import torch
import joblib
from huggingface_hub import hf_hub_download

# Download files
model_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="best_mlp_model.pth")
architecture_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="model_architecture.py")
scaler_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="scaler.pkl")
config_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="config.json")

# Load architecture
import importlib.util
spec = importlib.util.spec_from_file_location("model_architecture", architecture_path)
model_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(model_module)

# Load model
model = model_module.MLP(input_dim=22, hidden_dims=[128, 64, 32], dropout_rate=0.3)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()

# Load scaler
scaler = joblib.load(scaler_path)

# Make prediction
# X_new should be a pandas DataFrame or numpy array with the correct features
X_scaled = scaler.transform(X_new)
X_tensor = torch.FloatTensor(X_scaled)
with torch.no_grad():
    xg_prediction = model(X_tensor).numpy()

Training

Architecture: Multi-Layer Perceptron with 3 hidden layers
Hidden Dimensions: 128 → 64 → 32
Input Features: 22 features
Output: Binary probability (goal vs no goal)
Framework: PyTorch
Dropout Rate: 0.3
Activation: ReLU (hidden layers), Sigmoid (output)
Normalization: Batch Normalization after each hidden layer

The model was trained on football shot event data with:

Binary Cross Entropy loss
Adam optimizer (lr=0.001, weight_decay=1e-5)
ReduceLROnPlateau scheduler
Batch size: 256
Epochs: 50

License

MIT