Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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180 lines
9.1 KiB
180 lines
9.1 KiB
#!/usr/bin/env python3
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# License: MIT License
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import unittest
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import os
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import glob
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import numpy as np
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import pandas as pd
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from pyctbn.legacy.utility.json_importer import JsonImporter
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import json
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class TestJsonImporter(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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cls.read_files = glob.glob(os.path.join('./tests/data', "*.json"))
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def test_init(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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self.assertEqual(j1._samples_label, 'samples')
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self.assertEqual(j1._structure_label, 'dyn.str')
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self.assertEqual(j1._variables_label, 'variables')
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self.assertEqual(j1._time_key, 'Time')
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self.assertEqual(j1._variables_key, 'Name')
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self.assertEqual(j1._file_path, "./tests/data/networks_and_trajectories_binary_data_01_3.json")
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self.assertIsNone(j1._df_samples_list)
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self.assertIsNone(j1.variables)
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self.assertIsNone(j1.structure)
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self.assertEqual(j1.concatenated_samples,[])
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self.assertIsNone(j1.sorter)
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self.assertIsNone(j1._array_indx)
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self.assertIsInstance(j1._raw_data, list)
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def test_read_json_file_found(self):
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data_set = {"key1": [1, 2, 3], "key2": [4, 5, 6]}
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with open('data.json', 'w') as f:
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json.dump(data_set, f)
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path = os.getcwd()
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path = path + '/data.json'
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j1 = JsonImporter(path, '', '', '', '', '')
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self.assertTrue(self.ordered([data_set]) == self.ordered(j1._raw_data))
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os.remove('data.json')
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def test_read_json_file_not_found(self):
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path = os.getcwd()
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path = path + '/data.json'
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self.assertRaises(FileNotFoundError, JsonImporter, path, '', '', '', '', '')
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def test_build_sorter(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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df_samples_list = j1.normalize_trajectories(j1._raw_data, 0, j1._samples_label)
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sorter = j1.build_sorter(df_samples_list[0])
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self.assertListEqual(sorter, list(df_samples_list[0].columns.values)[1:])
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def test_normalize_trajectories(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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df_samples_list = j1.normalize_trajectories(j1._raw_data, 0, j1._samples_label)
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self.assertEqual(len(df_samples_list), len(j1._raw_data[0][j1._samples_label]))
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def test_normalize_trajectories_wrong_indx(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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self.assertRaises(IndexError, j1.normalize_trajectories, j1._raw_data, 474, j1._samples_label)
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def test_normalize_trajectories_wrong_key(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'sample', 'dyn.str', 'variables', 'Time', 'Name')
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self.assertRaises(KeyError, j1.normalize_trajectories, j1._raw_data, 0, j1._samples_label)
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def test_compute_row_delta_single_samples_frame(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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j1._array_indx = 0
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j1._df_samples_list = j1.import_trajectories(j1._raw_data)
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sample_frame = j1._df_samples_list[0]
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original_copy = sample_frame.copy()
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columns_header = list(sample_frame.columns.values)
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shifted_cols_header = [s + "S" for s in columns_header[1:]]
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new_sample_frame = j1.compute_row_delta_sigle_samples_frame(sample_frame, columns_header[1:],
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shifted_cols_header)
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self.assertEqual(len(list(sample_frame.columns.values)) + len(shifted_cols_header),
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len(list(new_sample_frame.columns.values)))
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self.assertEqual(sample_frame.shape[0] - 1, new_sample_frame.shape[0])
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for indx, row in new_sample_frame.iterrows():
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self.assertAlmostEqual(row['Time'],
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original_copy.iloc[indx + 1]['Time'] - original_copy.iloc[indx]['Time'])
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for indx, row in new_sample_frame.iterrows():
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np.array_equal(np.array(row[columns_header[1:]],dtype=int),
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np.array(original_copy.iloc[indx][columns_header[1:]],dtype=int))
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np.array_equal(np.array(row[shifted_cols_header], dtype=int),
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np.array(original_copy.iloc[indx + 1][columns_header[1:]], dtype=int))
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def test_compute_row_delta_in_all_frames(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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j1._array_indx = 0
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j1._df_samples_list = j1.import_trajectories(j1._raw_data)
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j1._sorter = j1.build_sorter(j1._df_samples_list[0])
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j1.compute_row_delta_in_all_samples_frames(j1._df_samples_list)
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self.assertEqual(list(j1._df_samples_list[0].columns.values),
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list(j1.concatenated_samples.columns.values)[:len(list(j1._df_samples_list[0].columns.values))])
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self.assertEqual(list(j1.concatenated_samples.columns.values)[0], j1._time_key)
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def test_compute_row_delta_in_all_frames_not_init_sorter(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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j1._array_indx = 0
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j1._df_samples_list = j1.import_trajectories(j1._raw_data)
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self.assertRaises(RuntimeError, j1.compute_row_delta_in_all_samples_frames, j1._df_samples_list)
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def test_clear_data_frame_list(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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j1._array_indx = 0
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j1._df_samples_list = j1.import_trajectories(j1._raw_data)
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j1._sorter = j1.build_sorter(j1._df_samples_list[0])
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j1.compute_row_delta_in_all_samples_frames(j1._df_samples_list)
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j1.clear_data_frame_list()
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for df in j1._df_samples_list:
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self.assertTrue(df.empty)
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def test_clear_concatenated_frame(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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j1.import_data(0)
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j1.clear_concatenated_frame()
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self.assertTrue(j1.concatenated_samples.empty)
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def test_import_variables(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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sorter = ['X', 'Y', 'Z']
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raw_data = [{'variables':{"Name": ['X', 'Y', 'Z'], "value": [3, 3, 3]}}]
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j1._array_indx = 0
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df_var = j1.import_variables(raw_data)
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self.assertEqual(list(df_var[j1._variables_key]), sorter)
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def test_import_structure(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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raw_data = [{"dyn.str":[{"From":"X","To":"Z"},{"From":"Y","To":"Z"},{"From":"Z","To":"Y"}]}]
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j1._array_indx = 0
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df_struct = j1.import_structure(raw_data)
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self.assertIsInstance(df_struct, pd.DataFrame)
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def test_import_sampled_cims(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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raw_data = j1.read_json_file()
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j1._array_indx = 0
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j1._df_samples_list = j1.import_trajectories(raw_data)
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j1._sorter = j1.build_sorter(j1._df_samples_list[0])
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cims = j1.import_sampled_cims(raw_data, 0, 'dyn.cims')
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self.assertEqual(list(cims.keys()), j1.sorter)
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def test_dataset_id(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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array_indx = 0
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j1.import_data(array_indx)
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self.assertEqual(array_indx, j1.dataset_id())
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def test_file_path(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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self.assertEqual(j1.file_path, "./tests/data/networks_and_trajectories_binary_data_01_3.json")
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def test_import_data(self):
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j1 = JsonImporter("./tests/data/networks_and_trajectories_binary_data_02_10_1.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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j1.import_data(0)
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self.assertEqual(list(j1.variables[j1._variables_key]),
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list(j1.concatenated_samples.columns.values[1:len(j1.variables[j1._variables_key]) + 1]))
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print(j1.variables)
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print(j1.structure)
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print(j1.concatenated_samples)
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def ordered(self, obj):
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if isinstance(obj, dict):
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return sorted((k, self.ordered(v)) for k, v in obj.items())
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if isinstance(obj, list):
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return sorted(self.ordered(x) for x in obj)
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else:
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return obj
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if __name__ == '__main__':
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unittest.main()
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