skpro.utils.plotting.plot_crossplot_loss#

skpro.utils.plotting.plot_crossplot_loss(y_true, y_pred, metric, ax=None)[source]#

Cross-loss-plot for probabilistic regression.

Plots:

x-axis: ground truth values \(y_i\)
y-axis: loss of the prediction \(\widehat{y}_i\) corresponding to \(y_i\), as calculated by metric.evaluate_by_index

Parameters:

y_truearray-like, [n_samples, n_targets]: Ground truth values
y_predskpro distribution, or predict_var return, [n_samples, n_targets]: Predicted values
metricskpro metric: Metric to calculate the loss
axmatplotlib axes, optional: Axes to plot on, if None, a new figure is created and returned

Returns:

axmatplotlib axes: Axes containing the plot If ax was None, a new figure is created and returned If ax was not None, the same ax is returned with plot added

Examples

>>> from skpro.utils.plotting import plot_crossplot_loss
>>> from skpro.metrics import CRPS
>>> from skpro.regression.residual import ResidualDouble
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.datasets import load_diabetes
>>>
>>> X, y = load_diabetes(return_X_y=True, as_frame=True)
>>> reg_mean = LinearRegression()
>>> reg_resid = RandomForestRegressor()
>>> reg_proba = ResidualDouble(reg_mean, reg_resid)
>>>
>>> reg_proba.fit(X, y)
ResidualDouble(...)
>>> y_pred = reg_proba.predict_proba(X)
>>> crps_metric = CRPS()
>>> plot_crossplot_loss(y, y_pred, crps_metric)