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Merge pull request #2075 from hroff-1902/hyperopt-cleanup2

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minor: hyperopt cleanups and output improvements
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Matthias 2019-08-01 07:08:50 +02:00 committed by GitHub
commit 4c005e7086
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2 changed files with 43 additions and 43 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..')

19
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -370,13 +370,13 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
def test_log_results_if_loss_improves(hyperopt, capsys) -> None: def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
hyperopt.current_best_loss = 2 hyperopt.current_best_loss = 2
hyperopt.total_epochs = 2
hyperopt.log_results( hyperopt.log_results(
{ {
'loss': 1, 'loss': 1,
'current_tries': 1, 'current_epoch': 1,
'total_tries': 2, 'results_explanation': 'foo.',
'result': 'foo.', 'is_initial_point': False
'initial_point': False
} }
) )
out, err = capsys.readouterr() out, err = capsys.readouterr()
@ -433,7 +433,7 @@ def test_roi_table_generation(hyperopt) -> None:
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0} assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_optimizer(mocker, default_conf, caplog) -> None: def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock()) dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock()) mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch( mocker.patch(
@ -443,7 +443,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
parallel = mocker.patch( parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel', 'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}]) MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
) )
patch_exchange(mocker) patch_exchange(mocker)
@ -457,8 +457,11 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
hyperopt.strategy.tickerdata_to_dataframe = MagicMock() hyperopt.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.start() hyperopt.start()
parallel.assert_called_once() parallel.assert_called_once()
assert log_has('Best result:\nfoo result\nwith values:\n', caplog.record_tuples)
out, err = capsys.readouterr()
assert 'Best result:\n* 1/1: foo result Objective: 1.00000\nwith values:\n' in out
assert dumper.called assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations # Should be called twice, once for tickerdata, once to save evaluations
assert dumper.call_count == 2 assert dumper.call_count == 2
@ -598,7 +601,7 @@ def test_generate_optimizer(mocker, default_conf) -> None:
} }
response_expected = { response_expected = {
'loss': 1.9840569076926293, 'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC ' 'results_explanation': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'( 2.31Σ%). Avg duration 100.0 mins.', '( 2.31Σ%). Avg duration 100.0 mins.',
'params': optimizer_param 'params': optimizer_param
} }