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359 lines
14 KiB
359 lines
14 KiB
from analytic_types.segment import Segment |
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import utils |
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import unittest |
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import numpy as np |
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import pandas as pd |
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import math |
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import random |
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RELATIVE_TOLERANCE = 1e-1 |
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class TestUtils(unittest.TestCase): |
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#example test for test's workflow purposes |
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def test_segment_parsion(self): |
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self.assertTrue(True) |
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def test_confidence_all_normal_value(self): |
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segment = [1, 2, 0, 6, 8, 5, 3] |
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utils_result = utils.find_confidence(segment)[0] |
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result = 4.0 |
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self.assertTrue(math.isclose(utils_result, result, rel_tol = RELATIVE_TOLERANCE)) |
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def test_confidence_all_nan_value(self): |
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segment = [np.nan, np.nan, np.nan, np.nan] |
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self.assertEqual(utils.find_confidence(segment)[0], 0) |
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def test_confidence_with_nan_value(self): |
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data = [np.nan, np.nan, 0, 8] |
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utils_result = utils.find_confidence(data)[0] |
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result = 4.0 |
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self.assertTrue(math.isclose(utils_result, result, rel_tol = RELATIVE_TOLERANCE)) |
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def test_interval_all_normal_value(self): |
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data = [1, 2, 1, 2, 4, 1, 2, 4, 5, 6] |
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data = pd.Series(data) |
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center = 4 |
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window_size = 2 |
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result = [1, 2, 4, 1, 2] |
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self.assertEqual(list(utils.get_interval(data, center, window_size)), result) |
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def test_interval_wrong_ws(self): |
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data = [1, 2, 4, 1, 2, 4] |
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data = pd.Series(data) |
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center = 3 |
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window_size = 6 |
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result = [1, 2, 4, 1, 2, 4] |
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self.assertEqual(list(utils.get_interval(data, center, window_size)), result) |
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def test_subtract_min_without_nan(self): |
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segment = [1, 2, 4, 1, 2, 4] |
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segment = pd.Series(segment) |
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result = [0, 1, 3, 0, 1, 3] |
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utils_result = list(utils.subtract_min_without_nan(segment)) |
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self.assertEqual(utils_result, result) |
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def test_subtract_min_with_nan(self): |
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segment = [np.nan, 2, 4, 1, 2, 4] |
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segment = pd.Series(segment) |
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result = [2, 4, 1, 2, 4] |
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utils_result = list(utils.subtract_min_without_nan(segment)[1:]) |
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self.assertEqual(utils_result, result) |
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def test_get_convolve(self): |
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data = [1, 2, 3, 2, 2, 0, 2, 3, 4, 3, 2, 1, 1, 2, 3, 4, 3, 2, 0] |
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data = pd.Series(data) |
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pattern_index = [2, 8, 15] |
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window_size = 2 |
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av_model = [1, 2, 3, 2, 1] |
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result = [] |
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self.assertNotEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) |
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def test_get_convolve_with_nan(self): |
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data = [1, 2, 3, 2, np.nan, 0, 2, 3, 4, np.nan, 2, 1, 1, 2, 3, 4, 3, np.nan, 0] |
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data = pd.Series(data) |
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pattern_index = [2, 8, 15] |
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window_size = 2 |
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av_model = [1, 2, 3, 2, 1] |
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result = utils.get_convolve(pattern_index, av_model, data, window_size) |
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for val in result: |
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self.assertFalse(np.isnan(val)) |
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def test_get_convolve_empty_data(self): |
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data = [] |
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pattern_index = [] |
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window_size = 2 |
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window_size_zero = 0 |
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av_model = [] |
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result = [] |
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self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) |
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self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size_zero), result) |
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def test_find_jump_parameters_center(self): |
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segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5] |
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segment = pd.Series(segment) |
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jump_center = [10, 11] |
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self.assertIn(utils.find_pattern_center(segment, 0, 'jump'), jump_center) |
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def test_find_jump_parameters_height(self): |
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segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5] |
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segment = pd.Series(segment) |
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jump_height = [3.5, 4] |
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self.assertGreaterEqual(utils.find_parameters(segment, 0, 'jump')[0], jump_height[0]) |
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self.assertLessEqual(utils.find_parameters(segment, 0, 'jump')[0], jump_height[1]) |
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def test_find_jump_parameters_length(self): |
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segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5] |
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segment = pd.Series(segment) |
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jump_length = 2 |
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self.assertEqual(utils.find_parameters(segment, 0, 'jump')[1], jump_length) |
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def test_find_drop_parameters_center(self): |
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segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] |
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segment = pd.Series(segment) |
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drop_center = [14, 15, 16] |
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self.assertIn(utils.find_pattern_center(segment, 0, 'drop'), drop_center) |
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def test_find_drop_parameters_height(self): |
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segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] |
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segment = pd.Series(segment) |
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drop_height = [3.5, 4] |
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self.assertGreaterEqual(utils.find_parameters(segment, 0, 'drop')[0], drop_height[0]) |
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self.assertLessEqual(utils.find_parameters(segment, 0, 'drop')[0], drop_height[1]) |
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def test_find_drop_parameters_length(self): |
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segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] |
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segment = pd.Series(segment) |
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drop_length = 2 |
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self.assertEqual(utils.find_parameters(segment, 0, 'drop')[1], drop_length) |
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def test_get_av_model_empty_data(self): |
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patterns_list = [] |
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result = [] |
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self.assertEqual(utils.get_av_model(patterns_list), result) |
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def test_get_av_model_normal_data(self): |
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patterns_list = [[1, 1, 1], [2, 2, 2],[3,3,3]] |
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result = [2.0, 2.0, 2.0] |
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self.assertEqual(utils.get_av_model(patterns_list), result) |
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def test_get_distribution_density(self): |
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segment = [1, 1, 1, 3, 5, 5, 5] |
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segment = pd.Series(segment) |
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result = (3, 5, 1) |
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self.assertEqual(utils.get_distribution_density(segment), result) |
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def test_get_distribution_density_right(self): |
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data = [1.0, 5.0, 5.0, 4.0] |
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data = pd.Series(data) |
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median = 3.0 |
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max_line = 5.0 |
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min_line = 1.0 |
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utils_result = utils.get_distribution_density(data) |
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self.assertTrue(math.isclose(utils_result[0], median, rel_tol = RELATIVE_TOLERANCE)) |
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self.assertTrue(math.isclose(utils_result[1], max_line, rel_tol = RELATIVE_TOLERANCE)) |
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self.assertTrue(math.isclose(utils_result[2], min_line, rel_tol = RELATIVE_TOLERANCE)) |
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def test_get_distribution_density_left(self): |
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data = [1.0, 1.0, 2.0, 1.0, 5.0] |
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data = pd.Series(data) |
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median = 3.0 |
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max_line = 5.0 |
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min_line = 1.0 |
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utils_result = utils.get_distribution_density(data) |
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self.assertTrue(math.isclose(utils_result[0], median, rel_tol = RELATIVE_TOLERANCE)) |
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self.assertTrue(math.isclose(utils_result[1], max_line, rel_tol = RELATIVE_TOLERANCE)) |
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self.assertTrue(math.isclose(utils_result[2], min_line, rel_tol = RELATIVE_TOLERANCE)) |
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def test_get_distribution_density_short_data(self): |
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data = [1.0, 5.0] |
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data = pd.Series(data) |
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segment = [1.0] |
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segment = pd.Series(segment) |
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utils_result_data = utils.get_distribution_density(data) |
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utils_result_segment = utils.get_distribution_density(segment) |
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self.assertEqual(len(utils_result_data), 3) |
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self.assertEqual(utils_result_segment, (0, 0, 0)) |
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def test_get_distribution_density_with_nans(self): |
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segment = [np.NaN, 1, 1, 1, np.NaN, 3, 5, 5, 5, np.NaN] |
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segment = pd.Series(segment) |
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result = (3, 5, 1) |
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self.assertEqual(utils.get_distribution_density(segment), result) |
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def test_find_pattern_jump_center(self): |
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data = [1.0, 1.0, 1.0, 5.0, 5.0, 5.0] |
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data = pd.Series(data) |
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median = 3.0 |
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result = 3 |
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self.assertEqual(result, utils.find_pattern_center(data, 0, 'jump')) |
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def test_get_convolve_wrong_index(self): |
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data = [1.0, 5.0, 2.0, 1.0, 6.0, 2.0] |
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data = pd.Series(data) |
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segemnts = [1, 11] |
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av_model = [0.0, 4.0, 0.0] |
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window_size = 1 |
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try: |
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utils.get_convolve(segemnts, av_model, data, window_size) |
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except ValueError: |
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self.fail('Method get_convolve raised unexpectedly') |
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def test_get_av_model_for_different_length(self): |
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patterns_list = [[1.0, 1.0, 2.0], [4.0, 4.0], [2.0, 2.0, 2.0], [3.0, 3.0], []] |
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try: |
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utils.get_av_model(patterns_list) |
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except ValueError: |
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self.fail('Method get_convolve raised unexpectedly') |
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def test_find_nan_indexes(self): |
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data = [1, 1, 1, 0, 0, np.nan, None, []] |
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data = pd.Series(data) |
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result = [5, 6] |
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self.assertEqual(utils.find_nan_indexes(data), result) |
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def test_find_nan_indexes_normal_values(self): |
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data = [1, 1, 1, 0, 0, 0, 1, 1] |
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data = pd.Series(data) |
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result = [] |
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self.assertEqual(utils.find_nan_indexes(data), result) |
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def test_find_nan_indexes_empty_values(self): |
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data = [] |
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result = [] |
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self.assertEqual(utils.find_nan_indexes(data), result) |
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def test_create_correlation_data(self): |
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data = [random.randint(10, 999) for _ in range(10000)] |
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data = pd.Series(data) |
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pattern_model = [100, 200, 500, 300, 100] |
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ws = 2 |
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result = 6000 |
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corr_data = utils.get_correlation_gen(data, ws, pattern_model) |
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corr_data = list(corr_data) |
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self.assertGreaterEqual(len(corr_data), result) |
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def test_inverse_segment(self): |
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data = pd.Series([1,2,3,4,3,2,1]) |
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result = pd.Series([3,2,1,0,1,2,3]) |
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utils_result = utils.inverse_segment(data) |
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for ind, val in enumerate(utils_result): |
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self.assertEqual(val, result[ind]) |
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def test_get_end_of_segment_equal(self): |
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data = pd.Series([5,4,3,2,1,0,0,0]) |
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result_list = [4, 5, 6] |
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self.assertIn(utils.get_end_of_segment(data, False), result_list) |
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def test_get_end_of_segment_greater(self): |
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data = pd.Series([5,4,3,2,1,0,1,2,3]) |
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result_list = [4, 5, 6] |
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self.assertIn(utils.get_end_of_segment(data, False), result_list) |
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def test_get_borders_of_peaks(self): |
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data = pd.Series([1,0,1,2,3,2,1,0,0,1,2,3,4,3,2,2,1,0,1,2,3,4,5,3,2,1,0]) |
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pattern_center = [4, 12, 22] |
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ws = 3 |
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confidence = 1.5 |
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result = [(1, 7), (9, 15), (19, 25)] |
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self.assertEqual(utils.get_borders_of_peaks(pattern_center, data, ws, confidence), result) |
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def test_get_borders_of_peaks_for_trough(self): |
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data = pd.Series([4,4,5,5,3,1,3,5,5,6,3,2]) |
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pattern_center = [5] |
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ws = 5 |
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confidence = 3 |
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result = [(3, 7)] |
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self.assertEqual(utils.get_borders_of_peaks(pattern_center, data, ws, confidence, inverse = True), result) |
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def test_get_start_and_end_of_segments(self): |
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segments = [[1, 2, 3, 4], [5, 6, 7], [8], [], [12, 12]] |
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result = [[1, 4], [5, 7], [8, 8], [12, 12]] |
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utils_result = utils.get_start_and_end_of_segments(segments) |
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for got, expected in zip(utils_result, result): |
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self.assertEqual(got, expected) |
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def test_get_start_and_end_of_segments_empty(self): |
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segments = [] |
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result = [] |
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utils_result = utils.get_start_and_end_of_segments(segments) |
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self.assertEqual(result, utils_result) |
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def test_merge_intersecting_segments(self): |
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test_cases = [ |
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{ |
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'index': [Segment(10, 20), Segment(30, 40)], |
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'result': [[10, 20], [30, 40]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(10, 20), Segment(13, 23), Segment(15, 17), Segment(20, 40)], |
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'result': [[10, 40]], |
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'step': 0, |
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}, |
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{ |
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'index': [], |
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'result': [], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(10, 20)], |
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'result': [[10, 20]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(10, 20), Segment(13, 23), Segment(25, 30), Segment(35, 40)], |
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'result': [[10, 23], [25, 30], [35, 40]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(10, 50), Segment(5, 40), Segment(15, 25), Segment(6, 50)], |
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'result': [[5, 50]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(5, 10), Segment(10, 20), Segment(25, 50)], |
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'result': [[5, 20], [25, 50]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(20, 40), Segment(10, 15), Segment(50, 60)], |
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'result': [[10, 15], [20, 40], [50, 60]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(20, 40), Segment(10, 20), Segment(50, 60)], |
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'result': [[10, 40], [50, 60]], |
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'step': 0, |
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}, |
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{ |
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'index': [Segment(10, 10), Segment(20, 20), Segment(30, 30)], |
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'result': [[10, 30]], |
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'step': 10, |
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}, |
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] |
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for case in test_cases: |
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utils_result = utils.merge_intersecting_segments(case['index'], case['step']) |
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for got, expected in zip(utils_result, case['result']): |
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self.assertEqual(got.from_timestamp, expected[0]) |
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self.assertEqual(got.to_timestamp, expected[1]) |
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def test_serialize(self): |
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segment_list = [Segment(100,200)] |
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serialize_list = utils.meta.SerializableList(segment_list) |
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meta_result = utils.meta.serialize(serialize_list) |
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expected_result = [{ 'from': 100, 'to': 200 }] |
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self.assertEqual(meta_result, expected_result) |
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def test_remove_duplicates_and_sort(self): |
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a1 = [1, 3, 5] |
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a2 = [8, 3, 6] |
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expected_result = [1, 3, 5, 6, 8] |
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utils_result = utils.remove_duplicates_and_sort(a1+a2) |
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self.assertEqual(utils_result, expected_result) |
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self.assertEqual([], []) |
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if __name__ == '__main__': |
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unittest.main()
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