arviz.plot_separation — ArviZ dev documentation (original) (raw)
arviz.plot_separation(idata=None, y=None, y_hat=None, y_hat_line=False, expected_events=False, figsize=None, textsize=None, color='C0', legend=True, ax=None, plot_kwargs=None, y_hat_line_kwargs=None, exp_events_kwargs=None, backend=None, backend_kwargs=None, show=None)[source]#
Separation plot for binary outcome models.
Model predictions are sorted and plotted using a color code according to the observed data.
Parameters:
idataInferenceData
arviz.InferenceData object.
Observed data. If str, idata must be present and contain the observed data group
Posterior predictive samples for y. It must have the same shape as y. If str or None, idata must contain the posterior predictive group.
y_hat_linebool, optional
Plot the sorted y_hat predictions.
expected_eventsbool, optional
Plot the total number of expected events.
figsizefigure size tuple, optional
If None, size is (8 + numvars, 8 + numvars)
textsize: int, optional
Text size for labels. If None it will be autoscaled based on figsize.
colorstr, optional
Color to assign to the positive class. The negative class will be plotted using the same color and an alpha=0.3 transparency.
legendbool, optional
Show the legend of the figure.
ax: axes, optional
Matplotlib axes or bokeh figures.
plot_kwargsdict, optional
Additional keywords passed to matplotlib.axes.Axes.bar() orbokeh:bokeh.plotting.Figure.vbar() for separation plot.
y_hat_line_kwargsdict, optional
Additional keywords passed to ax.plot for y_hat line.
exp_events_kwargsdict, optional
Additional keywords passed to ax.scatter for expected_events marker.
backend: str, optional
Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.
backend_kwargs: bool, optional
These are kwargs specific to the backend being used, passed tomatplotlib.pyplot.subplots() orbokeh.plotting.figure().
showbool, optional
Call backend show function.
Returns:
axesmatplotlib axes or bokeh figures
See also
Plot for posterior/prior predictive checks.
References
Examples
Separation plot for a logistic regression model.
import arviz as az idata = az.load_arviz_data('classification10d') az.plot_separation(idata=idata, y='outcome', y_hat='outcome', figsize=(8, 1))
