def test_infer_posterior(self):
expected = Beta(alpha=1 + 4, beta=1 + 6)
actual = BetaBinomialConjugate.infer_posterior(self.series)
self.assertEqual(expected, actual)
def plot_conditional_prob_densities(
self,
categorical: Union[DataDistributionMixin, DataCategoriesMixin],
hdi: float = 0.95,
width: float = 0.8,
num_segments: int = 100,
color: Color = 'k',
color_min: Optional[Color] = None,
color_mean: Optional[Color] = None,
edge_color: Optional[Color] = None,
axf: Optional[AxesFormatter] = None) -> AxesFormatter:
"""
Plot conditional probability densities of the data, split by the
categories of an Ordinal or Nominal distribution.
:param categorical: Nominal or Ordinal distribution.
:param hdi: Highest Density Interval width for each distribution.
:param width: Width of each density bar.
:param num_segments: Number of segments to plot per density.
:param color: Color for the densest part of each distribution.
:param color_min: Color for the sparsest part of each distribution,
if different to color.
:param color_mean: Color for mean data markers.
:param edge_color: Optional color for the edge of each density bar.
:param axf: Optional AxesFormatter to plot on.
"""
axf = axf or AxesFormatter()
cats = categorical.categories
n_cats = len(cats)
yy_min, yy_max = inf, -inf
# filter categorical data
shared_ix = list(
set(self._data.index).intersection(categorical.data.index))
cat_data = categorical.data.loc[shared_ix]
ratio_data = self._data.loc[shared_ix]
for c, category in enumerate(categorical.categories):
cat_ratio_data = ratio_data.loc[cat_data == category]
if len(cat_ratio_data) == 0:
continue
# fit distribution and find limits for HDI
cat_dist = BetaBinomialConjugate.infer_posterior(cat_ratio_data)
# cat_dist = Beta.fit(data=cat_ratio_data)
y_min, y_max = cat_dist.hdi(hdi)
yy_min, yy_max = min(y_min, yy_min), max(y_max, yy_max)
# plot density
axf.add_v_density(x=c + 1,
y_to_z=cat_dist.pdf().at(
linspace(y_min, y_max, num_segments + 1)),
color=color,
color_min=color_min,
edge_color=edge_color,
width=width)
# plot descriptive statistics lines
if color_mean is not None:
mean = cat_ratio_data.mean()
axf.add_line(x=[c + 0.55, c + 1.45],
y=[mean, mean],
color=color_mean)
# labels
axf.set_text(title=f'{hdi: .0%} HDIs of $p(' + r'p_{' + self.name +
r'}' + f'|{categorical.name})$',
x_label=categorical.name,
y_label=r'$p_{' + self.name + r'}$')
# axes
axf.set_x_lim(0, n_cats + 1)
yy_range = yy_max - yy_min
axf.set_y_lim(yy_min - yy_range * 0.05, yy_max + yy_range * 0.05)
axf.y_ticks.set_locations(linspace(0, 1, 11))
axf.x_ticks.set_locations(range(1, n_cats + 1)).set_labels(cats)
return axf