameters())
total_parameters = sum([np.prod(p.size()) for p in model_parameters])
print('Total number of parameters : ', total_parameters * 2)
print('Model : SASRec Contrastive')
print('Dataset : ', dataset)
print('Experiment ID', args.exp_id)
print('Seed: ', args.seed)
print('Hyperparams: ')
print('##############################################')
print('Batch_size: ', args.batch_size)
print('Hidden_size: ', args.hidden_factor)
print('Learning Rate: ', args.lr)
print('Discount: ', args.discount)
print('Contrastive Loss: ', contrastive_objective)
print('Augmentation : ', augmentation)
print('Reward buy: ', args.r_buy)
print('Reward click: ', args.r_click)
print('##############################################')
print('Initial Evaluation.')
evaluate(SASRec1, total_step, dataset)
for i in range(args.epoch):
for j in range(num_batches):
batch = replay_buffer.sample(n=args.batch_size).to_dict()
next_state = list(batch['next_state'].values())
len_next_state = list(batch['len_next_states'].values())
next_states = np.asarray(next_state)
len_next_states = np.asarray(len_next_state)
# double q learning, pointer is for selecting which network
# is target and which is main.
pointer = np.random.randint(0, 2)
# Set in train mode.
SASRec1.train()
SASRec2.train()
if pointer == 0:
mainQN = SASRec1
target_QN = SASRec2
else:
mainQN = SASRec2
target_QN = SASRec1
target_model_output = target_QN(next_states, len_next_states)
target_Qs = target_QN.output1(target_model_output)
# here mainQN.len_state == len_next_state
main_model_output = mainQN(next_states, len_next_states)
target_Qs_selector = mainQN.output1(main_model_output)
# Set target_Qs to 0 for states where episode ends
is_done = list(batch['is_done'].values())
for index in range(target_Qs.shape[0]):
if is_done[index]:
target_Qs[index] = torch.Tensor(np.zeros([item_num])).to(device)
state = list(batch['state'].values())
len_state = list(batch['len_state'].values())
action = list(batch['action'].values())
is_buy = list(batch['is_buy'].values())
reward = []
for k in range(len(is_buy)):
reward.append(reward_buy if is_buy[k] == 1 else reward_click)
discount = [args.discount] * len(action)
states = np.asarray(state)
len_states = np.asarray(len_state)
actions = torch.Tensor(np.asarray(action)).long().to(device)
rewards = torch.Tensor(np.asarray(reward)).to(device)
discounts = torch.Tensor(np.asarray(discount)).to(device)
if total_step < switch_interval:
main_model_current_state = mainQN(states, len_states)
q_tm1 = mainQN.output1(main_model_current_state)
q_loss, td_error, best_action = mainQN.double_qlearning(
q_tm1, actions, rewards, discounts, target_Qs,
target_Qs_selector)
predictions = mainQN.output2(main_model_current_state)
if augmentation == 'permutation':
rng = np.random.default_rng()
states_permuted = rng.permuted(states, axis=1)
logits_aug_state = mainQN(states_permuted, len_states)
elif augmentation == 'crop':
cropped_seq = utils.mask_crop_2d_array(states, pad_item)
logits_aug_state = mainQN(cropped_seq, len_states)
elif augmentation == 'mask':
masked_seq = utils.mask_2d_array(states, pad_item)
logits_aug_state = mainQN(masked_seq, len_states)
else:
raise ValueError('Invalid augmentation.')
if contrastive_objective == "InfoNCECosine":
contrastive_loss = utils.info_nce_cosim_loss(main_model_current_state,
logits_aug_state,
total_step,
console)
elif contrastive_objective == "InfoNCE":
main_model_current_state = main_model_current_state.unsqueeze(-1)
logits_aug_state = logits_aug_state.unsqueeze(-1)
contrastive_loss = utils.contrastive_loss(main_model_current_state,
logits_aug_state)
elif contrastive_objective == "Hierarchical":
main_model_current_state = main_model_current_state.unsqueeze(-1)
logits_aug_state = logits_aug_state.unsqueeze(-1)
contrastive_loss = utils.hierarchical_contrastive_loss(main_model_current_state,
logits_aug_state)
mainQN.opt.zero_grad()
loss = mainQN.celoss1(predictions, actions)
final_loss = torch.mean(loss + q_loss + contrastive_loss)
final_loss.backward()
mainQN.opt.step()
if total_step % console == 0:
print("the loss in %dth batch is: %f" %
(total_step, final_loss))
else:
main_model_current_state = mainQN(states, len_states)
q_tm1 = mainQN.output1(main_model_current_state)
q_loss, td_error, best_action = mainQN.double_qlearning(
q_tm1, actions, rewards, discounts, target_Qs,
target_Qs_selector)
predictions = mainQN.output2(main_model_current_state)
with torch.no_grad():
q_indexed = mainQN.batched_index(q_tm1, actions)
if augmentation == 'permutation':
rng = np.random.default_rng()
states_permuted = rng.permuted(states, axis=1)
logits_aug_state = mainQN(states_permuted, len_states)
elif augmentation == 'crop':
cropped_seq = utils.mask_crop_2d_array(states, pad_item)
logits_aug_state = mainQN(cropped_seq, len_states)
elif augmentation == 'mask':
masked_seq = utils.mask_2d_array(states, pad_item)
logits_aug_state = mainQN(masked_seq, len_states)
else:
raise ValueError('Invalid augmentation.')
if contrastive_objective == "InfoNCECosine":
contrastive_loss = utils.info_nce_cosim_loss(main_model_current_state,
logits_aug_state,
total_step,
console)
elif contrastive_objective == "InfoNCE":
main_model_current_state = main_model_current_state.unsqueeze(-1)
logits_aug_state = logits_aug_state.unsqueeze(-1)
contrastive_loss = utils.contrastive_loss(main_model_current_state,
logits_aug_state)
elif contrastive_objective == "Hierarchical":
main_model_current_state = main_model_current_state.unsqueeze(-1)
logits_aug_state = logits_aug_state.unsqueeze(-1)
contrastive_loss = utils.hierarchical_contrastive_loss(main_model_current_state,
logits_aug_state)
celoss2 = mainQN.celoss2(predictions, actions)
loss_multi = torch.multiply(q_indexed, celoss2)
mainQN.opt2.zero_grad()
final_loss = torch.mean(loss_multi + q_loss + contrastive_loss)
final_loss.backward()
mainQN.opt2.step()
if total_step % console == 0:
print("the loss in %dth batch is: %f" %
(total_step, final_loss))
total_step += 1
if total_step % eval_interval == 0:
evaluate(mainQN, total_step, dataset)