BF plot: Use arviz's kde instead of the one from scipy

CHANGELOG.md

@@ -4,6 +4,8 @@
 
 ### New features
 
+- Bayes Factor plot: Use arviz's kde instead of the one from scipy ([2237](https://github.com/arviz-devs/arviz/pull/2237))
+
 ### Maintenance and fixes
 
 ### Deprecation

arviz/plots/backends/matplotlib/bfplot.py

@@ -1,5 +1,4 @@
 import matplotlib.pyplot as plt
-import numpy as np
 
 from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser
 from ...distplot import plot_dist
@@ -10,11 +9,8 @@ def plot_bf(
     ax,
     bf_10,
     bf_01,
-    xlim,
     prior,
     posterior,
-    prior_pdf,
-    posterior_pdf,
     ref_val,
     prior_at_ref_val,
     posterior_at_ref_val,
@@ -52,14 +48,22 @@ def plot_bf(
     if ax is None:
         _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs)
 
-    x = np.linspace(*xlim, 5000)
-
-    if posterior.dtype.kind == "f":
-        ax.plot(x, prior_pdf(x), color=colors[0], label="Prior", **plot_kwargs)
-        ax.plot(x, posterior_pdf(x), color=colors[1], label="Posterior", **plot_kwargs)
-    elif posterior.dtype.kind == "i":
-        plot_dist(prior, color=colors[0], label="Prior", ax=ax, hist_kwargs=hist_kwargs)
-        plot_dist(posterior, color=colors[1], label="Posterior", ax=ax, hist_kwargs=hist_kwargs)
+    plot_dist(
+        prior,
+        color=colors[0],
+        label="Prior",
+        ax=ax,
+        plot_kwargs=plot_kwargs,
+        hist_kwargs=hist_kwargs,
+    )
+    plot_dist(
+        posterior,
+        color=colors[1],
+        label="Posterior",
+        ax=ax,
+        plot_kwargs=plot_kwargs,
+        hist_kwargs=hist_kwargs,
+    )
 
     ax.plot(ref_val, posterior_at_ref_val, "ko", lw=1.5)
     ax.plot(ref_val, prior_at_ref_val, "ko", lw=1.5)

arviz/plots/bfplot.py

@@ -1,10 +1,12 @@
 # Plotting and reporting Bayes Factor given idata, var name, prior distribution and reference value
+# pylint: disable=unbalanced-tuple-unpacking
 import logging
 
-from scipy.stats import gaussian_kde
+from numpy import interp
 
 from ..data.utils import extract
 from .plot_utils import get_plotting_function
+from ..stats.density_utils import _kde_linear
 
 _log = logging.getLogger(__name__)
 
@@ -14,7 +16,6 @@ def plot_bf(
     var_name,
     prior=None,
     ref_val=0,
-    xlim=None,
     colors=("C0", "C1"),
     figsize=None,
     textsize=None,
@@ -47,8 +48,6 @@ def plot_bf(
         In case we want to use different prior, for example for sensitivity analysis.
     ref_val : int, default 0
         Point-null for Bayes factor estimation.
-    xlim :  tuple, optional
-        Set the x limits, which might be used for visualization purposes.
     colors : tuple, default ('C0', 'C1')
         Tuple of valid Matplotlib colors. First element for the prior, second for the posterior.
     figsize : (float, float), optional
@@ -94,7 +93,7 @@ def plot_bf(
         ...     prior={"a":np.random.normal(0, 1, 5000)})
         >>> az.plot_bf(idata, var_name="a", ref_val=0)
     """
-    posterior = extract(idata, var_names=var_name)
+    posterior = extract(idata, var_names=var_name).values
 
     if ref_val > posterior.max() or ref_val < posterior.min():
         _log.warning(
@@ -106,21 +105,15 @@ def plot_bf(
         _log.warning("Posterior distribution has {posterior.ndim} dimensions")
 
     if prior is None:
-        prior = extract(idata, var_names=var_name, group="prior")
-
-    if xlim is None:
-        xlim = (prior.min(), prior.max())
+        prior = extract(idata, var_names=var_name, group="prior").values
 
     if posterior.dtype.kind == "f":
-        posterior_pdf = gaussian_kde(posterior)
-        prior_pdf = gaussian_kde(prior)
-
-        posterior_at_ref_val = posterior_pdf(ref_val)
-        prior_at_ref_val = prior_pdf(ref_val)
+        posterior_grid, posterior_pdf = _kde_linear(posterior)
+        prior_grid, prior_pdf = _kde_linear(prior)
+        posterior_at_ref_val = interp(ref_val, posterior_grid, posterior_pdf)
+        prior_at_ref_val = interp(ref_val, prior_grid, prior_pdf)
 
     elif posterior.dtype.kind == "i":
-        prior_pdf = None
-        posterior_pdf = None
         posterior_at_ref_val = (posterior == ref_val).mean()
         prior_at_ref_val = (prior == ref_val).mean()
 
@@ -131,11 +124,8 @@ def plot_bf(
         ax=ax,
         bf_10=bf_10.item(),
         bf_01=bf_01.item(),
-        xlim=xlim,
         prior=prior,
         posterior=posterior,
-        prior_pdf=prior_pdf,
-        posterior_pdf=posterior_pdf,
         ref_val=ref_val,
         prior_at_ref_val=prior_at_ref_val,
         posterior_at_ref_val=posterior_at_ref_val,