Densityplot: add support for discrete variables (#2878)

* densityplot: add support for discrete variables

* fix error with xticks, add to release notes

* fix typo

Author: aloctavodia

RELEASE-NOTES.md

@@ -15,6 +15,7 @@
   Optionally it can plot divergences.
 - Plots of discrete distributions in the docstrings
 - Add logitnormal distribution
+- Densityplot: add support for discrete variables
 
 ### Fixes
 

pymc3/plots/densityplot.py

@@ -12,8 +12,9 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
                 colors='cycle', outline=True, hpd_markers='', shade=0., bw=4.5, figsize=None,
                 textsize=12, plot_transformed=False, ax=None):
     """
-    Generates KDE plots truncated at their 100*(1-alpha)% credible intervals from a trace or list of
-    traces. KDE plots are grouped per variable and colors assigned to models.
+    Generates KDE plots for continuous variables and histograms for discretes ones.
+    Plots are truncated at their 100*(1-alpha)% credible intervals. Plots are grouped
+    per variable and colors assigned to models.
 
     Parameters
     ----------
@@ -32,11 +33,11 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
         Defaults to 'mean'.
     colors : list or string, optional
         List with valid matplotlib colors, one color per model. Alternative a string can be passed.
-        If the string is `cycle `, it will automatically choose a color per model from matplolib's
+        If the string is `cycle`, it will automatically choose a color per model from matplolib's
         cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all
-        models. Defaults to 'C0' (blueish in most matplotlib styles)
+        models. Defaults to `cycle`.
     outline : boolean
-        Use a line to draw the truncated KDE and. Defaults to True
+        Use a line to draw KDEs and histograms. Default to True
     hpd_markers : str
         A valid `matplotlib.markers` like 'v', used to indicate the limits of the hpd interval.
         Defaults to empty string (no marker).
@@ -64,7 +65,7 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
 
     """
     if point_estimate not in ('mean', 'median', None):
-        raise ValueError("Point estimate should be 'mean' or 'median'")
+        raise ValueError("Point estimate should be 'mean', 'median' or None")
 
     if not isinstance(trace, (list, tuple)):
         trace = [trace]
@@ -77,7 +78,8 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
         else:
             models = ['']
     elif len(models) != lenght_trace:
-        raise ValueError("The number of names for the models does not match the number of models")
+        raise ValueError(
+            "The number of names for the models does not match the number of models")
 
     lenght_models = len(models)
 
@@ -97,8 +99,8 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
     if figsize is None:
         figsize = (6, len(varnames) * 2)
 
-    fig, kplot = plt.subplots(len(varnames), 1, squeeze=False, figsize=figsize)
-    kplot = kplot.flatten()
+    fig, dplot = plt.subplots(len(varnames), 1, squeeze=False, figsize=figsize)
+    dplot = dplot.flatten()
 
     for v_idx, vname in enumerate(varnames):
         for t_idx, tr in enumerate(trace):
@@ -108,23 +110,24 @@ def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='m
                 if k > 1:
                     vec = np.split(vec.T.ravel(), k)
                     for i in range(k):
-                        _kde_helper(vec[i], vname, colors[t_idx], bw, alpha, point_estimate,
-                                    hpd_markers, outline, shade, kplot[v_idx])
+                        _d_helper(vec[i], vname, colors[t_idx], bw, alpha, point_estimate,
+                                  hpd_markers, outline, shade, dplot[v_idx])
+
                 else:
-                    _kde_helper(vec, vname, colors[t_idx], bw, alpha, point_estimate,
-                                hpd_markers, outline, shade, kplot[v_idx])
+                    _d_helper(vec, vname, colors[t_idx], bw, alpha, point_estimate,
+                              hpd_markers, outline, shade, dplot[v_idx])
 
     if lenght_trace > 1:
         for m_idx, m in enumerate(models):
-            kplot[0].plot([], label=m, c=colors[m_idx])
-        kplot[0].legend(fontsize=textsize)
+            dplot[0].plot([], label=m, c=colors[m_idx])
+        dplot[0].legend(fontsize=textsize)
 
     fig.tight_layout()
 
-    return kplot
+    return dplot
 
 
-def _kde_helper(vec, vname, c, bw, alpha, point_estimate, hpd_markers, outline, shade, ax):
+def _d_helper(vec, vname, c, bw, alpha, point_estimate, hpd_markers, outline, shade, ax):
     """
     vec : array
         1D array from trace
@@ -145,34 +148,42 @@ def _kde_helper(vec, vname, c, bw, alpha, point_estimate, hpd_markers, outline,
         (opaque). Defaults to 0.
     ax : matplotlib axes
     """
-    density, l, u = fast_kde(vec, bw)
-    x = np.linspace(l, u, len(density))
-    hpd_ = hpd(vec, alpha)
-    cut = (x >= hpd_[0]) & (x <= hpd_[1])
-
-    xmin = x[cut][0]
-    xmax = x[cut][-1]
-    ymin = density[cut][0]
-    ymax = density[cut][-1]
-
-    if outline:
-        ax.plot(x[cut], density[cut], color=c)
-        ax.plot([xmin, xmin], [-0.5, ymin], color=c, ls='-')
-        ax.plot([xmax, xmax], [-0.5, ymax], color=c, ls='-')
+    if vec.dtype.kind == 'f':
+        density, l, u = fast_kde(vec)
+        x = np.linspace(l, u, len(density))
+        hpd_ = hpd(vec, alpha)
+        cut = (x >= hpd_[0]) & (x <= hpd_[1])
+
+        xmin = x[cut][0]
+        xmax = x[cut][-1]
+        ymin = density[cut][0]
+        ymax = density[cut][-1]
+
+        if outline:
+            ax.plot(x[cut], density[cut], color=c)
+            ax.plot([xmin, xmin], [-ymin/100, ymin], color=c, ls='-')
+            ax.plot([xmax, xmax], [-ymax/100, ymax], color=c, ls='-')
+
+        if shade:
+            ax.fill_between(x, density, where=cut, color=c, alpha=shade)
+
+    else:
+        xmin, xmax = hpd(vec, alpha)
+        bins = range(xmin, xmax+1)
+        if outline:
+            ax.hist(vec, bins=bins, color=c, histtype='step')
+        ax.hist(vec, bins=bins, color=c, alpha=shade)
 
     if hpd_markers:
         ax.plot(xmin, 0, 'v', color=c, markeredgecolor='k')
         ax.plot(xmax, 0, 'v', color=c, markeredgecolor='k')
 
-    if shade:
-        ax.fill_between(x, density, where=cut, color=c, alpha=shade)
-
     if point_estimate is not None:
         if point_estimate == 'mean':
             ps = np.mean(vec)
-        if point_estimate == 'median':
+        elif point_estimate == 'median':
             ps = np.median(vec)
-        ax.plot(ps, 0, 'o', color=c, markeredgecolor='k')
+        ax.plot(ps, -0.001, 'o', color=c, markeredgecolor='k')
 
     ax.set_yticks([])
     ax.set_title(vname)