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Merge pull request #2269 from hroff-1902/hyperopt-cleanup4

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minor: Cleanup in hyperopt
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Matthias 2019-09-17 06:45:46 +02:00 committed by GitHub
commit 27238d97d5
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2 changed files with 13 additions and 10 deletions
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22
freqtrade/optimize/hyperopt.py
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@ -73,9 +73,11 @@ class Hyperopt:
self.trials: List = [] self.trials: List = []
# Populate functions here (hasattr is slow so should not be run during "regular" operations) # Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
self.backtesting.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
if hasattr(self.custom_hyperopt, 'populate_buy_trend'): if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.backtesting.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore self.backtesting.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'): if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.backtesting.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore self.backtesting.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
@ -109,7 +111,9 @@ class Hyperopt:
p.unlink() p.unlink()
def get_args(self, params): def get_args(self, params):
dimensions = self.hyperopt_space()
dimensions = self.dimensions
# Ensure the number of dimensions match # Ensure the number of dimensions match
# the number of parameters in the list x. # the number of parameters in the list x.
if len(params) != len(dimensions): if len(params) != len(dimensions):
@ -322,9 +326,9 @@ class Hyperopt:
f'Total profit {total_profit: 11.8f} {stake_cur} ' f'Total profit {total_profit: 11.8f} {stake_cur} '
f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.') f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
def get_optimizer(self, cpu_count) -> Optimizer: def get_optimizer(self, dimensions, cpu_count) -> Optimizer:
return Optimizer( return Optimizer(
self.hyperopt_space(), dimensions,
base_estimator="ET", base_estimator="ET",
acq_optimizer="auto", acq_optimizer="auto",
n_initial_points=INITIAL_POINTS, n_initial_points=INITIAL_POINTS,
@ -370,9 +374,6 @@ class Hyperopt:
(max_date - min_date).days (max_date - min_date).days
) )
self.backtesting.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data) preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
dump(preprocessed, self.tickerdata_pickle) dump(preprocessed, self.tickerdata_pickle)
@ -387,7 +388,8 @@ class Hyperopt:
config_jobs = self.config.get('hyperopt_jobs', -1) config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}') logger.info(f'Number of parallel jobs set as: {config_jobs}')
opt = self.get_optimizer(config_jobs) self.dimensions = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.config.get('print_colorized', False): if self.config.get('print_colorized', False):
colorama_init(autoreset=True) colorama_init(autoreset=True)
@ -398,9 +400,9 @@ class Hyperopt:
logger.info(f'Effective number of parallel workers used: {jobs}') logger.info(f'Effective number of parallel workers used: {jobs}')
EVALS = max(self.total_epochs // jobs, 1) EVALS = max(self.total_epochs // jobs, 1)
for i in range(EVALS): for i in range(EVALS):
asked = opt.ask(n_points=jobs) asked = self.opt.ask(n_points=jobs)
f_val = self.run_optimizer_parallel(parallel, asked) f_val = self.run_optimizer_parallel(parallel, asked)
opt.tell(asked, [v['loss'] for v in f_val]) self.opt.tell(asked, [v['loss'] for v in f_val])
for j in range(jobs): for j in range(jobs):
current = i * jobs + j current = i * jobs + j
val = f_val[j] val = f_val[j]

1
tests/optimize/test_hyperopt.py
View File

@ -560,6 +560,7 @@ def test_generate_optimizer(mocker, default_conf) -> None:
} }
hyperopt = Hyperopt(default_conf) hyperopt = Hyperopt(default_conf)
hyperopt.dimensions = hyperopt.hyperopt_space()
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values())) generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
assert generate_optimizer_value == response_expected assert generate_optimizer_value == response_expected