From bddfc1b78363faa7d160655286392efcc99bcc72 Mon Sep 17 00:00:00 2001 From: Alexandr Velikiy <39257464+VargBurz@users.noreply.github.com> Date: Sun, 2 Dec 2018 21:35:25 +0300 Subject: [PATCH] Add unit tests for methods in utils #271 (#274) --- analytics/tests/test_utils.py | 124 +++++++++++++++++++++++++++++++++- 1 file changed, 123 insertions(+), 1 deletion(-) diff --git a/analytics/tests/test_utils.py b/analytics/tests/test_utils.py index bf12199..0dde71a 100644 --- a/analytics/tests/test_utils.py +++ b/analytics/tests/test_utils.py @@ -1,11 +1,133 @@ +import utils import unittest +import numpy as np +import pandas as pd +import math class TestUtils(unittest.TestCase): #example test for test's workflow purposes def test_segment_parsion(self): self.assertTrue(True) - + + def test_confidence_all_normal_value(self): + segment = [1, 2, 0, 6, 8, 5, 3] + utils_result = utils.find_confidence(segment) + result = 1.6 + relative_tolerance = 1e-2 + self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance)) + + def test_confidence_all_nan_value(self): + segment = [np.NaN, np.NaN, np.NaN, np.NaN] + self.assertEqual(utils.find_confidence(segment), 0) + + def test_confidence_with_nan_value(self): + data = [np.NaN, np.NaN, 0, 8] + utils_result = utils.find_confidence(data) + result = 1.6 + relative_tolerance = 1e-2 + self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance)) + + def test_interval_all_normal_value(self): + data = [1, 2, 1, 2, 4, 1, 2, 4, 5, 6] + data = pd.Series(data) + center = 4 + window_size = 2 + result = [1, 2, 4, 1, 2] + self.assertEqual(list(utils.get_interval(data, center, window_size)), result) + + def test_interval_wrong_ws(self): + data = [1, 2, 4, 1, 2, 4] + data = pd.Series(data) + center = 3 + window_size = 6 + result = [1, 2, 4, 1, 2, 4] + self.assertEqual(list(utils.get_interval(data, center, window_size)), result) + + def test_subtract_min_without_nan(self): + segment = [1, 2, 4, 1, 2, 4] + segment = pd.Series(segment) + result = [0, 1, 3, 0, 1, 3] + utils_result = list(utils.subtract_min_without_nan(segment)) + self.assertEqual(utils_result, result) + + def test_subtract_min_with_nan(self): + segment = [np.NaN, 2, 4, 1, 2, 4] + segment = pd.Series(segment) + result = [2, 4, 1, 2, 4] + utils_result = list(utils.subtract_min_without_nan(segment)[1:]) + self.assertEqual(utils_result, result) + + def test_get_convolve(self): + data = [1, 2, 3, 2, 2, 0, 2, 3, 4, 3, 2, 1, 1, 2, 3, 4, 3, 2, 0] + data = pd.Series(data) + pattern_index = [2, 8, 15] + window_size = 2 + av_model = [1, 2, 3, 2, 1] + result = [] + self.assertNotEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) + + def test_get_convolve_with_nan(self): + data = [1, 2, 3, 2, np.NaN, 0, 2, 3, 4, np.NaN, 2, 1, 1, 2, 3, 4, 3, np.NaN, 0] + data = pd.Series(data) + pattern_index = [2, 8, 15] + window_size = 2 + av_model = [1, 2, 3, 2, 1] + result = utils.get_convolve(pattern_index, av_model, data, window_size) + for val in result: + self.assertFalse(np.isnan(val)) + + def test_get_convolve_empty_data(self): + data = [] + pattern_index = [] + window_size = 2 + av_model = [] + result = [] + self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) + + def test_get_distribution_density(self): + segment = [1, 1, 1, 3, 5, 5, 5] + segment = pd.Series(segment) + result = (3, 5, 1) + self.assertEqual(utils.get_distribution_density(segment), result) + + def test_find_jump_parameters_center(self): + 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] + segment = pd.Series(segment) + jump_center = [10, 11] + self.assertIn(utils.find_jump_parameters(segment, 0)[0], jump_center) + + def test_find_jump_parameters_height(self): + 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] + segment = pd.Series(segment) + jump_height = [3.5, 4] + self.assertGreaterEqual(utils.find_jump_parameters(segment, 0)[1], jump_height[0]) + self.assertLessEqual(utils.find_jump_parameters(segment, 0)[1], jump_height[1]) + + def test_find_jump_parameters_length(self): + 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] + segment = pd.Series(segment) + jump_length = 2 + self.assertEqual(utils.find_jump_parameters(segment, 0)[2], jump_length) + + def test_find_drop_parameters_center(self): + 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] + segment = pd.Series(segment) + drop_center = [14, 15] + self.assertIn(utils.find_drop_parameters(segment, 0)[0], drop_center) + + def test_find_drop_parameters_height(self): + 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] + segment = pd.Series(segment) + drop_height = [3.5, 4] + self.assertGreaterEqual(utils.find_drop_parameters(segment, 0)[1], drop_height[0]) + self.assertLessEqual(utils.find_drop_parameters(segment, 0)[1], drop_height[1]) + + def test_find_drop_parameters_length(self): + 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] + segment = pd.Series(segment) + drop_length = 2 + self.assertEqual(utils.find_drop_parameters(segment, 0)[2], drop_length) if __name__ == '__main__': unittest.main()