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chore: improtve method sorting

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Matthias
2024-11-10 15:55:26 +01:00
parent 365c454da1
commit 119b73ead2
1 changed files with 28 additions and 28 deletions
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56
freqtrade/optimize/hyperopt/hyperopt_optimizer.py
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@@ -137,34 +137,6 @@ class HyperOptimizer:
# and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params, strict=False)}
def get_optimizer(
self,
cpu_count: int,
random_state: int,
initial_points: int,
model_queue_size: int,
) -> Optimizer:
dimensions = self.dimensions
estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
acq_optimizer = "sampling"
if isinstance(estimator, str):
if estimator not in ("GP", "RF", "ET", "GBRT"):
raise OperationalException(f"Estimator {estimator} not supported.")
else:
acq_optimizer = "auto"
logger.info(f"Using estimator {estimator}.")
return Optimizer(
dimensions,
base_estimator=estimator,
acq_optimizer=acq_optimizer,
n_initial_points=initial_points,
acq_optimizer_kwargs={"n_jobs": cpu_count},
random_state=random_state,
model_queue_size=model_queue_size,
)
def _get_params_details(self, params: dict) -> dict:
"""
Return the params for each space
@@ -403,6 +375,34 @@ class HyperOptimizer:
"total_profit": total_profit,
}
def get_optimizer(
self,
cpu_count: int,
random_state: int,
initial_points: int,
model_queue_size: int,
) -> Optimizer:
dimensions = self.dimensions
estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
acq_optimizer = "sampling"
if isinstance(estimator, str):
if estimator not in ("GP", "RF", "ET", "GBRT"):
raise OperationalException(f"Estimator {estimator} not supported.")
else:
acq_optimizer = "auto"
logger.info(f"Using estimator {estimator}.")
return Optimizer(
dimensions,
base_estimator=estimator,
acq_optimizer=acq_optimizer,
n_initial_points=initial_points,
acq_optimizer_kwargs={"n_jobs": cpu_count},
random_state=random_state,
model_queue_size=model_queue_size,
)
def advise_and_trim(self, data: dict[str, DataFrame]) -> dict[str, DataFrame]:
preprocessed = self.backtesting.strategy.advise_all_indicators(data)