How to use the fbprophet.diagnostics.performance_metrics function in fbprophet

In [0, 1]. Defaults to 0.1.
    ax: Optional matplotlib axis on which to plot. If not given, a new figure
        will be created.
    figsize: Optional tuple width, height in inches.

    Returns
    -------
    a matplotlib figure.
    """
    if ax is None:
        fig = plt.figure(facecolor='w', figsize=figsize)
        ax = fig.add_subplot(111)
    else:
        fig = ax.get_figure()
    # Get the metric at the level of individual predictions, and with the rolling window.
    df_none = performance_metrics(df_cv, metrics=[metric], rolling_window=-1)
    df_h = performance_metrics(df_cv, metrics=[metric], rolling_window=rolling_window)

    # Some work because matplotlib does not handle timedelta
    # Target ~10 ticks.
    tick_w = max(df_none['horizon'].astype('timedelta64[ns]')) / 10.
    # Find the largest time resolution that has <1 unit per bin.
    dts = ['D', 'h', 'm', 's', 'ms', 'us', 'ns']
    dt_names = [
        'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds',
        'nanoseconds'
    ]
    dt_conversions = [
        24 * 60 * 60 * 10 ** 9,
        60 * 60 * 10 ** 9,
        60 * 10 ** 9,
        10 ** 9,

ax: Optional matplotlib axis on which to plot. If not given, a new figure
        will be created.
    figsize: Optional tuple width, height in inches.

    Returns
    -------
    a matplotlib figure.
    """
    if ax is None:
        fig = plt.figure(facecolor='w', figsize=figsize)
        ax = fig.add_subplot(111)
    else:
        fig = ax.get_figure()
    # Get the metric at the level of individual predictions, and with the rolling window.
    df_none = performance_metrics(df_cv, metrics=[metric], rolling_window=-1)
    df_h = performance_metrics(df_cv, metrics=[metric], rolling_window=rolling_window)

    # Some work because matplotlib does not handle timedelta
    # Target ~10 ticks.
    tick_w = max(df_none['horizon'].astype('timedelta64[ns]')) / 10.
    # Find the largest time resolution that has <1 unit per bin.
    dts = ['D', 'h', 'm', 's', 'ms', 'us', 'ns']
    dt_names = [
        'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds',
        'nanoseconds'
    ]
    dt_conversions = [
        24 * 60 * 60 * 10 ** 9,
        60 * 60 * 10 ** 9,
        60 * 10 ** 9,
        10 ** 9,
        10 ** 6,

Background:

    This model validation function is still under construction and will be updated during a future release.


    """

    count_of_time_units = len(dates)
    #print(count_of_time_units)
    initial_size = str(int(count_of_time_units * 0.20)) + " days"
    horizon_size = str(int(count_of_time_units * 0.10)) + " days"
    period_size = str(int(count_of_time_units * 0.05)) + " days"

    df_cv = cross_validation(model, initial=initial_size, horizon=horizon_size, period=period_size)
    #df_cv = cross_validation(model,initial='730 days', period='180 days', horizon = '365 days')
    df_p = performance_metrics(df_cv)

    #print(df_cv.head(100))
    #print(df_p.head(100))

    mape_score_avg = str(round(df_p['mape'].mean()*100,2)) + "%"

    return mape_score_avg