Simon Ebner
|
df033d92ef
|
Improve performance of decimalspace.py
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()
```
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2021-10-24 18:14:24 +02:00 |
Matthias