There is no point in regenerating them and it will cause some overhead as all space classes will be recreated for every epoch.
468 lines
20 KiB
Python
468 lines
20 KiB
Python
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
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"""
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This module contains the hyperopt logic
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"""
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import logging
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import random
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import warnings
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from datetime import datetime, timezone
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from math import ceil
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from operator import itemgetter
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import progressbar
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from colorama import Fore, Style
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from colorama import init as colorama_init
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from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
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from pandas import DataFrame
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from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
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from freqtrade.data.converter import trim_dataframe
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from freqtrade.data.history import get_timerange
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from freqtrade.misc import file_dump_json, plural
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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from freqtrade.optimize.optimize_reports import generate_strategy_stats
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from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
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from freqtrade.strategy import IStrategy
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# Suppress scikit-learn FutureWarnings from skopt
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=FutureWarning)
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from skopt import Optimizer
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from skopt.space import Dimension
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progressbar.streams.wrap_stderr()
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progressbar.streams.wrap_stdout()
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logger = logging.getLogger(__name__)
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INITIAL_POINTS = 30
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# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
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# in the skopt model queue, to optimize memory consumption
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SKOPT_MODEL_QUEUE_SIZE = 10
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MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
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class Hyperopt:
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"""
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Hyperopt class, this class contains all the logic to run a hyperopt simulation
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To run a backtest:
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hyperopt = Hyperopt(config)
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hyperopt.start()
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"""
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custom_hyperopt: IHyperOpt
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def __init__(self, config: Dict[str, Any]) -> None:
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self.buy_space: List[Dimension] = []
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self.sell_space: List[Dimension] = []
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self.roi_space: List[Dimension] = []
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self.stoploss_space: List[Dimension] = []
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self.trailing_space: List[Dimension] = []
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self.dimensions: List[Dimension] = []
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self.config = config
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self.backtesting = Backtesting(self.config)
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if not self.config.get('hyperopt'):
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self.custom_hyperopt = HyperOptAuto(self.config)
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else:
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self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
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self.custom_hyperopt.strategy = self.backtesting.strategy
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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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time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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strategy = str(self.config['strategy'])
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self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
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f'strategy_{strategy}_hyperopt_results_{time_now}.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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self.current_best_loss = 100
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self.clean_hyperopt()
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self.num_epochs_saved = 0
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# Previous evaluations
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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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self.backtesting.strategy.advise_indicators = ( # type: ignore
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self.custom_hyperopt.populate_indicators) # type: ignore
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if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
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self.backtesting.strategy.advise_buy = ( # type: ignore
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self.custom_hyperopt.populate_buy_trend) # type: ignore
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if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
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self.backtesting.strategy.advise_sell = ( # type: ignore
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self.custom_hyperopt.populate_sell_trend) # type: ignore
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# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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self.max_open_trades = self.config['max_open_trades']
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else:
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logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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self.max_open_trades = 0
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self.position_stacking = self.config.get('position_stacking', False)
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if HyperoptTools.has_space(self.config, 'sell'):
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# Make sure use_sell_signal is enabled
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if 'ask_strategy' not in self.config:
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self.config['ask_strategy'] = {}
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self.config['ask_strategy']['use_sell_signal'] = True
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self.print_all = self.config.get('print_all', False)
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self.hyperopt_table_header = 0
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self.print_colorized = self.config.get('print_colorized', False)
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self.print_json = self.config.get('print_json', False)
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@staticmethod
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def get_lock_filename(config: Dict[str, Any]) -> str:
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return str(config['user_data_dir'] / 'hyperopt.lock')
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def clean_hyperopt(self) -> None:
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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.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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p.unlink()
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def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
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# Ensure the number of dimensions match
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# the number of parameters in the list.
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if len(raw_params) != len(dimensions):
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raise ValueError('Mismatch in number of search-space dimensions.')
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# Return a dict where the keys are the names of the dimensions
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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_results(self) -> None:
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"""
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Save hyperopt results to file
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"""
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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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# Store hyperopt filename
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latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
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file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
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log=False)
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def _get_params_details(self, params: Dict) -> Dict:
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"""
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Return the params for each space
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"""
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result: Dict = {}
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if HyperoptTools.has_space(self.config, 'buy'):
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result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
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if HyperoptTools.has_space(self.config, 'sell'):
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result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
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if HyperoptTools.has_space(self.config, 'roi'):
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result['roi'] = self.custom_hyperopt.generate_roi_table(params)
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if HyperoptTools.has_space(self.config, 'stoploss'):
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result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
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if HyperoptTools.has_space(self.config, 'trailing'):
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result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
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return result
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def print_results(self, results) -> None:
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"""
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Log results if it is better than any previous evaluation
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TODO: this should be moved to HyperoptTools too
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"""
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is_best = results['is_best']
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if self.print_all or is_best:
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print(
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HyperoptTools.get_result_table(
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self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header
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)
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)
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self.hyperopt_table_header = 2
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def init_spaces(self):
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"""
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Assign the dimensions in the hyperoptimization space.
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"""
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if HyperoptTools.has_space(self.config, 'buy'):
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logger.debug("Hyperopt has 'buy' space")
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self.buy_space = self.custom_hyperopt.indicator_space()
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if HyperoptTools.has_space(self.config, 'sell'):
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logger.debug("Hyperopt has 'sell' space")
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self.sell_space = self.custom_hyperopt.sell_indicator_space()
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if HyperoptTools.has_space(self.config, 'roi'):
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logger.debug("Hyperopt has 'roi' space")
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self.roi_space = self.custom_hyperopt.roi_space()
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if HyperoptTools.has_space(self.config, 'stoploss'):
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logger.debug("Hyperopt has 'stoploss' space")
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self.stoploss_space = self.custom_hyperopt.stoploss_space()
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if HyperoptTools.has_space(self.config, 'trailing'):
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logger.debug("Hyperopt has 'trailing' space")
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self.trailing_space = self.custom_hyperopt.trailing_space()
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self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
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self.stoploss_space + self.trailing_space)
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def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
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"""
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Used Optimize function. Called once per epoch to optimize whatever is configured.
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Keep this function as optimized as possible!
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"""
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backtest_start_time = datetime.now(timezone.utc)
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params_dict = self._get_params_dict(self.dimensions, raw_params)
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params_details = self._get_params_details(params_dict)
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# Apply parameters
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if HyperoptTools.has_space(self.config, 'roi'):
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self.backtesting.strategy.minimal_roi = ( # type: ignore
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self.custom_hyperopt.generate_roi_table(params_dict))
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if HyperoptTools.has_space(self.config, 'buy'):
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self.backtesting.strategy.advise_buy = ( # type: ignore
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self.custom_hyperopt.buy_strategy_generator(params_dict))
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if HyperoptTools.has_space(self.config, 'sell'):
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self.backtesting.strategy.advise_sell = ( # type: ignore
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self.custom_hyperopt.sell_strategy_generator(params_dict))
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if HyperoptTools.has_space(self.config, 'stoploss'):
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self.backtesting.strategy.stoploss = params_dict['stoploss']
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if HyperoptTools.has_space(self.config, 'trailing'):
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d = self.custom_hyperopt.generate_trailing_params(params_dict)
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self.backtesting.strategy.trailing_stop = d['trailing_stop']
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self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
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self.backtesting.strategy.trailing_stop_positive_offset = \
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d['trailing_stop_positive_offset']
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self.backtesting.strategy.trailing_only_offset_is_reached = \
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d['trailing_only_offset_is_reached']
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processed = load(self.data_pickle_file)
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bt_results = self.backtesting.backtest(
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processed=processed,
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start_date=self.min_date.datetime,
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end_date=self.max_date.datetime,
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max_open_trades=self.max_open_trades,
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position_stacking=self.position_stacking,
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enable_protections=self.config.get('enable_protections', False),
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)
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backtest_end_time = datetime.now(timezone.utc)
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bt_results.update({
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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})
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return self._get_results_dict(bt_results, self.min_date, self.max_date,
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params_dict, params_details,
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processed=processed)
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def _get_results_dict(self, backtesting_results, min_date, max_date,
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params_dict, params_details, processed: Dict[str, DataFrame]
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) -> Dict[str, Any]:
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strat_stats = generate_strategy_stats(
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processed, self.backtesting.strategy.get_strategy_name(),
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backtesting_results, min_date, max_date, market_change=0
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)
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results_explanation = HyperoptTools.format_results_explanation_string(
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strat_stats, self.config['stake_currency'])
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trade_count = strat_stats['total_trades']
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total_profit = strat_stats['profit_total']
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# If this evaluation contains too short amount of trades to be
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# interesting -- consider it as 'bad' (assigned max. loss value)
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# in order to cast this hyperspace point away from optimization
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# path. We do not want to optimize 'hodl' strategies.
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loss: float = MAX_LOSS
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if trade_count >= self.config['hyperopt_min_trades']:
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loss = self.calculate_loss(results=backtesting_results['results'],
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trade_count=trade_count,
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min_date=min_date.datetime, max_date=max_date.datetime,
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config=self.config, processed=processed)
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return {
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'loss': loss,
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'params_dict': params_dict,
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'params_details': params_details,
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'results_metrics': strat_stats,
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'results_explanation': results_explanation,
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'total_profit': total_profit,
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}
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def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
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return Optimizer(
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dimensions,
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base_estimator="ET",
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acq_optimizer="auto",
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n_initial_points=INITIAL_POINTS,
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acq_optimizer_kwargs={'n_jobs': cpu_count},
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random_state=self.random_state,
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model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
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)
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def run_optimizer_parallel(self, parallel, asked, i) -> List:
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return parallel(delayed(
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wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
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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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def start(self) -> None:
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self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
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logger.info(f"Using optimizer random state: {self.random_state}")
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self.hyperopt_table_header = -1
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# Initialize spaces ...
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self.init_spaces()
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data, timerange = self.backtesting.load_bt_data()
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logger.info("Dataload complete. Calculating indicators")
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preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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preprocessed[pair] = trim_dataframe(df, timerange,
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startup_candles=self.backtesting.required_startup)
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self.min_date, self.max_date = get_timerange(preprocessed)
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logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(self.max_date - self.min_date).days} days)..')
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dump(preprocessed, self.data_pickle_file)
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# We don't need exchange instance anymore while running hyperopt
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self.backtesting.exchange.close()
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self.backtesting.exchange._api = None # type: ignore
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self.backtesting.exchange._api_async = None # type: ignore
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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.backtesting.strategy.dp = None # type: ignore
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IStrategy.dp = None # type: ignore
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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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config_jobs = self.config.get('hyperopt_jobs', -1)
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logger.info(f'Number of parallel jobs set as: {config_jobs}')
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self.opt = self.get_optimizer(self.dimensions, config_jobs)
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if self.print_colorized:
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colorama_init(autoreset=True)
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try:
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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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# Define progressbar
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if self.print_colorized:
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widgets = [
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' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
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' (', progressbar.Percentage(), ')] ',
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progressbar.Bar(marker=progressbar.AnimatedMarker(
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fill='\N{FULL BLOCK}',
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fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
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marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
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)),
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' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
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]
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else:
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widgets = [
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' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
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' (', progressbar.Percentage(), ')] ',
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progressbar.Bar(marker=progressbar.AnimatedMarker(
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fill='\N{FULL BLOCK}',
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)),
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' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
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]
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with progressbar.ProgressBar(
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max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
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widgets=widgets
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) as pbar:
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EVALS = ceil(self.total_epochs / jobs)
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for i in range(EVALS):
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# Correct the number of epochs to be processed for the last
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# iteration (should not exceed self.total_epochs in total)
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n_rest = (i + 1) * jobs - self.total_epochs
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current_jobs = jobs - n_rest if n_rest > 0 else jobs
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asked = self.opt.ask(n_points=current_jobs)
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f_val = self.run_optimizer_parallel(parallel, asked, i)
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self.opt.tell(asked, [v['loss'] for v in f_val])
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# Calculate progressbar outputs
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for j, val in enumerate(f_val):
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# Use human-friendly indexes here (starting from 1)
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current = i * jobs + j + 1
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val['current_epoch'] = current
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val['is_initial_point'] = current <= INITIAL_POINTS
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logger.debug(f"Optimizer epoch evaluated: {val}")
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is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
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# This value is assigned here and not in the optimization method
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# to keep proper order in the list of results. That's because
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# evaluations can take different time. Here they are aligned in the
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# order they will be shown to the user.
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val['is_best'] = is_best
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self.print_results(val)
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|
|
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if is_best:
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self.current_best_loss = val['loss']
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self.epochs.append(val)
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|
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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_results()
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|
|
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pbar.update(current)
|
|
|
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except KeyboardInterrupt:
|
|
print('User interrupted..')
|
|
|
|
self._save_results()
|
|
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
|
|
f"saved to '{self.results_file}'.")
|
|
|
|
if self.epochs:
|
|
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
|
|
best_epoch = sorted_epochs[0]
|
|
HyperoptTools.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
|
|
# a chance to be evaluated.
|
|
print("No epochs evaluated yet, no best result.")
|