Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
194 lines
9.3 KiB
194 lines
9.3 KiB
4 years ago
|
|
||
|
4 years ago
|
# License: MIT License
|
||
|
|
|
||
|
|
|
||
|
4 years ago
|
import unittest
|
||
|
|
import glob
|
||
|
|
import os
|
||
|
|
import networkx as nx
|
||
|
|
import numpy as np
|
||
|
|
import itertools
|
||
|
|
|
||
3 years ago
|
from pyctbn.legacy.structure_graph.sample_path import SamplePath
|
||
|
|
from pyctbn.legacy.structure_graph.network_graph import NetworkGraph
|
||
|
|
from pyctbn.legacy.utility.json_importer import JsonImporter
|
||
|
4 years ago
|
|
||
|
|
|
||
|
|
class TestNetworkGraph(unittest.TestCase):
|
||
|
|
@classmethod
|
||
|
|
def setUpClass(cls):
|
||
|
3 years ago
|
cls.read_files = glob.glob(os.path.join('./tests/data', "*.json"))
|
||
|
4 years ago
|
cls.importer = JsonImporter(cls.read_files[2], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
|
||
|
4 years ago
|
cls.importer.import_data(0)
|
||
|
|
cls.s1 = SamplePath(cls.importer)
|
||
|
|
cls.s1.build_trajectories()
|
||
|
|
cls.s1.build_structure()
|
||
|
|
|
||
|
|
def test_init(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
self.assertEqual(self.s1.structure, g1._graph_struct)
|
||
|
|
self.assertIsInstance(g1._graph, nx.DiGraph)
|
||
|
|
self.assertIsNone(g1.time_scalar_indexing_strucure)
|
||
|
|
self.assertIsNone(g1.transition_scalar_indexing_structure)
|
||
|
|
self.assertIsNone(g1.transition_filtering)
|
||
|
|
self.assertIsNone(g1.p_combs)
|
||
|
|
|
||
|
|
def test_add_nodes(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
for n1, n2 in zip(g1.nodes, self.s1.structure.nodes_labels):
|
||
|
|
self.assertEqual(n1, n2)
|
||
|
|
|
||
|
|
def test_add_edges(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for e in self.s1.structure.edges:
|
||
|
|
self.assertIn(tuple(e), g1.edges)
|
||
|
|
|
||
|
|
def test_fast_init(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
g1.fast_init(node)
|
||
|
|
self.assertIsNotNone(g1._graph.nodes)
|
||
|
|
self.assertIsNotNone(g1._graph.edges)
|
||
|
|
self.assertIsInstance(g1._time_scalar_indexing_structure, np.ndarray)
|
||
|
|
self.assertIsInstance(g1._transition_scalar_indexing_structure, np.ndarray)
|
||
|
|
self.assertIsInstance(g1._time_filtering, np.ndarray)
|
||
|
|
self.assertIsInstance(g1._transition_filtering, np.ndarray)
|
||
|
|
self.assertIsInstance(g1._p_combs_structure, np.ndarray)
|
||
|
|
self.assertIsInstance(g1._aggregated_info_about_nodes_parents, tuple)
|
||
|
|
|
||
|
|
def test_get_ordered_by_indx_set_of_parents(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
|
||
|
|
for indx in range(len(aggr_info[0]) - 1 ):
|
||
|
|
self.assertLess(g1.get_node_indx(aggr_info[0][indx]), g1.get_node_indx(aggr_info[0][indx + 1]))
|
||
|
|
for par, par_indx in zip(aggr_info[0], aggr_info[1]):
|
||
|
|
self.assertEqual(g1.get_node_indx(par), par_indx)
|
||
|
|
for par, par_val in zip(aggr_info[0], aggr_info[2]):
|
||
|
|
self.assertEqual(g1._graph_struct.get_states_number(par), par_val)
|
||
|
|
|
||
|
|
def test_build_time_scalar_indexing_structure_for_a_node(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
|
||
|
|
self.aux_build_time_scalar_indexing_structure_for_a_node(g1, node, aggr_info[1],
|
||
|
|
aggr_info[0], aggr_info[2])
|
||
|
|
|
||
|
|
def aux_build_time_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels, parents_vals):
|
||
|
|
node_states = graph.get_states_number(node_id)
|
||
|
|
time_scalar_indexing = NetworkGraph.build_time_scalar_indexing_structure_for_a_node(node_states, parents_vals)
|
||
|
|
self.assertEqual(len(time_scalar_indexing), len(parents_indxs) + 1)
|
||
|
|
merged_list = parents_labels[:]
|
||
|
|
merged_list.insert(0, node_id)
|
||
|
|
vals_list = []
|
||
|
|
for node in merged_list:
|
||
|
|
vals_list.append(graph.get_states_number(node))
|
||
|
|
t_vec = np.array(vals_list)
|
||
|
|
t_vec = t_vec.cumprod()
|
||
|
|
self.assertTrue(np.array_equal(time_scalar_indexing, t_vec))
|
||
|
|
|
||
|
|
def test_build_transition_scalar_indexing_structure_for_a_node(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
|
||
|
|
self.aux_build_transition_scalar_indexing_structure_for_a_node(g1, node, aggr_info[1],
|
||
|
|
aggr_info[0], aggr_info[2])
|
||
|
|
|
||
|
|
def aux_build_transition_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels,
|
||
|
|
parents_values):
|
||
|
|
node_states = graph.get_states_number(node_id)
|
||
|
|
transition_scalar_indexing = graph.build_transition_scalar_indexing_structure_for_a_node(node_states,
|
||
|
|
parents_values)
|
||
|
|
self.assertEqual(len(transition_scalar_indexing), len(parents_indxs) + 2)
|
||
|
|
merged_list = parents_labels[:]
|
||
|
|
merged_list.insert(0, node_id)
|
||
|
|
merged_list.insert(0, node_id)
|
||
|
|
vals_list = []
|
||
|
|
for node_id in merged_list:
|
||
|
|
vals_list.append(graph.get_states_number(node_id))
|
||
|
|
m_vec = np.array([vals_list])
|
||
|
|
m_vec = m_vec.cumprod()
|
||
|
|
self.assertTrue(np.array_equal(transition_scalar_indexing, m_vec))
|
||
|
|
|
||
|
|
def test_build_time_columns_filtering_structure_for_a_node(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
|
||
|
|
self.aux_build_time_columns_filtering_structure_for_a_node(g1, node, aggr_info[1])
|
||
|
|
|
||
|
|
def aux_build_time_columns_filtering_structure_for_a_node(self, graph, node_id, p_indxs):
|
||
|
|
graph.build_time_columns_filtering_for_a_node(graph.get_node_indx(node_id), p_indxs)
|
||
|
|
single_filter = []
|
||
|
|
single_filter.append(graph.get_node_indx(node_id))
|
||
|
|
single_filter.extend(p_indxs)
|
||
|
|
self.assertTrue(np.array_equal(graph.build_time_columns_filtering_for_a_node(graph.get_node_indx(node_id),
|
||
|
|
p_indxs),np.array(single_filter)))
|
||
|
|
def test_build_transition_columns_filtering_structure(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
|
||
|
|
self.aux_build_time_columns_filtering_structure_for_a_node(g1, node, aggr_info[1])
|
||
|
|
|
||
|
|
def aux_build_transition_columns_filtering_structure(self, graph, node_id, p_indxs):
|
||
|
|
single_filter = []
|
||
|
|
single_filter.append(graph.get_node_indx(node_id) + graph._graph_struct.total_variables_number)
|
||
|
|
single_filter.append(graph.get_node_indx(node_id))
|
||
|
|
single_filter.extend(p_indxs)
|
||
|
|
self.assertTrue(np.array_equal(graph.build_transition_filtering_for_a_node(graph.get_node_indx(node_id),
|
||
|
|
|
||
|
|
p_indxs), np.array(single_filter)))
|
||
|
|
def test_build_p_combs_structure(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in self.s1.structure.nodes_labels:
|
||
|
|
aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
|
||
|
|
self.aux_build_p_combs_structure(g1, aggr_info[2])
|
||
|
|
|
||
|
|
def aux_build_p_combs_structure(self, graph, p_vals):
|
||
|
|
p_combs = graph.build_p_comb_structure_for_a_node(p_vals)
|
||
|
|
p_possible_vals = []
|
||
|
|
for val in p_vals:
|
||
|
|
vals = [v for v in range(val)]
|
||
|
|
p_possible_vals.extend(vals)
|
||
|
|
comb_struct = set(itertools.product(p_possible_vals,repeat=len(p_vals)))
|
||
|
|
for comb in comb_struct:
|
||
|
|
self.assertIn(np.array(comb), p_combs)
|
||
|
|
|
||
|
|
def test_get_parents_by_id(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node in g1.nodes:
|
||
|
|
self.assertListEqual(g1.get_parents_by_id(node), list(g1._graph.predecessors(node)))
|
||
|
|
|
||
|
|
def test_get_states_number(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node, val in zip(g1.nodes, g1.nodes_values):
|
||
|
|
self.assertEqual(val, g1.get_states_number(node))
|
||
|
|
|
||
|
|
def test_get_node_indx(self):
|
||
|
|
g1 = NetworkGraph(self.s1.structure)
|
||
|
|
g1.add_nodes(self.s1.structure.nodes_labels)
|
||
|
|
g1.add_edges(self.s1.structure.edges)
|
||
|
|
for node, indx in zip(g1.nodes, g1.nodes_indexes):
|
||
|
|
self.assertEqual(indx, g1.get_node_indx(node))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
unittest.main()
|