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Use skopt model_queue_size instead of custom hack

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hroff-1902 2020-04-29 10:49:25 +03:00
parent 32eaca9970
commit 726e52aaa7
1 changed files with 4 additions and 21 deletions
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25
freqtrade/optimize/hyperopt.py
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@ -49,9 +49,9 @@ logger = logging.getLogger(__name__)
INITIAL_POINTS = 30 INITIAL_POINTS = 30
# Keep no more than 2*SKOPT_MODELS_MAX_NUM models # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt models list # in the skopt model queue, to optimize memory consumption
SKOPT_MODELS_MAX_NUM = 10 SKOPT_MODEL_QUEUE_SIZE = 10
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
@ -563,25 +563,9 @@ class Hyperopt:
n_initial_points=INITIAL_POINTS, n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count}, acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.random_state, random_state=self.random_state,
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
) )
def fix_optimizer_models_list(self) -> None:
"""
WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
This may cease working when skopt updates if implementation of this intrinsic
part changes.
"""
n = len(self.opt.models) - SKOPT_MODELS_MAX_NUM
# Keep no more than 2*SKOPT_MODELS_MAX_NUM models in the skopt models list,
# remove the old ones. These are actually of no use, the current model
# from the estimator is the only one used in the skopt optimizer.
# Freqtrade code also does not inspect details of the models.
if n >= SKOPT_MODELS_MAX_NUM:
logger.debug(f"Fixing skopt models list, removing {n} old items...")
del self.opt.models[0:n]
def run_optimizer_parallel(self, parallel, asked, i) -> List: def run_optimizer_parallel(self, parallel, asked, i) -> List:
return parallel(delayed( return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked) wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@ -677,7 +661,6 @@ class Hyperopt:
asked = self.opt.ask(n_points=current_jobs) asked = self.opt.ask(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i) f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val]) self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
# Calculate progressbar outputs # Calculate progressbar outputs
for j, val in enumerate(f_val): for j, val in enumerate(f_val):