plots

plots

plot_murphy_diagram(y_obs, y_pred, weights=None, *, etas=100, functional='mean', level=0.5, ax=None)

Plot a Murphy diagram.

A Murphy diagram plots the scores of elementary scoring functions ElementaryScore over a range of their free parameter eta. This shows, if a model dominates all others over a wide class of scoring functions or if the ranking is very much dependent on the choice of scoring function. See Notes for further details.

Parameters:

Name	Type	Description	Default
y_obs	array-like of shape (n_obs)	Observed values of the response variable. For binary classification, y_obs is expected to be in the interval [0, 1].	required
y_pred	array-like of shape (n_obs) or (n_obs, n_models)	Predicted values of the conditional expectation of Y, E(Y|X).	required
weights	array-like of shape (n_obs) or None	Case weights.	None
etas	int or array - like	If an integer is given, equidistant points between min and max y values are generater. If an array-like is given, those points are used.	100
functional	str	The functional that is induced by the identification function V. Options are: "mean". Argument level is neglected. "median". Argument level is neglected. "expectile" "quantile"	'mean'
level	float	The level of the expectile of quantile. (Often called \(\alpha\).) It must be 0 < level < 1. level=0.5 and functional="expectile" gives the mean. level=0.5 and functional="quantile" gives the median.	0.5
ax	Axes	Axes object to draw the plot onto, otherwise uses the current Axes.	None

Returns:

Name	Type	Description
ax		Either the matplotlib axes or the plotly figure. This is configurable by setting the plot_backend via model_diagnostics.set_config or model_diagnostics.config_context.

Notes

For details, refer to [Ehm2015].

References

[Ehm2015]

W. Ehm, T. Gneiting, A. Jordan, F. Krüger. "Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings". arxiv:1503.08195.

Source code in src/model_diagnostics/scoring/plots.py

def plot_murphy_diagram(
    y_obs: npt.ArrayLike,
    y_pred: npt.ArrayLike,
    weights: Optional[npt.ArrayLike] = None,
    *,
    etas: Union[int, npt.ArrayLike] = 100,
    functional: str = "mean",
    level: float = 0.5,
    ax: Optional[mpl.axes.Axes] = None,
):
    r"""Plot a Murphy diagram.

    A Murphy diagram plots the scores of elementary scoring functions `ElementaryScore`
    over a range of their free parameter `eta`. This shows, if a model dominates all
    others over a wide class of scoring functions or if the ranking is very much
    dependent on the choice of scoring function.
    See [Notes](#notes) for further details.

    Parameters
    ----------
    y_obs : array-like of shape (n_obs)
        Observed values of the response variable.
        For binary classification, y_obs is expected to be in the interval [0, 1].
    y_pred : array-like of shape (n_obs) or (n_obs, n_models)
        Predicted values of the conditional expectation of Y, `E(Y|X)`.
    weights : array-like of shape (n_obs) or None
        Case weights.
    etas : int or array-like
        If an integer is given, equidistant points between min and max y values are
        generater. If an array-like is given, those points are used.
    functional : str
        The functional that is induced by the identification function `V`. Options are:

        - `"mean"`. Argument `level` is neglected.
        - `"median"`. Argument `level` is neglected.
        - `"expectile"`
        - `"quantile"`
    level : float
        The level of the expectile of quantile. (Often called \(\alpha\).)
        It must be `0 < level < 1`.
        `level=0.5` and `functional="expectile"` gives the mean.
        `level=0.5` and `functional="quantile"` gives the median.
    ax : matplotlib.axes.Axes
        Axes object to draw the plot onto, otherwise uses the current Axes.

    Returns
    -------
    ax :
        Either the matplotlib axes or the plotly figure. This is configurable by
        setting the `plot_backend` via
        [`model_diagnostics.set_config`][model_diagnostics.set_config] or
        [`model_diagnostics.config_context`][model_diagnostics.config_context].

    Notes
    -----
    For details, refer to `[Ehm2015]`.

    References
    ----------
    `[Ehm2015]`

    :   W. Ehm, T. Gneiting, A. Jordan, F. Krüger.
        "Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet
        Representations, and Forecast Rankings".
        [arxiv:1503.08195](https://arxiv.org/abs/1503.08195).
    """
    if ax is None:
        plot_backend = get_config()["plot_backend"]
        if plot_backend == "matplotlib":
            ax = plt.gca()
        else:
            import plotly.graph_objects as go

            fig = ax = go.Figure()
    elif isinstance(ax, mpl.axes.Axes):
        plot_backend = "matplotlib"
    elif is_plotly_figure(ax):
        import plotly.graph_objects as go

        plot_backend = "plotly"
        fig = ax
    else:
        msg = (
            "The ax argument must be None, a matplotlib Axes or a plotly Figure, "
            f"got {type(ax)}."
        )
        raise ValueError(msg)

    if (n_cols := length_of_second_dimension(y_obs)) > 0:
        if n_cols == 1:
            y_obs = get_second_dimension(y_obs, 0)
        else:
            msg = (
                f"Array-like y_obs has more than 2 dimensions, y_obs.shape[1]={n_cols}"
            )
            raise ValueError(msg)

    y_pred_min, y_pred_max = get_array_min_max(y_pred)
    y_obs_min, y_obs_max = get_array_min_max(y_obs)
    y_min, y_max = min(y_pred_min, y_obs_min), max(y_pred_max, y_obs_max)

    if y_min == y_max:
        msg = "All values y_obs and y_pred are one single and same value."
        raise ValueError(msg)
    elif isinstance(etas, numbers.Integral):
        etas = np.linspace(y_min, y_max, num=etas, endpoint=True)
    else:
        etas = np.asarray(etas).astype(float)
        if etas.ndim > 1:
            etas = etas.reshape(max(etas.shape))

    def elementary_score(y_obs, y_pred, weights, eta):
        sf = ElementaryScore(eta, functional=functional, level=level)
        return sf(y_obs=y_obs, y_pred=y_pred, weights=weights)

    n_pred = length_of_second_dimension(y_pred)
    pred_names, _ = get_sorted_array_names(y_pred)

    for i in range(len(pred_names)):
        y_pred_i = y_pred if n_pred == 0 else get_second_dimension(y_pred, i)

        y_plot = [
            elementary_score(y_obs=y_obs, y_pred=y_pred_i, weights=weights, eta=eta)
            for eta in etas
        ]
        label = pred_names[i] if n_pred >= 2 else None
        if plot_backend == "matplotlib":
            ax.plot(etas, y_plot, label=label)
        else:
            fig.add_scatter(
                x=etas,
                y=y_plot,
                mode="lines",
                line={"color": get_plotly_color(i)},
                name=label,
            )

    xlabel = "eta"
    ylabel = "score"
    title = "Murphy Diagram"
    if n_pred <= 1 and len(pred_names[0]) > 0:
        title = title + " " + pred_names[0]

    if plot_backend == "matplotlib":
        if n_pred >= 2:
            ax.legend()
        ax.set_title(title)
        ax.set(xlabel=xlabel, ylabel=ylabel)
    else:
        if n_pred <= 1:
            fig.update_layout(showlegend=False)
        fig.update_layout(xaxis_title=xlabel, yaxis_title=ylabel, title=title)

    return ax