bayesmark/builtin_opt/hyperopt_optimizer.py [88:108]:
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        round_to_values = {}
        for param_name in param_list:
            param_config = api_config[param_name]

            param_type = param_config["type"]

            param_space = param_config.get("space", None)
            param_range = param_config.get("range", None)
            param_values = param_config.get("values", None)

            # Some setup for case that whitelist of values is provided:
            values_only_type = param_type in ("cat", "ordinal")
            if (param_values is not None) and (not values_only_type):
                assert param_range is None
                param_values = np.unique(param_values)
                param_range = (param_values[0], param_values[-1])
                round_to_values[param_name] = interp1d(
                    param_values, param_values, kind="nearest", fill_value="extrapolate"
                )

            if param_type == "int":
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example_opt_root/scikit_optimizer.py [59:79]:
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        round_to_values = {}
        for param_name in param_list:
            param_config = api_config[param_name]

            param_type = param_config["type"]

            param_space = param_config.get("space", None)
            param_range = param_config.get("range", None)
            param_values = param_config.get("values", None)

            # Some setup for case that whitelist of values is provided:
            values_only_type = param_type in ("cat", "ordinal")
            if (param_values is not None) and (not values_only_type):
                assert param_range is None
                param_values = np.unique(param_values)
                param_range = (param_values[0], param_values[-1])
                round_to_values[param_name] = interp1d(
                    param_values, param_values, kind="nearest", fill_value="extrapolate"
                )

            if param_type == "int":
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