def model_to_dict()

in python/prophet/serialize.py [0:0]

• 42 lines of code
• 16 McCabe index (conditional complexity)

def model_to_dict(model):
    """Convert a Prophet model to a dictionary suitable for JSON serialization.

    Model must be fitted. Skips Stan objects that are not needed for predict.

    Can be reversed with model_from_dict.

    Parameters
    ----------
    model: Prophet model object.

    Returns
    -------
    dict that can be used to serialize a Prophet model as JSON or loaded back
    into a Prophet model.
    """
    if model.history is None:
        raise ValueError(
            "This can only be used to serialize models that have already been fit."
        )

    model_dict = {
        attribute: getattr(model, attribute) for attribute in SIMPLE_ATTRIBUTES
    }
    # Handle attributes of non-core types
    for attribute in PD_SERIES:
        if getattr(model, attribute) is None:
            model_dict[attribute] = None
        else:
            model_dict[attribute] = getattr(model, attribute).to_json(
                orient='split', date_format='iso'
            )
    for attribute in PD_TIMESTAMP:
        model_dict[attribute] = getattr(model, attribute).timestamp()
    for attribute in PD_TIMEDELTA:
        model_dict[attribute] = getattr(model, attribute).total_seconds()
    for attribute in PD_DATAFRAME:
        if getattr(model, attribute) is None:
            model_dict[attribute] = None
        else:
            model_dict[attribute] = getattr(model, attribute).to_json(orient='table', index=False)
    for attribute in NP_ARRAY:
        model_dict[attribute] = getattr(model, attribute).tolist()
    for attribute in ORDEREDDICT:
        model_dict[attribute] = [
            list(getattr(model, attribute).keys()),
            getattr(model, attribute),
        ]
    # Other attributes with special handling
    # fit_kwargs -> Transform any numpy types before serializing.
    # They do not need to be transformed back on deserializing.
    fit_kwargs = deepcopy(model.fit_kwargs)
    if 'init' in fit_kwargs:
        for k, v in fit_kwargs['init'].items():
            if isinstance(v, np.ndarray):
                fit_kwargs['init'][k] = v.tolist()
            elif isinstance(v, np.floating):
                fit_kwargs['init'][k] = float(v)
    model_dict['fit_kwargs'] = fit_kwargs

    # Params (Dict[str, np.ndarray])
    model_dict['params'] = {k: v.tolist() for k, v in model.params.items()}
    # Attributes that are skipped: stan_fit, stan_backend
    model_dict['__prophet_version'] = __version__
    return model_dict