Coverage for mlos_core/mlos_core/optimizers/random_optimizer.py: 100%

1# 

2# Copyright (c) Microsoft Corporation. 

3# Licensed under the MIT License. 

4# 

5""" 

6Contains the RandomOptimizer class. 

7""" 

8 

9from typing import Optional 

10 

11import pandas as pd 

12 

13from mlos_core.optimizers.optimizer import BaseOptimizer 

14 

15 

16class RandomOptimizer(BaseOptimizer): 

17 """Optimizer class that produces random suggestions. 

18 Useful for baseline comparison against Bayesian optimizers. 

19 

20 Parameters 

21 ---------- 

22 parameter_space : ConfigSpace.ConfigurationSpace 

23 The parameter space to optimize. 

24 """ 

25 

26 def _register(self, configurations: pd.DataFrame, scores: pd.Series, 

27 context: Optional[pd.DataFrame] = None) -> None: 

28 """Registers the given configurations and scores. 

29 

30 Doesn't do anything on the RandomOptimizer except storing configurations for logging. 

31 

32 Parameters 

33 ---------- 

34 configurations : pd.DataFrame 

35 Dataframe of configurations / parameters. The columns are parameter names and the rows are the configurations. 

36 

37 scores : pd.Series 

38 Scores from running the configurations. The index is the same as the index of the configurations. 

39 

40 context : None 

41 Not Yet Implemented. 

42 """ 

43 if context is not None: 

44 raise NotImplementedError() 

45 # should we pop them from self.pending_observations? 

46 

47 def _suggest(self, context: Optional[pd.DataFrame] = None) -> pd.DataFrame: 

48 """Suggests a new configuration. 

49 

50 Sampled at random using ConfigSpace. 

51 

52 Parameters 

53 ---------- 

54 context : None 

55 Not Yet Implemented. 

56 

57 Returns 

58 ------- 

59 configuration : pd.DataFrame 

60 Pandas dataframe with a single row. Column names are the parameter names. 

61 """ 

62 if context is not None: 

63 # not sure how that works here? 

64 raise NotImplementedError() 

65 return pd.DataFrame(dict(self.optimizer_parameter_space.sample_configuration()), index=[0]) 

66 

67 def register_pending(self, configurations: pd.DataFrame, 

68 context: Optional[pd.DataFrame] = None) -> None: 

69 raise NotImplementedError() 

70 # self._pending_observations.append((configurations, context))