Coverage for mlos_bench/mlos_bench/tests/tunables/test_tunables_size_props.py: 100%

1# 

2# Copyright (c) Microsoft Corporation. 

3# Licensed under the MIT License. 

4# 

5""" 

6Unit tests for checking tunable size properties. 

7""" 

8 

9import numpy as np 

10import pytest 

11 

12from mlos_bench.tunables.tunable import Tunable 

13 

14 

15# Note: these test do *not* check the ConfigSpace conversions for those same Tunables. 

16# That is checked indirectly via grid_search_optimizer_test.py 

17 

18def test_tunable_int_size_props() -> None: 

19 """Test tunable int size properties""" 

20 tunable = Tunable( 

21 name="test", 

22 config={ 

23 "type": "int", 

24 "range": [1, 5], 

25 "default": 3, 

26 }) 

27 assert tunable.span == 4 

28 assert tunable.cardinality == 5 

29 expected = [1, 2, 3, 4, 5] 

30 assert list(tunable.quantized_values or []) == expected 

31 assert list(tunable.values or []) == expected 

32 

33 

34def test_tunable_float_size_props() -> None: 

35 """Test tunable float size properties""" 

36 tunable = Tunable( 

37 name="test", 

38 config={ 

39 "type": "float", 

40 "range": [1.5, 5], 

41 "default": 3, 

42 }) 

43 assert tunable.span == 3.5 

44 assert tunable.cardinality == np.inf 

45 assert tunable.quantized_values is None 

46 assert tunable.values is None 

47 

48 

49def test_tunable_categorical_size_props() -> None: 

50 """Test tunable categorical size properties""" 

51 tunable = Tunable( 

52 name="test", 

53 config={ 

54 "type": "categorical", 

55 "values": ["a", "b", "c"], 

56 "default": "a", 

57 }) 

58 with pytest.raises(AssertionError): 

59 _ = tunable.span 

60 assert tunable.cardinality == 3 

61 assert tunable.values == ["a", "b", "c"] 

62 with pytest.raises(AssertionError): 

63 _ = tunable.quantized_values 

64 

65 

66def test_tunable_quantized_int_size_props() -> None: 

67 """Test quantized tunable int size properties""" 

68 tunable = Tunable( 

69 name="test", 

70 config={ 

71 "type": "int", 

72 "range": [100, 1000], 

73 "default": 100, 

74 "quantization": 100 

75 }) 

76 assert tunable.span == 900 

77 assert tunable.cardinality == 10 

78 expected = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000] 

79 assert list(tunable.quantized_values or []) == expected 

80 assert list(tunable.values or []) == expected 

81 

82 

83def test_tunable_quantized_float_size_props() -> None: 

84 """Test quantized tunable float size properties""" 

85 tunable = Tunable( 

86 name="test", 

87 config={ 

88 "type": "float", 

89 "range": [0, 1], 

90 "default": 0, 

91 "quantization": .1 

92 }) 

93 assert tunable.span == 1 

94 assert tunable.cardinality == 11 

95 expected = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] 

96 assert pytest.approx(list(tunable.quantized_values or []), 0.0001) == expected 

97 assert pytest.approx(list(tunable.values or []), 0.0001) == expected