issue resolved: opt.tell() is now called in the parent process
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hroff-1902
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@ -3,6 +3,7 @@
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"""
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This module contains the hyperopt logic
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"""
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import functools
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import logging
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import multiprocessing
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import os
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@ -16,6 +17,8 @@ from pprint import pprint
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from typing import Any, Dict, List
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from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
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from joblib._parallel_backends import LokyBackend
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from joblib import register_parallel_backend, parallel_backend
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from pandas import DataFrame
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from skopt import Optimizer
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from skopt.space import Dimension
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@ -37,6 +40,9 @@ TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
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EVALS_FRAME = 100
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_hyperopt = None
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class Hyperopt(Backtesting):
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"""
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Hyperopt class, this class contains all the logic to run a hyperopt simulation
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@ -69,6 +75,9 @@ class Hyperopt(Backtesting):
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self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle')
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self.trials: List = []
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self.opt = None
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self.f_val: List = []
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def get_args(self, params):
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dimensions = self.hyperopt_space()
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# Ensure the number of dimensions match
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@ -114,7 +123,7 @@ class Hyperopt(Backtesting):
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logger.info('ROI table:')
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pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4)
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def log_results_immediate(self) -> None:
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def log_results_immediate(self, n) -> None:
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print('.', end='')
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sys.stdout.flush()
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@ -169,7 +178,21 @@ class Hyperopt(Backtesting):
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return spaces
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def generate_optimizer(self, _params: Dict) -> Dict:
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"""
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Process params (a point in the hyper options space generated by the
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optimizer.ask() optimizer method) and run backtest() for it.
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This method is executed in one of the joblib worker processes.
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The optimizer.tell() function is executed in the joblib parent process
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in order to tell results to single instance of the optimizer rather than
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its copies serialized in the joblib worker processes.
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It receives result of this method as a part of the joblib batch completion
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callback function.
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"""
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params = self.get_args(_params)
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if self.has_space('roi'):
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self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
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@ -203,19 +226,14 @@ class Hyperopt(Backtesting):
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trade_count = len(results.index)
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trade_duration = results.trade_duration.mean()
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if trade_count == 0:
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return {
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'loss': MAX_LOSS,
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'params': params,
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'result': result_explanation,
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}
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loss = self.calculate_loss(total_profit, trade_count, trade_duration)
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loss = MAX_LOSS if trade_count == 0 else \
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self.calculate_loss(total_profit, trade_count, trade_duration)
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return {
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'loss': loss,
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'params': params,
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'result': result_explanation,
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'asked': _params,
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}
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def format_results(self, results: DataFrame) -> str:
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@ -239,20 +257,35 @@ class Hyperopt(Backtesting):
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base_estimator="ET",
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acq_optimizer="auto",
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n_initial_points=30,
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acq_optimizer_kwargs={'n_jobs': cpu_count}
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acq_optimizer_kwargs={'n_jobs': cpu_count},
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random_state=777,
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)
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def run_optimizer_parallel(self, parallel, opt, tries: int, first_try: int) -> List:
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def run_optimizer_parallel(self, parallel, tries: int, first_try: int) -> List:
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result = parallel(delayed(
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wrap_non_picklable_objects(self.parallel_tell_and_log))
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(opt, i, opt.ask()) for i in range(first_try, first_try + tries))
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wrap_non_picklable_objects(self.parallel_objective))
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(asked, i) for asked, i in
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zip(self.opt_generator(), range(first_try, first_try + tries)))
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return result
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def parallel_tell_and_log(self, opt, i, asked):
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f_val = self.generate_optimizer(asked)
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opt.tell(asked, f_val['loss'])
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self.log_results_immediate()
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return f_val
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def opt_generator(self):
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while True:
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if self.f_val:
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# print("opt.tell(): ",
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# [v['asked'] for v in self.f_val], [v['loss'] for v in self.f_val])
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functools.partial(
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self.opt.tell,
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([v['asked'] for v in self.f_val], [v['loss'] for v in self.f_val])
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)
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self.f_val = []
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yield self.opt.ask()
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def parallel_objective(self, asked, n):
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self.log_results_immediate(n)
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return self.generate_optimizer(asked)
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def parallel_callback(self, f_val):
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self.f_val.extend(f_val)
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def load_previous_results(self):
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""" read trials file if we have one """
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@ -283,12 +316,14 @@ class Hyperopt(Backtesting):
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cpus = multiprocessing.cpu_count()
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logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
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opt = self.get_optimizer(cpus)
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self.opt = self.get_optimizer(cpus)
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frames = self.total_tries // EVALS_FRAME
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last_frame_len = self.total_tries % EVALS_FRAME
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try:
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register_parallel_backend('custom', CustomImmediateResultBackend)
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with parallel_backend('custom'):
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with Parallel(n_jobs=cpus, verbose=0) as parallel:
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for frame in range(frames + 1):
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frame_start = frame * EVALS_FRAME
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@ -296,7 +331,7 @@ class Hyperopt(Backtesting):
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print(f"\n{frame_start+1}-{frame_start+frame_len}"
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f"/{self.total_tries}: ", end='')
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f_val = self.run_optimizer_parallel(
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parallel, opt,
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parallel,
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frame_len,
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frame_start
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)
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@ -337,5 +372,32 @@ def start(args: Namespace) -> None:
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"to understand how to configure hyperopt.")
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raise ValueError("--strategy configured but not supported for hyperopt")
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# Initialize backtesting object
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hyperopt = Hyperopt(config)
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hyperopt.start()
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global _hyperopt
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_hyperopt = Hyperopt(config)
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_hyperopt.start()
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class MultiCallback:
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def __init__(self, *callbacks):
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self.callbacks = [cb for cb in callbacks if cb]
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def __call__(self, out):
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for cb in self.callbacks:
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cb(out)
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class CustomImmediateResultBackend(LokyBackend):
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def callback(self, result):
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"""
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Our custom completion callback. Executed in the parent process.
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Use it to run Optimizer.tell() with immediate results of the backtest()
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evaluated in the joblib worker process.
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"""
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# Fetch results from the Future object passed to us.
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# Future object is assumed to be 'done' already.
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f_val = result.result().copy()
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_hyperopt.parallel_callback(f_val)
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def apply_async(self, func, callback=None):
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cbs = MultiCallback(callback, self.callback)
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return super().apply_async(func, cbs)
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@ -358,7 +358,12 @@ def test_generate_optimizer(mocker, default_conf) -> None:
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'loss': 1.9840569076926293,
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'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
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'(0.0231Σ%). Avg duration 100.0 mins.',
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'params': optimizer_param
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'params': optimizer_param,
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'asked': [
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0, 35, 0, 0, False, True, False, False, 'macd_cross_signal', 0, 75, 0, 0,
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False, True, False, False, 'macd_cross_signal', 60.0, 30.0, 20.0,
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0.01, 0.01, 0.1, -0.4
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]
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}
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hyperopt = Hyperopt(default_conf)
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