/
optimize.py
741 lines (602 loc) · 28.3 KB
/
optimize.py
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import copy
import json
import os
import pickle
import time
from typing import Any
from typing import Dict
import warnings
import lightgbm as lgb
import numpy as np
import tqdm
import optuna
from optuna.integration.lightgbm_tuner.alias import _handling_alias_metrics
from optuna.integration.lightgbm_tuner.alias import _handling_alias_parameters
from optuna import type_checking
if type_checking.TYPE_CHECKING:
from typing import Callable # NOQA
from typing import Generator # NOQA
from typing import List # NOQA
from typing import Optional # NOQA
from typing import Tuple # NOQA
from typing import Union # NOQA
from optuna.trial import FrozenTrial # NOQA
from optuna.study import Study # NOQA
from optuna.trial import Trial # NOQA
VALID_SET_TYPE = Union[List[lgb.Dataset], Tuple[lgb.Dataset, ...], lgb.Dataset]
# Define key names of `Trial.system_attrs`.
_ELAPSED_SECS_KEY = "lightgbm_tuner:elapsed_secs"
_AVERAGE_ITERATION_TIME_KEY = "lightgbm_tuner:average_iteration_time"
_STEP_NAME_KEY = "lightgbm_tuner:step_name"
_LGBM_PARAMS_KEY = "lightgbm_tuner:lgbm_params"
# EPS is used to ensure that a sampled parameter value is in pre-defined value range.
EPS = 1e-12
# Default value of tree_depth, used for upper bound of num_leaves.
DEFAULT_TUNER_TREE_DEPTH = 8
# Default parameter values described in the official webpage.
DEFAULT_LIGHTGBM_PARAMETERS = {
"lambda_l1": 0.0,
"lambda_l2": 0.0,
"num_leaves": 31,
"feature_fraction": 1.0,
"bagging_fraction": 1.0,
"bagging_freq": 0,
"min_child_samples": 20,
}
_logger = optuna.logging.get_logger(__name__)
class BaseTuner(object):
def __init__(self, lgbm_params=None, lgbm_kwargs=None):
# type: (Dict[str, Any], Dict[str,Any]) -> None
# Handling alias metrics.
if lgbm_params is not None:
_handling_alias_metrics(lgbm_params)
self.lgbm_params = lgbm_params or {}
self.lgbm_kwargs = lgbm_kwargs or {}
def _get_booster_best_score(self, booster):
# type: (lgb.Booster) -> float
metric = self.lgbm_params.get("metric", "binary_logloss")
# todo (smly): This implementation is different logic from the LightGBM's python bindings.
if type(metric) is str:
pass
elif type(metric) is list:
metric = metric[-1]
elif type(metric) is set:
metric = list(metric)[-1]
else:
raise NotImplementedError
valid_sets = self.lgbm_kwargs.get("valid_sets") # type: Optional[VALID_SET_TYPE]
if self.lgbm_kwargs.get("valid_names") is not None:
if type(self.lgbm_kwargs["valid_names"]) is str:
valid_name = self.lgbm_kwargs["valid_names"]
elif type(self.lgbm_kwargs["valid_names"]) in [list, tuple]:
valid_name = self.lgbm_kwargs["valid_names"][-1]
else:
raise NotImplementedError
elif type(valid_sets) is lgb.Dataset:
valid_name = "valid_0"
elif isinstance(valid_sets, (list, tuple)) and len(valid_sets) > 0:
valid_set_idx = len(valid_sets) - 1
valid_name = "valid_{}".format(valid_set_idx)
else:
raise NotImplementedError
metric = self._metric_with_eval_at(metric)
val_score = booster.best_score[valid_name][metric]
return val_score
def _metric_with_eval_at(self, metric):
# type: (str) -> str
if metric != "ndcg" and metric != "map":
return metric
eval_at = self.lgbm_params.get("eval_at")
if eval_at is None:
eval_at = self.lgbm_params.get("{}_at".format(metric))
if eval_at is None:
eval_at = self.lgbm_params.get("{}_eval_at".format(metric))
if eval_at is None:
# Set default value of LightGBM.
# See https://lightgbm.readthedocs.io/en/latest/Parameters.html#eval_at.
eval_at = [1, 2, 3, 4, 5]
# Optuna can handle only a single metric. Choose first one.
if type(eval_at) in [list, tuple]:
return "{}@{}".format(metric, eval_at[0])
if type(eval_at) is int:
return "{}@{}".format(metric, eval_at)
raise ValueError(
"The value of eval_at is expected to be int or a list/tuple of int."
"'{}' is specified.".format(eval_at)
)
def higher_is_better(self):
# type: () -> bool
metric_name = self.lgbm_params.get("metric", "binary_logloss")
return metric_name.startswith(("auc", "ndcg", "map"))
def compare_validation_metrics(self, val_score, best_score):
# type: (float, float) -> bool
if self.higher_is_better():
return val_score > best_score
else:
return val_score < best_score
class OptunaObjective(BaseTuner):
"""Objective for hyperparameter-tuning with Optuna."""
def __init__(
self,
target_param_names, # type: List[str]
lgbm_params, # type: Dict[str, Any]
train_set, # type: lgb.Dataset
lgbm_kwargs, # type: Dict[str, Any]
best_score, # type: float
step_name, # type: str
model_dir, # type: Optional[str]
pbar=None, # type: Optional[tqdm.tqdm]
):
self.target_param_names = target_param_names
self.pbar = pbar
self.lgbm_params = lgbm_params
self.lgbm_kwargs = lgbm_kwargs
self.train_set = train_set
self.report = [] # type: List[Dict[str, Any]]
self.trial_count = 0
self.best_score = best_score
self.best_booster_with_trial_number = None # type: Optional[Tuple[lgb.Booster, int]]
self.step_name = step_name
self.model_dir = model_dir
self._check_target_names_supported()
def _check_target_names_supported(self):
# type: () -> None
supported_param_names = [
"lambda_l1",
"lambda_l2",
"num_leaves",
"feature_fraction",
"bagging_fraction",
"bagging_freq",
"min_child_samples",
]
for target_param_name in self.target_param_names:
if target_param_name not in supported_param_names:
raise NotImplementedError("Parameter `{}` is not supported for tunning.")
def __call__(self, trial):
# type: (Trial) -> float
pbar_fmt = "{}, val_score: {:.6f}"
if self.pbar is not None:
self.pbar.set_description(pbar_fmt.format(self.step_name, self.best_score))
if "lambda_l1" in self.target_param_names:
self.lgbm_params["lambda_l1"] = trial.suggest_loguniform("lambda_l1", 1e-8, 10.0)
if "lambda_l2" in self.target_param_names:
self.lgbm_params["lambda_l2"] = trial.suggest_loguniform("lambda_l2", 1e-8, 10.0)
if "num_leaves" in self.target_param_names:
tree_depth = self.lgbm_params.get("max_depth", DEFAULT_TUNER_TREE_DEPTH)
max_num_leaves = 2 ** tree_depth if tree_depth > 0 else 2 ** DEFAULT_TUNER_TREE_DEPTH
self.lgbm_params["num_leaves"] = trial.suggest_int("num_leaves", 2, max_num_leaves)
if "feature_fraction" in self.target_param_names:
# `GridSampler` is used for sampling feature_fraction value.
# The value 1.0 for the hyperparameter is always sampled.
param_value = min(trial.suggest_uniform("feature_fraction", 0.4, 1.0 + EPS), 1.0)
self.lgbm_params["feature_fraction"] = param_value
if "bagging_fraction" in self.target_param_names:
# `TPESampler` is used for sampling bagging_fraction value.
# The value 1.0 for the hyperparameter might by sampled.
param_value = min(trial.suggest_uniform("bagging_fraction", 0.4, 1.0 + EPS), 1.0)
self.lgbm_params["bagging_fraction"] = param_value
if "bagging_freq" in self.target_param_names:
self.lgbm_params["bagging_freq"] = trial.suggest_int("bagging_freq", 1, 7)
if "min_child_samples" in self.target_param_names:
# `GridSampler` is used for sampling min_child_samples value.
# The value 1.0 for the hyperparameter is always sampled.
param_value = int(trial.suggest_uniform("min_child_samples", 5, 100 + EPS))
self.lgbm_params["min_child_samples"] = param_value
start_time = time.time()
booster = lgb.train(self.lgbm_params, self.train_set, **self.lgbm_kwargs)
val_score = self._get_booster_best_score(booster)
elapsed_secs = time.time() - start_time
average_iteration_time = elapsed_secs / booster.current_iteration()
if self.model_dir is not None:
path = os.path.join(self.model_dir, "{}.pkl".format(trial.number))
with open(path, "wb") as fout:
pickle.dump(booster, fout)
_logger.info("The booster of trial#{} was saved as {}.".format(trial.number, path))
if self.compare_validation_metrics(val_score, self.best_score):
self.best_score = val_score
self.best_booster_with_trial_number = (booster, trial.number)
if self.pbar is not None:
self.pbar.set_description(pbar_fmt.format(self.step_name, self.best_score))
self.pbar.update(1)
self.report.append(
dict(
# Since v1.2.0, action was concatenation of parameter names. Currently, it is
# explicitly given to distinguish steps which tune the same parameters.
action=self.step_name,
trial=self.trial_count,
value=str(trial.params),
val_score=val_score,
elapsed_secs=elapsed_secs,
average_iteration_time=average_iteration_time,
)
)
trial.set_system_attr(_ELAPSED_SECS_KEY, elapsed_secs)
trial.set_system_attr(_AVERAGE_ITERATION_TIME_KEY, average_iteration_time)
trial.set_system_attr(_STEP_NAME_KEY, self.step_name)
trial.set_system_attr(_LGBM_PARAMS_KEY, json.dumps(self.lgbm_params))
self.trial_count += 1
return val_score
class LightGBMTuner(BaseTuner):
"""Hyperparameter-tuning with Optuna for LightGBM.
Arguments and keyword arguments for `lightgbm.train()`_ can be passed.
The arguments that only :class:`~optuna.integration.lightgbm.LightGBMTuner` has are listed
below:
Args:
time_budget:
A time budget for parameter tuning in seconds.
best_params:
A dictionary to store the best parameters.
.. deprecated:: 1.4.0
Please use the ``params`` attribute of the best booster, which is obtained by
:meth:`~optuna.integration.lightgbm.LightGBMTuner.get_best_booster`.
tuning_history:
A List to store the history of parameter tuning.
.. deprecated:: 1.4.0
Please use the ``study`` argument to access optimization history.
study:
A :class:`~optuna.study.Study` instance to store optimization results. The
:class:`~optuna.trial.Trial` instances in it has the following system attributes:
``elapsed_secs`` is the elapsed time since the optimization starts.
``average_iteration_time`` is the average time of iteration to train the booster
model in the trial. ``lgbm_params`` is a JSON-serialized dictionary of LightGBM
parameters used in the trial.
optuna_callbacks:
List of Optuna callback functions that are invoked at the end of each trial.
Each function must accept two parameters with the following types in this order:
:class:`~optuna.study.Study` and :class:`~optuna.FrozenTrial`.
Please note that this is not a ``callbacks`` argument of `lightgbm.train()`_ .
model_dir:
A directory to save boosters. By default, it is set to :obj:`None` and no boosters are
saved. Please set shared directory (e.g., directories on NFS) if you want to access
:meth:`~optuna.integration.LightGBMTuner.get_best_booster` in distributed environments.
Otherwise, it may raise :obj:`ValueError`. If the directory does not exist, it will be
created. The filenames of the boosters will be ``{model_dir}/{trial_number}.pkl``
(e.g., ``./boosters/0.pkl``).
.. _lightgbm.train(): https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html
"""
def __init__(
self,
params, # type: Dict[str, Any]
train_set, # type: lgb.Dataset
num_boost_round=1000, # type: int
valid_sets=None, # type: Optional[VALID_SET_TYPE]
valid_names=None, # type: Optional[Any]
fobj=None, # type: Optional[Callable[..., Any]]
feval=None, # type: Optional[Callable[..., Any]]
feature_name="auto", # type: str
categorical_feature="auto", # type: str
early_stopping_rounds=None, # type: Optional[int]
evals_result=None, # type: Optional[Dict[Any, Any]]
verbose_eval=True, # type: Optional[bool]
learning_rates=None, # type: Optional[List[float]]
keep_training_booster=False, # type: Optional[bool]
callbacks=None, # type: Optional[List[Callable[..., Any]]]
time_budget=None, # type: Optional[int]
sample_size=None, # type: Optional[int]
best_params=None, # type: Optional[Dict[str, Any]]
tuning_history=None, # type: Optional[List[Dict[str, Any]]]
study=None, # type: Optional[Study]
optuna_callbacks=None, # type: Optional[List[Callable[[Study, FrozenTrial], None]]]
model_dir=None, # type: Optional[str]
verbosity=1, # type: Optional[int]
):
# type: (...) -> None
params = copy.deepcopy(params)
# Handling alias metrics.
_handling_alias_metrics(params)
args = [params, train_set]
kwargs = dict(
num_boost_round=num_boost_round,
valid_sets=valid_sets,
valid_names=valid_names,
fobj=fobj,
feval=feval,
feature_name=feature_name,
categorical_feature=categorical_feature,
early_stopping_rounds=early_stopping_rounds,
evals_result=evals_result,
verbose_eval=verbose_eval,
learning_rates=learning_rates,
keep_training_booster=keep_training_booster,
callbacks=callbacks,
time_budget=time_budget,
verbosity=verbosity,
sample_size=sample_size,
) # type: Dict[str, Any]
self._parse_args(*args, **kwargs)
self._best_booster_with_trial_number = None # type: Optional[Tuple[lgb.Booster, int]]
self._start_time = None # type: Optional[float]
self._model_dir = model_dir
if self._model_dir is not None and not os.path.exists(self._model_dir):
os.mkdir(self._model_dir)
if best_params is not None:
warnings.warn(
"The `best_params` argument is deprecated. "
"Please get the parameter values via `lightgbm.basic.Booster.params`.",
DeprecationWarning,
)
if tuning_history is not None:
warnings.warn(
"The `tuning_history` argument is deprecated. "
"Please use the `study` argument to access optimization history.",
DeprecationWarning,
)
self._best_params = {} if best_params is None else best_params
self.tuning_history = [] if tuning_history is None else tuning_history
# Set default parameters as best.
self._best_params.update(DEFAULT_LIGHTGBM_PARAMETERS)
if study is None:
self.study = optuna.create_study(
direction="maximize" if self.higher_is_better() else "minimize"
)
else:
self.study = study
if self.higher_is_better():
if self.study.direction != optuna.study.StudyDirection.MAXIMIZE:
metric_name = self.lgbm_params.get("metric", "binary_logloss")
raise ValueError(
"Study direction is inconsistent with the metric {}. "
"Please set 'maximize' as the direction.".format(metric_name)
)
else:
if self.study.direction != optuna.study.StudyDirection.MINIMIZE:
metric_name = self.lgbm_params.get("metric", "binary_logloss")
raise ValueError(
"Study direction is inconsistent with the metric {}. "
"Please set 'minimize' as the direction.".format(metric_name)
)
if valid_sets is None:
raise ValueError("`valid_sets` is required.")
self._optuna_callbacks = optuna_callbacks
@property
def best_score(self) -> float:
""""Return the score of the best booster."""
try:
return self.study.best_value
except ValueError:
# Return the default score because no trials have completed.
return -np.inf if self.higher_is_better() else np.inf
@property
def best_params(self) -> Dict[str, Any]:
"""Return parameters of the best booster."""
try:
return json.loads(self.study.best_trial.system_attrs[_LGBM_PARAMS_KEY])
except ValueError:
# Return the default score because no trials have completed.
params = copy.deepcopy(DEFAULT_LIGHTGBM_PARAMETERS)
# self.lgbm_params may contain parameters given by users.
params.update(self.lgbm_params)
return params
@property
def best_booster(self) -> lgb.Booster:
"""Return the best booster.
.. deprecated:: 1.4.0
Please get the best booster via
:class:`~optuna.integration.lightgbm.LightGBMTuner.get_best_booster` instead.
"""
warnings.warn(
"The `best_booster` attribute is deprecated. Please use `get_best_booster` instead.",
DeprecationWarning,
)
return self.get_best_booster()
def get_best_booster(self) -> lgb.Booster:
"""Return the best booster.
If the best booster cannot be found, :class:`ValueError` will be raised. To prevent the
errors, please save boosters by specifying the ``model_dir`` arguments of
:meth:`~optuna.integration.lightgbm.LightGBMTuner.__init__` when you resume tuning
or you run tuning in parallel.
"""
if self._best_booster_with_trial_number is not None:
if self._best_booster_with_trial_number[1] == self.study.best_trial.number:
return self._best_booster_with_trial_number[0]
if len(self.study.trials) == 0:
raise ValueError("The best booster is not available because no trials completed.")
# The best booster exists, but this instance does not have it.
# This may be due to resuming or parallelization.
if self._model_dir is None:
raise ValueError(
"The best booster cannot be found. It may be found in the other processes due to "
"resuming or distributed computing. Please set the `model_dir` argument of "
"`LightGBMTuner.__init__` and make sure that boosters are shared with all "
"processes."
)
best_trial = self.study.best_trial
path = os.path.join(self._model_dir, "{}.pkl".format(best_trial.number))
if not os.path.exists(path):
raise ValueError(
"The best booster cannot be found in {}. If you execute `LightGBMTuner` in "
"distributed environment, please use network file system (e.g., NFS) to share "
"models with multiple workers.".format(self._model_dir)
)
with open(path, "rb") as fin:
booster = pickle.load(fin)
return booster
def _get_params(self):
# type: () -> Dict[str, Any]
params = copy.deepcopy(self.lgbm_params)
params.update(self.best_params)
return params
def _parse_args(self, *args, **kwargs):
# type: (Any, Any) -> None
self.auto_options = {
option_name: kwargs.get(option_name)
for option_name in [
"time_budget",
"sample_size",
"best_params",
"tuning_history",
"verbosity",
]
}
# Split options.
for option_name in self.auto_options.keys():
if option_name in kwargs:
del kwargs[option_name]
self.lgbm_params = args[0]
self.train_set = args[1]
self.train_subset = None # Use for sampling.
self.lgbm_kwargs = kwargs
def run(self) -> None:
"""Perform the hyperparameter-tuning with given parameters."""
# Suppress log messages.
if self.auto_options["verbosity"] == 0:
optuna.logging.disable_default_handler()
self.lgbm_params["verbose"] = -1
self.lgbm_params["seed"] = 111
self.lgbm_kwargs["verbose_eval"] = False
# Handling aliases.
_handling_alias_parameters(self.lgbm_params)
# Sampling.
self.sample_train_set()
self.tune_feature_fraction()
self.tune_num_leaves()
self.tune_bagging()
self.tune_feature_fraction_stage2()
self.tune_regularization_factors()
self.tune_min_data_in_leaf()
def sample_train_set(self):
# type: () -> None
"""Make subset of `self.train_set` Dataset object."""
if self.auto_options["sample_size"] is None:
return
self.train_set.construct()
n_train_instance = self.train_set.get_label().shape[0]
if n_train_instance > self.auto_options["sample_size"]:
offset = n_train_instance - self.auto_options["sample_size"]
idx_list = offset + np.arange(self.auto_options["sample_size"])
self.train_subset = self.train_set.subset(idx_list)
def tune_feature_fraction(self, n_trials=7):
# type: (int) -> None
param_name = "feature_fraction"
param_values = np.linspace(0.4, 1.0, n_trials).tolist()
# TODO(toshihikoyanase): Remove catch_warnings after GridSampler becomes non-experimental.
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=optuna.exceptions.ExperimentalWarning)
sampler = optuna.samplers.GridSampler({param_name: param_values})
self.tune_params([param_name], len(param_values), sampler, "feature_fraction")
def tune_num_leaves(self, n_trials=20):
# type: (int) -> None
self.tune_params(["num_leaves"], n_trials, optuna.samplers.TPESampler(), "num_leaves")
def tune_bagging(self, n_trials=10):
# type: (int) -> None
self.tune_params(
["bagging_fraction", "bagging_freq"], n_trials, optuna.samplers.TPESampler(), "bagging"
)
def tune_feature_fraction_stage2(self, n_trials=6):
# type: (int) -> None
param_name = "feature_fraction"
best_feature_fraction = self.best_params[param_name]
param_values = np.linspace(
best_feature_fraction - 0.08, best_feature_fraction + 0.08, n_trials
).tolist()
param_values = [val for val in param_values if val >= 0.4 and val <= 1.0]
# TODO(toshihikoyanase): Remove catch_warnings after GridSampler becomes non-experimental.
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=optuna.exceptions.ExperimentalWarning)
sampler = optuna.samplers.GridSampler({param_name: param_values})
self.tune_params([param_name], len(param_values), sampler, "feature_fraction_stage2")
def tune_regularization_factors(self, n_trials=20):
# type: (int) -> None
self.tune_params(
["lambda_l1", "lambda_l2"],
n_trials,
optuna.samplers.TPESampler(),
"regularization_factors",
)
def tune_min_data_in_leaf(self):
# type: () -> None
param_name = "min_child_samples"
param_values = [5, 10, 25, 50, 100]
# TODO(toshihikoyanase): Remove catch_warnings after GridSampler becomes non-experimental.
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=optuna.exceptions.ExperimentalWarning)
sampler = optuna.samplers.GridSampler({param_name: param_values})
self.tune_params([param_name], len(param_values), sampler, "min_data_in_leaf")
def tune_params(self, target_param_names, n_trials, sampler, step_name):
# type: (List[str], int, optuna.samplers.BaseSampler, str) -> None
pbar = tqdm.tqdm(total=n_trials, ascii=True)
# Set current best parameters.
self.lgbm_params.update(self.best_params)
train_set = self.train_set
if self.train_subset is not None:
train_set = self.train_subset
objective = OptunaObjective(
target_param_names,
self.lgbm_params,
train_set,
self.lgbm_kwargs,
self.best_score,
step_name=step_name,
model_dir=self._model_dir,
pbar=pbar,
)
study = self._create_stepwise_study(self.study, step_name)
study.sampler = sampler
complete_trials = [
t
for t in study.trials
if t.state in (optuna.trial.TrialState.COMPLETE, optuna.trial.TrialState.PRUNED)
]
_n_trials = n_trials - len(complete_trials)
if self._start_time is None:
self._start_time = time.time()
if self.auto_options["time_budget"] is not None:
_timeout = self.auto_options["time_budget"] - (time.time() - self._start_time)
else:
_timeout = None
if _n_trials > 0:
try:
study.optimize(
objective,
n_trials=_n_trials,
timeout=_timeout,
catch=(),
callbacks=self._optuna_callbacks,
)
except ValueError:
# ValueError is raised by GridSampler when all combinations were examined.
# TODO(toshihikoyanase): Remove this try-except after Study.stop is implemented.
pass
pbar.close()
del pbar
# Add tuning history.
self.tuning_history += objective.report
if objective.best_booster_with_trial_number is not None:
self._best_booster_with_trial_number = objective.best_booster_with_trial_number
self._best_params.update(self.best_params)
def _create_stepwise_study(
self, study: "optuna.study.Study", step_name: str
) -> "optuna.study.Study":
# This class is assumed to be passed to a sampler and a pruner corresponding to the step.
class _StepwiseStudy(optuna.study.Study):
def __init__(self, study, step_name):
# type: (optuna.study.Study, str) -> None
super().__init__(
study_name=study.study_name,
storage=study._storage,
sampler=study.sampler,
pruner=study.pruner,
)
self._step_name = step_name
def get_trials(self, deepcopy=True):
# type: (bool) -> List[optuna.trial.FrozenTrial]
trials = super().get_trials(deepcopy=deepcopy)
return [t for t in trials if t.system_attrs.get(_STEP_NAME_KEY) == self._step_name]
@property
def best_trial(self):
# type: () -> optuna.trial.FrozenTrial
"""Return the best trial in the study.
Returns:
A :class:`~optuna.trial.FrozenTrial` object of the best trial.
"""
trials = self.get_trials(deepcopy=False)
trials = [t for t in trials if t.state is optuna.trial.TrialState.COMPLETE]
if len(trials) == 0:
raise ValueError("No trials are completed yet.")
if self.direction == optuna.study.StudyDirection.MINIMIZE:
best_trial = min(trials, key=lambda t: t.value)
else:
best_trial = max(trials, key=lambda t: t.value)
return copy.deepcopy(best_trial)
return _StepwiseStudy(study, step_name)