def get_default_prophet_parameter_search_space()

in kats/utils/parameter_tuning_utils.py [0:0]

• 48 lines of code
• 1 McCabe index (conditional complexity)

def get_default_prophet_parameter_search_space() -> List[Dict[str, Any]]:
    """Generates default search space as a list of dictionaries and returns it for prophet model.

    Each dictionary in the list corresponds to a hyperparameter, having properties
    defining that hyperparameter. Properties are name, type, value_type, values,
    is_ordered. Hyperparameters that are included: seasonality_prior_scale, yearly_seasonality,
    weekly_seasonality, daily_seasonality, seasonality_mode, changepoint_prior_scale,
    changepoint_range.

    Args:
        N/A

    Returns:
        As described above

    Raises:
        N/A
    """

    return [
        {
            "name": "seasonality_prior_scale",
            "type": "choice",
            "value_type": "float",
            "values": list(np.logspace(-2, 1, 10, endpoint=True)),
            "is_ordered": True,
        },
        {
            "name": "yearly_seasonality",
            "type": "choice",
            "value_type": "bool",
            "values": [True, False],
        },
        {
            "name": "weekly_seasonality",
            "type": "choice",
            "value_type": "bool",
            "values": [True, False],
        },
        {
            "name": "daily_seasonality",
            "type": "choice",
            "value_type": "bool",
            "values": [True, False],
        },
        {
            "name": "seasonality_mode",
            "type": "choice",
            "value_type": "str",
            "values": ["additive", "multiplicative"],
        },
        {
            "name": "changepoint_prior_scale",
            "type": "choice",
            "value_type": "float",
            "values": list(np.logspace(-3, 0, 10, endpoint=True)),
            "is_ordered": True,
        },
        {
            "name": "changepoint_range",
            "type": "choice",
            "value_type": "float",
            "values": list(np.arange(0.8, 0.96, 0.01)),  # last value is 0.95
            "is_ordered": True,
        },
    ]