python/prophet/plot.py [45:70]:
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):
    """Plot the Prophet forecast.

    Parameters
    ----------
    m: Prophet model.
    fcst: pd.DataFrame output of m.predict.
    ax: Optional matplotlib axes on which to plot.
    uncertainty: Optional boolean to plot uncertainty intervals, which will
        only be done if m.uncertainty_samples > 0.
    plot_cap: Optional boolean indicating if the capacity should be shown
        in the figure, if available.
    xlabel: Optional label name on X-axis
    ylabel: Optional label name on Y-axis
    figsize: Optional tuple width, height in inches.
    include_legend: Optional boolean to add legend to the plot.

    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()
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python/prophet/plot.py [478:517]:
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):
    """Plot a performance metric vs. forecast horizon from cross validation.

    Cross validation produces a collection of out-of-sample model predictions
    that can be compared to actual values, at a range of different horizons
    (distance from the cutoff). This computes a specified performance metric
    for each prediction, and aggregated over a rolling window with horizon.

    This uses prophet.diagnostics.performance_metrics to compute the metrics.
    Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'.

    rolling_window is the proportion of data included in the rolling window of
    aggregation. The default value of 0.1 means 10% of data are included in the
    aggregation for computing the metric.

    As a concrete example, if metric='mse', then this plot will show the
    squared error for each cross validation prediction, along with the MSE
    averaged over rolling windows of 10% of the data.

    Parameters
    ----------
    df_cv: The output from prophet.diagnostics.cross_validation.
    metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', 'coverage'].
    rolling_window: Proportion of data to use for rolling average of metric.
        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.
    color: Optional color for plot and error points, useful when plotting
        multiple model performances on one axis for comparison.

    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()
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