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bbf5d55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import os
import sys
import json
import optuna
import warnings
import torch
import torch.nn as nn
import pandas as pd
from torch.utils.data import Dataset, DataLoader
warnings.simplefilter(action='ignore', category=FutureWarning)
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src.logger import get_logger # pylint: disable=import-error
logger = get_logger(__name__)
class QuarterlyStockDataset(Dataset):
def __init__(self, df, sequence_length=90):
try:
self.sequence_length = sequence_length
self.samples = []
df = df.sort_values(by=["Ticker", "Date"]).reset_index(drop=True)
tickers = df['Ticker'].unique()
feature_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
for ticker in tickers:
ticker_df = df[df['Ticker'] == ticker]
data = ticker_df[feature_cols].values
ticker_id = ticker_df['Ticker_Encoded'].iloc[0]
for i in range(0, len(data) - sequence_length + 1, sequence_length):
window = data[i:i+sequence_length]
self.samples.append((torch.tensor(window, dtype=torch.float32),
torch.tensor(ticker_id, dtype=torch.long)))
print(f"Created {len(self.samples)} quarterly sequences across {len(tickers)} tickers.")
except Exception as e:
logger.error("Error batching dataset: %s", e)
raise
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
class LSTMAutoEncoder(nn.Module):
def __init__(self, input_dim, num_tickers, embed_dim=8, hidden_size=64, latent_dim=16, num_layers=1):
super(LSTMAutoEncoder, self).__init__()
self.ticker_embed = nn.Embedding(num_tickers, embed_dim)
# Encoder
self.encoder = nn.LSTM(input_dim + embed_dim, hidden_size, num_layers=num_layers, batch_first=True)
self.fc_enc = nn.Linear(hidden_size, latent_dim)
# Decoder
self.fc_dec = nn.Linear(latent_dim + embed_dim, hidden_size)
self.decoder = nn.LSTM(hidden_size, input_dim, num_layers=num_layers, batch_first=True)
def forward(self, x, ticker_id):
ticker_emb = self.ticker_embed(ticker_id).unsqueeze(1).repeat(1, x.size(1), 1)
x_in = torch.cat([x, ticker_emb], dim=2)
# Encoder
enc_out, (h, c) = self.encoder(x_in)
latent = self.fc_enc(enc_out[:, -1, :])
latent_cat = torch.cat([latent, self.ticker_embed(ticker_id)], dim=1)
latent_cat = latent_cat.unsqueeze(1).repeat(1, x.size(1), 1)
# Decoder
dec_input = self.fc_dec(latent_cat)
out_dec, _ = self.decoder(dec_input)
return out_dec
def objective(trial, df, sequence_length=90, device='cpu'):
try:
num_layers = trial.suggest_int("num_layers", 1, 3)
hidden_size = trial.suggest_categorical("hidden_size", [32, 64, 128])
latent_dim = trial.suggest_categorical("latent_dim", [8, 16, 32])
lr = trial.suggest_loguniform("lr", 1e-4, 1e-2)
embed_dim = trial.suggest_categorical("embed_dim", [4, 8, 16])
train_df = df[df['Date'] < '2023-01-01']
val_df = df[(df['Date'] >= '2023-01-01') & (df['Date'] < '2024-01-01')]
train_dataset = QuarterlyStockDataset(train_df, sequence_length)
val_dataset = QuarterlyStockDataset(val_df, sequence_length)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=False)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
num_tickers = df['Ticker_Encoded'].nunique()
model = LSTMAutoEncoder(
input_dim=5, num_tickers=num_tickers, embed_dim=embed_dim,
hidden_size=hidden_size, latent_dim=latent_dim, num_layers=num_layers
).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
epochs = 20
for epoch in range(epochs):
model.train()
total_train_loss = 0
for batch_x, batch_ticker in train_loader:
batch_x, batch_ticker = batch_x.to(device), batch_ticker.to(device)
optimizer.zero_grad()
recon = model(batch_x, batch_ticker)
loss = criterion(recon, batch_x)
loss.backward()
optimizer.step()
total_train_loss += loss.item()
model.eval()
total_val_loss = 0
with torch.no_grad():
for batch_x, batch_ticker in val_loader:
batch_x, batch_ticker = batch_x.to(device), batch_ticker.to(device)
recon = model(batch_x, batch_ticker)
loss = criterion(recon, batch_x)
total_val_loss += loss.item()
avg_val_loss = total_val_loss / len(val_loader)
return avg_val_loss
except Exception as e:
logger.error("Error training Model : %s", e)
raise
if __name__ == "__main__":
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
processed_data_path = os.path.join(base_dir, 'data', 'processed', 'stock_data.parquet')
model_path = os.path.join(base_dir, 'models', 'lstm_autoencoder.pth')
loss_path = os.path.join(base_dir, 'resources', 'loss_values.json')
hyperparams_path = os.path.join(base_dir, 'models', 'hyperparameters.json')
df = pd.read_parquet(processed_data_path)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
study = optuna.create_study(direction="minimize")
study.optimize(lambda trial: objective(trial, df, device=device), n_trials=10)
best_trial = study.best_trial
best_params = best_trial.params
train_df = df[df['Date'] < '2024-01-01']
full_dataset = QuarterlyStockDataset(train_df, sequence_length=90)
full_loader = DataLoader(full_dataset, batch_size=64, shuffle=False)
num_tickers = df['Ticker_Encoded'].nunique()
best_model = LSTMAutoEncoder(
input_dim=5,
num_tickers=num_tickers,
embed_dim=best_params.get('embed_dim', 8),
hidden_size=best_params['hidden_size'],
latent_dim=best_params['latent_dim'],
num_layers=best_params['num_layers']
).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(best_model.parameters(), lr=best_params['lr'])
epochs = 50
train_losses = []
for epoch in range(epochs):
best_model.train()
total_loss = 0
for batch_x, batch_ticker in full_loader:
batch_x, batch_ticker = batch_x.to(device), batch_ticker.to(device)
optimizer.zero_grad()
recon = best_model(batch_x, batch_ticker)
loss = criterion(recon, batch_x)
loss.backward()
optimizer.step()
total_loss += loss.item()
avg_loss = total_loss / len(full_loader)
train_losses.append(avg_loss)
print(f"Epoch [{epoch+1}/{epochs}] Loss: {avg_loss:.6f}")
torch.save(best_model.state_dict(), model_path)
with open(loss_path, 'w') as f:
json.dump(train_losses, f)
with open(hyperparams_path, 'w') as f:
json.dump(best_params, f)
print(f"Model, losses, and hyperparameters saved successfully.") |