Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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70 lines
2.8 KiB
70 lines
2.8 KiB
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# License: MIT License
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import unittest
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import numpy as np
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import glob
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import os
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from pyctbn.legacy.structure_graph.network_graph import NetworkGraph
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from pyctbn.legacy.structure_graph.sample_path import SamplePath
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from pyctbn.legacy.structure_graph.set_of_cims import SetOfCims
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from pyctbn.legacy.estimators.parameters_estimator import ParametersEstimator
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from pyctbn.legacy.utility.json_importer import JsonImporter
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class TestParametersEstimatior(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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cls.read_files = glob.glob(os.path.join('./tests/data/networks_and_trajectories_binary_data_01_3.json', "*.json"))
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cls.array_indx = 0
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cls.importer = JsonImporter('./tests/data/networks_and_trajectories_binary_data_01_3.json', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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cls.importer.import_data(cls.array_indx)
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cls.s1 = SamplePath(cls.importer)
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cls.s1.build_trajectories()
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cls.s1.build_structure()
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print(cls.s1.structure.edges)
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print(cls.s1.structure.nodes_values)
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def test_fast_init(self):
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for node in self.s1.structure.nodes_labels:
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g = NetworkGraph(self.s1.structure)
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g.fast_init(node)
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p1 = ParametersEstimator(self.s1.trajectories, g)
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self.assertEqual(p1._trajectories, self.s1.trajectories)
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self.assertEqual(p1._net_graph, g)
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self.assertIsNone(p1._single_set_of_cims)
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p1.fast_init(node)
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self.assertIsInstance(p1._single_set_of_cims, SetOfCims)
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def test_compute_parameters_for_node(self):
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for indx, node in enumerate(self.s1.structure.nodes_labels):
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print(node)
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g = NetworkGraph(self.s1.structure)
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g.fast_init(node)
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p1 = ParametersEstimator(self.s1.trajectories, g)
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p1.fast_init(node)
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sofc1 = p1.compute_parameters_for_node(node)
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sampled_cims = self.aux_import_sampled_cims('dyn.cims')
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sc = list(sampled_cims.values())
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self.equality_of_cims_of_node(sc[indx], sofc1._actual_cims)
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def equality_of_cims_of_node(self, sampled_cims, estimated_cims):
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self.assertEqual(len(sampled_cims), len(estimated_cims))
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for c1, c2 in zip(sampled_cims, estimated_cims):
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self.cim_equality_test(c1, c2.cim)
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def cim_equality_test(self, cim1, cim2):
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for r1, r2 in zip(cim1, cim2):
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self.assertTrue(np.all(np.isclose(r1, r2, 1e01)))
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def aux_import_sampled_cims(self, cims_label):
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i1 = JsonImporter('./tests/data/networks_and_trajectories_binary_data_01_3.json', '', '', '', '', '')
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raw_data = i1.read_json_file()
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return i1.import_sampled_cims(raw_data, self.array_indx, cims_label)
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if __name__ == '__main__':
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unittest.main()
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