Function plot_metric_graph

monai/utils/jupyter_utils.py:46–90

Plot metrics on a single graph with running averages plotted for selected keys. The values in `graphmap` should be lists of (timepoint, value) pairs as stored in MetricLogger objects. Args: ax: Axes object to plot into title: graph title graphmap: dictionary of

(
    ax: plt.Axes,
    title: str,
    graphmap: Mapping[str, list[float] | tuple[list[float], list[float]]],
    yscale: str = "log",
    avg_keys: tuple[str] = (LOSS_NAME,),
    window_fraction: int = 20,
)

def plot_metric_graph(
    ax: plt.Axes,
    title: str,
    graphmap: Mapping[str, list[float] | tuple[list[float], list[float]]],
    yscale: str = "log",
    avg_keys: tuple[str] = (LOSS_NAME,),
    window_fraction: int = 20,
) -> None:
    """
    Plot metrics on a single graph with running averages plotted for selected keys. The values in `graphmap`
    should be lists of (timepoint, value) pairs as stored in MetricLogger objects.

    Args:
        ax: Axes object to plot into
        title: graph title
        graphmap: dictionary of named graph values, which are lists of values or (index, value) pairs
        yscale: scale for y-axis compatible with `Axes.set_yscale`
        avg_keys: tuple of keys in `graphmap` to provide running average plots for
        window_fraction: what fraction of the graph value length to use as the running average window
    """
    from matplotlib.ticker import MaxNLocator

    for n, v in graphmap.items():
        if len(v) > 0:
            if isinstance(v[0], (tuple, list)):  # values are (x,y) pairs
                inds, vals = zip(*v)  # separate values into list of indices in X dimension and values
            else:
                inds, vals = tuple(range(len(v))), tuple(v)  # values are without indices, make indices for them

            ax.plot(inds, vals, label=f"{n} = {vals[-1]:.5g}")

            # if requested compute and plot a running average for the values using a fractional window size
            if n in avg_keys and len(v) > window_fraction:
                window = len(v) // window_fraction
                kernel = np.ones((window,)) / window
                ra = np.convolve((vals[0],) * (window - 1) + vals, kernel, mode="valid")

                ax.plot(inds, ra, label=f"{n} Avg = {ra[-1]:.5g}")

    ax.set_title(title)
    ax.set_yscale(yscale)
    ax.axis("on")
    ax.legend(bbox_to_anchor=(1, 1), loc=1, borderaxespad=0.0)
    ax.grid(True, "both", "both")
    ax.xaxis.set_major_locator(MaxNLocator(integer=True))


def plot_metric_images(