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move result logging out from optimizer

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Janne Sinivirta 2018-06-21 14:59:36 +03:00
parent c415014153
commit 8fee2e2409
1 changed files with 25 additions and 47 deletions
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72
freqtrade/optimize/hyperopt.py
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@ -50,7 +50,6 @@ class Hyperopt(Backtesting):
# to the number of days # to the number of days
self.target_trades = 600 self.target_trades = 600
self.total_tries = config.get('epochs', 0) self.total_tries = config.get('epochs', 0)
self.current_tries = 0
self.current_best_loss = 100 self.current_best_loss = 100
# max average trade duration in minutes # max average trade duration in minutes
@ -288,27 +287,8 @@ class Hyperopt(Backtesting):
trade_count = len(results.index) trade_count = len(results.index)
trade_duration = results.trade_duration.mean() trade_duration = results.trade_duration.mean()
if trade_count == 0 or trade_duration > self.max_accepted_trade_duration:
print('.', end='')
sys.stdout.flush()
return {
'status': STATUS_FAIL,
'loss': float('inf')
}
loss = self.calculate_loss(total_profit, trade_count, trade_duration) loss = self.calculate_loss(total_profit, trade_count, trade_duration)
self.current_tries += 1
self.log_results(
{
'loss': loss,
'current_tries': self.current_tries,
'total_tries': self.total_tries,
'result': result_explanation,
}
)
return { return {
'loss': loss, 'loss': loss,
'status': STATUS_OK, 'status': STATUS_OK,
@ -357,36 +337,34 @@ class Hyperopt(Backtesting):
self.total_tries self.total_tries
) )
try: # results = sorted(self.trials.results, key=itemgetter('loss'))
# best_parameters = fmin( # best_result = results[0]['result']
# fn=self.generate_optimizer, cpus = multiprocessing.cpu_count()
# space=self.hyperopt_space(), print(f'Found {cpus}. Let\'s make them scream!')
# algo=tpe.suggest,
# max_evals=self.total_tries,
# trials=self.trials
# )
# results = sorted(self.trials.results, key=itemgetter('loss')) opt = Optimizer(
# best_result = results[0]['result'] self.hyperopt_space(),
cpus = multiprocessing.cpu_count() base_estimator="ET",
print(f'Found {cpus}. Let\'s make them scream!') acq_optimizer="auto",
n_initial_points=30,
acq_optimizer_kwargs={'n_jobs': -1}
)
opt = Optimizer(self.hyperopt_space(), base_estimator="ET", acq_optimizer="auto", n_initial_points=30, acq_optimizer_kwargs={'n_jobs': -1}) with Parallel(n_jobs=-1) as parallel:
for i in range(self.total_tries//cpus):
asked = opt.ask(n_points=cpus)
f_val = parallel(delayed(self.generate_optimizer)(v) for v in asked)
opt.tell(asked, [i['loss'] for i in f_val])
with Parallel(n_jobs=-1) as parallel: for j in range(cpus):
for i in range(self.total_tries//cpus): self.log_results(
asked = opt.ask(n_points=cpus) {
#asked = opt.ask() 'loss': f_val[j]['loss'],
#f_val = self.generate_optimizer(asked) 'current_tries': i * cpus + j,
f_val = parallel(delayed(self.generate_optimizer)(v) for v in asked) 'total_tries': self.total_tries,
opt.tell(asked, [i['loss'] for i in f_val]) 'result': f_val[j]['result'],
print(f'got value {f_val}') }
)
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display # Improve best parameter logging display
# if best_parameters: # if best_parameters: