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AEncoder.py
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AEncoder.py
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import argparse
import logging
import os
import numpy as np
import torch
from torch import nn
import torch.optim as optim
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
import pytorch_lightning as pl
import torch.nn.functional as F
from Preprocess import preprocessing
# AEncoder
#------------------------------------------------------------------------------#
class JaneStreetDataset:
def __init__(self, dataset, targets):
self.dataset = dataset
self.targets = targets
def __len__(self):
return self.dataset.shape[0]
def __getitem__(self, item):
return {
'x': torch.tensor(self.dataset[item, :], dtype=torch.float),
'y': torch.tensor(self.targets[item], dtype=torch.float)
}
#-------------------------------------------#
class DataModule(pl.LightningDataModule):
def __init__(self, data, targets, BATCH_SIZE, fold = None):
super().__init__()
self.BATCH_SIZE = BATCH_SIZE
self.data = data
self.targets = targets
self.fold = fold
def preapre_data(self):
pass
def setup(self, stage=None):
train_data, train_targets = self.data, self.targets
self.train_dataset = JaneStreetDataset(
dataset = train_data, #train_data.values
targets = train_targets
)
def train_dataloader(self):
train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_size=self.BATCH_SIZE
)
return train_loader
def valid_dataloader(self):
return None
def test_dataloader(self):
return None
#-------------------------------------------#
# Encoder - Decoder
class LitAutoEncoder(pl.LightningModule):
def __init__(self, input_shape):
super().__init__()
self.encoder = nn.Sequential(
nn.BatchNorm1d(input_shape),
nn.Linear(input_shape, 64),
nn.ReLU(),
nn.Linear(64, 32)
)
self.decoder = nn.Sequential(
nn.Dropout(.2),
nn.Linear(32, 64),
nn.ReLU(),
nn.Linear(64, input_shape)
)
def forward(self, x):
# in lightning, forward defines the prediction/inference actions
embedding = self.encoder(x)
return embedding
def training_step(self, batch, batch_idx):
x = batch['x']
#x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
return loss
#def training_epoch_end(self, outputs):
# TODO: add roc_auc
#avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
#logs = {'train_loss': avg_loss}
#return {'log': logs, 'progress_bar': logs}
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def AEncoder(X, y, train=False, EPOCHS = 10, BATCH_SIZE = 4096):
model_dir = os.path.join('checkpoints','encoder.pkl')
#X, y, _ = preprocessing()
NUM_FEATURES = X.shape[1]
early_stop_callback = EarlyStopping(
monitor='train_loss', min_delta=0.00, patience=10, verbose=True, mode='min'
)
GPU = int(torch.cuda.is_available())
if train:
DataLoader = DataModule(data=X, targets=y, BATCH_SIZE=BATCH_SIZE)
trainer = pl.Trainer(gpus=GPU, max_epochs=EPOCHS, weights_summary='full', callbacks=[early_stop_callback])
AEncoder = LitAutoEncoder(input_shape=NUM_FEATURES)
trainer.fit(AEncoder, DataLoader)
torch.save(AEncoder.state_dict(), model_dir)
else:
AEncoder = LitAutoEncoder(input_shape=NUM_FEATURES)
AEncoder.load_state_dict(torch.load(model_dir))
return AEncoder