highlight random points in hyperopt results table
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Italo
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229b0b037e
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a3b401a762
@ -10,7 +10,7 @@ import warnings
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from datetime import datetime, timezone
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from math import ceil
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional, Tuple
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import progressbar
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import rapidjson
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@ -410,7 +410,7 @@ class Hyperopt:
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# Store non-trimmed data - will be trimmed after signal generation.
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dump(preprocessed, self.data_pickle_file)
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def get_asked_points(self, n_points: int) -> List[List[Any]]:
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def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
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'''
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Enforce points returned from `self.opt.ask` have not been already evaluated
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@ -424,20 +424,30 @@ class Hyperopt:
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'''
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i = 0
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asked_non_tried: List[List[Any]] = []
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is_random: List[bool] = []
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while i < 5 and len(asked_non_tried) < n_points:
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if i < 3:
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self.opt.cache_ = {}
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asked = self.opt.ask(n_points=n_points * 5)
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is_random = [False for _ in range(len(asked))]
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else:
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asked = self.opt.space.rvs(n_samples=n_points * 5)
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is_random = [True for _ in range(len(asked))]
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asked_non_tried += [x for x in asked
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if x not in self.opt.Xi
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and x not in asked_non_tried]
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is_random += [rand for x, rand in zip(asked, is_random)
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if x not in self.opt.Xi
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and x not in asked_non_tried]
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i += 1
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if asked_non_tried:
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return asked_non_tried[:min(len(asked_non_tried), n_points)]
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return (
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asked_non_tried[:min(len(asked_non_tried), n_points)],
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is_random[:min(len(asked_non_tried), n_points)]
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)
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else:
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return self.opt.ask(n_points=n_points)
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return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
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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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@ -503,7 +513,7 @@ class Hyperopt:
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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.get_asked_points(n_points=current_jobs)
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asked, is_random = self.get_asked_points(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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@ -522,6 +532,7 @@ class Hyperopt:
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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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val['is_random'] = is_random[j]
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self.print_results(val)
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if is_best:
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@ -322,12 +322,12 @@ class HyperoptTools():
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'results_metrics.profit_total', 'results_metrics.holding_avg',
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'results_metrics.max_drawdown',
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'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
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'loss', 'is_initial_point', 'is_best']]
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'loss', 'is_initial_point', 'is_random', 'is_best']]
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trials.columns = [
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'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
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'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
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'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
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]
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return trials
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@ -349,9 +349,11 @@ class HyperoptTools():
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trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
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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_initial_point'] | trials['is_random'], 'Best'] = '* '
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
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trials.loc[
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(trials['is_initial_point'] | trials['is_random']) & trials['is_best'],
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'Best'] = '* Best'
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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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# perc_multi = 1 if legacy_mode else 100
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@ -407,7 +409,7 @@ class HyperoptTools():
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trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][j]), Style.RESET_ALL)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
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if remove_header > 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='orgtbl',
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@ -2053,6 +2053,7 @@ def saved_hyperopt_results():
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'total_profit': -0.00125625,
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'current_epoch': 1,
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'is_initial_point': True,
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'is_random': False,
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'is_best': True,
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}, {
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@ -2069,6 +2070,7 @@ def saved_hyperopt_results():
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'total_profit': 6.185e-05,
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'current_epoch': 2,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': 14.241196856510731,
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@ -2079,6 +2081,7 @@ def saved_hyperopt_results():
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'total_profit': -0.13639474,
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'current_epoch': 3,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': 100000,
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@ -2086,7 +2089,7 @@ def saved_hyperopt_results():
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'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
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'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
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'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
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'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
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'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_random': False, 'is_best': False
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}, {
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'loss': 0.22195522184191518,
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'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
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@ -2096,6 +2099,7 @@ def saved_hyperopt_results():
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'total_profit': -0.002480140000000001,
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'current_epoch': 5,
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'is_initial_point': True,
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'is_random': False,
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'is_best': True
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}, {
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'loss': 0.545315889154162,
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@ -2106,6 +2110,7 @@ def saved_hyperopt_results():
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'total_profit': -0.0041773,
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'current_epoch': 6,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': 4.713497421432944,
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@ -2118,6 +2123,7 @@ def saved_hyperopt_results():
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'total_profit': -0.06339929,
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'current_epoch': 7,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': 20.0, # noqa: E501
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@ -2128,6 +2134,7 @@ def saved_hyperopt_results():
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'total_profit': 0.0,
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'current_epoch': 8,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': 2.4731817780991223,
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@ -2138,6 +2145,7 @@ def saved_hyperopt_results():
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'total_profit': -0.044050070000000004, # noqa: E501
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'current_epoch': 9,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': -0.2604606005845212, # noqa: E501
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@ -2148,6 +2156,7 @@ def saved_hyperopt_results():
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'total_profit': 0.00021629,
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'current_epoch': 10,
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'is_initial_point': True,
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'is_random': False,
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'is_best': True
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}, {
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'loss': 4.876465945994304, # noqa: E501
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@ -2159,6 +2168,7 @@ def saved_hyperopt_results():
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'total_profit': -0.07436117,
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'current_epoch': 11,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}, {
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'loss': 100000,
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@ -2169,6 +2179,7 @@ def saved_hyperopt_results():
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'total_profit': 0,
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'current_epoch': 12,
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'is_initial_point': True,
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'is_random': False,
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'is_best': False
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}
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]
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@ -41,6 +41,7 @@ def generate_result_metrics():
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'max_drawdown_abs': 0.001,
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'loss': 0.001,
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'is_initial_point': 0.001,
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'is_random': False,
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'is_best': 1,
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}
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@ -247,6 +248,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
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'total_profit': 0,
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'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
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'is_initial_point': False,
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'is_random': False,
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'is_best': True
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}
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)
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