Source code for arviz.plots.separationplot

"""Separation plot for discrete outcome models."""

import warnings

import numpy as np
import xarray as xr

from import InferenceData
from ..rcparams import rcParams
from .plot_utils import get_plotting_function

[docs] def 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, ): """Separation plot for binary outcome models. Model predictions are sorted and plotted using a color code according to the observed data. Parameters ---------- idata : InferenceData :class:`arviz.InferenceData` object. y : array, DataArray or str Observed data. If str, ``idata`` must be present and contain the observed data group y_hat : array, DataArray or str 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_line : bool, optional Plot the sorted ``y_hat`` predictions. expected_events : bool, optional Plot the total number of expected events. figsize : figure 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``. color : str, optional Color to assign to the positive class. The negative class will be plotted using the same color and an `alpha=0.3` transparency. legend : bool, optional Show the legend of the figure. ax: axes, optional Matplotlib axes or bokeh figures. plot_kwargs : dict, optional Additional keywords passed to :meth:`` or :meth:`bokeh:bokeh.plotting.Figure.vbar` for separation plot. y_hat_line_kwargs : dict, optional Additional keywords passed to ax.plot for ``y_hat`` line. exp_events_kwargs : dict, 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 to :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`. show : bool, optional Call backend show function. Returns ------- axes : matplotlib axes or bokeh figures See Also -------- plot_ppc : Plot for posterior/prior predictive checks. References ---------- .. [1] Greenhill, B. *et al.*, The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models, *American Journal of Political Science*, (2011) see Examples -------- Separation plot for a logistic regression model. .. plot:: :context: close-figs >>> import arviz as az >>> idata = az.load_arviz_data('classification10d') >>> az.plot_separation(idata=idata, y='outcome', y_hat='outcome', figsize=(8, 1)) """ label_y_hat = "y_hat" if idata is not None and not isinstance(idata, InferenceData): raise ValueError("idata must be of type InferenceData or None") if idata is None: if not all(isinstance(arg, (np.ndarray, xr.DataArray)) for arg in (y, y_hat)): raise ValueError( "y and y_hat must be array or DataArray when idata is None " f"but they are of types {[type(arg) for arg in (y, y_hat)]}" ) else: if y_hat is None and isinstance(y, str): label_y_hat = y y_hat = y elif y_hat is None: raise ValueError("y_hat cannot be None if y is not a str") if isinstance(y, str): y = idata.observed_data[y].values elif not isinstance(y, (np.ndarray, xr.DataArray)): raise ValueError(f"y must be of types array, DataArray or str, not {type(y)}") if isinstance(y_hat, str): label_y_hat = y_hat y_hat = idata.posterior_predictive[y_hat].mean(dim=("chain", "draw")).values elif not isinstance(y_hat, (np.ndarray, xr.DataArray)): raise ValueError(f"y_hat must be of types array, DataArray or str, not {type(y_hat)}") if len(y) != len(y_hat): warnings.warn( "y and y_hat must be the same length", UserWarning, ) locs = np.linspace(0, 1, len(y_hat)) width = np.diff(locs).mean() separation_kwargs = dict( y=y, y_hat=y_hat, y_hat_line=y_hat_line, label_y_hat=label_y_hat, expected_events=expected_events, figsize=figsize, textsize=textsize, color=color, legend=legend, locs=locs, width=width, ax=ax, plot_kwargs=plot_kwargs, y_hat_line_kwargs=y_hat_line_kwargs, exp_events_kwargs=exp_events_kwargs, backend_kwargs=backend_kwargs, show=show, ) if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() plot = get_plotting_function("plot_separation", "separationplot", backend) axes = plot(**separation_kwargs) return axes