mirror of
https://github.com/freqtrade/freqtrade.git
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333 lines
12 KiB
Python
333 lines
12 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 sys
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from datetime import datetime
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from math import ceil
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from pathlib import Path
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from typing import Any
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import rapidjson
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from joblib import Parallel, cpu_count, delayed, wrap_non_picklable_objects
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from joblib.externals import cloudpickle
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from rich.console import Console
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from freqtrade.constants import FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
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from freqtrade.enums import HyperoptState
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import file_dump_json, plural
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from freqtrade.optimize.hyperopt.hyperopt_optimizer import HyperOptimizer
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from freqtrade.optimize.hyperopt.hyperopt_output import HyperoptOutput
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from freqtrade.optimize.hyperopt_tools import (
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HyperoptStateContainer,
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HyperoptTools,
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hyperopt_serializer,
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)
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from freqtrade.util import get_progress_tracker
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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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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 start a hyperopt run:
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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: Config) -> None:
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self._hyper_out: HyperoptOutput = HyperoptOutput(streaming=True)
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self.config = config
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self.analyze_per_epoch = self.config.get("analyze_per_epoch", False)
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HyperoptStateContainer.set_state(HyperoptState.STARTUP)
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if self.config.get("hyperopt"):
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raise OperationalException(
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"Using separate Hyperopt files has been removed in 2021.9. Please convert "
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"your existing Hyperopt file to the new Hyperoptable strategy interface"
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)
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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 = (
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self.config["user_data_dir"]
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/ "hyperopt_results"
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/ f"strategy_{strategy}_{time_now}.fthypt"
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)
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self.data_pickle_file = (
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self.config["user_data_dir"] / "hyperopt_results" / "hyperopt_tickerdata.pkl"
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)
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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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self.current_best_epoch: dict[str, Any] | None = None
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if HyperoptTools.has_space(self.config, "sell"):
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# Make sure use_exit_signal is enabled
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self.config["use_exit_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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self.hyperopter = HyperOptimizer(self.config)
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@staticmethod
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def get_lock_filename(config: Config) -> 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 hyperopt_pickle_magic(self, bases) -> None:
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"""
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Hyperopt magic to allow strategy inheritance across files.
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For this to properly work, we need to register the module of the imported class
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to pickle as value.
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"""
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for modules in bases:
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if modules.__name__ != "IStrategy":
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cloudpickle.register_pickle_by_value(sys.modules[modules.__module__])
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self.hyperopt_pickle_magic(modules.__bases__)
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def _save_result(self, epoch: dict) -> None:
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"""
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Save hyperopt results to file
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Store one line per epoch.
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While not a valid json object - this allows appending easily.
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:param epoch: result dictionary for this epoch.
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"""
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epoch[FTHYPT_FILEVERSION] = 2
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with self.results_file.open("a") as f:
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rapidjson.dump(
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epoch,
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f,
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default=hyperopt_serializer,
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number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN,
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)
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f.write("\n")
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self.num_epochs_saved += 1
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logger.debug(
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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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)
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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)}, log=False)
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def print_results(self, results: dict[str, Any]) -> 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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self._hyper_out.add_data(
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self.config,
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[results],
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self.total_epochs,
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self.print_all,
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)
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def run_optimizer_parallel(self, parallel: Parallel, asked: list[list]) -> list[dict[str, Any]]:
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"""Start optimizer in a parallel way"""
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return parallel(
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delayed(wrap_non_picklable_objects(self.hyperopter.generate_optimizer))(v)
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for v in asked
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)
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def _set_random_state(self, random_state: int | None) -> int:
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return random_state or random.randint(1, 2**16 - 1) # noqa: S311
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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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Steps:
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1. Try to get points using `self.opt.ask` first
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2. Discard the points that have already been evaluated
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3. Retry using `self.opt.ask` up to 3 times
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4. If still some points are missing in respect to `n_points`, random sample some points
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5. Repeat until at least `n_points` points in the `asked_non_tried` list
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6. Return a list with length truncated at `n_points`
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"""
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def unique_list(a_list):
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new_list = []
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for item in a_list:
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if item not in new_list:
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new_list.append(item)
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return new_list
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i = 0
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asked_non_tried: list[list[Any]] = []
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is_random_non_tried: 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 = unique_list(self.opt.ask(n_points=n_points * 5 if i > 0 else n_points))
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is_random = [False for _ in range(len(asked))]
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else:
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asked = unique_list(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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is_random_non_tried += [
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rand
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for x, rand in zip(asked, is_random, strict=False)
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if x not in self.opt.Xi and x not in asked_non_tried
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]
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asked_non_tried += [
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x for x in asked if x not in self.opt.Xi and x not in asked_non_tried
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]
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i += 1
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if asked_non_tried:
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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_non_tried[: 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), [False for _ in range(n_points)]
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def evaluate_result(self, val: dict[str, Any], current: int, is_random: bool):
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"""
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Evaluate results returned from generate_optimizer
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"""
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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("Optimizer epoch evaluated: %s", 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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val["is_random"] = is_random
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self.print_results(val)
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if is_best:
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self.current_best_loss = val["loss"]
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self.current_best_epoch = val
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self._save_result(val)
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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"))
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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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self.hyperopter.prepare_hyperopt()
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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.hyperopter.get_optimizer(
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config_jobs, self.random_state, INITIAL_POINTS, SKOPT_MODEL_QUEUE_SIZE
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)
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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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console = Console(
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color_system="auto" if self.print_colorized else None,
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)
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# Define progressbar
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with get_progress_tracker(
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console=console,
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cust_callables=[self._hyper_out],
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) as pbar:
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task = pbar.add_task("Epochs", total=self.total_epochs)
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start = 0
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if self.analyze_per_epoch:
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# First analysis not in parallel mode when using --analyze-per-epoch.
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# This allows dataprovider to load it's informative cache.
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asked, is_random = self.get_asked_points(n_points=1)
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f_val0 = self.hyperopter.generate_optimizer(asked[0])
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self.opt.tell(asked, [f_val0["loss"]])
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self.evaluate_result(f_val0, 1, is_random[0])
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pbar.update(task, advance=1)
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start += 1
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evals = ceil((self.total_epochs - start) / 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 - start)
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current_jobs = jobs - n_rest if n_rest > 0 else 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)
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self.opt.tell(asked, [v["loss"] for v in f_val])
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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 + start
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self.evaluate_result(val, current, is_random[j])
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pbar.update(task, advance=1)
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except KeyboardInterrupt:
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print("User interrupted..")
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logger.info(
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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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)
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if self.current_best_epoch:
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HyperoptTools.try_export_params(
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self.config,
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self.hyperopter.get_strategy_name(),
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self.current_best_epoch,
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)
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HyperoptTools.show_epoch_details(
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self.current_best_epoch, self.total_epochs, self.print_json
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)
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elif self.num_epochs_saved > 0:
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print(
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f"No good result found for given optimization function in {self.num_epochs_saved} "
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f"{plural(self.num_epochs_saved, 'epoch')}."
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)
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else:
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# This is printed when Ctrl+C is pressed quickly, before first epochs have
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# a chance to be evaluated.
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print("No epochs evaluated yet, no best result.")
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