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issue resolved: opt.tell() is now called in the parent process

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This commit is contained in:
hroff-1902 2019-04-16 08:24:38 +03:00
parent a8399533e2
commit f22caa78ed
2 changed files with 100 additions and 33 deletions
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106
freqtrade/optimize/hyperopt.py
View File

@ -3,6 +3,7 @@
"""
This module contains the hyperopt logic
"""
import functools
import logging
import multiprocessing
import os
@ -16,6 +17,8 @@ from pprint import pprint
from typing import Any, Dict, List
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
from joblib._parallel_backends import LokyBackend
from joblib import register_parallel_backend, parallel_backend
from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension
@ -37,6 +40,9 @@ TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
EVALS_FRAME = 100
_hyperopt = None
class Hyperopt(Backtesting):
"""
Hyperopt class, this class contains all the logic to run a hyperopt simulation
@ -69,6 +75,9 @@ class Hyperopt(Backtesting):
self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle')
self.trials: List = []
self.opt = None
self.f_val: List = []
def get_args(self, params):
dimensions = self.hyperopt_space()
# Ensure the number of dimensions match
@ -114,7 +123,7 @@ class Hyperopt(Backtesting):
logger.info('ROI table:')
pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4)
def log_results_immediate(self) -> None:
def log_results_immediate(self, n) -> None:
print('.', end='')
sys.stdout.flush()
@ -169,7 +178,21 @@ class Hyperopt(Backtesting):
return spaces
def generate_optimizer(self, _params: Dict) -> Dict:
"""
Process params (a point in the hyper options space generated by the
optimizer.ask() optimizer method) and run backtest() for it.
This method is executed in one of the joblib worker processes.
The optimizer.tell() function is executed in the joblib parent process
in order to tell results to single instance of the optimizer rather than
its copies serialized in the joblib worker processes.
It receives result of this method as a part of the joblib batch completion
callback function.
"""
params = self.get_args(_params)
if self.has_space('roi'):
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
@ -203,19 +226,14 @@ class Hyperopt(Backtesting):
trade_count = len(results.index)
trade_duration = results.trade_duration.mean()
if trade_count == 0:
return {
'loss': MAX_LOSS,
'params': params,
'result': result_explanation,
}
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
loss = MAX_LOSS if trade_count == 0 else \
self.calculate_loss(total_profit, trade_count, trade_duration)
return {
'loss': loss,
'params': params,
'result': result_explanation,
'asked': _params,
}
def format_results(self, results: DataFrame) -> str:
@ -239,20 +257,35 @@ class Hyperopt(Backtesting):
base_estimator="ET",
acq_optimizer="auto",
n_initial_points=30,
acq_optimizer_kwargs={'n_jobs': cpu_count}
acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=777,
)
def run_optimizer_parallel(self, parallel, opt, tries: int, first_try: int) -> List:
def run_optimizer_parallel(self, parallel, tries: int, first_try: int) -> List:
result = parallel(delayed(
wrap_non_picklable_objects(self.parallel_tell_and_log))
(opt, i, opt.ask()) for i in range(first_try, first_try + tries))
wrap_non_picklable_objects(self.parallel_objective))
(asked, i) for asked, i in
zip(self.opt_generator(), range(first_try, first_try + tries)))
return result
def parallel_tell_and_log(self, opt, i, asked):
f_val = self.generate_optimizer(asked)
opt.tell(asked, f_val['loss'])
self.log_results_immediate()
return f_val
def opt_generator(self):
while True:
if self.f_val:
# print("opt.tell(): ",
# [v['asked'] for v in self.f_val], [v['loss'] for v in self.f_val])
functools.partial(
self.opt.tell,
([v['asked'] for v in self.f_val], [v['loss'] for v in self.f_val])
)
self.f_val = []
yield self.opt.ask()
def parallel_objective(self, asked, n):
self.log_results_immediate(n)
return self.generate_optimizer(asked)
def parallel_callback(self, f_val):
self.f_val.extend(f_val)
def load_previous_results(self):
""" read trials file if we have one """
@ -283,12 +316,14 @@ class Hyperopt(Backtesting):
cpus = multiprocessing.cpu_count()
logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
opt = self.get_optimizer(cpus)
self.opt = self.get_optimizer(cpus)
frames = self.total_tries // EVALS_FRAME
last_frame_len = self.total_tries % EVALS_FRAME
try:
register_parallel_backend('custom', CustomImmediateResultBackend)
with parallel_backend('custom'):
with Parallel(n_jobs=cpus, verbose=0) as parallel:
for frame in range(frames + 1):
frame_start = frame * EVALS_FRAME
@ -296,7 +331,7 @@ class Hyperopt(Backtesting):
print(f"\n{frame_start+1}-{frame_start+frame_len}"
f"/{self.total_tries}: ", end='')
f_val = self.run_optimizer_parallel(
parallel, opt,
parallel,
frame_len,
frame_start
)
@ -337,5 +372,32 @@ def start(args: Namespace) -> None:
"to understand how to configure hyperopt.")
raise ValueError("--strategy configured but not supported for hyperopt")
# Initialize backtesting object
hyperopt = Hyperopt(config)
hyperopt.start()
global _hyperopt
_hyperopt = Hyperopt(config)
_hyperopt.start()
class MultiCallback:
def __init__(self, *callbacks):
self.callbacks = [cb for cb in callbacks if cb]
def __call__(self, out):
for cb in self.callbacks:
cb(out)
class CustomImmediateResultBackend(LokyBackend):
def callback(self, result):
"""
Our custom completion callback. Executed in the parent process.
Use it to run Optimizer.tell() with immediate results of the backtest()
evaluated in the joblib worker process.
"""
# Fetch results from the Future object passed to us.
# Future object is assumed to be 'done' already.
f_val = result.result().copy()
_hyperopt.parallel_callback(f_val)
def apply_async(self, func, callback=None):
cbs = MultiCallback(callback, self.callback)
return super().apply_async(func, cbs)

7
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -358,7 +358,12 @@ def test_generate_optimizer(mocker, default_conf) -> None:
'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'(0.0231Σ%). Avg duration 100.0 mins.',
'params': optimizer_param
'params': optimizer_param,
'asked': [
0, 35, 0, 0, False, True, False, False, 'macd_cross_signal', 0, 75, 0, 0,
False, True, False, False, 'macd_cross_signal', 60.0, 30.0, 20.0,
0.01, 0.01, 0.1, -0.4
]
}
hyperopt = Hyperopt(default_conf)