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import sys
import os
import json
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from models.forecasting.lstm import LSTMForecaster
def load_test_data(csv_path="../data/processed/merged_features.csv", seq_len=30, test_ratio=0.2):
df = pd.read_csv(csv_path)
df = df.select_dtypes(include=[np.number]).dropna()
data = df.values
split = int(len(data) * (1 - test_ratio))
test_data = data[split:]
X_test, y_test = [], []
for i in range(len(test_data) - seq_len - 1):
X_test.append(test_data[i:i+seq_len])
y_test.append(test_data[i+seq_len][0])
X_test = torch.tensor(np.array(X_test), dtype=torch.float32)
y_test = torch.tensor(np.array(y_test), dtype=torch.float32).unsqueeze(1)
return DataLoader(TensorDataset(X_test, y_test), batch_size=32), X_test.shape[2]
def evaluate_model(test_loader, model_path="./trained_models/lstm_forecaster.pt", config_path="./trained_models/config.json"):
with open(config_path, "r") as f:
config = json.load(f)
model = LSTMForecaster(
input_size=config["input_size"],
hidden_size=config["hidden_size"],
num_layers=config["num_layers"],
output_size=config["output_size"]
)
model.load_state_dict(torch.load(model_path))
model.eval()
preds, targets = [], []
with torch.no_grad():
for X, y in test_loader:
out = model(X)
preds.append(out.numpy())
targets.append(y.numpy())
preds = np.concatenate(preds)
targets = np.concatenate(targets)
mse = mean_squared_error(targets, preds)
mae = mean_absolute_error(targets, preds)
r2 = r2_score(targets, preds)
print("\n📊 Evaluation Metrics:")
print(f"➡️ MSE: {mse:.4f}")
print(f"➡️ MAE: {mae:.4f}")
print(f"➡️ R2 Score: {r2:.4f}")
os.makedirs("outputs", exist_ok=True)
plt.figure(figsize=(12, 5))
plt.plot(targets, label='Actual', color='blue')
plt.plot(preds, label='Predicted', color='orange')
plt.title("📈 LSTM Forecast vs Actual")
plt.xlabel("Time Step")
plt.ylabel("Value")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig("outputs/evaluation_plot.png")
plt.show()
if __name__ == "__main__":
test_loader, input_size = load_test_data()
print(f"🧪 Detected input feature size: {input_size}")
evaluate_model(test_loader)
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