Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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84 lines
2.3 KiB
84 lines
2.3 KiB
import sys
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sys.path.append("../../classes/")
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import glob
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import math
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import os
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import unittest
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import networkx as nx
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import numpy as np
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import pandas as pd
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import psutil
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from line_profiler import LineProfiler
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import copy
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import json
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import utility.cache as ch
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import structure_graph.sample_path as sp
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import estimators.structure_score_based_estimator as se
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import utility.json_importer as ji
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import utility.sample_importer as si
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class TestTabuSearch(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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#cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
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with open("../../data/networks_and_trajectories_binary_data_01_3.json") as f:
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raw_data = json.load(f)
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trajectory_list_raw= raw_data[0]["samples"]
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trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw]
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variables= pd.DataFrame(raw_data[0]["variables"])
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prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"])
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cls.importer = si.SampleImporter(
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trajectory_list=trajectory_list,
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variables=variables,
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prior_net_structure=prior_net_structure
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)
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cls.importer.import_data()
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cls.s1 = sp.SamplePath(cls.importer)
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#cls.traj = cls.s1.concatenated_samples
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# print(len(cls.traj))
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cls.s1 = sp.SamplePath(cls.importer)
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cls.s1.build_trajectories()
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cls.s1.build_structure()
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cls.s1.clear_memory()
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def test_structure(self):
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true_edges = copy.deepcopy(self.s1.structure.edges)
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true_edges = set(map(tuple, true_edges))
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se1 = se.StructureScoreBasedEstimator(self.s1)
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edges = se1.estimate_structure(
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max_parents = None,
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iterations_number = 100,
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patience = 20,
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tabu_length = 10,
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tabu_rules_duration = 10,
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optimizer = 'tabu',
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disable_multiprocessing=False
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)
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self.assertEqual(edges, true_edges)
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if __name__ == '__main__':
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unittest.main()
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