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Refactor hyperopt to extract evaluate_result

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This commit is contained in:
Matthias 2022-09-11 11:54:31 +02:00
parent a48923c0e4
commit 32e13d65c3
1 changed files with 29 additions and 20 deletions
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49
freqtrade/optimize/hyperopt.py
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@ -290,7 +290,7 @@ class Hyperopt:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict[str, Any]:
"""
Used Optimize function.
Called once per epoch to optimize whatever is configured.
@ -410,7 +410,9 @@ class Hyperopt:
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
def run_optimizer_parallel(self, parallel: Parallel, asked: List[List], i: int) -> List:
def run_optimizer_parallel(
self, parallel: Parallel, asked: List[List], i: int) -> List[Dict[str, Any]]:
""" Start optimizer in a parallel way """
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@ -514,6 +516,30 @@ class Hyperopt:
]
return widgets
def evaluate_result(self, val: Dict[str, Any], current: int, is_random: bool):
"""
Evaluate results returned from generate_optimizer
"""
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug("Optimizer epoch evaluated: %s", val)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
logger.info(f"Using optimizer random state: {self.random_state}")
@ -569,25 +595,8 @@ class Hyperopt:
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
self.evaluate_result(val, current, is_random[j])
pbar.update(current)