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highlight random points in hyperopt results table

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Italo
2022-03-30 00:29:14 +01:00
parent 229b0b037e
commit a3b401a762
4 changed files with 37 additions and 11 deletions
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21
freqtrade/optimize/hyperopt.py
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@@ -10,7 +10,7 @@ import warnings
from datetime import datetime, timezone
from math import ceil
from pathlib import Path
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, Tuple
import progressbar
import rapidjson
@@ -410,7 +410,7 @@ class Hyperopt:
# 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[List[Any]]:
def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
'''
Enforce points returned from `self.opt.ask` have not been already evaluated
@@ -424,20 +424,30 @@ class Hyperopt:
'''
i = 0
asked_non_tried: List[List[Any]] = []
is_random: List[bool] = []
while i < 5 and len(asked_non_tried) < n_points:
if i < 3:
self.opt.cache_ = {}
asked = self.opt.ask(n_points=n_points * 5)
is_random = [False for _ in range(len(asked))]
else:
asked = self.opt.space.rvs(n_samples=n_points * 5)
is_random = [True for _ in range(len(asked))]
asked_non_tried += [x for x in asked
if x not in self.opt.Xi
and x not in asked_non_tried]
is_random += [rand for x, rand in zip(asked, is_random)
if x not in self.opt.Xi
and x not in asked_non_tried]
i += 1
if asked_non_tried:
return asked_non_tried[:min(len(asked_non_tried), n_points)]
return (
asked_non_tried[:min(len(asked_non_tried), n_points)],
is_random[:min(len(asked_non_tried), n_points)]
)
else:
return self.opt.ask(n_points=n_points)
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
@@ -503,7 +513,7 @@ class Hyperopt:
n_rest = (i + 1) * jobs - self.total_epochs
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked = self.get_asked_points(n_points=current_jobs)
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
@@ -522,6 +532,7 @@ class Hyperopt:
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best: