def test_infer_posteriors_with_stats(self):
b__0_3 = Beta(1 + 0, 1 + 3)
b__1_2 = Beta(1 + 1, 1 + 2)
b__2_1 = Beta(1 + 2, 1 + 1)
b__3_0 = Beta(1 + 3, 1 + 0)
expected = DataFrame(data=[
(1, 1, 'c', 1, b__0_3, b__0_3.mean(), b__0_3.interval(.95)),
(2, 1, 'c', 1, b__1_2, b__1_2.mean(), b__1_2.interval(.95)),
(1, 2, 'c', 1, b__2_1, b__2_1.mean(), b__2_1.interval(.95)),
(2, 2, 'c', 1, b__3_0, b__3_0.mean(), b__3_0.interval(.95)),
(1, 1, 'd', 1, b__3_0, b__3_0.mean(), b__3_0.interval(.95)),
(2, 1, 'd', 1, b__2_1, b__2_1.mean(), b__2_1.interval(.95)),
(1, 2, 'd', 1, b__1_2, b__1_2.mean(), b__1_2.interval(.95)),
(2, 2, 'd', 1, b__0_3, b__0_3.mean(), b__0_3.interval(.95))
],
columns=[
'a', 'b', 'prob_var', 'prob_val', 'Beta',
'mean', 'interval__0.95'
])
actual = BetaBinomialConjugate.infer_posteriors(
data=self.binomial_data,
prob_vars=['c', 'd'],
cond_vars=['a', 'b'],
stats=['mean', {
'interval': 0.95
}])
for _, row in expected.iterrows():
actual_beta = actual.loc[(actual['a'] == row['a']) &
(actual['b'] == row['b']) &
(actual['prob_var'] == row['prob_var']) &
(actual['prob_val'] == row['prob_val']),
'Beta'].iloc[0]
self.assertTrue(row['Beta'] == actual_beta)
def test_infer_posteriors(self):
b__0_3 = Beta(1 + 0, 1 + 3)
b__1_2 = Beta(1 + 1, 1 + 2)
b__2_1 = Beta(1 + 2, 1 + 1)
b__3_0 = Beta(1 + 3, 1 + 0)
expected = DataFrame(
data=[(1, 1, 'c', 1, b__0_3), (2, 1, 'c', 1, b__1_2),
(1, 2, 'c', 1, b__2_1), (2, 2, 'c', 1, b__3_0),
(1, 1, 'd', 1, b__3_0), (2, 1, 'd', 1, b__2_1),
(1, 2, 'd', 1, b__1_2), (2, 2, 'd', 1, b__0_3)],
columns=['a', 'b', 'prob_var', 'prob_val', 'Beta'])
actual = BetaBinomialConjugate.infer_posteriors(
data=self.binomial_data,
prob_vars=['c', 'd'],
cond_vars=['a', 'b'])
for _, row in expected.iterrows():
actual_beta = actual.loc[(actual['a'] == row['a']) &
(actual['b'] == row['b']) &
(actual['prob_var'] == row['prob_var']) &
(actual['prob_val'] == row['prob_val']),
'Beta'].iloc[0]
self.assertTrue(row['Beta'] == actual_beta)