926 lines
40 KiB
Python
926 lines
40 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 functools
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import locale
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import logging
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import random
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import sys
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import warnings
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from collections import OrderedDict, deque
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from math import factorial, log
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from numpy import iinfo, int32
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from operator import itemgetter
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from pathlib import Path
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from pprint import pprint
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from typing import Any, Dict, List, Optional
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import rapidjson
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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 joblib import parallel_backend
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from multiprocessing import Manager
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from queue import Queue
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from pandas import DataFrame, json_normalize, isna
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from tabulate import tabulate
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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.exceptions import OperationalException
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from freqtrade.misc import plural, round_dict
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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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import freqtrade.optimize.hyperopt_backend as backend
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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.resolvers.hyperopt_resolver import (HyperOptLossResolver, HyperOptResolver)
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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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# Additional regressors already pluggable into the optimizer
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# from sklearn.linear_model import ARDRegression, BayesianRidge
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# possibly interesting regressors that need predict method override
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# from sklearn.ensemble import HistGradientBoostingRegressor
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# from xgboost import XGBoostRegressor
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logger = logging.getLogger(__name__)
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# supported strategies when asking for multiple points to the optimizer
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NEXT_POINT_METHODS = ["cl_min", "cl_mean", "cl_max"]
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NEXT_POINT_METHODS_LENGTH = 3
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MAX_LOSS = iinfo(int32).max # 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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def __init__(self, config: Dict[str, Any]) -> None:
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self.config = config
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self.backtesting = Backtesting(self.config)
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self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
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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'] / 'hyperopt_results' /
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'hyperopt_results.pickle')
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self.opts_file = (self.config['user_data_dir'] / 'hyperopt_results' /
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'hyperopt_optimizers.pickle')
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self.tickerdata_pickle = (self.config['user_data_dir'] / 'hyperopt_results' /
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'hyperopt_tickerdata.pkl')
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self.n_jobs = self.config.get('hyperopt_jobs', -1)
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if self.n_jobs < 0:
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self.n_jobs = cpu_count() // 2 or 1
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self.effort = self.config['effort'] if 'effort' in self.config else 0
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self.total_epochs = self.config['epochs'] if 'epochs' in self.config else 0
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self.max_epoch = 0
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self.max_epoch_reached = False
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self.min_epochs = 0
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self.epochs_limit = lambda: self.total_epochs or self.max_epoch
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# a guessed number extracted by the space dimensions
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self.search_space_size = 0
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# total number of candles being backtested
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self.n_samples = 0
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self.current_best_loss = MAX_LOSS
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self.current_best_epoch = 0
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self.epochs_since_last_best: List = []
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self.avg_best_occurrence = 0
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if not self.config.get('hyperopt_continue'):
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self.clean_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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# evaluations
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self.trials: List = []
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# optimizers
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self.opts: List[Optimizer] = []
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self.opt: Optimizer = None
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if 'multi_opt' in self.config and self.config['multi_opt']:
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self.multi = True
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backend.manager = Manager()
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backend.optimizers = backend.manager.Queue()
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backend.results_board = backend.manager.Queue(maxsize=1)
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backend.results_board.put([])
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self.opt_base_estimator = 'GBRT'
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self.opt_acq_optimizer = 'sampling'
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default_n_points = 2
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else:
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backend.manager = Manager()
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backend.results = backend.manager.Queue()
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self.multi = False
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self.opt_base_estimator = 'GP'
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self.opt_acq_optimizer = 'lbfgs'
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default_n_points = 1
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# in single opt assume runs are expensive so default to 1 point per ask
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self.n_points = self.config.get('points_per_opt', default_n_points)
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# if 0 n_points are given, don't use any base estimator (akin to random search)
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if self.n_points < 1:
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self.n_points = 1
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self.opt_base_estimator = 'DUMMY'
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self.opt_acq_optimizer = 'sampling'
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# models are only needed for posterior eval
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self.n_models = max(16, self.n_jobs)
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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 = \
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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 = \
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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 = \
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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 self.has_space('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.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.tickerdata_pickle, self.trials_file, self.opts_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, raw_params: List[Any]) -> Dict:
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dimensions: List[Dimension] = self.dimensions
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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_trials(self, final: bool = False) -> None:
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"""
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Save hyperopt trials to file
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"""
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num_trials = len(self.trials)
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print()
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if num_trials > self.num_trials_saved:
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logger.info(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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self.save_opts()
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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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def save_opts(self) -> None:
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""" Save optimizers state to disk. The minimum required state could also be constructed
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from the attributes [ models, space, rng ] with Xi, yi loaded from trials """
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# synchronize with saved trials
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opts = []
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n_opts = 0
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if self.multi:
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while not backend.optimizers.empty():
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opts.append(backend.optimizers.get())
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n_opts = len(opts)
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for opt in opts:
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backend.optimizers.put(opt)
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else:
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if self.opt:
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n_opts = 1
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opts = [self.opt]
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logger.info(f"Saving {n_opts} {plural(n_opts, 'optimizer')}.")
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dump(opts, self.opts_file)
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@staticmethod
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def _read_trials(trials_file: Path) -> List:
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"""
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Read hyperopt trials 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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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 self.has_space('buy'):
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result['buy'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('buy')}
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if self.has_space('sell'):
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result['sell'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('sell')}
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if self.has_space('roi'):
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result['roi'] = self.custom_hyperopt.generate_roi_table(params)
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if self.has_space('stoploss'):
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result['stoploss'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('stoploss')}
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if self.has_space('trailing'):
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result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
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return result
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@staticmethod
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def print_epoch_details(results, total_epochs: int, print_json: bool,
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no_header: bool = False, header_str: str = None) -> None:
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"""
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Display details of the hyperopt result
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"""
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params = results.get('params_details', {})
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# Default header string
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if header_str is None:
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header_str = "Best result"
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if not no_header:
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explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
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print(f"\n{header_str}:\n\n{explanation_str}\n")
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if print_json:
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result_dict: Dict = {}
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for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
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Hyperopt._params_update_for_json(result_dict, params, s)
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print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
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else:
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Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
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Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
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Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
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Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
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Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
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@staticmethod
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def _params_update_for_json(result_dict, params, space: str) -> None:
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if space in params:
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space_params = Hyperopt._space_params(params, space)
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if space in ['buy', 'sell']:
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result_dict.setdefault('params', {}).update(space_params)
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elif space == 'roi':
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# Convert keys in min_roi dict to strings because
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# rapidjson cannot dump dicts with integer keys...
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# OrderedDict is used to keep the numeric order of the items
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# in the dict.
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result_dict['minimal_roi'] = OrderedDict(
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(str(k), v) for k, v in space_params.items()
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)
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else: # 'stoploss', 'trailing'
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result_dict.update(space_params)
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@staticmethod
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def _params_pretty_print(params, space: str, header: str) -> None:
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if space in params:
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space_params = Hyperopt._space_params(params, space, 5)
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if space == 'stoploss':
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print(header, space_params.get('stoploss'))
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else:
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print(header)
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pprint(space_params, indent=4)
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@staticmethod
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def _space_params(params, space: str, r: int = None) -> Dict:
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d = params[space]
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# Round floats to `r` digits after the decimal point if requested
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return round_dict(d, r) if r else d
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@staticmethod
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def is_best_loss(results, current_best_loss: float) -> bool:
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return results['loss'] < current_best_loss
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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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"""
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is_best = results['is_best']
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if self.print_all or is_best:
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self.print_results_explanation(results, self.epochs_limit(), self.print_all,
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self.print_colorized)
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@staticmethod
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def print_results_explanation(results, total_epochs, highlight_best: bool,
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print_colorized: bool) -> None:
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"""
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Log results explanation string
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"""
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explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
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# Colorize output
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if print_colorized:
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if results['total_profit'] > 0:
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explanation_str = Fore.GREEN + explanation_str
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if highlight_best and results['is_best']:
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explanation_str = Style.BRIGHT + explanation_str
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print(explanation_str)
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@staticmethod
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def _format_explanation_string(results, total_epochs) -> str:
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return (("*" if 'is_initial_point' in results and results['is_initial_point'] else " ") +
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f"{results['current_epoch']:5d}/{total_epochs}: " +
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f"{results['results_explanation']} " +
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f"Objective: {results['loss']:.5f}")
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@staticmethod
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def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool) -> None:
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"""
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Log result table
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"""
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if not results:
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return
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.avg_profit', 'results_metrics.total_profit',
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'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
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'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '*'
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials['Objective'] = trials['Objective'].astype(str)
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Epoch'] = trials['Epoch'].apply(
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lambda x: "{}/{}".format(x, total_epochs))
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x) if not isna(x) else x)
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trials['Profit'] = trials['Profit'].apply(
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lambda x: '{:,.2f}%'.format(x) if not isna(x) else x)
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trials['Total profit'] = trials['Total profit'].apply(
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lambda x: '{: 11.8f} '.format(x) + config['stake_currency'] if not isna(x) else x)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f}m'.format(x) if not isna(x) else x)
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if print_colorized:
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for i in range(len(trials)):
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if trials.loc[i]['is_profit']:
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for z in range(len(trials.loc[i])-3):
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trials.iat[i, z] = "{}{}{}".format(Fore.GREEN,
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str(trials.loc[i][z]), Fore.RESET)
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if trials.loc[i]['is_best'] and highlight_best:
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for z in range(len(trials.loc[i])-3):
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trials.iat[i, z] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][z]), Style.RESET_ALL)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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print(tabulate(trials.to_dict(orient='list'), headers='keys', tablefmt='psql',
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stralign="right"))
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def has_space(self, space: str) -> bool:
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"""
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Tell if the space value is contained in the configuration
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"""
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# The 'trailing' space is not included in the 'default' set of spaces
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if space == 'trailing':
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return any(s in self.config['spaces'] for s in [space, 'all'])
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else:
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return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
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def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
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"""
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Return the dimensions in the hyperoptimization space.
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:param space: Defines hyperspace to return dimensions for.
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If None, then the self.has_space() will be used to return dimensions
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for all hyperspaces used.
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"""
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spaces: List[Dimension] = []
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if space == 'buy' or (space is None and self.has_space('buy')):
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logger.debug("Hyperopt has 'buy' space")
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spaces += self.custom_hyperopt.indicator_space()
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if space == 'sell' or (space is None and self.has_space('sell')):
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logger.debug("Hyperopt has 'sell' space")
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spaces += self.custom_hyperopt.sell_indicator_space()
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if space == 'roi' or (space is None and self.has_space('roi')):
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logger.debug("Hyperopt has 'roi' space")
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spaces += self.custom_hyperopt.roi_space()
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if space == 'stoploss' or (space is None and self.has_space('stoploss')):
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logger.debug("Hyperopt has 'stoploss' space")
|
|
spaces += self.custom_hyperopt.stoploss_space()
|
|
|
|
if space == 'trailing' or (space is None and self.has_space('trailing')):
|
|
logger.debug("Hyperopt has 'trailing' space")
|
|
spaces += self.custom_hyperopt.trailing_space()
|
|
|
|
return spaces
|
|
|
|
def backtest_params(self, raw_params: List[Any], iteration=None) -> Dict:
|
|
"""
|
|
Used Optimize function. Called once per epoch to optimize whatever is configured.
|
|
Keep this function as optimized as possible!
|
|
"""
|
|
params_dict = self._get_params_dict(raw_params)
|
|
params_details = self._get_params_details(params_dict)
|
|
|
|
if self.has_space('roi'):
|
|
self.backtesting.strategy.minimal_roi = \
|
|
self.custom_hyperopt.generate_roi_table(params_dict)
|
|
|
|
if self.has_space('buy'):
|
|
self.backtesting.strategy.advise_buy = \
|
|
self.custom_hyperopt.buy_strategy_generator(params_dict)
|
|
|
|
if self.has_space('sell'):
|
|
self.backtesting.strategy.advise_sell = \
|
|
self.custom_hyperopt.sell_strategy_generator(params_dict)
|
|
|
|
if self.has_space('stoploss'):
|
|
self.backtesting.strategy.stoploss = params_dict['stoploss']
|
|
|
|
if self.has_space('trailing'):
|
|
d = self.custom_hyperopt.generate_trailing_params(params_dict)
|
|
self.backtesting.strategy.trailing_stop = d['trailing_stop']
|
|
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
|
|
self.backtesting.strategy.trailing_stop_positive_offset = \
|
|
d['trailing_stop_positive_offset']
|
|
self.backtesting.strategy.trailing_only_offset_is_reached = \
|
|
d['trailing_only_offset_is_reached']
|
|
|
|
processed = load(self.tickerdata_pickle)
|
|
|
|
min_date, max_date = get_timerange(processed)
|
|
|
|
backtesting_results = self.backtesting.backtest(
|
|
processed=processed,
|
|
stake_amount=self.config['stake_amount'],
|
|
start_date=min_date,
|
|
end_date=max_date,
|
|
max_open_trades=self.max_open_trades,
|
|
position_stacking=self.position_stacking,
|
|
)
|
|
return self._get_results_dict(backtesting_results, min_date, max_date, params_dict,
|
|
params_details)
|
|
|
|
def _get_results_dict(self, backtesting_results, min_date, max_date, params_dict,
|
|
params_details):
|
|
results_metrics = self._calculate_results_metrics(backtesting_results)
|
|
results_explanation = self._format_results_explanation_string(results_metrics)
|
|
|
|
trade_count = results_metrics['trade_count']
|
|
total_profit = results_metrics['total_profit']
|
|
|
|
# If this evaluation contains too short amount of trades to be
|
|
# interesting -- consider it as 'bad' (assigned max. loss value)
|
|
# in order to cast this hyperspace point away from optimization
|
|
# path. We do not want to optimize 'hodl' strategies.
|
|
loss: float = MAX_LOSS
|
|
if trade_count >= self.config['hyperopt_min_trades']:
|
|
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
|
|
min_date=min_date.datetime, max_date=max_date.datetime)
|
|
return {
|
|
'loss': loss,
|
|
'params_dict': params_dict,
|
|
'params_details': params_details,
|
|
'results_metrics': results_metrics,
|
|
'results_explanation': results_explanation,
|
|
'total_profit': total_profit,
|
|
}
|
|
|
|
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
|
|
return {
|
|
'trade_count': len(backtesting_results.index),
|
|
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
|
|
'total_profit': backtesting_results.profit_abs.sum(),
|
|
'profit': backtesting_results.profit_percent.sum() * 100.0,
|
|
'duration': backtesting_results.trade_duration.mean(),
|
|
}
|
|
|
|
def _format_results_explanation_string(self, results_metrics: Dict) -> str:
|
|
"""
|
|
Return the formatted results explanation in a string
|
|
"""
|
|
stake_cur = self.config['stake_currency']
|
|
return (f"{results_metrics['trade_count']:6d} trades. "
|
|
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
|
|
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
|
|
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
|
|
f"Avg duration {results_metrics['duration']:5.1f} min."
|
|
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
|
|
|
|
def get_next_point_strategy(self):
|
|
""" Choose a strategy randomly among the supported ones, used in multi opt mode
|
|
to increase the diversion of the searches of each optimizer """
|
|
return NEXT_POINT_METHODS[random.randrange(0, NEXT_POINT_METHODS_LENGTH)]
|
|
|
|
def get_optimizer(self, dimensions: List[Dimension], n_jobs: int,
|
|
n_initial_points: int) -> Optimizer:
|
|
" Construct an optimizer object "
|
|
# https://github.com/scikit-learn/scikit-learn/issues/14265
|
|
# lbfgs uses joblib threading backend so n_jobs has to be reduced
|
|
# to avoid oversubscription
|
|
if self.opt_acq_optimizer == 'lbfgs':
|
|
n_jobs = 1
|
|
return Optimizer(
|
|
dimensions,
|
|
base_estimator=self.opt_base_estimator,
|
|
acq_optimizer=self.opt_acq_optimizer,
|
|
n_initial_points=n_initial_points,
|
|
acq_optimizer_kwargs={'n_jobs': n_jobs},
|
|
model_queue_size=self.n_models,
|
|
random_state=self.random_state,
|
|
)
|
|
|
|
def run_backtest_parallel(self, parallel: Parallel, tries: int, first_try: int,
|
|
jobs: int) -> List:
|
|
""" launch parallel in single opt mode, return the evaluated epochs """
|
|
result = parallel(
|
|
delayed(wrap_non_picklable_objects(self.parallel_objective))(asked, backend.results, i)
|
|
for asked, i in zip(self.opt_ask_and_tell(jobs, tries),
|
|
range(first_try, first_try + tries)))
|
|
return result
|
|
|
|
def run_multi_backtest_parallel(self, parallel: Parallel, tries: int, first_try: int,
|
|
jobs: int) -> List:
|
|
""" launch parallel in multi opt mode, return the evaluated epochs"""
|
|
results = parallel(
|
|
delayed(wrap_non_picklable_objects(self.parallel_opt_objective))(
|
|
i, backend.optimizers, jobs, backend.results_board)
|
|
for i in range(first_try, first_try + tries))
|
|
# each worker will return a list containing n_points, so compact into a single list
|
|
return functools.reduce(lambda x, y: [*x, *y], results)
|
|
|
|
def opt_ask_and_tell(self, jobs: int, tries: int):
|
|
"""
|
|
loop to manage optimizer state in single optimizer mode, everytime a job is
|
|
dispatched, we check the optimizer for points, to ask and to tell if any,
|
|
but only fit a new model every n_points, because if we fit at every result previous
|
|
points become invalid.
|
|
"""
|
|
vals = []
|
|
to_ask: deque = deque()
|
|
evald: List[List] = []
|
|
fit = False
|
|
for r in range(tries):
|
|
while not backend.results.empty():
|
|
vals.append(backend.results.get())
|
|
if vals:
|
|
self.opt.tell([list(v['params_dict'].values()) for v in vals],
|
|
[v['loss'] for v in vals],
|
|
fit=fit)
|
|
if fit:
|
|
fit = False
|
|
vals = []
|
|
|
|
if not to_ask:
|
|
self.opt.update_next()
|
|
to_ask.extend(self.opt.ask(n_points=self.n_points))
|
|
fit = True
|
|
a = to_ask.popleft()
|
|
while a in evald and len(to_ask) > 0:
|
|
logger.info('this point was evaluated before...')
|
|
a = to_ask.popleft()
|
|
evald.append(a)
|
|
yield a
|
|
|
|
def parallel_opt_objective(self, n: int, optimizers: Queue, jobs: int, results_board: Queue):
|
|
"""
|
|
objective run in multi opt mode, optimizers share the results as soon as they are completed
|
|
"""
|
|
self.log_results_immediate(n)
|
|
# fetch an optimizer instance
|
|
opt = optimizers.get()
|
|
# tell new points if any
|
|
results = results_board.get()
|
|
past_Xi = []
|
|
past_yi = []
|
|
for idx, res in enumerate(results):
|
|
unsubscribe = False
|
|
vals = res[0] # res[1] is the counter
|
|
for v in vals:
|
|
if list(v['params_dict'].values()) not in opt.Xi:
|
|
past_Xi.append(list(v['params_dict'].values()))
|
|
past_yi.append(v['loss'])
|
|
# decrease counter
|
|
if not unsubscribe:
|
|
unsubscribe = True
|
|
if unsubscribe:
|
|
results[idx][1] -= 1
|
|
if results[idx][1] < 1:
|
|
del results[idx]
|
|
# put back the updated results
|
|
results_board.put(results)
|
|
if len(past_Xi) > 0:
|
|
opt.tell(past_Xi, past_yi, fit=False)
|
|
opt.update_next()
|
|
|
|
# ask for points according to config
|
|
asked = opt.ask(n_points=self.n_points, strategy=self.get_next_point_strategy())
|
|
# run the backtest for each point
|
|
f_val = [self.backtest_params(e) for e in asked]
|
|
# tell the optimizer the results
|
|
Xi = [list(v['params_dict'].values()) for v in f_val]
|
|
yi = [v['loss'] for v in f_val]
|
|
opt.tell(Xi, yi, fit=False)
|
|
# update the board with the new results
|
|
results = results_board.get()
|
|
results.append([f_val, jobs - 1])
|
|
results_board.put(results)
|
|
# send back the updated optimizer
|
|
optimizers.put(opt)
|
|
return f_val
|
|
|
|
def parallel_objective(self, asked, results: Queue, n=0):
|
|
""" objective run in single opt mode, run the backtest, store the results into a queue """
|
|
self.log_results_immediate(n)
|
|
v = self.backtest_params(asked)
|
|
results.put(v)
|
|
return v
|
|
|
|
def log_results_immediate(self, n) -> None:
|
|
""" Signals that a new job has been scheduled"""
|
|
print('.', end='')
|
|
sys.stdout.flush()
|
|
|
|
def log_results(self, f_val, frame_start, total_epochs: int) -> int:
|
|
"""
|
|
Log results if it is better than any previous evaluation
|
|
"""
|
|
print()
|
|
current = frame_start + 1
|
|
i = 0
|
|
for i, v in enumerate(f_val):
|
|
is_best = self.is_best_loss(v, self.current_best_loss)
|
|
current = frame_start + i + 1
|
|
v['is_best'] = is_best
|
|
v['current_epoch'] = current
|
|
v['is_initial_point'] = current <= self.n_initial_points
|
|
logger.debug(f"Optimizer epoch evaluated: {v}")
|
|
if is_best:
|
|
self.current_best_loss = v['loss']
|
|
self.update_max_epoch(v, current)
|
|
self.print_results(v)
|
|
self.trials.append(v)
|
|
# Save results and optimizersafter every batch
|
|
self.save_trials()
|
|
# give up if no best since max epochs
|
|
if current + 1 > self.epochs_limit():
|
|
self.max_epoch_reached = True
|
|
return i
|
|
|
|
@staticmethod
|
|
def load_previous_results(trials_file: Path) -> List:
|
|
"""
|
|
Load data for epochs from the file if we have one
|
|
"""
|
|
trials: List = []
|
|
if trials_file.is_file() and trials_file.stat().st_size > 0:
|
|
trials = Hyperopt._read_trials(trials_file)
|
|
if trials[0].get('is_best') is None:
|
|
raise OperationalException(
|
|
"The file with Hyperopt results is incompatible with this version "
|
|
"of Freqtrade and cannot be loaded.")
|
|
logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
|
|
return trials
|
|
|
|
@staticmethod
|
|
def load_previous_optimizers(opts_file: Path) -> List:
|
|
""" Load the state of previous optimizers from file """
|
|
opts: List[Optimizer] = []
|
|
if opts_file.is_file() and opts_file.stat().st_size > 0:
|
|
opts = load(opts_file)
|
|
n_opts = len(opts)
|
|
if n_opts > 0 and type(opts[-1]) != Optimizer:
|
|
raise OperationalException("The file storing optimizers state might be corrupted "
|
|
"and cannot be loaded.")
|
|
else:
|
|
logger.info(f"Loaded {n_opts} previous {plural(n_opts, 'optimizer')} from disk.")
|
|
return opts
|
|
|
|
def _set_random_state(self, random_state: Optional[int]) -> int:
|
|
return random_state or random.randint(1, 2**16 - 1)
|
|
|
|
@staticmethod
|
|
def calc_epochs(dimensions: List[Dimension], n_jobs: int, effort: float, total_epochs: int):
|
|
""" Compute a reasonable number of initial points and
|
|
a minimum number of epochs to evaluate """
|
|
n_dimensions = len(dimensions)
|
|
n_parameters = 0
|
|
# sum all the dimensions discretely, granting minimum values
|
|
for d in dimensions:
|
|
if type(d).__name__ == 'Integer':
|
|
n_parameters += max(1, d.high - d.low)
|
|
elif type(d).__name__ == 'Real':
|
|
n_parameters += max(10, int(d.high - d.low))
|
|
else:
|
|
n_parameters += len(d.bounds)
|
|
# guess the size of the search space as the count of the
|
|
# unordered combination of the dimensions entries
|
|
search_space_size = int(
|
|
(factorial(n_parameters) /
|
|
(factorial(n_parameters - n_dimensions) * factorial(n_dimensions))))
|
|
# logger.info(f'Search space size: {search_space_size}')
|
|
if search_space_size < n_jobs:
|
|
# don't waste if the space is small
|
|
n_initial_points = n_jobs
|
|
min_epochs = n_jobs
|
|
elif total_epochs > 0:
|
|
n_initial_points = total_epochs // 3 if total_epochs > n_jobs * 3 else n_jobs
|
|
min_epochs = n_initial_points
|
|
else:
|
|
# extract coefficients from the search space and the jobs count
|
|
log_sss = int(log(search_space_size, 10))
|
|
log_jobs = int(log(n_jobs, 2)) if n_jobs > 4 else 2
|
|
jobs_ip = log_jobs * log_sss
|
|
# never waste
|
|
n_initial_points = log_sss if jobs_ip > search_space_size else jobs_ip
|
|
# it shall run for this much, I say
|
|
min_epochs = int(max(n_initial_points, n_jobs) * (1 + effort) + n_initial_points)
|
|
return n_initial_points, min_epochs, search_space_size
|
|
|
|
def update_max_epoch(self, val: Dict, current: int):
|
|
""" calculate max epochs: store the number of non best epochs
|
|
between each best, and get the mean of that value """
|
|
if val['is_initial_point'] is not True:
|
|
self.epochs_since_last_best.append(current - self.current_best_epoch)
|
|
self.avg_best_occurrence = (sum(self.epochs_since_last_best) //
|
|
len(self.epochs_since_last_best))
|
|
self.current_best_epoch = current
|
|
self.max_epoch = int(
|
|
(self.current_best_epoch + self.avg_best_occurrence + self.min_epochs) *
|
|
(1 + self.effort))
|
|
if self.max_epoch > self.search_space_size:
|
|
self.max_epoch = self.search_space_size
|
|
print()
|
|
logger.info(f'Max epoch set to: {self.epochs_limit()}')
|
|
|
|
def setup_optimizers(self):
|
|
""" Setup the optimizers objects, try to load from disk, or create new ones """
|
|
# try to load previous optimizers
|
|
opts = self.load_previous_optimizers(self.opts_file)
|
|
|
|
if self.multi:
|
|
if len(opts) > 0:
|
|
# put the restored optimizers in the queue and clear them from the object
|
|
for opt in opts:
|
|
backend.optimizers.put(opt)
|
|
# generate as many optimizers as are still needed to fill the job count
|
|
remaining = self.n_jobs - backend.optimizers.qsize()
|
|
if remaining > 0:
|
|
opt = self.get_optimizer(self.dimensions, self.n_jobs, self.n_initial_points)
|
|
for _ in range(remaining): # generate optimizers
|
|
# random state is preserved
|
|
backend.optimizers.put(
|
|
opt.copy(random_state=opt.rng.randint(0,
|
|
iinfo(int32).max)))
|
|
del opt
|
|
else:
|
|
# if we have more than 1 optimizer but are using single opt,
|
|
# pick one discard the rest...
|
|
if len(opts) > 0:
|
|
self.opt = opts[-1]
|
|
else:
|
|
self.opt = self.get_optimizer(self.dimensions, self.n_jobs, self.n_initial_points)
|
|
del opts[:]
|
|
|
|
def start(self) -> None:
|
|
""" Broom Broom """
|
|
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
|
|
logger.info(f"Using optimizer random state: {self.random_state}")
|
|
|
|
data, timerange = self.backtesting.load_bt_data()
|
|
|
|
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
|
|
|
|
# Trim startup period from analyzed dataframe
|
|
for pair, df in preprocessed.items():
|
|
preprocessed[pair] = trim_dataframe(df, timerange)
|
|
self.n_samples += len(preprocessed[pair])
|
|
min_date, max_date = get_timerange(data)
|
|
|
|
logger.info(
|
|
'Hyperopting with data from %s up to %s (%s days)..',
|
|
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
|
|
)
|
|
dump(preprocessed, self.tickerdata_pickle)
|
|
|
|
# We don't need exchange instance anymore while running hyperopt
|
|
self.backtesting.exchange = None # type: ignore
|
|
|
|
self.trials = self.load_previous_results(self.trials_file)
|
|
|
|
logger.info(f"Found {cpu_count()} CPU cores. Let's make them scream!")
|
|
logger.info(f'Number of parallel jobs set as: {self.n_jobs}')
|
|
|
|
self.dimensions: List[Dimension] = self.hyperopt_space()
|
|
self.n_initial_points, self.min_epochs, self.search_space_size = self.calc_epochs(
|
|
self.dimensions, self.n_jobs, self.effort, self.total_epochs)
|
|
# reduce random points by the number of optimizers in multi mode
|
|
if self.multi:
|
|
self.n_initial_points = self.n_initial_points // self.n_jobs
|
|
logger.info(f"Min epochs set to: {self.min_epochs}")
|
|
# if total epochs are not set, max_epoch takes its place
|
|
if self.total_epochs < 1:
|
|
self.max_epoch = int(self.min_epochs + len(self.trials))
|
|
# initialize average best occurrence
|
|
self.avg_best_occurrence = self.min_epochs // self.n_jobs
|
|
|
|
logger.info(f'Initial points: {self.n_initial_points}')
|
|
if self.print_colorized:
|
|
colorama_init(autoreset=True)
|
|
|
|
self.setup_optimizers()
|
|
try:
|
|
if self.multi:
|
|
jobs_scheduler = self.run_multi_backtest_parallel
|
|
else:
|
|
jobs_scheduler = self.run_backtest_parallel
|
|
with parallel_backend('loky', inner_max_num_threads=2):
|
|
with Parallel(n_jobs=self.n_jobs, verbose=0, backend='loky') as parallel:
|
|
# update epochs count
|
|
prev_batch = -1
|
|
epochs_so_far = len(self.trials)
|
|
while prev_batch < epochs_so_far:
|
|
prev_batch = epochs_so_far
|
|
# pad the batch length to the number of jobs to avoid desaturation
|
|
batch_len = (self.avg_best_occurrence + self.n_jobs -
|
|
self.avg_best_occurrence % self.n_jobs)
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|
# when using multiple optimizers each worker performs
|
|
# n_points (epochs) in 1 dispatch but this reduces the batch len too much
|
|
# if self.multi: batch_len = batch_len // self.n_points
|
|
# don't go over the limit
|
|
if epochs_so_far + batch_len > self.epochs_limit():
|
|
batch_len = self.epochs_limit() - epochs_so_far
|
|
print(
|
|
f"{epochs_so_far+1}-{epochs_so_far+batch_len}"
|
|
f"/{self.epochs_limit()}: ",
|
|
end='')
|
|
f_val = jobs_scheduler(parallel, batch_len, epochs_so_far, self.n_jobs)
|
|
saved = self.log_results(f_val, epochs_so_far, self.epochs_limit())
|
|
# stop if no epochs have been evaluated
|
|
if not saved or batch_len < 1:
|
|
break
|
|
# log_results add
|
|
epochs_so_far += saved
|
|
if self.max_epoch_reached:
|
|
logger.info("Max epoch reached, terminating.")
|
|
break
|
|
|
|
except KeyboardInterrupt:
|
|
print('User interrupted..')
|
|
|
|
self.save_trials(final=True)
|
|
|
|
if self.trials:
|
|
sorted_trials = sorted(self.trials, key=itemgetter('loss'))
|
|
results = sorted_trials[0]
|
|
self.print_epoch_details(results, self.epochs_limit(), self.print_json)
|
|
else:
|
|
# 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.")
|
|
|
|
def __getstate__(self):
|
|
state = self.__dict__.copy()
|
|
del state['trials']
|
|
return state
|