File size: 7,989 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
# conditional_wgangp_train.py
import os
import sys
import json
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.preprocessing import StandardScaler
from torch.utils.data import Dataset, DataLoader
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
try:
from src.logger import get_logger
logger = get_logger(__name__)
except Exception:
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class LatentTickerDataset(Dataset):
def __init__(self, latent_path, ticker_path):
self.latents = np.load(latent_path)
self.tickers = np.load(ticker_path)
assert self.latents.shape[0] == self.tickers.shape[0], "Latents and tickers length mismatch"
def __len__(self):
return self.latents.shape[0]
def __getitem__(self, idx):
x = self.latents[idx].astype(np.float32)
y = int(self.tickers[idx])
return x, y
class ConditionalGenerator(nn.Module):
def __init__(self, noise_dim, embed_dim, num_tickers, latent_dim, hidden_dim=128):
super().__init__()
self.ticker_emb = nn.Embedding(num_tickers, embed_dim)
input_dim = noise_dim + embed_dim
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.LeakyReLU(0.2),
nn.Linear(hidden_dim, hidden_dim),
nn.LeakyReLU(0.2),
nn.Linear(hidden_dim, latent_dim)
)
def forward(self, z, ticker_ids):
emb = self.ticker_emb(ticker_ids)
x = torch.cat([z, emb], dim=1)
return self.net(x)
class ConditionalDiscriminator(nn.Module):
def __init__(self, latent_dim, embed_dim, num_tickers, hidden_dim=128):
super().__init__()
self.ticker_emb = nn.Embedding(num_tickers, embed_dim)
input_dim = latent_dim + embed_dim
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.LeakyReLU(0.2),
nn.Linear(hidden_dim, hidden_dim),
nn.LeakyReLU(0.2),
nn.Linear(hidden_dim, 1)
)
def forward(self, x, ticker_ids):
emb = self.ticker_emb(ticker_ids)
x_cat = torch.cat([x, emb], dim=1)
return self.net(x_cat)
def gradient_penalty_cond(D, real, fake, ticker_ids, device):
"""Compute gradient penalty for conditional discriminator D(x, ticker_ids)."""
batch_size = real.size(0)
alpha = torch.rand(batch_size, 1).to(device)
interpolates = (alpha * real + (1 - alpha) * fake).requires_grad_(True)
d_interpolates = D(interpolates, ticker_ids)
grad_outputs = torch.ones_like(d_interpolates).to(device)
gradients = torch.autograd.grad(
outputs=d_interpolates,
inputs=interpolates,
grad_outputs=grad_outputs,
create_graph=True,
retain_graph=True,
only_inputs=True
)[0]
gradients = gradients.view(batch_size, -1)
gp = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
return gp
if __name__ == "__main__":
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
latent_path = os.path.join(base_dir, "data", "processed", "latent_vectors.npy")
ticker_path = os.path.join(base_dir, "data", "processed", "sequence_tickers.npy")
models_dir = os.path.join(base_dir, "models")
resources_dir = os.path.join(base_dir, "resources")
os.makedirs(models_dir, exist_ok=True)
os.makedirs(resources_dir, exist_ok=True)
logger.info("Loading latent vectors from: %s", latent_path)
latent_vectors = np.load(latent_path)
logger.info("Loaded latent vectors shape: %s", latent_vectors.shape)
logger.info("Loading sequence ticker IDs from: %s", ticker_path)
sequence_tickers = np.load(ticker_path)
logger.info("Loaded ticker IDs shape: %s", sequence_tickers.shape)
scaler = StandardScaler()
latent_scaled = scaler.fit_transform(latent_vectors)
scaler_save = {"mean": scaler.mean_.tolist(), "scale": scaler.scale_.tolist()}
np.save(os.path.join(resources_dir, "latent_scaler.npy"), scaler_save)
logger.info("Saved latent scaler params to resources.")
dataset = LatentTickerDataset(latent_path, ticker_path)
dataset.latents = latent_scaled
batch_size = 256
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=True, num_workers=2)
noise_dim = 64
hidden_dim = 128
n_epochs = 300
lr = 1e-4
lambda_gp = 10
n_critic = 5
embed_dim = 16
latent_dim = latent_scaled.shape[1]
num_tickers = int(sequence_tickers.max()) + 1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
G = ConditionalGenerator(noise_dim=noise_dim, embed_dim=embed_dim,
num_tickers=num_tickers, latent_dim=latent_dim,
hidden_dim=hidden_dim).to(device)
D = ConditionalDiscriminator(latent_dim=latent_dim, embed_dim=embed_dim,
num_tickers=num_tickers, hidden_dim=hidden_dim).to(device)
opt_G = optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.9))
opt_D = optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.9))
losses = {"epoch": [], "D_loss": [], "G_loss": []}
logger.info("Starting Conditional WGAN-GP training...")
for epoch in range(n_epochs):
D_losses_epoch = []
G_losses_epoch = []
for real_batch, tickers_batch in tqdm(loader, desc=f"Epoch {epoch+1}/{n_epochs}", leave=False):
real = real_batch.to(device)
tickers = tickers_batch.to(device).long()
bsize = real.size(0)
for _ in range(n_critic):
z = torch.randn(bsize, noise_dim).to(device)
fake = G(z, tickers)
d_real = D(real, tickers)
d_fake = D(fake.detach(), tickers)
gp = gradient_penalty_cond(D, real, fake.detach(), tickers, device)
d_loss = -(d_real.mean() - d_fake.mean()) + lambda_gp * gp
opt_D.zero_grad()
d_loss.backward()
opt_D.step()
z = torch.randn(bsize, noise_dim).to(device)
fake = G(z, tickers)
g_loss = -D(fake, tickers).mean()
opt_G.zero_grad()
g_loss.backward()
opt_G.step()
D_losses_epoch.append(d_loss.item())
G_losses_epoch.append(g_loss.item())
mean_D = float(np.mean(D_losses_epoch)) if len(D_losses_epoch) else 0.0
mean_G = float(np.mean(G_losses_epoch)) if len(G_losses_epoch) else 0.0
losses["epoch"].append(epoch + 1)
losses["D_loss"].append(mean_D)
losses["G_loss"].append(mean_G)
logger.info(f"[{epoch+1}/{n_epochs}] D_loss={mean_D:.4f}, G_loss={mean_G:.4f}")
losses_df = pd.DataFrame(losses)
losses_csv_path = os.path.join(resources_dir, "latent_gan_losses.csv")
losses_df.to_csv(losses_csv_path, index=False)
logger.info("Saved training losses to %s", losses_csv_path)
torch.save(G.state_dict(), os.path.join(models_dir, "latent_gan_generator_conditional.pth"))
torch.save(D.state_dict(), os.path.join(models_dir, "latent_gan_discriminator_conditional.pth"))
logger.info("Saved GAN models to models/")
with open(os.path.join(resources_dir, "gan_config.json"), "w") as f:
json.dump({
"model": "WGAN-GP-conditional",
"noise_dim": noise_dim,
"latent_dim": latent_dim,
"hidden_dim": hidden_dim,
"epochs": n_epochs,
"batch_size": batch_size,
"lr": lr,
"lambda_gp": lambda_gp,
"n_critic": n_critic,
"embed_dim": embed_dim,
"num_tickers": num_tickers
}, f, indent=4)
logger.info("Saved GAN config to resources/gan_config.json")
logger.info("Training completed successfully.")
|