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CaserTorch.py
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import torch
import torch.nn as nn
import argparse
import pandas as pd
import os
import time
import numpy as np
from utility import pad_history, calculate_hit, set_device
from shutil import copyfile
import train_eval
def parse_args():
parser = argparse.ArgumentParser(description="Run supervised GRU.")
parser.add_argument('--mode', default='train',
help='Train or test the model. "train" or "test"')
parser.add_argument('--epochs', type=int, default=30,
help='Number of max epochs.')
parser.add_argument('--data', nargs='?', default='data',
help='data directory')
parser.add_argument('--resume', type=int, default=1,
help='flag for resume. 1: resume training; 0: train from start')
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('--num_filters', type=int, default=16,
help='Number of filters per filter size (default: 128)')
parser.add_argument('--filter_sizes', nargs='?', default='[2,3,4]',
help='Specify the filter_size')
parser.add_argument('--dropout_rate', default=0.1, type=float)
return parser.parse_args()
class CaserTorch(nn.Module):
def __init__(self, hidden_size, item_num, state_size, device,num_filters,filter_sizes,
dropout_rate):
super(CaserTorch, self).__init__()
self.hidden_size = hidden_size
self.item_num = int(item_num)
self.state_size = state_size
self.device = device
self.filter_sizes=eval(filter_sizes)
self.num_filters=num_filters
self.dropout_rate=dropout_rate
self.item_embeddings = nn.Embedding(
num_embeddings=item_num+1,
embedding_dim=self.hidden_size,
# padding_idx=padding_idx
)
# init embedding
nn.init.normal_(self.item_embeddings.weight, 0, 0.01)
#Horizontal Convolutional Layer
self.horizontal_cnn = nn.ModuleList([nn.Conv2d(1,self.num_filters,(i,self.hidden_size))for i in self.filter_sizes])
#Vertical Convolutional Layer
self.vertical_cnn = nn.Conv2d(1,self.num_filters,(self.state_size,1))
#Fully Connected Layer
self.vertical_dim = self.num_filters * self.hidden_size
self.num_filters_total = self.num_filters * len(self.filter_sizes)
final_dim = self.vertical_dim + self.num_filters_total
self.fc = nn.Linear(final_dim,item_num)
#dropout
self.dropout = nn.Dropout(self.dropout_rate)
def forward(self, inputs, inputs_lengths):
input_emb = self.item_embeddings(inputs)
mask = torch.ne(inputs, self.item_num).float().unsqueeze(-1)
input_emb *= mask
batch_size=inputs.size(0)
embedded_chars_expanded = torch.reshape(input_emb,(batch_size,1,self.state_size,self.hidden_size))
pooled_outputs = []
for cnn in self.horizontal_cnn:
h_out = nn.functional.relu(cnn(embedded_chars_expanded))
h_out = h_out.squeeze()
p_out = nn.functional.max_pool1d(h_out,h_out.size(2))
pooled_outputs.append(p_out)
h_pool = torch.cat(pooled_outputs,1)
h_pool_flat = h_pool.view(-1,self.num_filters_total)
v_out = nn.functional.relu(self.vertical_cnn(embedded_chars_expanded))
v_flat = v_out.view(-1,self.vertical_dim)
out = torch.cat([h_pool_flat,v_flat],1)
out = self.dropout(out)
out = self.fc(out)
return out
class CaserEvaluator(train_eval.Evaluator):
def get_prediction(self, model, states, len_states, device):
prediction = model(states.to(device).long(), len_states.to(device).long())
return prediction
class CaserTrainer(train_eval.Trainer):
def create_model(self):
caserTorch = CaserTorch(hidden_size=self.args.hidden_factor, item_num=self.item_num,
state_size=self.state_size, device=self.device,num_filters=self.args.num_filters,
filter_sizes=self.args.filter_sizes,dropout_rate=self.args.dropout_rate)
return caserTorch
def get_model_out(self, state, len_state):
out = self.model(state, len_state)
return out
def get_evaluator(self, device, args, data_directory, state_size, item_num):
caser_evaluator = CaserEvaluator(device, args, data_directory, state_size, item_num)
return caser_evaluator
def create_optimizer(self):
optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.lr)
return optimizer
def get_criterion(self):
criterion = nn.CrossEntropyLoss()
return criterion
TRAIN = 'train'
TEST = 'test'
def train_model(args, device, state_size, item_num):
caser_trainer = CaserTrainer('caser_RC15', args, device, state_size, item_num)
caser_trainer.train(train_loader)
def test_model(device, args, data_directory, state_size, item_num):
caserTorch = CaserTorch(hidden_size=args.hidden_factor, item_num=item_num,
state_size=state_size, device=device)
checkpoint_handler = train_eval.CheckpointHandler('caser_RC15', device)
optimizer = torch.optim.Adam(caserTorch.parameters(), lr=args.lr)
_, _ = checkpoint_handler.load_from_checkpoint(True, caserTorch, optimizer)
caserTorch.to(device)
caser_evaluator = CaserEvaluator(device, args, data_directory, state_size, item_num)
caser_evaluator.evaluate(caserTorch, 'test')
if __name__ == '__main__':
# Network parameters
args = parse_args()
device = set_device()
data_directory = args.data
state_size, item_num = train_eval.get_stats(data_directory)
train_loader = train_eval.prepare_dataloader(data_directory, args.batch_size)
if args.mode.lower() == TRAIN:
train_model(args, device, state_size, item_num)
else:
test_model(device, args, data_directory, state_size, item_num)