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22b374f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | import plotly.offline as pyo
import logging
import sys
import optuna
from sklearn import model_selection
from scipy.stats import randint, uniform
import gym
from Trainer import *
from PPO_HypConfig import *
from Loader import *
all_optimizers =[
'optuna',
'sklearn'
]
class Dummy:
def __init__(self):
pass
def fit(self):
pass
class OptunaTuner:
def __init__(self, env_name, algo, directory, optimizer, ts, iterations, n_trials, counter):
self.env_name = env_name
self.algorithm = algo
self.directory = directory
self.optimizer = optimizer
self.timestep = ts
self.iterations = iterations
self.n_trials = n_trials
self.exe_number = counter
def create_study_db(self):
optuna.logging.get_logger("optuna").addHandler(logging.StreamHandler(sys.stdout))
study_name = f"study-{self.exe_number}" # Unique identifier of the study.
storage_dir = f"logs/{self.directory}"
if(not os.path.exists(storage_dir)):
os.makedirs(storage_dir)
storage_name = f"sqlite:///{storage_dir}/{study_name}.db"
return study_name, storage_name
def select_pruner(self):
return None
def select_sampler(self):
return None
def study_loader(self):
study_name = f"study-{self.exe_number}" # Unique identifier of the study.
storage_dir = f"logs/{self.directory}"
storage_name = f"sqlite:///{storage_dir}/{study_name}.db"
study = optuna.load_study(
study_name=study_name,
storage=storage_name
)
trials_df = study.trials_dataframe()
print(trials_df.head())
return study
def study_summaries(self):
study_name = f"study-{self.exe_number}" # Unique identifier of the study.
storage_dir = f"logs/{self.directory}"
storage_name = f"sqlite:///{storage_dir}/{study_name}.db"
summary = optuna.get_all_study_summaries(
storage_name, include_best_trial=True
)
print(summary)
for i,j in enumerate(summary):
print(f"-----------STUDY-{i}-----------")
for attribute in [attr for attr in dir(j) if not attr.startswith('__')]:
if(attribute=="system_attrs" or attribute=="user_attrs"):
continue
print(attribute, ':', getattr(j, attribute))
print()
pass
def under_testing_py(self):
study = self.study_loader()
trial = study.ask()
try:
optuna.testing.objectives.fail_objective(trial)
except ValueError:
print("Objective function failed!")
## To Prune
optuna.testing.objectives.pruned_objective(trial)
## Dummy trial
study = optuna.testing.visualization.prepare_study_with_trials()
def under_terminators(self):
pass
def return_study(self, bool_store_study=True, bool_select_pruner=True, bool_select_sampler=True):
study_name, storage_name, selected_pruner, selected_sampler = [None] * 4
if(bool_store_study):
study_name, storage_name = self.create_study_db()
if(bool_select_pruner):
selected_pruner = self.select_pruner()
if(bool_select_sampler):
selected_sampler = self.select_sampler()
lie = storage_name is not None
study = optuna.create_study(
storage=storage_name,
sampler=selected_sampler,
pruner=selected_pruner,
study_name=study_name,
direction='maximize',
load_if_exists=lie,
directions=None
)
return study
def call_optuna(self, store_study=True):
study = self.return_study()
study.optimize(self.objective, n_trials=self.n_trials)
def objective(self, trial):
m = MegaTrainer(self.env_name, self.algorithm)
counter = m.learn(
self.timestep,
self.iterations,
MegaHandler().request_next_HypConfig(trial, m.env, f"logs/{self.directory}"),
self.directory,
trial=trial
)
m_load = MegaLoader(self.env_name, self.algorithm, self.directory)
return m_load.load(counter, self.n_trials)["total_return"]
def visul(self):
study = self.study_loader()
if(not "_optimization_history.py"):
pyo.plot(optuna.visualization.plot_optimization_history(study))
if(not "_timeline.py"):
pyo.plot(optuna.visualization.plot_timeline(study))
if(not "_slice.py"):
pyo.plot(optuna.visualization.plot_slice(study))
if(not "_pareto_front.py"): ## this is used for multi-objective study
pyo.plot(optuna.visualization.plot_pareto_front(study))
if(not "_param_importances.py"):
pyo.plot(optuna.visualization.plot_param_importances(study))
if(not "_parallel_coordinate.py"):
pyo.plot(optuna.visualization.plot_parallel_coordinate(study))
if(not "_intermediate_values.py"):
pyo.plot(optuna.visualization.plot_intermediate_values(study))
if(not "_edf.py"):
pyo.plot(optuna.visualization.plot_edf(study))
if(not "_contour.py"):
pyo.plot(optuna.visualization.plot_contour(study))
class HyperPilotRL:
def __init__(self, env_name, algo, optimizer, timesteps, iterations, n_trials, counter):
self.env_name = env_name
self.algorithm = algo
self.directory = f"{optimizer}/{counter}/{env_name}/{algo}"
self.optimizer = optimizer
self.timestep = timesteps
self.iterations = iterations
self.n_trials = n_trials
self.execution_number = counter
if(self.optimizer == "optuna"):
o = OptunaTuner(env_name, algo, self.directory, optimizer, timesteps, iterations, n_trials, counter)
o.call_optuna()
o.study_summaries()
# o.visul()
elif(self.optimizer == "default"):
pass
else:
raise NameError("Enter optimizer is not supported.\nAvailable optimizers are", all_optimizers)
## optimizer = ['optuna']
def learning_curve():
pass
def cutoff_learn_according_to_max_time_allocation():
pass
if(__name__=="__main__"):
h = HyperPilotRL(
"CartPole-v1",
"PPO",
"optuna",
timesteps=1000,
iterations=100,
n_trials=10,
counter=102644
)
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