def test_mle_support_for_states_data(self):
row = pd.DataFrame([[True, False, None, 'dataset-a', 2.5]],
columns=['X1', 'X2', 'X3', 'dataset_id', 'weight'])
pi = pd.DataFrame([[0.6, 0.4], [0.2, 0.8], [0.5, 0.5]],
index=['dataset-a', 'dataset-b', 'dataset-c'],
columns=['K0', 'K1'])
p = pd.DataFrame([[0.1, 0.2, 0.3], [0.9, 0.8, 0.7]],
index=['K0', 'K1'],
columns=['X1', 'X2', 'X3'])
model = MultiDatasetMixtureModel(pi, p)
# see the test for log_support for how the expected support of:
# K0 K1
# 0 0.048 0.072
# has been estimated, making $K1$ the most likely state
expected_mle_states = pd.Series(['K1'], index=row.index)
actual_mle_states = model.mle_states(row)
assert_series_equal(expected_mle_states,
actual_mle_states,
check_names=False)
def test_complete_likelihood_for_icl(self):
data = pd.DataFrame([[False, False, None, 'dataset-a', 2.5],
[True, False, False, 'dataset-b', 3],
[True, None, False, 'dataset-c', 1],
[False, True, False, 'dataset-a', 2]],
columns=['X1', 'X2', 'X3', 'dataset_id', 'weight'])
pi = pd.DataFrame([[0.6, 0.4], [0.2, 0.8], [0.5, 0.5]],
index=['dataset-a', 'dataset-b', 'dataset-c'],
columns=['K0', 'K1'])
p = pd.DataFrame([[0.1, 0.2, 0.3], [0.98, 0.89, 0.51]],
index=['K0', 'K1'],
columns=['X1', 'X2', 'X3'])
model = MultiDatasetMixtureModel(pi, p)
mle_states = model.mle_states(data)
expected_ans = 0
for ix, state in mle_states.iteritems():
dataset = data.loc[ix, 'dataset_id']
row_ans = np.log(pi.loc[dataset, state])
for col, x_ik in data.loc[ix].iteritems():
if col in ['dataset_id', 'weight']:
continue
if x_ik is None:
if p.loc[state, col] >= 0.5:
x_ik = 1
else:
x_ik = 0
if x_ik == 1:
row_ans += np.log(p.loc[state, col])
else:
row_ans += np.log(1 - p.loc[state, col])
expected_ans += row_ans * data.loc[ix, 'weight']
actual_ans = model.complete_mle_log_likelihood(data)
self.assertAlmostEqual(expected_ans, actual_ans)