Merge pull request #2075 from hroff-1902/hyperopt-cleanup2
minor: hyperopt cleanups and output improvements
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Matthias
GitHub
commit
4c005e7086
@ -51,7 +51,7 @@ class Hyperopt(Backtesting):
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self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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self.total_tries = config.get('epochs', 0)
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self.total_epochs = config.get('epochs', 0)
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self.current_best_loss = 100
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if not self.config.get('hyperopt_continue'):
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@ -124,13 +124,12 @@ class Hyperopt(Backtesting):
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"""
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results = sorted(self.trials, key=itemgetter('loss'))
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best_result = results[0]
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logger.info(
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'Best result:\n%s\nwith values:\n',
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best_result['result']
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)
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log_str = self.format_results_logstring(best_result)
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print(f"\nBest result:\n{log_str}\nwith values:")
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pprint(best_result['params'], indent=4)
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if 'roi_t1' in best_result['params']:
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logger.info('ROI table:')
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print("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(self, results) -> None:
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@ -139,22 +138,26 @@ class Hyperopt(Backtesting):
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"""
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print_all = self.config.get('print_all', False)
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if print_all or results['loss'] < self.current_best_loss:
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# Output human-friendly index here (starting from 1)
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current = results['current_tries'] + 1
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total = results['total_tries']
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res = results['result']
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loss = results['loss']
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self.current_best_loss = results['loss']
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log_msg = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
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log_msg = f'*{log_msg}' if results['initial_point'] else f' {log_msg}'
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log_str = self.format_results_logstring(results)
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if print_all:
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print(log_msg)
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print(log_str)
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else:
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print('\n' + log_msg)
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print('\n' + log_str)
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else:
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print('.', end='')
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sys.stdout.flush()
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def format_results_logstring(self, results) -> str:
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# Output human-friendly index here (starting from 1)
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current = results['current_epoch'] + 1
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total = self.total_epochs
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res = results['results_explanation']
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loss = results['loss']
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self.current_best_loss = results['loss']
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log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
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log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
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return log_str
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def has_space(self, space: str) -> bool:
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"""
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Tell if a space value is contained in the configuration
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@ -214,7 +217,7 @@ class Hyperopt(Backtesting):
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'end_date': max_date,
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}
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)
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result_explanation = self.format_results(results)
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results_explanation = self.format_results(results)
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trade_count = len(results.index)
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@ -226,7 +229,7 @@ class Hyperopt(Backtesting):
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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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'results_explanation': results_explanation,
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}
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loss = self.calculate_loss(results=results, trade_count=trade_count,
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@ -235,12 +238,12 @@ class Hyperopt(Backtesting):
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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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'results_explanation': results_explanation,
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}
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def format_results(self, results: DataFrame) -> str:
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"""
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Return the format result in a string
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Return the formatted results explanation in a string
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"""
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trades = len(results.index)
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avg_profit = results.profit_percent.mean() * 100.0
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@ -323,25 +326,19 @@ class Hyperopt(Backtesting):
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with Parallel(n_jobs=config_jobs) as parallel:
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jobs = parallel._effective_n_jobs()
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logger.info(f'Effective number of parallel workers used: {jobs}')
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EVALS = max(self.total_tries // jobs, 1)
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EVALS = max(self.total_epochs // jobs, 1)
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for i in range(EVALS):
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asked = opt.ask(n_points=jobs)
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f_val = self.run_optimizer_parallel(parallel, asked)
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opt.tell(asked, [i['loss'] for i in f_val])
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self.trials += f_val
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opt.tell(asked, [v['loss'] for v in f_val])
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for j in range(jobs):
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current = i * jobs + j
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self.log_results({
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'loss': f_val[j]['loss'],
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'current_tries': current,
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'initial_point': current < INITIAL_POINTS,
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'total_tries': self.total_tries,
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'result': f_val[j]['result'],
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})
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logger.debug(f"Optimizer params: {f_val[j]['params']}")
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for j in range(jobs):
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logger.debug(f"Optimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
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val = f_val[j]
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val['current_epoch'] = current
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val['is_initial_point'] = current < INITIAL_POINTS
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self.log_results(val)
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self.trials.append(val)
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logger.debug(f"Optimizer epoch evaluated: {val}")
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except KeyboardInterrupt:
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print('User interrupted..')
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@ -370,13 +370,13 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
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def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
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hyperopt.current_best_loss = 2
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hyperopt.total_epochs = 2
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hyperopt.log_results(
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{
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'loss': 1,
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'current_tries': 1,
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'total_tries': 2,
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'result': 'foo.',
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'initial_point': False
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'current_epoch': 1,
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'results_explanation': 'foo.',
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'is_initial_point': False
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}
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)
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out, err = capsys.readouterr()
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@ -433,7 +433,7 @@ def test_roi_table_generation(hyperopt) -> None:
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assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
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def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
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def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
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dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
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mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
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mocker.patch(
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@ -443,7 +443,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}])
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
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)
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patch_exchange(mocker)
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@ -457,8 +457,11 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
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hyperopt.strategy.tickerdata_to_dataframe = MagicMock()
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hyperopt.start()
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parallel.assert_called_once()
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assert log_has('Best result:\nfoo result\nwith values:\n', caplog.record_tuples)
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out, err = capsys.readouterr()
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assert 'Best result:\n* 1/1: foo result Objective: 1.00000\nwith values:\n' in out
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assert dumper.called
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# Should be called twice, once for tickerdata, once to save evaluations
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assert dumper.call_count == 2
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@ -598,8 +601,8 @@ def test_generate_optimizer(mocker, default_conf) -> None:
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}
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response_expected = {
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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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'( 2.31Σ%). Avg duration 100.0 mins.',
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'results_explanation': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
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'( 2.31Σ%). Avg duration 100.0 mins.',
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'params': optimizer_param
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}
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