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fix duplicate params in same batch also

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viotemp1
2025-05-29 15:41:47 +03:00
parent 17cd0452ec
commit dfae7ca2ec
1 changed files with 21 additions and 17 deletions
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38
freqtrade/optimize/hyperopt/hyperopt.py
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@@ -15,7 +15,7 @@ from typing import Any
import rapidjson
from joblib import Parallel, cpu_count
from optuna.trial import Trial, TrialState
from optuna.trial import FrozenTrial, Trial, TrialState
from freqtrade.constants import FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
from freqtrade.enums import HyperoptState
@@ -171,15 +171,19 @@ class Hyperopt:
asked.append(self.opt.ask(dimensions))
return asked
def duplicate_optuna_asked_points(self, trial: Trial) -> bool:
def duplicate_optuna_asked_points(self, trial: Trial, asked_trials: list[FrozenTrial]) -> bool:
asked_trials_no_dups: list[FrozenTrial] = []
trials_to_consider = trial.study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])
# Check whether we already evaluated the sampled `params`.
for t in reversed(trials_to_consider):
if trial.params == t.params:
# logger.warning(
# f"duplicate trial: Trial {trial.number} has same params as {t.number}"
# )
return True
# Check whether same`params` in one batch (asked_trials). Autosampler is doing this.
for t in asked_trials:
if t.params not in asked_trials_no_dups:
asked_trials_no_dups.append(t)
if len(asked_trials_no_dups) != len(asked_trials):
return True
return False
def get_asked_points(self, n_points: int, dimensions: dict) -> tuple[list[Any], list[bool]]:
@@ -189,26 +193,26 @@ class Hyperopt:
Steps:
1. Try to get points using `self.opt.ask` first
2. Discard the points that have already been evaluated
3. Retry using `self.opt.ask` up to 5 times
3. Retry using `self.opt.ask` up to `n_points` times
"""
asked_non_tried: list[list[Any]] = []
asked_duplicates: list[Trial] = []
asked_non_tried: list[FrozenTrial] = []
optuna_asked_trials = self.get_optuna_asked_points(n_points=n_points, dimensions=dimensions)
asked_non_tried += [
x for x in optuna_asked_trials if not self.duplicate_optuna_asked_points(x)
x
for x in optuna_asked_trials
if not self.duplicate_optuna_asked_points(x, optuna_asked_trials)
]
i = 0
while i < 5 and len(asked_non_tried) < n_points:
while i < 2 * n_points and len(asked_non_tried) < n_points:
asked_new = self.get_optuna_asked_points(n_points=1, dimensions=dimensions)[0]
if not self.duplicate_optuna_asked_points(asked_new):
if not self.duplicate_optuna_asked_points(asked_new, asked_non_tried):
asked_non_tried.append(asked_new)
else:
asked_duplicates.append(asked_new)
i += 1
if len(asked_duplicates) > 0 and len(asked_non_tried) < n_points:
for asked_duplicate in asked_duplicates:
logger.warning(f"duplicate params for Epoch {asked_duplicate.number}")
self.count_skipped_epochs += len(asked_duplicates)
if len(asked_non_tried) < n_points:
logger.warning(
"duplicate params detected. Please check if search space is not too small!"
)
self.count_skipped_epochs += n_points - len(asked_non_tried)
return asked_non_tried, [False for _ in range(len(asked_non_tried))]