Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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147 lines
5.2 KiB
147 lines
5.2 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_score
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import estimators.structure_constraint_based_estimator as se_constr
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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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pass
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def test_constr(self):
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list_constraint= [0,1]
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list_cardinality= [[2,"binary"],[3,"ternary"], [4,"quaternary"]]
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list_dens = [["0.1","_01"],["0.2","_02"], ["0.3",""], ["0.4","_04"] ]
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for constr in list_constraint:
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for card in list_cardinality:
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for dens in list_dens:
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if card[0] == 4:
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list_vals= [3,4,5,6,10,15]
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else:
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list_vals= [3,4,5,6,10,15,20]
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for var_n in list_vals:
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patience = 25
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var_number= var_n
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if var_number > 11:
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patience = 30
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if var_number > 16:
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patience = 35
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cardinality = card[0]
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cardinality_string = card[1]
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density= dens[0]
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density_string = dens[1]
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constraint = constr
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index = 1
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num_networks=10
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while index <= num_networks:
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with open(f"/home/alessandro/Documents/ctbn_cba/data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}/{index}.json") as f:
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raw_data = json.load(f)
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trajectory_list_raw= raw_data["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["variables"])
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prior_net_structure = pd.DataFrame(raw_data["dyn.str"])
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self.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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self.importer.import_data()
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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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if constr == 0:
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se1 = se_score.StructureScoreBasedEstimator(self.s1)
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set_list_edges = se1.estimate_structure(
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max_parents = None,
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iterations_number = 100,
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patience = patience,
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tabu_length = var_number,
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tabu_rules_duration = var_number,
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optimizer = 'tabu'
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)
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else:
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se1 = se_constr.StructureConstraintBasedEstimator(self.s1,0.1,0.1)
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set_list_edges = se1.estimate_structure(disable_multiprocessing=False)
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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(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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