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Add some errorhandling for custom estimator

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Matthias
2021-09-16 07:13:25 +02:00
parent c0811ae896
commit 994c3c3a4c
2 changed files with 14 additions and 4 deletions
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14
freqtrade/optimize/hyperopt.py
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@@ -45,7 +45,7 @@ progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
INITIAL_POINTS = 30 INITIAL_POINTS = 5
# Keep no more than SKOPT_MODEL_QUEUE_SIZE models # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption # in the skopt model queue, to optimize memory consumption
@@ -366,13 +366,19 @@ class Hyperopt:
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer: def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
estimator = self.custom_hyperopt.generate_estimator() estimator = self.custom_hyperopt.generate_estimator()
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}.") logger.info(f"Using estimator {estimator}.")
# TODO: Impact of changing acq_optimizer to "sampling" is unclear
# (other than that it fails with other optimizers when using custom sklearn regressors)
return Optimizer( return Optimizer(
dimensions, dimensions,
base_estimator=estimator, base_estimator=estimator,
acq_optimizer="sampling", acq_optimizer=acq_optimizer,
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,

4
tests/optimize/test_hyperopt.py
View File

@@ -884,6 +884,10 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
assert hyperopt.backtesting.strategy.buy_rsi.value != 35 assert hyperopt.backtesting.strategy.buy_rsi.value != 35
assert hyperopt.backtesting.strategy.sell_rsi.value != 74 assert hyperopt.backtesting.strategy.sell_rsi.value != 74
hyperopt.custom_hyperopt.generate_estimator = lambda *args, **kwargs: 'ET1'
with pytest.raises(OperationalException, match="Estimator ET1 not supported."):
hyperopt.get_optimizer([], 2)
def test_SKDecimal(): def test_SKDecimal():
space = SKDecimal(1, 2, decimals=2) space = SKDecimal(1, 2, decimals=2)