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fixes, moved points setup to its function

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This commit is contained in:
orehunt 2020-03-10 09:10:10 +01:00
parent 29e9faf167
commit ece0ddba38
1 changed files with 26 additions and 23 deletions
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49
freqtrade/optimize/hyperopt.py
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@ -15,7 +15,7 @@ from numpy import iinfo, int32
from operator import itemgetter from operator import itemgetter
from pathlib import Path from pathlib import Path
from pprint import pprint from pprint import pprint
from typing import Any, Dict, List, Optional from typing import Any, Dict, List, Optional, Tuple, Set
import rapidjson import rapidjson
from colorama import Fore, Style from colorama import Fore, Style
@ -55,7 +55,7 @@ logger = logging.getLogger(__name__)
NEXT_POINT_METHODS = ["cl_min", "cl_mean", "cl_max"] NEXT_POINT_METHODS = ["cl_min", "cl_mean", "cl_max"]
NEXT_POINT_METHODS_LENGTH = 3 NEXT_POINT_METHODS_LENGTH = 3
VOID_LOSS = iinfo(int32).max # just a big enough number to be bad result in loss optimization VOID_LOSS = iinfo(int32).max # just a big enough number to be bad result in loss optimization
class Hyperopt: class Hyperopt:
@ -555,7 +555,7 @@ class Hyperopt:
# only exclude results at the beginning when void loss is yet to be set # only exclude results at the beginning when void loss is yet to be set
void_filtered = list(filter(lambda v: v["loss"] != VOID_LOSS, vals)) void_filtered = list(filter(lambda v: v["loss"] != VOID_LOSS, vals))
else: else:
if opt.void_loss == VOID_LOSS: # set void loss once if opt.void_loss == VOID_LOSS: # set void loss once
opt.void_loss = max(opt.yi) opt.void_loss = max(opt.yi)
void_filtered = [] void_filtered = []
# default bad losses to set void_loss # default bad losses to set void_loss
@ -616,7 +616,7 @@ class Hyperopt:
""" """
vals = [] vals = []
to_ask: deque = deque() to_ask: deque = deque()
evald: set(Tuple) = set() evald: Set[Tuple] = set()
fit = False fit = False
opt = self.opt opt = self.opt
for r in range(tries): for r in range(tries):
@ -625,10 +625,10 @@ class Hyperopt:
if vals: if vals:
# filter losses # filter losses
void_filtered = self.filter_void_losses(vals, opt) void_filtered = self.filter_void_losses(vals, opt)
if vals: # again if all are filtered if void_filtered: # again if all are filtered
opt.tell([list(v['params_dict'].values()) for v in void_filtered], opt.tell([list(v['params_dict'].values()) for v in void_filtered],
[v['loss'] for v in vals], [v['loss'] for v in void_filtered],
fit=fit) fit=fit)
if fit: if fit:
fit = False fit = False
del vals[:], void_filtered[:] del vals[:], void_filtered[:]
@ -841,7 +841,7 @@ class Hyperopt:
for _ in range(remaining): # generate optimizers for _ in range(remaining): # generate optimizers
# random state is preserved # random state is preserved
opt_copy = opt.copy(random_state=opt.rng.randint(0, opt_copy = opt.copy(random_state=opt.rng.randint(0,
iinfo(int32).max)) iinfo(int32).max))
opt_copy.void_loss = VOID_LOSS opt_copy.void_loss = VOID_LOSS
backend.optimizers.put(opt_copy) backend.optimizers.put(opt_copy)
del opt, opt_copy del opt, opt_copy
@ -857,6 +857,23 @@ class Hyperopt:
self.opt.void_loss = VOID_LOSS self.opt.void_loss = VOID_LOSS
del opts[:] del opts[:]
def setup_points(self):
self.n_initial_points, self.min_epochs, self.search_space_size = self.calc_epochs(
self.dimensions, self.n_jobs, self.effort, self.total_epochs, self.n_points
)
logger.info(f"Min epochs set to: {self.min_epochs}")
# reduce random points by n_points in multi mode because asks are per job
if self.multi:
self.opt_n_initial_points = self.n_initial_points // self.n_points
else:
self.opt_n_initial_points = self.n_initial_points
logger.info(f'Initial points: {self.n_initial_points}')
# if total epochs are not set, max_epoch takes its place
if self.total_epochs < 1:
self.max_epoch = int(self.min_epochs + len(self.trials))
# initialize average best occurrence
self.avg_best_occurrence = self.min_epochs // self.n_jobs
def start(self) -> None: def start(self) -> None:
""" Broom Broom """ """ Broom Broom """
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None)) self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
@ -887,22 +904,8 @@ class Hyperopt:
logger.info(f'Number of parallel jobs set as: {self.n_jobs}') logger.info(f'Number of parallel jobs set as: {self.n_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space() self.dimensions: List[Dimension] = self.hyperopt_space()
self.n_initial_points, self.min_epochs, self.search_space_size = self.calc_epochs( self.setup_points()
self.dimensions, self.n_jobs, self.effort, self.total_epochs, self.n_points
)
# reduce random points by n_points in multi mode because asks are per job
if self.multi:
self.opt_n_initial_points = self.n_initial_points // self.n_points
else:
self.opt_n_initial_points = self.n_initial_points
logger.info(f"Min epochs set to: {self.min_epochs}")
# if total epochs are not set, max_epoch takes its place
if self.total_epochs < 1:
self.max_epoch = int(self.min_epochs + len(self.trials))
# initialize average best occurrence
self.avg_best_occurrence = self.min_epochs // self.n_jobs
logger.info(f'Initial points: {self.n_initial_points}')
if self.print_colorized: if self.print_colorized:
colorama_init(autoreset=True) colorama_init(autoreset=True)