Coverage for src / sparkle / CLI / help / global_variables.py: 55%

1#!/usr/bin/env python3 

2"""Definitions of constants broadly used in Sparkle.""" 

3 

4from __future__ import annotations 

5from typing import TYPE_CHECKING 

6import ast 

7from argparse import Namespace 

8import random 

9import numpy as np 

10 

11from sparkle.platform.settings_objects import Settings 

12 

13if TYPE_CHECKING: 

14 from sparkle.configurator.configurator import ConfigurationScenario 

15 from sparkle.selector import SelectionScenario 

16 from sparkle.structures import PerformanceDataFrame 

17 

18__settings: Settings = None 

19 

20 

21def settings(argsv: Namespace = None) -> Settings: 

22 """Function to get the global settings object.""" 

23 global __settings 

24 if __settings is None: 

25 __settings = Settings(Settings.DEFAULT_settings_path, argsv=argsv) 

26 # Set global random state 

27 max_seed = 2**32 - 1 

28 latest_ini = Settings(Settings.DEFAULT_previous_settings_path) 

29 # Determine seed priority: latest.ini > __settings > random 

30 seed = latest_ini.seed or __settings.seed or random.randint(0, max_seed) 

31 # Set global RNG states 

32 np.random.seed(seed) 

33 random.seed(seed) 

34 __settings.random_state = seed 

35 # Next seed will be saved in latest.ini when the cli script calls 'write_used_settings()' 

36 next_seed = random.randint(0, max_seed) 

37 __settings.seed = next_seed 

38 

39 elif argsv is not None: 

40 __settings.apply_arguments(argsv) 

41 

42 return __settings 

43 

44 

45__configuration_scenarios: list[ConfigurationScenario] = None 

46__selection_scenarios: list[SelectionScenario] = None 

47 

48 

49def configuration_scenarios(refresh: bool = False) -> list[ConfigurationScenario]: 

50 """Fetch all known configuration scenarios.""" 

51 global __configuration_scenarios 

52 config_path = Settings.DEFAULT_configuration_output 

53 if __configuration_scenarios is None or refresh: 

54 # NOTE: Import here for platform speedup 

55 from sparkle.configurator.implementations import ( 

56 SMAC2Scenario, 

57 SMAC3Scenario, 

58 ParamILSScenario, 

59 IRACEScenario, 

60 ) 

61 

62 __configuration_scenarios = [] 

63 for f in config_path.glob("*/*/*.*"): # We look for files at depth three 

64 if "scenario" not in f.name: 

65 continue 

66 if "SMAC2" in str(f): 

67 __configuration_scenarios.append(SMAC2Scenario.from_file(f)) 

68 elif "SMAC3" in str(f): 

69 __configuration_scenarios.append(SMAC3Scenario.from_file(f)) 

70 elif "ParamILS" in str(f): 

71 __configuration_scenarios.append(ParamILSScenario.from_file(f)) 

72 elif "IRACE" in str(f): 

73 __configuration_scenarios.append(IRACEScenario.from_file(f)) 

74 return __configuration_scenarios 

75 

76 

77def selection_scenarios(refresh: bool = False) -> list[SelectionScenario]: 

78 """Fetch all known selection scenarios.""" 

79 global __selection_scenarios 

80 selection_path = Settings.DEFAULT_selection_output 

81 if __selection_scenarios is None or refresh: 

82 # NOTE: Import here for speedup 

83 from sparkle.selector import SelectionScenario 

84 

85 __selection_scenarios = [] 

86 for f in selection_path.glob("*/*/*.txt"): # We look for files at depth three 

87 if "scenario" not in f.name: 

88 continue 

89 __selection_scenarios.append(SelectionScenario.from_file(f)) 

90 return __selection_scenarios 

91 

92 

93def parallel_portfolio_scenarios() -> list[PerformanceDataFrame]: 

94 """Fetch all known parallel portfolio scenarios.""" 

95 parallel_portfolio_path = Settings.DEFAULT_parallel_portfolio_output 

96 # NOTE: Import here to speedup 

97 from sparkle.structures import PerformanceDataFrame 

98 

99 return [PerformanceDataFrame(f) for f in parallel_portfolio_path.glob("*/*.csv")] 

100 

101 

102reference_list_dir = Settings.DEFAULT_reference_dir 

103extractor_nickname_list_path = reference_list_dir / "sparkle_extractor_nickname_list.txt" 

104solver_nickname_list_path = reference_list_dir / "sparkle_solver_nickname_list.txt" 

105instances_nickname_path = reference_list_dir / "sparkle_instance_nickname_list.txt" 

106 

107file_storage_data_mapping = { 

108 solver_nickname_list_path: {}, 

109 instances_nickname_path: {}, 

110 extractor_nickname_list_path: {}, 

111} 

112 

113for data_path in file_storage_data_mapping.keys(): 

114 if data_path.exists(): 

115 with data_path.open("r+") as fo: 

116 file_storage_data_mapping[data_path] = ast.literal_eval(fo.read()) 

117 

118solver_nickname_mapping = file_storage_data_mapping[solver_nickname_list_path] 

119instance_set_nickname_mapping = file_storage_data_mapping[instances_nickname_path] 

120extractor_nickname_mapping = file_storage_data_mapping[extractor_nickname_list_path]