Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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179 lines
5.5 KiB
179 lines
5.5 KiB
#!/usr/bin/env python3
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# License: MIT License
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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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import json
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import pandas as pd
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from pyctbn.legacy.structure_graph.sample_path import SamplePath
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from pyctbn.legacy.estimators.structure_constraint_based_estimator import StructureConstraintBasedEstimator
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from pyctbn.legacy.utility.sample_importer import SampleImporter
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import copy
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class TestStructureConstraintBasedEstimator(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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with open("./tests/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 = 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 = SamplePath(cls.importer)
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cls.s1.build_trajectories()
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cls.s1.build_structure()
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def test_structure_1(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 = StructureConstraintBasedEstimator(self.s1,0.1,0.1)
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edges = se1.estimate_structure(processes_number=2)
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self.assertFalse(se1.spurious_edges())
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self.assertEqual(edges, true_edges)
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def test_structure_2(self):
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with open("./tests/data/networks_and_trajectories_binary_data_02_10_1.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 = 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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#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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self.s1 = 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 = StructureConstraintBasedEstimator(self.s1,0.1,0.1)
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edges = se1.estimate_structure(True)
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'calculate precision and recall'
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n_missing_edges = 0
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n_added_fake_edges = 0
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n_added_fake_edges = len(edges.difference(true_edges))
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n_missing_edges = len(true_edges.difference(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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self.assertGreaterEqual(precision,0.75)
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self.assertGreaterEqual(recall,0.75)
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def test_structure_3(self):
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with open("./tests/data/networks_and_trajectories_ternary_data_01_6_1.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 = 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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#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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self.s1 = 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 = StructureConstraintBasedEstimator(self.s1,0.1,0.1)
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edges = se1.estimate_structure(True)
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'calculate precision and recall'
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n_missing_edges = 0
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n_added_fake_edges = 0
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n_added_fake_edges = len(edges.difference(true_edges))
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n_missing_edges = len(true_edges.difference(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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self.assertGreaterEqual(precision,0.75)
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self.assertGreaterEqual(recall,0.75)
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if __name__ == '__main__':
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unittest.main()
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