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hyperopt cleanup and output improvements

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hroff-1902 2019-07-30 11:47:28 +03:00
parent 0677472c56
commit 8f1f416a52
1 changed files with 31 additions and 34 deletions
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65
freqtrade/optimize/hyperopt.py
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@ -51,7 +51,7 @@ class Hyperopt(Backtesting):
self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
self.total_tries = config.get('epochs', 0) self.total_epochs = config.get('epochs', 0)
self.current_best_loss = 100 self.current_best_loss = 100
if not self.config.get('hyperopt_continue'): if not self.config.get('hyperopt_continue'):
@ -124,13 +124,12 @@ class Hyperopt(Backtesting):
""" """
results = sorted(self.trials, key=itemgetter('loss')) results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0] best_result = results[0]
logger.info(
'Best result:\n%s\nwith values:\n', log_str = self.format_results_logstring(best_result)
best_result['result'] print(f"\nBest result:\n{log_str}\nwith values:")
)
pprint(best_result['params'], indent=4) pprint(best_result['params'], indent=4)
if 'roi_t1' in best_result['params']: if 'roi_t1' in best_result['params']:
logger.info('ROI table:') print("ROI table:")
pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4) pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4)
def log_results(self, results) -> None: def log_results(self, results) -> None:
@ -139,22 +138,26 @@ class Hyperopt(Backtesting):
""" """
print_all = self.config.get('print_all', False) print_all = self.config.get('print_all', False)
if print_all or results['loss'] < self.current_best_loss: if print_all or results['loss'] < self.current_best_loss:
# Output human-friendly index here (starting from 1) log_str = self.format_results_logstring(results)
current = results['current_tries'] + 1
total = results['total_tries']
res = results['result']
loss = results['loss']
self.current_best_loss = results['loss']
log_msg = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
log_msg = f'*{log_msg}' if results['initial_point'] else f' {log_msg}'
if print_all: if print_all:
print(log_msg) print(log_str)
else: else:
print('\n' + log_msg) print('\n' + log_str)
else: else:
print('.', end='') print('.', end='')
sys.stdout.flush() sys.stdout.flush()
def format_results_logstring(self, results) -> str:
# Output human-friendly index here (starting from 1)
current = results['current_epoch'] + 1
total = self.total_epochs
res = results['results_explanation']
loss = results['loss']
self.current_best_loss = results['loss']
log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
return log_str
def has_space(self, space: str) -> bool: def has_space(self, space: str) -> bool:
""" """
Tell if a space value is contained in the configuration Tell if a space value is contained in the configuration
@ -214,7 +217,7 @@ class Hyperopt(Backtesting):
'end_date': max_date, 'end_date': max_date,
} }
) )
result_explanation = self.format_results(results) results_explanation = self.format_results(results)
trade_count = len(results.index) trade_count = len(results.index)
@ -226,7 +229,7 @@ class Hyperopt(Backtesting):
return { return {
'loss': MAX_LOSS, 'loss': MAX_LOSS,
'params': params, 'params': params,
'result': result_explanation, 'results_explanation': results_explanation,
} }
loss = self.calculate_loss(results=results, trade_count=trade_count, loss = self.calculate_loss(results=results, trade_count=trade_count,
@ -235,12 +238,12 @@ class Hyperopt(Backtesting):
return { return {
'loss': loss, 'loss': loss,
'params': params, 'params': params,
'result': result_explanation, 'results_explanation': results_explanation,
} }
def format_results(self, results: DataFrame) -> str: def format_results(self, results: DataFrame) -> str:
""" """
Return the format result in a string Return the formatted results explanation in a string
""" """
trades = len(results.index) trades = len(results.index)
avg_profit = results.profit_percent.mean() * 100.0 avg_profit = results.profit_percent.mean() * 100.0
@ -323,25 +326,19 @@ class Hyperopt(Backtesting):
with Parallel(n_jobs=config_jobs) as parallel: with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs() jobs = parallel._effective_n_jobs()
logger.info(f'Effective number of parallel workers used: {jobs}') logger.info(f'Effective number of parallel workers used: {jobs}')
EVALS = max(self.total_tries // jobs, 1) EVALS = max(self.total_epochs // jobs, 1)
for i in range(EVALS): for i in range(EVALS):
asked = opt.ask(n_points=jobs) asked = opt.ask(n_points=jobs)
f_val = self.run_optimizer_parallel(parallel, asked) f_val = self.run_optimizer_parallel(parallel, asked)
opt.tell(asked, [i['loss'] for i in f_val]) opt.tell(asked, [v['loss'] for v in f_val])
self.trials += f_val
for j in range(jobs): for j in range(jobs):
current = i * jobs + j current = i * jobs + j
self.log_results({ val = f_val[j]
'loss': f_val[j]['loss'], val['current_epoch'] = current
'current_tries': current, val['is_initial_point'] = current < INITIAL_POINTS
'initial_point': current < INITIAL_POINTS, self.log_results(val)
'total_tries': self.total_tries, self.trials.append(val)
'result': f_val[j]['result'], logger.debug(f"Optimizer epoch evaluated: {val}")
})
logger.debug(f"Optimizer params: {f_val[j]['params']}")
for j in range(jobs):
logger.debug(f"Optimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
except KeyboardInterrupt: except KeyboardInterrupt:
print('User interrupted..') print('User interrupted..')