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https://github.com/Floriansylvain/FDJ-SLAYER.git
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feat: stats analysis & viz w/ unit tests
This commit is contained in:
Florian Sylvain
@@ -0,0 +1,199 @@
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"""
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Unit tests for the StatsAnalysis class in fdj_slayer.stats module
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"""
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import unittest
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from unittest.mock import patch
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import math
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from fdj_slayer.stats import StatsAnalysis
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class TestStatsAnalysis(unittest.TestCase):
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"""Tests for the StatsAnalysis class"""
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def setUp(self):
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"""Set up test fixtures before each test method"""
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self.stats_analyzer = StatsAnalysis(
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max_number=50, max_star=12, number_of_numbers=5, number_of_stars=2)
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self.sample_draws = [
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{'numbers': [1, 10, 20, 30, 40], 'stars': [1, 10]},
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{'numbers': [5, 15, 25, 35, 45], 'stars': [5, 12]},
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{'numbers': [2, 12, 22, 32, 42], 'stars': [2, 11]},
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{'numbers': [3, 13, 23, 33, 43], 'stars': [3, 8]},
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{'numbers': [4, 14, 24, 34, 44], 'stars': [4, 9]}
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]
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def test_init(self):
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"""Test initialization with correct parameters"""
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self.assertEqual(self.stats_analyzer.max_number, 50)
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self.assertEqual(self.stats_analyzer.max_star, 12)
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self.assertEqual(self.stats_analyzer.number_of_numbers, 5)
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self.assertEqual(self.stats_analyzer.number_of_stars, 2)
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def test_extract_numbers_and_stars(self):
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"""Test extraction of numbers and stars from draws"""
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all_numbers, all_stars = self.stats_analyzer._extract_numbers_and_stars(
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self.sample_draws)
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expected_numbers = [1, 10, 20, 30, 40, 5, 15, 25, 35, 45, 2, 12, 22, 32, 42,
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3, 13, 23, 33, 43, 4, 14, 24, 34, 44]
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expected_stars = [1, 10, 5, 12, 2, 11, 3, 8, 4, 9]
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self.assertEqual(sorted(all_numbers), sorted(expected_numbers))
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self.assertEqual(sorted(all_stars), sorted(expected_stars))
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def test_calculate_frequencies(self):
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"""Test frequency calculation"""
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values = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
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max_value = 5
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frequencies = self.stats_analyzer._calculate_frequencies(
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values, max_value)
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self.assertEqual(frequencies, {1: 1, 2: 2, 3: 3, 4: 4, 5: 0})
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self.assertEqual(sum(frequencies.values()), len(values))
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def test_find_min_max_frequencies(self):
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"""Test finding minimum and maximum frequencies"""
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counts = {1: 5, 2: 3, 3: 3, 4: 10, 5: 0}
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min_max = self.stats_analyzer._find_min_max_frequencies(counts)
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self.assertEqual(min_max["min"], ([5], 0))
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self.assertEqual(min_max["max"], ([4], 10))
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counts = {1: 5, 2: 5, 3: 0, 4: 0, 5: 10, 6: 10}
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min_max = self.stats_analyzer._find_min_max_frequencies(counts)
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self.assertEqual(sorted(min_max["min"][0]), [3, 4])
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self.assertEqual(min_max["min"][1], 0)
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self.assertEqual(sorted(min_max["max"][0]), [5, 6])
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self.assertEqual(min_max["max"][1], 10)
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def test_calculate_statistics(self):
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"""Test statistics calculation"""
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counts = {1: 5, 2: 10, 3: 15}
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expected_freq = 10
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min_max = {"min": ([1], 5), "max": ([3], 15)}
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stats_data = self.stats_analyzer._calculate_statistics(
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counts, expected_freq, min_max)
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expected_std_dev = math.sqrt(
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sum((count - expected_freq)**2 for count in [5, 10, 15]) / 3)
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expected_variation_pct = (15 - 5) / expected_freq * 100
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self.assertAlmostEqual(stats_data["std_dev"], expected_std_dev)
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self.assertAlmostEqual(
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stats_data["variation_pct"], expected_variation_pct)
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def test_assess_randomness(self):
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"""Test randomness assessment based on p-value"""
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self.assertEqual(
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self.stats_analyzer._assess_randomness(0.06), "likely random")
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self.assertEqual(self.stats_analyzer._assess_randomness(
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0.05), "possibly biased")
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self.assertEqual(self.stats_analyzer._assess_randomness(
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0.04), "possibly biased")
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self.assertEqual(
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self.stats_analyzer._assess_randomness(0.8), "likely random")
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self.assertEqual(self.stats_analyzer._assess_randomness(
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0.01), "possibly biased")
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@patch('scipy.stats.chisquare')
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def test_perform_chi_square_test(self, mock_chisquare):
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"""Test chi-square test with mocked scipy function"""
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mock_chisquare.return_value = (1.234, 0.567)
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counts = {1: 5, 2: 7, 3: 9}
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expected_freq = 7
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max_value = 3
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result = self.stats_analyzer._perform_chi_square_test(
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counts, expected_freq, max_value)
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mock_chisquare.assert_called_once_with([5, 7, 9], [7, 7, 7])
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self.assertEqual(result, {"chi2": 1.234, "p_value": 0.567})
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def test_analyze_dataset(self):
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"""Test dataset analysis with mocked internal methods"""
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values = [1, 1, 2, 2, 2, 3, 3, 4]
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value_type = "number"
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max_value = 5
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with patch.object(self.stats_analyzer, '_calculate_frequencies') as mock_calc_freq, \
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patch.object(self.stats_analyzer, '_perform_chi_square_test') as mock_chi2, \
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patch.object(self.stats_analyzer, '_find_min_max_frequencies') as mock_min_max, \
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patch.object(self.stats_analyzer, '_calculate_statistics') as mock_stats, \
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patch.object(self.stats_analyzer, '_assess_randomness') as mock_assess:
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mock_calc_freq.return_value = {1: 2, 2: 3, 3: 2, 4: 1, 5: 0}
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mock_chi2.return_value = {"chi2": 2.5, "p_value": 0.6}
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mock_min_max.return_value = {"min": ([5], 0), "max": ([2], 3)}
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mock_stats.return_value = {"std_dev": 1.2, "variation_pct": 30.0}
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mock_assess.return_value = "likely random"
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results = self.stats_analyzer._analyze_dataset(
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values, value_type, max_value)
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self.assertEqual(results["number_frequencies"], {
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1: 2, 2: 3, 3: 2, 4: 1, 5: 0})
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self.assertEqual(
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results["expected_number_freq"], len(values) / max_value)
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self.assertEqual(results["number_chi2"], 2.5)
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self.assertEqual(results["p_value_numbers"], 0.6)
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self.assertEqual(results["min_number"], ([5], 0))
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self.assertEqual(results["max_number"], ([2], 3))
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self.assertEqual(results["number_std_dev"], 1.2)
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self.assertEqual(results["number_variation_pct"], 30.0)
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self.assertEqual(results["number_assessment"], "likely random")
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@patch.object(StatsAnalysis, '_analyze_dataset')
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@patch.object(StatsAnalysis, '_extract_numbers_and_stars')
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def test_analyze_randomness(self, mock_extract, mock_analyze):
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"""Test analyze_randomness with mocked methods to verify method interactions"""
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mock_extract.return_value = ([1, 2, 3], [4, 5])
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mock_analyze.side_effect = [
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{"number_key": "number_value"},
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{"star_key": "star_value"}
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]
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results = self.stats_analyzer.analyze_randomness(self.sample_draws)
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mock_extract.assert_called_once_with(self.sample_draws)
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self.assertEqual(mock_analyze.call_count, 2)
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mock_analyze.assert_any_call([1, 2, 3], "number", 50)
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mock_analyze.assert_any_call([4, 5], "star", 12)
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self.assertEqual(results, {
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"sample_size": len(self.sample_draws),
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"number_key": "number_value",
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"star_key": "star_value"
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})
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@patch('scipy.stats.chisquare')
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def test_analyze_randomness_integration(self, mock_chisquare):
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"""Integration test for analyze_randomness with real data"""
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mock_chisquare.return_value = (2.0, 0.8)
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results = self.stats_analyzer.analyze_randomness(self.sample_draws)
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self.assertEqual(results["sample_size"], len(self.sample_draws))
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self.assertIn("number_frequencies", results)
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self.assertIn("star_frequencies", results)
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self.assertEqual(
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len(results["number_frequencies"]), self.stats_analyzer.max_number)
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self.assertEqual(
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len(results["star_frequencies"]), self.stats_analyzer.max_star)
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self.assertEqual(results["number_assessment"], "likely random")
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self.assertEqual(results["star_assessment"], "likely random")
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def test_analyze_randomness_empty_draws(self):
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"""Test behavior with empty draws list"""
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with self.assertRaises(ValueError):
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self.stats_analyzer.analyze_randomness([])
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if __name__ == '__main__':
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unittest.main()
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