Coverage for moptipy / api / _process_ss_log.py: 91%

1"""A process with logging and different search and solution space.""" 

2from typing import Final 

3 

4from pycommons.io.path import Path 

5from pycommons.types import type_error 

6 

7from moptipy.api._process_base import _TIME_IN_NS, _check_log_time 

8from moptipy.api._process_no_ss import _write_log 

9from moptipy.api._process_ss import _ProcessSS 

10from moptipy.api.algorithm import Algorithm 

11from moptipy.api.encoding import Encoding 

12from moptipy.api.objective import Objective 

13from moptipy.api.space import Space 

14from moptipy.utils.logger import Logger 

15 

16 

17class _ProcessSSLog(_ProcessSS): 

18 """A process with logging and different search and solution space.""" 

19 

20 def __init__(self, 

21 solution_space: Space, 

22 objective: Objective, 

23 algorithm: Algorithm, 

24 log_file: Path, 

25 search_space: Space | None = None, 

26 encoding: Encoding | None = None, 

27 rand_seed: int | None = None, 

28 max_fes: int | None = None, 

29 max_time_millis: int | None = None, 

30 goal_f: int | float | None = None, 

31 log_all_fes: bool = False) -> None: 

32 """ 

33 Perform the internal initialization. Do not call directly. 

34 

35 :param solution_space: the solution space. 

36 :param objective: the objective function 

37 :param algorithm: the optimization algorithm 

38 :param search_space: the search space. 

39 :param encoding: the encoding 

40 :param log_file: the optional log file 

41 :param rand_seed: the optional random seed 

42 :param max_fes: the maximum permitted function evaluations 

43 :param max_time_millis: the maximum runtime in milliseconds 

44 :param goal_f: the goal objective value. if it is reached, the 

45 process is terminated 

46 :param log_all_fes: should every single FE be logged? 

47 """ 

48 super().__init__(solution_space=solution_space, 

49 objective=objective, 

50 algorithm=algorithm, 

51 log_file=log_file, 

52 search_space=search_space, 

53 encoding=encoding, 

54 rand_seed=rand_seed, 

55 max_fes=max_fes, 

56 max_time_millis=max_time_millis, 

57 goal_f=goal_f) 

58 if not isinstance(log_file, str): 

59 raise type_error(log_file, "log_file", str) 

60 if not isinstance(log_all_fes, bool): 

61 raise type_error(log_all_fes, "log_all_fes", bool) 

62 #: `True` if all FEs are logged, `False` to only log improvements. 

63 self.__log_all: Final[bool] = log_all_fes 

64 #: The in-memory log 

65 self.__log: list[list[int | float]] = [] 

66 #: the quick access to the log appending method 

67 self.__log_append = self.__log.append 

68 

69 def evaluate(self, x) -> float | int: 

70 if self._terminated: 

71 if self._knows_that_terminated: 

72 raise ValueError("The process has been terminated and the " 

73 "algorithm knows it.") 

74 return self._current_best_f 

75 

76 current_y: Final = self._current_y 

77 self._g(x, current_y) 

78 result: Final[int | float] = self._f(current_y) 

79 self._current_fes = current_fes = self._current_fes + 1 

80 do_term: bool = current_fes >= self._end_fes 

81 do_log: bool = self.__log_all 

82 ctn: int = 0 

83 

84 if result < self._current_best_f: 

85 self._last_improvement_fe = current_fes 

86 self._current_best_f = result 

87 self.copy(self._current_best_x, x) 

88 self._current_y = self._current_best_y 

89 self._current_best_y = current_y 

90 self._current_time_nanos = ctn = _TIME_IN_NS() 

91 self._last_improvement_time_nanos = ctn 

92 do_term = do_term or (result <= self._end_f) 

93 do_log = True 

94 

95 if do_log: 

96 if ctn <= 0: 

97 self._current_time_nanos = ctn = _TIME_IN_NS() 

98 self.__log_append([current_fes, ctn, result]) 

99 

100 if do_term: 

101 self.terminate() 

102 

103 return result 

104 

105 def register(self, x, f: int | float) -> None: 

106 if self._terminated: 

107 if self._knows_that_terminated: 

108 raise ValueError("The process has been terminated and the " 

109 "algorithm knows it.") 

110 return 

111 

112 self._current_fes = current_fes = self._current_fes + 1 

113 do_term: bool = current_fes >= self._end_fes 

114 do_log: bool = self.__log_all 

115 ctn: int = 0 

116 

117 if f < self._current_best_f: 

118 self._last_improvement_fe = current_fes 

119 self._current_best_f = f 

120 self.copy(self._current_best_x, x) 

121 current_y: Final = self._current_y 

122 self._g(x, current_y) 

123 self._current_y = self._current_best_y 

124 self._current_best_y = current_y 

125 self._current_time_nanos = ctn = _TIME_IN_NS() 

126 self._last_improvement_time_nanos = ctn 

127 do_term = do_term or (f <= self._end_f) 

128 do_log = True 

129 

130 if do_log: 

131 if ctn <= 0: 

132 self._current_time_nanos = ctn = _TIME_IN_NS() 

133 self.__log_append([current_fes, ctn, f]) 

134 

135 if do_term: 

136 self.terminate() 

137 

138 def _check_timing(self) -> None: 

139 super()._check_timing() 

140 _check_log_time(self._start_time_nanos, self._current_time_nanos, 

141 self.__log) 

142 

143 def _write_log(self, logger: Logger) -> None: 

144 _write_log(self.__log, self._start_time_nanos, logger) 

145 del self.__log 

146 super()._write_log(logger) 

147 

148 def __str__(self) -> str: 

149 return "LoggingProcessWithSearchSpace"