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Update hyperopt.py 

1. Try to get points using `self.opt.ask` first
        2. Discard the points that have already been evaluated
        3. Retry using `self.opt.ask` up to 3 times
        4. If still some points are missing in respect to `n_points`, random sample some points
        5. Repeat until at least `n_points` points in the `asked_non_tried` list
        6. Return a list with legth truncated at `n_points`
This commit is contained in:
Italo 2022-03-20 15:41:14 +00:00
parent d2a5448305
commit d796ce0935
1 changed files with 27 additions and 2 deletions
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29
freqtrade/optimize/hyperopt.py
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@ -413,6 +413,31 @@ class Hyperopt:
f'({(self.max_date - self.min_date).days} days)..')
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
def get_asked_points(self, n_points: int) -> List[Any]:
'''
Steps:
1. Try to get points using `self.opt.ask` first
2. Discard the points that have already been evaluated
3. Retry using `self.opt.ask` up to 3 times
4. If still some points are missing in respect to `n_points`, random sample some points
5. Repeat until at least `n_points` points in the `asked_non_tried` list
6. Return a list with legth truncated at `n_points`
'''
i = 0
asked_non_tried = []
while i < 100:
if len(asked_non_tried) < n_points:
if i < 3:
asked = self.opt.ask(n_points=n_points)
else:
# use random sample if `self.opt.ask` returns points points already tried
asked = self.opt.space.rvs(n_samples=n_points * 5)
asked_non_tried += [x for x in asked if x not in self.opt.Xi and x not in asked_non_tried]
i += 1
else:
break
return asked_non_tried[:n_points]
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
@ -478,11 +503,11 @@ class Hyperopt:
n_rest = (i + 1) * jobs - self.total_epochs
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked = self.opt.ask(n_points=current_jobs)
asked = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
res = self.opt.tell(asked, [v['loss'] for v in f_val])
self.plot_optimizer(res, path='user_data/scripts', convergence=False, regret=False, r2=True, objective=True, jobs=jobs)
self.plot_optimizer(res, path='user_data/scripts', convergence=False, regret=False, r2=False, objective=True, jobs=jobs)
if res.models and hasattr(res.models[-1], "kernel_"):
print(f'kernel: {res.models[-1].kernel_}')