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Unverified Commit dd31208a authored by James Lamb's avatar James Lamb Committed by GitHub
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[ci] [python-package] enable ruff-format on all Python code (#6336)

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python-package/lightgbm/__init__.py
View file @ dd31208a
......@@ -23,14 +23,33 @@ except ImportError:
pass
_version_path = Path(__file__).absolute().parent / 'VERSION.txt'
_version_path = Path(__file__).absolute().parent / "VERSION.txt"
if _version_path.is_file():
__version__ = _version_path.read_text(encoding='utf-8').strip()
__version__ = _version_path.read_text(encoding="utf-8").strip()
__all__ = ['Dataset', 'Booster', 'CVBooster', 'Sequence',
'register_logger',
'train', 'cv',
'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker',
'DaskLGBMRegressor', 'DaskLGBMClassifier', 'DaskLGBMRanker',
'log_evaluation', 'record_evaluation', 'reset_parameter', 'early_stopping', 'EarlyStopException',
'plot_importance', 'plot_split_value_histogram', 'plot_metric', 'plot_tree', 'create_tree_digraph']
__all__ = [
"Dataset",
"Booster",
"CVBooster",
"Sequence",
"register_logger",
"train",
"cv",
"LGBMModel",
"LGBMRegressor",
"LGBMClassifier",
"LGBMRanker",
"DaskLGBMRegressor",
"DaskLGBMClassifier",
"DaskLGBMRanker",
"log_evaluation",
"record_evaluation",
"reset_parameter",
"early_stopping",
"EarlyStopException",
"plot_importance",
"plot_split_value_histogram",
"plot_metric",
"plot_tree",
"create_tree_digraph",
]
python-package/lightgbm/basic.py
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python-package/lightgbm/callback.py
View file @ dd31208a
......@@ -18,21 +18,21 @@ if TYPE_CHECKING:
from .engine import CVBooster
__all__ = [
'EarlyStopException',
'early_stopping',
'log_evaluation',
'record_evaluation',
'reset_parameter',
"EarlyStopException",
"early_stopping",
"log_evaluation",
"record_evaluation",
"reset_parameter",
]
_EvalResultDict = Dict[str, Dict[str, List[Any]]]
_EvalResultTuple = Union[
_LGBM_BoosterEvalMethodResultType,
_LGBM_BoosterEvalMethodResultWithStandardDeviationType
_LGBM_BoosterEvalMethodResultWithStandardDeviationType,
]
_ListOfEvalResultTuples = Union[
List[_LGBM_BoosterEvalMethodResultType],
List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]
List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType],
]
......@@ -95,8 +95,8 @@ class _LogEvaluationCallback:
def __call__(self, env: CallbackEnv) -> None:
if self.period > 0 and env.evaluation_result_list and (env.iteration + 1) % self.period == 0:
result = '\t'.join([_format_eval_result(x, self.show_stdv) for x in env.evaluation_result_list])
_log_info(f'[{env.iteration + 1}]\t{result}')
result = "\t".join([_format_eval_result(x, self.show_stdv) for x in env.evaluation_result_list])
_log_info(f"[{env.iteration + 1}]\t{result}")
def log_evaluation(period: int = 1, show_stdv: bool = True) -> _LogEvaluationCallback:
......@@ -133,7 +133,7 @@ class _RecordEvaluationCallback:
self.before_iteration = False
if not isinstance(eval_result, dict):
raise TypeError('eval_result should be a dictionary')
raise TypeError("eval_result should be a dictionary")
self.eval_result = eval_result
def _init(self, env: CallbackEnv) -> None:
......@@ -152,8 +152,8 @@ class _RecordEvaluationCallback:
if len(item) == 4:
self.eval_result[data_name].setdefault(eval_name, [])
else:
self.eval_result[data_name].setdefault(f'{eval_name}-mean', [])
self.eval_result[data_name].setdefault(f'{eval_name}-stdv', [])
self.eval_result[data_name].setdefault(f"{eval_name}-mean", [])
self.eval_result[data_name].setdefault(f"{eval_name}-stdv", [])
def __call__(self, env: CallbackEnv) -> None:
if env.iteration == env.begin_iteration:
......@@ -171,8 +171,8 @@ class _RecordEvaluationCallback:
data_name, eval_name = item[1].split()
res_mean = item[2]
res_stdv = item[4] # type: ignore[misc]
self.eval_result[data_name][f'{eval_name}-mean'].append(res_mean)
self.eval_result[data_name][f'{eval_name}-stdv'].append(res_stdv)
self.eval_result[data_name][f"{eval_name}-mean"].append(res_mean)
self.eval_result[data_name][f"{eval_name}-stdv"].append(res_stdv)
def record_evaluation(eval_result: Dict[str, Dict[str, List[Any]]]) -> Callable:
......@@ -230,8 +230,10 @@ class _ResetParameterCallback:
elif callable(value):
new_param = value(env.iteration - env.begin_iteration)
else:
raise ValueError("Only list and callable values are supported "
"as a mapping from boosting round index to new parameter value.")
raise ValueError(
"Only list and callable values are supported "
"as a mapping from boosting round index to new parameter value."
)
if new_param != env.params.get(key, None):
new_parameters[key] = new_param
if new_parameters:
......@@ -276,9 +278,8 @@ class _EarlyStoppingCallback:
stopping_rounds: int,
first_metric_only: bool = False,
verbose: bool = True,
min_delta: Union[float, List[float]] = 0.0
min_delta: Union[float, List[float]] = 0.0,
) -> None:
if not isinstance(stopping_rounds, int) or stopping_rounds <= 0:
raise ValueError(f"stopping_rounds should be an integer and greater than 0. got: {stopping_rounds}")
......@@ -298,7 +299,7 @@ class _EarlyStoppingCallback:
self.best_iter: List[int] = []
self.best_score_list: List[_ListOfEvalResultTuples] = []
self.cmp_op: List[Callable[[float, float], bool]] = []
self.first_metric = ''
self.first_metric = ""
def _gt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
return curr_score > best_score + delta
......@@ -321,29 +322,24 @@ class _EarlyStoppingCallback:
def _init(self, env: CallbackEnv) -> None:
if env.evaluation_result_list is None or env.evaluation_result_list == []:
raise ValueError(
"For early stopping, at least one dataset and eval metric is required for evaluation"
)
raise ValueError("For early stopping, at least one dataset and eval metric is required for evaluation")
is_dart = any(env.params.get(alias, "") == 'dart' for alias in _ConfigAliases.get("boosting"))
is_dart = any(env.params.get(alias, "") == "dart" for alias in _ConfigAliases.get("boosting"))
if is_dart:
self.enabled = False
_log_warning('Early stopping is not available in dart mode')
_log_warning("Early stopping is not available in dart mode")
return
# validation sets are guaranteed to not be identical to the training data in cv()
if isinstance(env.model, Booster):
only_train_set = (
len(env.evaluation_result_list) == 1
and self._is_train_set(
only_train_set = len(env.evaluation_result_list) == 1 and self._is_train_set(
ds_name=env.evaluation_result_list[0][0],
eval_name=env.evaluation_result_list[0][1].split(" ")[0],
env=env
)
env=env,
)
if only_train_set:
self.enabled = False
_log_warning('Only training set found, disabling early stopping.')
_log_warning("Only training set found, disabling early stopping.")
return
if self.verbose:
......@@ -355,26 +351,26 @@ class _EarlyStoppingCallback:
n_datasets = len(env.evaluation_result_list) // n_metrics
if isinstance(self.min_delta, list):
if not all(t >= 0 for t in self.min_delta):
raise ValueError('Values for early stopping min_delta must be non-negative.')
raise ValueError("Values for early stopping min_delta must be non-negative.")
if len(self.min_delta) == 0:
if self.verbose:
_log_info('Disabling min_delta for early stopping.')
_log_info("Disabling min_delta for early stopping.")
deltas = [0.0] * n_datasets * n_metrics
elif len(self.min_delta) == 1:
if self.verbose:
_log_info(f'Using {self.min_delta[0]} as min_delta for all metrics.')
_log_info(f"Using {self.min_delta[0]} as min_delta for all metrics.")
deltas = self.min_delta * n_datasets * n_metrics
else:
if len(self.min_delta) != n_metrics:
raise ValueError('Must provide a single value for min_delta or as many as metrics.')
raise ValueError("Must provide a single value for min_delta or as many as metrics.")
if self.first_metric_only and self.verbose:
_log_info(f'Using only {self.min_delta[0]} as early stopping min_delta.')
_log_info(f"Using only {self.min_delta[0]} as early stopping min_delta.")
deltas = self.min_delta * n_datasets
else:
if self.min_delta < 0:
raise ValueError('Early stopping min_delta must be non-negative.')
raise ValueError("Early stopping min_delta must be non-negative.")
if self.min_delta > 0 and n_metrics > 1 and not self.first_metric_only and self.verbose:
_log_info(f'Using {self.min_delta} as min_delta for all metrics.')
_log_info(f"Using {self.min_delta} as min_delta for all metrics.")
deltas = [self.min_delta] * n_datasets * n_metrics
# split is needed for "<dataset type> <metric>" case (e.g. "train l1")
......@@ -382,18 +378,19 @@ class _EarlyStoppingCallback:
for eval_ret, delta in zip(env.evaluation_result_list, deltas):
self.best_iter.append(0)
if eval_ret[3]: # greater is better
self.best_score.append(float('-inf'))
self.best_score.append(float("-inf"))
self.cmp_op.append(partial(self._gt_delta, delta=delta))
else:
self.best_score.append(float('inf'))
self.best_score.append(float("inf"))
self.cmp_op.append(partial(self._lt_delta, delta=delta))
def _final_iteration_check(self, env: CallbackEnv, eval_name_splitted: List[str], i: int) -> None:
if env.iteration == env.end_iteration - 1:
if self.verbose:
best_score_str = '\t'.join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]])
_log_info('Did not meet early stopping. '
f'Best iteration is:\n[{self.best_iter[i] + 1}]\t{best_score_str}')
best_score_str = "\t".join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]])
_log_info(
"Did not meet early stopping. " f"Best iteration is:\n[{self.best_iter[i] + 1}]\t{best_score_str}"
)
if self.first_metric_only:
_log_info(f"Evaluated only: {eval_name_splitted[-1]}")
raise EarlyStopException(self.best_iter[i], self.best_score_list[i])
......@@ -409,7 +406,7 @@ class _EarlyStoppingCallback:
"Please report it at https://github.com/microsoft/LightGBM/issues"
)
# self.best_score_list is initialized to an empty list
first_time_updating_best_score_list = (self.best_score_list == [])
first_time_updating_best_score_list = self.best_score_list == []
for i in range(len(env.evaluation_result_list)):
score = env.evaluation_result_list[i][2]
if first_time_updating_best_score_list or self.cmp_op[i](score, self.best_score[i]):
......@@ -426,12 +423,14 @@ class _EarlyStoppingCallback:
if self._is_train_set(
ds_name=env.evaluation_result_list[i][0],
eval_name=eval_name_splitted[0],
env=env
env=env,
):
continue # train data for lgb.cv or sklearn wrapper (underlying lgb.train)
elif env.iteration - self.best_iter[i] >= self.stopping_rounds:
if self.verbose:
eval_result_str = '\t'.join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]])
eval_result_str = "\t".join(
[_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]]
)
_log_info(f"Early stopping, best iteration is:\n[{self.best_iter[i] + 1}]\t{eval_result_str}")
if self.first_metric_only:
_log_info(f"Evaluated only: {eval_name_splitted[-1]}")
......@@ -439,7 +438,12 @@ class _EarlyStoppingCallback:
self._final_iteration_check(env, eval_name_splitted, i)
def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbose: bool = True, min_delta: Union[float, List[float]] = 0.0) -> _EarlyStoppingCallback:
def early_stopping(
stopping_rounds: int,
first_metric_only: bool = False,
verbose: bool = True,
min_delta: Union[float, List[float]] = 0.0,
) -> _EarlyStoppingCallback:
"""Create a callback that activates early stopping.
Activates early stopping.
......@@ -473,4 +477,9 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
callback : _EarlyStoppingCallback
The callback that activates early stopping.
"""
return _EarlyStoppingCallback(stopping_rounds=stopping_rounds, first_metric_only=first_metric_only, verbose=verbose, min_delta=min_delta)
return _EarlyStoppingCallback(
stopping_rounds=stopping_rounds,
first_metric_only=first_metric_only,
verbose=verbose,
min_delta=min_delta,
)
python-package/lightgbm/compat.py
View file @ dd31208a
......@@ -8,6 +8,7 @@ try:
from pandas import DataFrame as pd_DataFrame
from pandas import Series as pd_Series
from pandas import concat
try:
from pandas import CategoricalDtype as pd_CategoricalDtype
except ImportError:
......@@ -40,15 +41,18 @@ except ImportError:
try:
from numpy.random import Generator as np_random_Generator
except ImportError:
class np_random_Generator: # type: ignore
"""Dummy class for np.random.Generator."""
def __init__(self, *args, **kwargs):
pass
"""matplotlib"""
try:
import matplotlib # noqa: F401
MATPLOTLIB_INSTALLED = True
except ImportError:
MATPLOTLIB_INSTALLED = False
......@@ -56,6 +60,7 @@ except ImportError:
"""graphviz"""
try:
import graphviz # noqa: F401
GRAPHVIZ_INSTALLED = True
except ImportError:
GRAPHVIZ_INSTALLED = False
......@@ -63,6 +68,7 @@ except ImportError:
"""datatable"""
try:
import datatable
if hasattr(datatable, "Frame"):
dt_DataTable = datatable.Frame
else:
......@@ -85,6 +91,7 @@ try:
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import assert_all_finite, check_array, check_X_y
try:
from sklearn.exceptions import NotFittedError
from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold
......@@ -155,6 +162,7 @@ try:
from dask.dataframe import DataFrame as dask_DataFrame
from dask.dataframe import Series as dask_Series
from dask.distributed import Client, Future, default_client, wait
DASK_INSTALLED = True
except ImportError:
DASK_INSTALLED = False
......@@ -195,6 +203,7 @@ except ImportError:
def __init__(self, *args, **kwargs):
pass
"""pyarrow"""
try:
import pyarrow.compute as pa_compute
......@@ -205,6 +214,7 @@ try:
from pyarrow.cffi import ffi as arrow_cffi
from pyarrow.types import is_floating as arrow_is_floating
from pyarrow.types import is_integer as arrow_is_integer
PYARROW_INSTALLED = True
except ImportError:
PYARROW_INSTALLED = False
......@@ -266,4 +276,5 @@ except ImportError:
def _LGBMCpuCount(only_physical_cores: bool = True) -> int:
return cpu_count()
__all__: List[str] = []
python-package/lightgbm/dask.py
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python-package/lightgbm/engine.py
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python-package/lightgbm/libpath.py
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......@@ -16,17 +16,19 @@ def find_lib_path() -> List[str]:
List of all found library paths to LightGBM.
"""
curr_path = Path(__file__).absolute()
dll_path = [curr_path.parents[1],
curr_path.parents[0] / 'bin',
curr_path.parents[0] / 'lib']
if system() in ('Windows', 'Microsoft'):
dll_path.append(curr_path.parents[1] / 'Release')
dll_path.append(curr_path.parents[1] / 'windows' / 'x64' / 'DLL')
dll_path = [p / 'lib_lightgbm.dll' for p in dll_path]
dll_path = [
curr_path.parents[1],
curr_path.parents[0] / "bin",
curr_path.parents[0] / "lib",
]
if system() in ("Windows", "Microsoft"):
dll_path.append(curr_path.parents[1] / "Release")
dll_path.append(curr_path.parents[1] / "windows" / "x64" / "DLL")
dll_path = [p / "lib_lightgbm.dll" for p in dll_path]
else:
dll_path = [p / 'lib_lightgbm.so' for p in dll_path]
dll_path = [p / "lib_lightgbm.so" for p in dll_path]
lib_path = [str(p) for p in dll_path if p.is_file()]
if not lib_path:
dll_path_joined = '\n'.join(map(str, dll_path))
raise Exception(f'Cannot find lightgbm library file in following paths:\n{dll_path_joined}')
dll_path_joined = "\n".join(map(str, dll_path))
raise Exception(f"Cannot find lightgbm library file in following paths:\n{dll_path_joined}")
return lib_path
python-package/lightgbm/plotting.py
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python-package/lightgbm/sklearn.py
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python-package/pyproject.toml
View file @ dd31208a
......@@ -114,7 +114,6 @@ exclude = [
"compile/*.py",
"external_libs/*.py",
"lightgbm-python/*.py",
"python-package/*.py",
]
indent-style = "space"
quote-style = "double"
......
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