Hyperopt cleanup, do not use 'trials'
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@ -75,8 +75,8 @@ class Hyperopt:
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self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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self.trials_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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self.results_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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self.data_pickle_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_tickerdata.pkl')
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self.total_epochs = config.get('epochs', 0)
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@ -88,10 +88,10 @@ class Hyperopt:
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else:
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logger.info("Continuing on previous hyperopt results.")
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self.num_trials_saved = 0
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self.num_epochs_saved = 0
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# Previous evaluations
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self.trials: List = []
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self.epochs: List = []
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# Populate functions here (hasattr is slow so should not be run during "regular" operations)
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if hasattr(self.custom_hyperopt, 'populate_indicators'):
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@ -132,7 +132,7 @@ class Hyperopt:
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"""
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Remove hyperopt pickle files to restart hyperopt.
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"""
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for f in [self.data_pickle_file, self.trials_file]:
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for f in [self.data_pickle_file, self.results_file]:
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p = Path(f)
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if p.is_file():
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logger.info(f"Removing `{p}`.")
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@ -151,27 +151,26 @@ class Hyperopt:
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# and the values are taken from the list of parameters.
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return {d.name: v for d, v in zip(dimensions, raw_params)}
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def save_trials(self, final: bool = False) -> None:
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def _save_results(self) -> None:
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"""
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Save hyperopt trials to file
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Save hyperopt results to file
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"""
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num_trials = len(self.trials)
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if num_trials > self.num_trials_saved:
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logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
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dump(self.trials, self.trials_file)
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self.num_trials_saved = num_trials
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if final:
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logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
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f"saved to '{self.trials_file}'.")
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num_epochs = len(self.epochs)
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if num_epochs > self.num_epochs_saved:
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logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
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dump(self.epochs, self.results_file)
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self.num_epochs_saved = num_epochs
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logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
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f"saved to '{self.results_file}'.")
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@staticmethod
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def _read_trials(trials_file: Path) -> List:
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def _read_results(results_file: Path) -> List:
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"""
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Read hyperopt trials file
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Read hyperopt results from file
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"""
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logger.info("Reading Trials from '%s'", trials_file)
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trials = load(trials_file)
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return trials
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logger.info("Reading epochs from '%s'", results_file)
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data = load(results_file)
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return data
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def _get_params_details(self, params: Dict) -> Dict:
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"""
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@ -588,19 +587,20 @@ class Hyperopt:
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wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
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@staticmethod
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def load_previous_results(trials_file: Path) -> List:
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def load_previous_results(results_file: Path) -> List:
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"""
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Load data for epochs from the file if we have one
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"""
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trials: List = []
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if trials_file.is_file() and trials_file.stat().st_size > 0:
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trials = Hyperopt._read_trials(trials_file)
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if trials[0].get('is_best') is None:
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epochs: List = []
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if results_file.is_file() and results_file.stat().st_size > 0:
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epochs = Hyperopt._read_results(results_file)
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# Detection of some old format, without 'is_best' field saved
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if epochs[0].get('is_best') is None:
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raise OperationalException(
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"The file with Hyperopt results is incompatible with this version "
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"of Freqtrade and cannot be loaded.")
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logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
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return trials
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logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
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return epochs
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def _set_random_state(self, random_state: Optional[int]) -> int:
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return random_state or random.randint(1, 2**16 - 1)
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@ -628,7 +628,7 @@ class Hyperopt:
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self.backtesting.exchange = None # type: ignore
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self.backtesting.pairlists = None # type: ignore
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self.trials = self.load_previous_results(self.trials_file)
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self.epochs = self.load_previous_results(self.results_file)
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cpus = cpu_count()
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logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
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@ -698,23 +698,25 @@ class Hyperopt:
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if is_best:
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self.current_best_loss = val['loss']
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self.trials.append(val)
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self.epochs.append(val)
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# Save results after each best epoch and every 100 epochs
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if is_best or current % 100 == 0:
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self.save_trials()
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self._save_results()
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pbar.update(current)
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except KeyboardInterrupt:
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print('User interrupted..')
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self.save_trials(final=True)
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self._save_results()
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logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
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f"saved to '{self.results_file}'.")
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if self.trials:
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sorted_trials = sorted(self.trials, key=itemgetter('loss'))
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results = sorted_trials[0]
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self.print_epoch_details(results, self.total_epochs, self.print_json)
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if self.epochs:
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sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
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best_epoch = sorted_epochs[0]
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self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
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else:
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# This is printed when Ctrl+C is pressed quickly, before first epochs have
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# a chance to be evaluated.
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@ -1,5 +1,6 @@
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# pragma pylint: disable=missing-docstring,W0212,C0103
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import locale
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import logging
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, List
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@ -56,14 +57,14 @@ def hyperopt_results():
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# Functions for recurrent object patching
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def create_trials(mocker, hyperopt, testdatadir) -> List[Dict]:
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def create_results(mocker, hyperopt, testdatadir) -> List[Dict]:
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"""
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When creating trials, mock the hyperopt Trials so that *by default*
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When creating results, mock the hyperopt so that *by default*
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- we don't create any pickle'd files in the filesystem
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- we might have a pickle'd file so make sure that we return
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false when looking for it
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"""
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hyperopt.trials_file = testdatadir / 'optimize/ut_trials.pickle'
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hyperopt.results_file = testdatadir / 'optimize/ut_results.pickle'
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mocker.patch.object(Path, "is_file", MagicMock(return_value=False))
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stat_mock = MagicMock()
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@ -477,28 +478,30 @@ def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
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assert caplog.record_tuples == []
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def test_save_trials_saves_trials(mocker, hyperopt, testdatadir, caplog) -> None:
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trials = create_trials(mocker, hyperopt, testdatadir)
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def test_save_results_saves_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
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epochs = create_results(mocker, hyperopt, testdatadir)
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mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
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trials_file = testdatadir / 'optimize' / 'ut_trials.pickle'
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results_file = testdatadir / 'optimize' / 'ut_results.pickle'
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hyperopt.trials = trials
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hyperopt.save_trials(final=True)
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assert log_has(f"1 epoch saved to '{trials_file}'.", caplog)
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caplog.set_level(logging.DEBUG)
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hyperopt.epochs = epochs
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hyperopt._save_results()
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assert log_has(f"1 epoch saved to '{results_file}'.", caplog)
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mock_dump.assert_called_once()
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hyperopt.trials = trials + trials
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hyperopt.save_trials(final=True)
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assert log_has(f"2 epochs saved to '{trials_file}'.", caplog)
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hyperopt.epochs = epochs + epochs
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hyperopt._save_results()
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assert log_has(f"2 epochs saved to '{results_file}'.", caplog)
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def test_read_trials_returns_trials_file(mocker, hyperopt, testdatadir, caplog) -> None:
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trials = create_trials(mocker, hyperopt, testdatadir)
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mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials)
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trials_file = testdatadir / 'optimize' / 'ut_trials.pickle'
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hyperopt_trial = hyperopt._read_trials(trials_file)
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assert log_has(f"Reading Trials from '{trials_file}'", caplog)
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assert hyperopt_trial == trials
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def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
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epochs = create_results(mocker, hyperopt, testdatadir)
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mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=epochs)
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results_file = testdatadir / 'optimize' / 'ut_results.pickle'
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hyperopt_epochs = hyperopt._read_results(results_file)
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assert log_has(f"Reading epochs from '{results_file}'", caplog)
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assert hyperopt_epochs == epochs
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mock_load.assert_called_once()
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