Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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129 lines
6.0 KiB
129 lines
6.0 KiB
import sys
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sys.path.append("/Users/Zalum/Desktop/Tesi/CTBN_Project/main_package/classes/")
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import unittest
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import pandas as pd
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import numpy as np
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import sample_path as sp
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import structure as st
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import network_graph as ng
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import parameters_estimator as pe
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class TestStructure(unittest.TestCase):
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def setUp(self):
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self.structure_frame = pd.DataFrame([{"From":"X","To":"Z"}, {"From":"Y","To":"Z"},
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{"From":"Z","To":"Y"} ])
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self.variables_frame = pd.DataFrame([{"Name":"X","Value":3},{"Name":"Y","Value":3},{"Name":"Z","Value":3}])
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def test_init(self):
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s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
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self.assertTrue(self.structure_frame.equals(s1.structure_frame))
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self.assertTrue(self.variables_frame.equals(s1.variables_frame))
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self.assertEqual(self.variables_frame.columns.values[0], s1.name_label)
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self.assertEqual(self.variables_frame.columns.values[1], s1.value_label)
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#print(len(self.variables_frame.index))
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self.assertEqual(len(self.variables_frame.index), s1.total_variables_number)
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def test_list_of_edges(self):
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s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
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records = self.structure_frame.to_records(index=False)
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result = list(records)
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for e1, e2 in zip(result, s1.list_of_edges()):
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self.assertEqual(e1, e2)
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def test_list_of_nodes_labels(self):
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s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
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self.assertEqual(list(self.variables_frame['Name']), s1.list_of_nodes_labels())
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def test_get_node_id(self):
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s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
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for indx, var in enumerate(list(self.variables_frame['Name'])):
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self.assertEqual(var, s1.get_node_id(indx))
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def test_get_node_indx(self):
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filtered_frame = self.variables_frame.drop(self.variables_frame[self.variables_frame['Name'] == 'Y'].index)
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#print(filtered_frame)
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s1 = st.Structure(self.structure_frame, filtered_frame, len(self.variables_frame.index))
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for indx, var in zip(filtered_frame.index, filtered_frame['Name']):
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self.assertEqual(indx, s1.get_node_indx(var))
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def test_list_of_node_indxs(self):
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filtered_frame = self.variables_frame.drop(self.variables_frame[self.variables_frame['Name'] == 'Y'].index)
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# print(filtered_frame)
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s1 = st.Structure(self.structure_frame, filtered_frame, len(self.variables_frame.index))
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for indx1, indx2 in zip(filtered_frame.index, s1.list_of_nodes_indexes()):
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self.assertEqual(indx1, indx2)
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def test_get_positional_node_indx(self):
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filtered_frame = self.variables_frame.drop(self.variables_frame[self.variables_frame['Name'] == 'Y'].index)
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# print(filtered_frame)
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s1 = st.Structure(self.structure_frame, filtered_frame, len(self.variables_frame.index))
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for indx, var in enumerate(s1.list_of_nodes_labels()):
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self.assertEqual(indx, s1.get_positional_node_indx(var))
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def test_get_states_number(self):
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s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
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for indx, row in self.variables_frame.iterrows():
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self.assertEqual(row[1], s1.get_states_number(row[0]))
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def test_get_states_numeber_by_indx(self):
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s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
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for indx, row in self.variables_frame.iterrows():
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self.assertEqual(row[1], s1.get_states_number_by_indx(indx))
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def test_new_init(self):
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#self.variables_frame.drop(self.variables_frame[(self.variables_frame['Name'] == 'Y')].index, inplace=True)
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"""labels = self.variables_frame['Name'].to_list()
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indxs = self.variables_frame.index.to_numpy()
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vals = self.variables_frame['Value'].to_numpy()
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edges = list(self.structure_frame.to_records(index=False))
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print(labels)
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print(indxs)
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print(vals)
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print(edges)
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s1 = st.Structure(labels, indxs, vals, edges, len(self.variables_frame.index))
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#print(s1.get_node_id(2))
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print(s1.get_node_indx('Z'))
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print(s1.get_positional_node_indx('Z'))
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print(s1.get_states_number('Z'))
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print(s1.get_states_number_by_indx(1))
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[CIM:
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[[-4.82318981 1.18421625 3.63997346]
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[ 4.44726473 -9.20141291 4.755239 ]
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[ 2.93950444 4.36292948 -7.30152554]], CIM:
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[[-6.0336893 1.69212904 4.34235011]
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[ 3.32692085 -5.03977237 1.7137923 ]
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[ 3.65519241 3.81402509 -7.46819716]], CIM:
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[[-6.78778897 1.98559721 4.80306557]
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[ 1.23811008 -6.26366842 5.0265376 ]
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[ 3.02720526 4.0256821 -7.05222539]]]
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array([ 3, 9, 27])
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array([3, 9])
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array([1, 2])
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array([4, 1, 2])
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"""
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sp1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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sp1.build_trajectories()
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sp1.build_structure()
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st1 = st.Structure(['X','Y','Z'], np.array([0,1,2]), np.array([3,3,3]), [('Z','X'),('Y', 'X')], sp1.total_variables_count)
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g1 = ng.NetworkGraph(st1)
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g1.init_graph()
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print("M Vector",g1.transition_scalar_indexing_structure)
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print("Time Vecotr",g1.time_scalar_indexing_strucure)
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print("Time Filter",g1.time_filtering)
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print("M Filter",g1.transition_filtering)
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print(g1.p_combs)
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print("AGG STR", g1.aggregated_info_about_nodes_parents)
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p1 = pe.ParametersEstimator(sp1,g1)
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p1.init_sets_cims_container()
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p1.compute_parameters_for_node('X')
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#print(p1.sets_of_cims_struct.get_cims_of_node(0,[1,0]))
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print(p1.sets_of_cims_struct.sets_of_cims[1].actual_cims)
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#print(p1.sets_of_cims_struct.sets_of_cims[2].get_cims_where_parents_except_last_are_in_state(np.array([0])))
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#print(p1.sets_of_cims_struct.sets_of_cims[0].p_combs)
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if __name__ == '__main__':
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unittest.main()
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