//  [python]
def perform_hyperparameter_search(
    data_origin: str, models: List[str], result_dir: str, save_top_n: int = 10
):
    """
    Perform hyperparameter search for a list of models and save the results into a directory.

    Parameters
    ----------
    data_origin: str
        Name of data set models should be evaluated on.
    models: List[str]
        List specifiying the names of models.
    result_dir: str
        Directory that results should be saved to.
    save_top_n: int
        Save the top n parameter configuration. Default is 10.
    """

    data_loader = load_data_from_origin(args.data_origin)
    dh = DataHandler(**data_loader)

    train_data, _, val_data = dh.load_data_splits()
    feat_names = dh.load_feature_names()
    target_name = dh.load_target_name()

    with tqdm(total=get_num_runs(models)) as progress_bar:

        for model_name in models:

            X_train = train_data[feat_names].values
            X_val = val_data[feat_names].values

            # Scale and impute
            if model_name != "HI-VAE":
                pipe = pipeline.Pipeline(
                    [("scaler", StandardScaler()), ("imputer", SimpleImputer())]
                )
                X_train = pipe.fit_transform(X_train)
                X_val = pipe.transform(X_val)

            y_train, y_val = (
                train_data[target_name].values,
                val_data[target_name].values,
            )

            progress_bar.postfix = f"(model: {model_name})"
            progress_bar.update()
            scores = {}
            model_type = MODEL_CLASSES[model_name]

            sampled_params = sample_hyperparameters(model_name, data_origin)

            for run, param_set in enumerate(sampled_params):

                if model_name in NEURAL_MODELS - DEEP_KERNELS:
                    param_set.update(input_size=len(feat_names))

                model = model_type(**param_set)

                try:
                    try:
                        model.fit(X_train, y_train, **TRAIN_PARAMS[model_name])
                    except AttributeError:
                        model.train(X_train, y_train, **TRAIN_PARAMS[model_name])

                    preds = model.predict(X_val)

                    # Neural predictors: Use the AUC-ROC score
                    if model_name in NEURAL_PREDICTORS | DEEP_KERNELS:
                        # When model training goes completely awry
                        if np.isnan(preds).all():
                            score = 0

                        else:
                            preds = preds[:, 1]
                            score = roc_auc_score(
                                y_true=y_val[~np.isnan(preds)],
                                y_score=preds[~np.isnan(preds)],
                            )
                            print(f"Score: {score}")

                    # Auto-encoders: Use mean negative reconstruction error (because score are sorted descendingly)
                    elif model_name in AUTOENCODERS:
                        score = -float(preds.mean())

                    # PPCA: Just use the (mean) log-likelihood
                    else:
                        score = preds.mean()

                # In case of nans due bad training parameters
                except (ValueError, RuntimeError) as e:
                    print(f"There was an error: '{str(e)}', run aborted.")
                    score = -np.inf

                if np.isnan(score):
                    score = -np.inf

                scores[run] = {"score": score, "hyperparameters": param_set}
                progress_bar.update(1)

                # Rank and save results
                # Do after every experiment in case anything goes wrong
                sorted_scores = dict(
                    list(
                        sorted(
                            scores.items(),
                            key=lambda run: run[1]["score"],
                            reverse=True,
                        )
                    )[:save_top_n]
                )
                model_result_dir = f"{result_dir}/{data_origin}/"

                if not os.path.exists(model_result_dir):
                    os.makedirs(model_result_dir)

                with open(f"{model_result_dir}/{model_name}.json", "w") as result_file:
                    result_file.write(json.dumps(sorted_scores, indent=4, default=str))