def test_map_approx(self):
subjs = 40
data, params_true = kabuki.generate.gen_rand_data(gen_func_df,
{'A':{'loc':0, 'scale':1}, 'B': {'loc':2, 'scale':1}},
subj_noise={'loc':.1}, size=1000, subjs=subjs)
model = HNodeSimple(data, depends_on={'mu': 'condition'})
model.approximate_map()
model.approximate_map(individual_subjs=False)
counter = 0
for condition, subj_params in params_true.iteritems():
nodes = model.nodes_db[model.nodes_db['condition'] == condition]
for idx, params in enumerate(subj_params):
nodes_subj = nodes[nodes['subj_idx'] == idx]
for param_name, value in params.iteritems():
if param_name != 'loc':
continue
node = nodes_subj[nodes_subj.knode_name == 'mu_subj']
assert len(node) == 1, "Only one node should have been left after slicing."
abs_error = np.abs(node.map[0] - value)
self.assertTrue(abs_error < .2)
counter += 1
# looping for each condition (i.e. twice)
self.assertEqual(counter, subjs*2)
