df033d92ef
decimalspace.py is heavily used in the hyperoptimization. The following
benchmark code runs an optimization which is taken from optimizing a
real strategy (wtc).
The optimized version takes on my machine approx. 11/12s compared to the
original 32s. Results are equivalent in both cases.
```
import freqtrade.optimize.space
import numpy as np
import skopt
import timeit
def init():
Decimal = freqtrade.optimize.space.decimalspace.SKDecimal
Integer = skopt.space.space.Integer
dimensions = [Decimal(low=-1.0,
high=1.0,
decimals=4,
prior='uniform',
transform='identity')] * 20
return skopt.Optimizer(
dimensions,
base_estimator="ET",
acq_optimizer="auto",
n_initial_points=5,
acq_optimizer_kwargs={'n_jobs': 96},
random_state=0,
model_queue_size=10,
)
def test():
opt = init()
actual = opt.ask(n_points=2)
expected = [[
0.7515, -0.4723, -0.6941, -0.7988, 0.0448, 0.8605, -0.108, 0.5399,
0.763, -0.2948, 0.8345, -0.7683, 0.7077, -0.2478, -0.333, 0.8575,
0.6108, 0.4514, 0.5982, 0.3506
], [
0.5563, 0.7386, -0.6407, 0.9073, -0.5211, -0.8167, -0.3771,
-0.0318, 0.2861, 0.1176, 0.0943, -0.6077, -0.9317, -0.5372,
-0.4934, -0.3637, -0.8035, -0.8627, -0.5399, 0.6036
]]
absdiff = np.max(np.abs(np.asarray(expected) - np.asarray(actual)))
assert absdiff < 1e-5
def time():
opt = init()
print('dt', timeit.timeit("opt.ask(n_points=20)", globals=locals()))
if __name__ == "__main__":
test()
time()
```
38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
import numpy as np
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from skopt.space import Integer
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class SKDecimal(Integer):
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def __init__(self, low, high, decimals=3, prior="uniform", base=10, transform=None,
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name=None, dtype=np.int64):
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self.decimals = decimals
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self.pow_dot_one = pow(0.1, self.decimals)
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self.pow_ten = pow(10, self.decimals)
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_low = int(low * self.pow_ten)
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_high = int(high * self.pow_ten)
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# trunc to precision to avoid points out of space
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self.low_orig = round(_low * self.pow_dot_one, self.decimals)
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self.high_orig = round(_high * self.pow_dot_one, self.decimals)
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super().__init__(_low, _high, prior, base, transform, name, dtype)
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def __repr__(self):
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return "Decimal(low={}, high={}, decimals={}, prior='{}', transform='{}')".format(
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self.low_orig, self.high_orig, self.decimals, self.prior, self.transform_)
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def __contains__(self, point):
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if isinstance(point, list):
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point = np.array(point)
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return self.low_orig <= point <= self.high_orig
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def transform(self, Xt):
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return super().transform([int(v * self.pow_ten) for v in Xt])
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def inverse_transform(self, Xt):
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res = super().inverse_transform(Xt)
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# equivalent to [round(x * pow(0.1, self.decimals), self.decimals) for x in res]
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return [int(v) / self.pow_ten for v in res]
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