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Save epochs at intermediate points

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hroff-1902 2019-11-23 11:32:33 +03:00
parent 99eeb2e605
commit 097cdcb57a
1 changed files with 33 additions and 13 deletions
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46
freqtrade/optimize/hyperopt.py
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@ -77,6 +77,8 @@ class Hyperopt:
# Previous evaluations # Previous evaluations
self.trials: List = [] self.trials: List = []
self.num_trials_saved = 0
# Populate functions here (hasattr is slow so should not be run during "regular" operations) # Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'): if hasattr(self.custom_hyperopt, 'populate_indicators'):
self.backtesting.strategy.advise_indicators = \ self.backtesting.strategy.advise_indicators = \
@ -132,13 +134,17 @@ class Hyperopt:
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)} arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict return arg_dict
def save_trials(self) -> None: def save_trials(self, final: bool = False) -> None:
""" """
Save hyperopt trials to file Save hyperopt trials to file
""" """
if self.trials: num_trials = len(self.trials)
logger.info("Saving %d evaluations to '%s'", len(self.trials), self.trials_file) if num_trials > self.num_trials_saved:
logger.info(f"Saving {num_trials} epochs.")
dump(self.trials, self.trials_file) dump(self.trials, self.trials_file)
self.num_trials_saved = num_trials
if final:
logger.info(f"{num_trials} epochs were saved to '{self.trials_file}'.")
def read_trials(self) -> List: def read_trials(self) -> List:
""" """
@ -153,6 +159,12 @@ class Hyperopt:
""" """
Display Best hyperopt result Display Best hyperopt result
""" """
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.
if not self.trials:
print("No epochs evaluated yet, no best result.")
return
results = sorted(self.trials, key=itemgetter('loss')) results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0] best_result = results[0]
params = best_result['params'] params = best_result['params']
@ -197,12 +209,20 @@ class Hyperopt:
# Also round to 5 digits after the decimal point # Also round to 5 digits after the decimal point
print(f"Stoploss: {round(params.get('stoploss'), 5)}") print(f"Stoploss: {round(params.get('stoploss'), 5)}")
def is_best(self, results) -> bool:
return results['loss'] < self.current_best_loss
def log_results(self, results) -> None: def log_results(self, results) -> None:
""" """
Log results if it is better than any previous evaluation Log results if it is better than any previous evaluation
""" """
print_all = self.config.get('print_all', False) print_all = self.config.get('print_all', False)
is_best_loss = results['loss'] < self.current_best_loss is_best_loss = self.is_best(results)
if not print_all:
print('.', end='' if results['current_epoch'] % 100 != 0 else None)
sys.stdout.flush()
if print_all or is_best_loss: if print_all or is_best_loss:
if is_best_loss: if is_best_loss:
self.current_best_loss = results['loss'] self.current_best_loss = results['loss']
@ -217,13 +237,9 @@ class Hyperopt:
print(log_str) print(log_str)
else: else:
print(f'\n{log_str}') print(f'\n{log_str}')
else:
print('.', end='')
sys.stdout.flush()
def format_results_logstring(self, results) -> str: def format_results_logstring(self, results) -> str:
# Output human-friendly index here (starting from 1) current = results['current_epoch']
current = results['current_epoch'] + 1
total = self.total_epochs total = self.total_epochs
res = results['results_explanation'] res = results['results_explanation']
loss = results['loss'] loss = results['loss']
@ -422,15 +438,19 @@ class Hyperopt:
self.opt.tell(asked, [v['loss'] for v in f_val]) self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list() self.fix_optimizer_models_list()
for j in range(jobs): for j in range(jobs):
current = i * jobs + j # Use human-friendly index here (starting from 1)
current = i * jobs + j + 1
val = f_val[j] val = f_val[j]
val['current_epoch'] = current val['current_epoch'] = current
val['is_initial_point'] = current < INITIAL_POINTS val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best(val)
self.log_results(val) self.log_results(val)
self.trials.append(val) self.trials.append(val)
logger.debug(f"Optimizer epoch evaluated: {val}") if is_best or current % 100 == 0:
self.save_trials()
except KeyboardInterrupt: except KeyboardInterrupt:
print('User interrupted..') print('User interrupted..')
self.save_trials() self.save_trials(final=True)
self.log_trials_result() self.log_trials_result()