cilipep/ai-agent-with-tslab/hybrid-ensemble

class SklearnModelWrapper:
    def __init__(self, model, window, n_features):
        self.model = model
        self.window = window
        self.n_features = n_features
    def __call__(self, x):
        # x: (batch, seq, features) tensor
        x_np = x.detach().cpu().numpy()
        X_flat = x_np.reshape(x_np.shape[0], -1)  # flatten sequences
        proba = self.model.predict_proba(X_flat)[:, 1]
        eps = 1e-7
        proba = np.clip(proba, eps, 1 - eps)
        logits = np.log(proba / (1 - proba))  # convert to logit
        return torch.tensor(logits, dtype=torch.float32).unsqueeze(1)

class HybridEnsemble:
    def __init__(self, neural_models, sklearn_wrappers, weights=None):
        self.all_models = neural_models + sklearn_wrappers
        if weights is None:
            self.weights = [1.0 / len(self.all_models)] * len(self.all_models)
        else:
            total = sum(weights)
            self.weights = [w / total for w in weights]
    def __call__(self, x):
        logits = torch.stack([m(x) for m in self.all_models])
        weights = torch.tensor(self.weights, device=x.device).view(-1, 1, 1)
        return (logits * weights).sum(dim=0)