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Merge pull request #3054 from Fredrik81/progress-bar

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Hyperopt: Progressbar during hyperopt
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Matthias 2020-04-12 09:32:52 +02:00 committed by GitHub
commit 18a6c98a82
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5 changed files with 97 additions and 55 deletions
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4
freqtrade/commands/hyperopt_commands.py
View File

@ -52,8 +52,8 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if not export_csv:
try:
Hyperopt.print_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized, 0)
print(Hyperopt.get_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')

6
freqtrade/loggers.py
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@ -18,13 +18,13 @@ def _set_loggers(verbosity: int = 0) -> None:
"""
logging.getLogger('requests').setLevel(
logging.INFO if verbosity <= 1 else logging.DEBUG
logging.INFO if verbosity <= 1 else logging.DEBUG
)
logging.getLogger("urllib3").setLevel(
logging.INFO if verbosity <= 1 else logging.DEBUG
logging.INFO if verbosity <= 1 else logging.DEBUG
)
logging.getLogger('ccxt.base.exchange').setLevel(
logging.INFO if verbosity <= 2 else logging.DEBUG
logging.INFO if verbosity <= 2 else logging.DEBUG
)
logging.getLogger('telegram').setLevel(logging.INFO)

140
freqtrade/optimize/hyperopt.py
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@ -7,7 +7,6 @@ This module contains the hyperopt logic
import locale
import logging
import random
import sys
import warnings
from math import ceil
from collections import OrderedDict
@ -18,10 +17,10 @@ from typing import Any, Dict, List, Optional
import rapidjson
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects)
from pandas import DataFrame, json_normalize, isna
import progressbar
import tabulate
from os import path
import io
@ -43,7 +42,8 @@ with warnings.catch_warnings():
from skopt import Optimizer
from skopt.space import Dimension
progressbar.streams.wrap_stderr()
progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__)
@ -266,21 +266,33 @@ class Hyperopt:
Log results if it is better than any previous evaluation
"""
is_best = results['is_best']
if not self.print_all:
# Print '\n' after each 100th epoch to separate dots from the log messages.
# Otherwise output is messy on a terminal.
print('.', end='' if results['current_epoch'] % 100 != 0 else None) # type: ignore
sys.stdout.flush()
if self.print_all or is_best:
if not self.print_all:
# Separate the results explanation string from dots
print("\n")
self.print_result_table(self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header)
print(
self.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
)
)
self.hyperopt_table_header = 2
def get_results(self, results) -> str:
"""
Log results if it is better than any previous evaluation
"""
output = ''
is_best = results['is_best']
if self.print_all or is_best:
output = self.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
)
self.hyperopt_table_header = 2
return output
@staticmethod
def print_results_explanation(results, total_epochs, highlight_best: bool,
print_colorized: bool) -> None:
@ -304,13 +316,13 @@ class Hyperopt:
f"Objective: {results['loss']:.5f}")
@staticmethod
def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> None:
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return
return ''
tabulate.PRESERVE_WHITESPACE = True
@ -381,7 +393,7 @@ class Hyperopt:
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
print(table)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
@ -654,47 +666,75 @@ class Hyperopt:
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()
logger.info(f'Effective number of parallel workers used: {jobs}')
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
n_rest = (i + 1) * jobs - self.total_epochs
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked = self.opt.ask(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
with progressbar.ProgressBar(
maxval=self.total_epochs, redirect_stdout=True, redirect_stderr=True,
widgets=widgets
) as pbar:
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
n_rest = (i + 1) * jobs - self.total_epochs
current_jobs = jobs - n_rest if n_rest > 0 else jobs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
asked = self.opt.ask(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
is_best = self.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
self.print_results(val)
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.trials.append(val)
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self.save_trials()
pbar.update(current)
if is_best:
self.current_best_loss = val['loss']
self.trials.append(val)
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self.save_trials()
except KeyboardInterrupt:
print('User interrupted..')

1
requirements-hyperopt.txt
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@ -7,3 +7,4 @@ scikit-learn==0.22.2.post1
scikit-optimize==0.7.4
filelock==3.0.12
joblib==0.14.1
progressbar2==3.50.1

1
setup.py
View File

@ -24,6 +24,7 @@ hyperopt = [
'scikit-optimize',
'filelock',
'joblib',
'progressbar2',
]
develop = [