Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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109 lines
3.1 KiB
109 lines
3.1 KiB
4 years ago
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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 psutil
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from line_profiler import LineProfiler
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import copy
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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_constraint_based_estimator as se
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import utility.json_importer as ji
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class TestTabuSearch(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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pass
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def test_constr(self):
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list_vals= [3,4,5,6,10,15]
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list_dens = [["0.1","_01"],["0.2","_02"], ["0.3",""], ["0.4","_04"] ]
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for dens in list_dens:
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for var_n in list_vals:
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var_number= var_n
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cardinality = 4
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cardinality_string = "quaternary"
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density= dens[0]
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density_string = dens[1]
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constraint = 1
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index = 0
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num_networks=10
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if var_number > 9:
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num_networks=3
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while index < num_networks:
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#cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
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self.importer = ji.JsonImporter(f"../../data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}.json",
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'samples', 'dyn.str', 'variables', 'Time', 'Name', index )
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self.s1 = sp.SamplePath(self.importer)
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self.s1.build_trajectories()
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self.s1.build_structure()
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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.StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
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se1.ctpc_algorithm()
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set_list_edges = set(se1.complete_graph.edges)
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n_added_fake_edges = len(set_list_edges.difference(true_edges))
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n_missing_edges = len(true_edges.difference(set_list_edges))
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n_true_positive = len(true_edges) - n_missing_edges
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precision = n_true_positive / (n_true_positive + n_added_fake_edges)
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recall = n_true_positive / (n_true_positive + n_missing_edges)
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f1_measure = round(2* (precision*recall) / (precision+recall),3)
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# print(f"n archi reali non trovati: {n_missing_edges}")
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# print(f"n archi non reali aggiunti: {n_added_fake_edges}")
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print(true_edges)
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print(set_list_edges)
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print(f"precision: {precision} ")
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print(f"recall: {recall} ")
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with open("../results/results.csv", 'a+') as fi:
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fi.write(f"{constraint},{var_number},{density},{cardinality},{index},{f1_measure},{round(precision,3)},{round(recall,3)}")
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index += 1
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self.assertEqual(set_list_edges, true_edges)
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if __name__ == '__main__':
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unittest.main()
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