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Keep dimensions stored in hyperopt class 

There is no point in regenerating them and it will cause some
overhead as all space classes will be recreated for every epoch.
This commit is contained in:
Matthias 2021-05-02 09:35:10 +02:00
parent ced5cc7ce2
commit 46f0f66039
2 changed files with 33 additions and 33 deletions
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63
freqtrade/optimize/hyperopt.py
View File

@ -65,6 +65,13 @@ class Hyperopt:
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
@ -139,9 +146,7 @@ class Hyperopt:
logger.info(f"Removing `{p}`.")
p.unlink()
def _get_params_dict(self, raw_params: List[Any]) -> Dict:
dimensions: List[Dimension] = self.dimensions
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
# the number of parameters in the list.
@ -175,16 +180,13 @@ class Hyperopt:
result: Dict = {}
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('buy')}
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('sell')}
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = self.custom_hyperopt.generate_roi_table(params)
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('stoploss')}
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
@ -207,38 +209,32 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
def init_spaces(self):
"""
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
Assign the dimensions in the hyperoptimization space.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and HyperoptTools.has_space(self.config, 'buy')):
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and HyperoptTools.has_space(self.config, 'sell')):
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and HyperoptTools.has_space(self.config, 'roi')):
if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
self.roi_space = self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None
and HyperoptTools.has_space(self.config, 'stoploss')):
if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None
and HyperoptTools.has_space(self.config, 'trailing')):
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space()
return spaces
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
self.stoploss_space + self.trailing_space)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
@ -246,9 +242,10 @@ class Hyperopt:
Keep this function as optimized as possible!
"""
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(raw_params)
params_dict = self._get_params_dict(self.dimensions, raw_params)
params_details = self._get_params_details(params_dict)
# Apply parameters
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
@ -294,7 +291,8 @@ class Hyperopt:
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details, processed: Dict[str, DataFrame]):
params_dict, params_details, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
strat_stats = generate_strategy_stats(
processed, self.backtesting.strategy.get_strategy_name(),
@ -347,6 +345,8 @@ class Hyperopt:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
# Initialize spaces ...
self.init_spaces()
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
@ -377,7 +377,6 @@ class Hyperopt:
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:

3
tests/optimize/test_hyperopt.py
View File

@ -695,7 +695,8 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.min_date = Arrow(2017, 12, 10)
hyperopt.max_date = Arrow(2017, 12, 13)
hyperopt.dimensions = hyperopt.hyperopt_space()
hyperopt.init_spaces()
hyperopt.dimensions = hyperopt.dimensions
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
assert generate_optimizer_value == response_expected