colosseum.experiment.config 

 1from dataclasses import dataclass
 2from typing import Type
 3
 4import yaml
 5
 6from colosseum.emission_maps import EmissionMap
 7from colosseum.utils import ensure_folder
 8
 9
10@dataclass(frozen=True)
11class ExperimentConfig:
12    n_seeds: int
13    """is the number of seed each agent/MDP interaction is repeated."""
14    n_steps: int
15    """is the optimization horizon."""
16    max_interaction_time_s: float
17    """is the maximum training time given to the agent in seconds."""
18    log_performance_indicators_every: int
19    """is the number of step after which the performance indicators are calculated and stored."""
20    emission_map: Type["EmissionMap"] = None
21    """is the emission map for the MDP."""
22
23    def store_at(self, dest_folder: str):
24        with open(ensure_folder(dest_folder) + "experiment_config.yml", "w") as f:
25            conf = {
26                "n_seeds": self.n_seeds,
27                "n_steps": self.n_steps,
28                "max_interaction_time_s": self.max_interaction_time_s,
29                "log_performance_indicators_every": self.log_performance_indicators_every,
30            }
31            if self.emission_map is not None:
32                conf["emission_map"] = self.emission_map.__name__
33
34            yaml.dump(conf, f)
@dataclass(frozen=True)
class ExperimentConfig: 
11@dataclass(frozen=True)
12class ExperimentConfig:
13    n_seeds: int
14    """is the number of seed each agent/MDP interaction is repeated."""
15    n_steps: int
16    """is the optimization horizon."""
17    max_interaction_time_s: float
18    """is the maximum training time given to the agent in seconds."""
19    log_performance_indicators_every: int
20    """is the number of step after which the performance indicators are calculated and stored."""
21    emission_map: Type["EmissionMap"] = None
22    """is the emission map for the MDP."""
23
24    def store_at(self, dest_folder: str):
25        with open(ensure_folder(dest_folder) + "experiment_config.yml", "w") as f:
26            conf = {
27                "n_seeds": self.n_seeds,
28                "n_steps": self.n_steps,
29                "max_interaction_time_s": self.max_interaction_time_s,
30                "log_performance_indicators_every": self.log_performance_indicators_every,
31            }
32            if self.emission_map is not None:
33                conf["emission_map"] = self.emission_map.__name__
34
35            yaml.dump(conf, f)
ExperimentConfig( n_seeds: int, n_steps: int, max_interaction_time_s: float, log_performance_indicators_every: int, emission_map: Type[colosseum.emission_maps.base.EmissionMap] = None)
n_seeds: int

is the number of seed each agent/MDP interaction is repeated.

n_steps: int

is the optimization horizon.

max_interaction_time_s: float

is the maximum training time given to the agent in seconds.

log_performance_indicators_every: int

is the number of step after which the performance indicators are calculated and stored.

emission_map: Type[colosseum.emission_maps.base.EmissionMap] = None

is the emission map for the MDP.

def store_at(self, dest_folder: str): 
24    def store_at(self, dest_folder: str):
25        with open(ensure_folder(dest_folder) + "experiment_config.yml", "w") as f:
26            conf = {
27                "n_seeds": self.n_seeds,
28                "n_steps": self.n_steps,
29                "max_interaction_time_s": self.max_interaction_time_s,
30                "log_performance_indicators_every": self.log_performance_indicators_every,
31            }
32            if self.emission_map is not None:
33                conf["emission_map"] = self.emission_map.__name__
34
35            yaml.dump(conf, f)