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import os, json, math, pickle
from datetime import datetime, timedelta
import numpy as np
import yfinance as yf
from sklearn.metrics import mean_absolute_error, mean_squared_error
import torch
from models import StockLSTM
os.environ["CUDA_VISIBLE_DEVICES"] = ""
ARTIFACTS_DIR = "artifacts"
@torch.no_grad()
def evaluate(symbol: str):
base = os.path.join(ARTIFACTS_DIR, symbol.upper())
model = StockLSTM(input_dim=1, hidden_dim=64, num_layers=2, dropout=0.2)
model.load_state_dict(torch.load(os.path.join(base, "model.pt"), map_location="cpu"))
model.eval()
with open(os.path.join(base, "scaler.pkl"), "rb") as f:
scaler = pickle.load(f)
with open(os.path.join(base, "meta.json"), "r") as f:
meta = json.load(f)
seq_len = meta["seq_len"]
end = datetime.utcnow().date()
start = end - timedelta(days=5*365)
df = yf.download(symbol, start=start.isoformat(), end=end.isoformat(), progress=False, auto_adjust=True)
data = df[["Close"]].dropna().values
scaled = scaler.transform(data)
split_idx = int(len(scaled) * 0.8)
test_scaled = scaled[split_idx - seq_len:] # include tail of train for continuity
# build sequences
X, y = [], []
for i in range(seq_len, len(test_scaled)):
X.append(test_scaled[i-seq_len:i])
y.append(test_scaled[i])
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
X_t = torch.from_numpy(X) # [N, T, 1]
pred_scaled = model(X_t).numpy()
pred = scaler.inverse_transform(pred_scaled)
y_true = scaler.inverse_transform(y)
rmse = math.sqrt(mean_squared_error(y_true, pred))
mae = mean_absolute_error(y_true, pred)
return {"symbol": symbol.upper(), "rmse": rmse, "mae": mae, "n": len(y_true)}
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