diff --git a/docs/Python-API.rst b/docs/Python-API.rst index 60389216bb2b..ef249ad4c700 100644 --- a/docs/Python-API.rst +++ b/docs/Python-API.rst @@ -55,3 +55,11 @@ Plotting plot_metric plot_tree create_tree_digraph + +Utilities +--------- + +.. autosummary:: + :toctree: pythonapi/ + + register_logger diff --git a/python-package/lightgbm/__init__.py b/python-package/lightgbm/__init__.py index f8d3bce3078f..44f2e56679f0 100644 --- a/python-package/lightgbm/__init__.py +++ b/python-package/lightgbm/__init__.py @@ -3,7 +3,7 @@ Contributors: https://github.com/microsoft/LightGBM/graphs/contributors. """ -from .basic import Booster, Dataset +from .basic import Booster, Dataset, register_logger from .callback import (early_stopping, print_evaluation, record_evaluation, reset_parameter) from .engine import cv, train, CVBooster @@ -28,6 +28,7 @@ __version__ = version_file.read().strip() __all__ = ['Dataset', 'Booster', 'CVBooster', + 'register_logger', 'train', 'cv', 'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker', 'print_evaluation', 'record_evaluation', 'reset_parameter', 'early_stopping', diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py index f52776f320ca..5b0a73f4ed20 100644 --- a/python-package/lightgbm/basic.py +++ b/python-package/lightgbm/basic.py @@ -5,8 +5,10 @@ import json import os import warnings -from tempfile import NamedTemporaryFile from collections import OrderedDict +from functools import wraps +from logging import Logger +from tempfile import NamedTemporaryFile import numpy as np import scipy.sparse @@ -15,9 +17,64 @@ from .libpath import find_lib_path +class _DummyLogger: + def info(self, msg): + print(msg) + + def warning(self, msg): + warnings.warn(msg, stacklevel=3) + + +_LOGGER = _DummyLogger() + + +def register_logger(logger): + """Register custom logger. + + Parameters + ---------- + logger : logging.Logger + Custom logger. + """ + if not isinstance(logger, Logger): + raise TypeError("Logger should inherit logging.Logger class") + global _LOGGER + _LOGGER = logger + + +def _normalize_native_string(func): + """Join log messages from native library which come by chunks.""" + msg_normalized = [] + + @wraps(func) + def wrapper(msg): + nonlocal msg_normalized + if msg.strip() == '': + msg = ''.join(msg_normalized) + msg_normalized = [] + return func(msg) + else: + msg_normalized.append(msg) + + return wrapper + + +def _log_info(msg): + _LOGGER.info(msg) + + +def _log_warning(msg): + _LOGGER.warning(msg) + + +@_normalize_native_string +def _log_native(msg): + _LOGGER.info(msg) + + def _log_callback(msg): - """Redirect logs from native library into Python console.""" - print("{0:s}".format(msg.decode('utf-8')), end='') + """Redirect logs from native library into Python.""" + _log_native("{0:s}".format(msg.decode('utf-8'))) def _load_lib(): @@ -329,8 +386,8 @@ def convert_from_sliced_object(data): """Fix the memory of multi-dimensional sliced object.""" if isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray): if not data.flags.c_contiguous: - warnings.warn("Usage of np.ndarray subset (sliced data) is not recommended " - "due to it will double the peak memory cost in LightGBM.") + _log_warning("Usage of np.ndarray subset (sliced data) is not recommended " + "due to it will double the peak memory cost in LightGBM.") return np.copy(data) return data @@ -620,7 +677,7 @@ def predict(self, data, start_iteration=0, num_iteration=-1, preds, nrow = self.__pred_for_np2d(data.to_numpy(), start_iteration, num_iteration, predict_type) else: try: - warnings.warn('Converting data to scipy sparse matrix.') + _log_warning('Converting data to scipy sparse matrix.') csr = scipy.sparse.csr_matrix(data) except BaseException: raise TypeError('Cannot predict data for type {}'.format(type(data).__name__)) @@ -1103,9 +1160,9 @@ def _lazy_init(self, data, label=None, reference=None, .co_varnames[:getattr(self.__class__, '_lazy_init').__code__.co_argcount]) for key, _ in params.items(): if key in args_names: - warnings.warn('{0} keyword has been found in `params` and will be ignored.\n' - 'Please use {0} argument of the Dataset constructor to pass this parameter.' - .format(key)) + _log_warning('{0} keyword has been found in `params` and will be ignored.\n' + 'Please use {0} argument of the Dataset constructor to pass this parameter.' + .format(key)) # user can set verbose with params, it has higher priority if not any(verbose_alias in params for verbose_alias in _ConfigAliases.get("verbosity")) and silent: params["verbose"] = -1 @@ -1126,7 +1183,7 @@ def _lazy_init(self, data, label=None, reference=None, if categorical_indices: for cat_alias in _ConfigAliases.get("categorical_feature"): if cat_alias in params: - warnings.warn('{} in param dict is overridden.'.format(cat_alias)) + _log_warning('{} in param dict is overridden.'.format(cat_alias)) params.pop(cat_alias, None) params['categorical_column'] = sorted(categorical_indices) @@ -1172,7 +1229,7 @@ def _lazy_init(self, data, label=None, reference=None, self.set_group(group) if isinstance(predictor, _InnerPredictor): if self._predictor is None and init_score is not None: - warnings.warn("The init_score will be overridden by the prediction of init_model.") + _log_warning("The init_score will be overridden by the prediction of init_model.") self._set_init_score_by_predictor(predictor, data) elif init_score is not None: self.set_init_score(init_score) @@ -1314,7 +1371,7 @@ def construct(self): if self.reference is not None: reference_params = self.reference.get_params() if self.get_params() != reference_params: - warnings.warn('Overriding the parameters from Reference Dataset.') + _log_warning('Overriding the parameters from Reference Dataset.') self._update_params(reference_params) if self.used_indices is None: # create valid @@ -1583,11 +1640,11 @@ def set_categorical_feature(self, categorical_feature): self.categorical_feature = categorical_feature return self._free_handle() elif categorical_feature == 'auto': - warnings.warn('Using categorical_feature in Dataset.') + _log_warning('Using categorical_feature in Dataset.') return self else: - warnings.warn('categorical_feature in Dataset is overridden.\n' - 'New categorical_feature is {}'.format(sorted(list(categorical_feature)))) + _log_warning('categorical_feature in Dataset is overridden.\n' + 'New categorical_feature is {}'.format(sorted(list(categorical_feature)))) self.categorical_feature = categorical_feature return self._free_handle() else: @@ -1840,8 +1897,8 @@ def get_data(self): elif isinstance(self.data, DataTable): self.data = self.data[self.used_indices, :] else: - warnings.warn("Cannot subset {} type of raw data.\n" - "Returning original raw data".format(type(self.data).__name__)) + _log_warning("Cannot subset {} type of raw data.\n" + "Returning original raw data".format(type(self.data).__name__)) self.need_slice = False if self.data is None: raise LightGBMError("Cannot call `get_data` after freed raw data, " @@ -2011,10 +2068,10 @@ def add_features_from(self, other): old_self_data_type) err_msg += ("Set free_raw_data=False when construct Dataset to avoid this" if was_none else "Freeing raw data") - warnings.warn(err_msg) + _log_warning(err_msg) self.feature_name = self.get_feature_name() - warnings.warn("Reseting categorical features.\n" - "You can set new categorical features via ``set_categorical_feature`` method") + _log_warning("Reseting categorical features.\n" + "You can set new categorical features via ``set_categorical_feature`` method") self.categorical_feature = "auto" self.pandas_categorical = None return self @@ -2834,7 +2891,7 @@ def model_from_string(self, model_str, verbose=True): self.handle, ctypes.byref(out_num_class))) if verbose: - print('Finished loading model, total used %d iterations' % int(out_num_iterations.value)) + _log_info('Finished loading model, total used %d iterations' % int(out_num_iterations.value)) self.__num_class = out_num_class.value self.pandas_categorical = _load_pandas_categorical(model_str=model_str) return self diff --git a/python-package/lightgbm/callback.py b/python-package/lightgbm/callback.py index 9140127c846b..c2db7a3cf991 100644 --- a/python-package/lightgbm/callback.py +++ b/python-package/lightgbm/callback.py @@ -1,10 +1,9 @@ # coding: utf-8 """Callbacks library.""" import collections -import warnings from operator import gt, lt -from .basic import _ConfigAliases +from .basic import _ConfigAliases, _log_info, _log_warning class EarlyStopException(Exception): @@ -67,7 +66,7 @@ def print_evaluation(period=1, show_stdv=True): def _callback(env): if period > 0 and env.evaluation_result_list and (env.iteration + 1) % period == 0: result = '\t'.join([_format_eval_result(x, show_stdv) for x in env.evaluation_result_list]) - print('[%d]\t%s' % (env.iteration + 1, result)) + _log_info('[%d]\t%s' % (env.iteration + 1, result)) _callback.order = 10 return _callback @@ -180,15 +179,14 @@ def _init(env): enabled[0] = not any(env.params.get(boost_alias, "") == 'dart' for boost_alias in _ConfigAliases.get("boosting")) if not enabled[0]: - warnings.warn('Early stopping is not available in dart mode') + _log_warning('Early stopping is not available in dart mode') return if not env.evaluation_result_list: raise ValueError('For early stopping, ' 'at least one dataset and eval metric is required for evaluation') if verbose: - msg = "Training until validation scores don't improve for {} rounds" - print(msg.format(stopping_rounds)) + _log_info("Training until validation scores don't improve for {} rounds".format(stopping_rounds)) # split is needed for " " case (e.g. "train l1") first_metric[0] = env.evaluation_result_list[0][1].split(" ")[-1] @@ -205,10 +203,10 @@ def _init(env): def _final_iteration_check(env, eval_name_splitted, i): if env.iteration == env.end_iteration - 1: if verbose: - print('Did not meet early stopping. Best iteration is:\n[%d]\t%s' % ( + _log_info('Did not meet early stopping. Best iteration is:\n[%d]\t%s' % ( best_iter[i] + 1, '\t'.join([_format_eval_result(x) for x in best_score_list[i]]))) if first_metric_only: - print("Evaluated only: {}".format(eval_name_splitted[-1])) + _log_info("Evaluated only: {}".format(eval_name_splitted[-1])) raise EarlyStopException(best_iter[i], best_score_list[i]) def _callback(env): @@ -232,10 +230,10 @@ def _callback(env): continue # train data for lgb.cv or sklearn wrapper (underlying lgb.train) elif env.iteration - best_iter[i] >= stopping_rounds: if verbose: - print('Early stopping, best iteration is:\n[%d]\t%s' % ( + _log_info('Early stopping, best iteration is:\n[%d]\t%s' % ( best_iter[i] + 1, '\t'.join([_format_eval_result(x) for x in best_score_list[i]]))) if first_metric_only: - print("Evaluated only: {}".format(eval_name_splitted[-1])) + _log_info("Evaluated only: {}".format(eval_name_splitted[-1])) raise EarlyStopException(best_iter[i], best_score_list[i]) _final_iteration_check(env, eval_name_splitted, i) _callback.order = 30 diff --git a/python-package/lightgbm/dask.py b/python-package/lightgbm/dask.py index a0c99b23f62e..14d349db961f 100644 --- a/python-package/lightgbm/dask.py +++ b/python-package/lightgbm/dask.py @@ -6,7 +6,6 @@ It is based on dask-lightgbm, which was based on dask-xgboost. """ -import logging import socket from collections import defaultdict from copy import deepcopy @@ -22,11 +21,9 @@ from dask import delayed from dask.distributed import Client, default_client, get_worker, wait -from .basic import _ConfigAliases, _LIB, _safe_call +from .basic import _ConfigAliases, _LIB, _log_warning, _safe_call from .sklearn import LGBMClassifier, LGBMRegressor, LGBMRanker -logger = logging.getLogger(__name__) - def _find_open_port(worker_ip: str, local_listen_port: int, ports_to_skip: Iterable[int]) -> int: """Find an open port. @@ -257,10 +254,10 @@ def _train(client, data, label, params, model_factory, sample_weight=None, group 'voting_parallel' } if tree_learner is None: - logger.warning('Parameter tree_learner not set. Using "data" as default') + _log_warning('Parameter tree_learner not set. Using "data" as default') params['tree_learner'] = 'data' elif tree_learner.lower() not in allowed_tree_learners: - logger.warning('Parameter tree_learner set to %s, which is not allowed. Using "data" as default' % tree_learner) + _log_warning('Parameter tree_learner set to %s, which is not allowed. Using "data" as default' % tree_learner) params['tree_learner'] = 'data' local_listen_port = 12400 diff --git a/python-package/lightgbm/engine.py b/python-package/lightgbm/engine.py index 9db8018d2902..51f1b7e6e9df 100644 --- a/python-package/lightgbm/engine.py +++ b/python-package/lightgbm/engine.py @@ -2,13 +2,12 @@ """Library with training routines of LightGBM.""" import collections import copy -import warnings from operator import attrgetter import numpy as np from . import callback -from .basic import Booster, Dataset, LightGBMError, _ConfigAliases, _InnerPredictor +from .basic import Booster, Dataset, LightGBMError, _ConfigAliases, _InnerPredictor, _log_warning from .compat import SKLEARN_INSTALLED, _LGBMGroupKFold, _LGBMStratifiedKFold @@ -146,12 +145,12 @@ def train(params, train_set, num_boost_round=100, for alias in _ConfigAliases.get("num_iterations"): if alias in params: num_boost_round = params.pop(alias) - warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias)) + _log_warning("Found `{}` in params. Will use it instead of argument".format(alias)) params["num_iterations"] = num_boost_round for alias in _ConfigAliases.get("early_stopping_round"): if alias in params: early_stopping_rounds = params.pop(alias) - warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias)) + _log_warning("Found `{}` in params. Will use it instead of argument".format(alias)) params["early_stopping_round"] = early_stopping_rounds first_metric_only = params.get('first_metric_only', False) @@ -525,12 +524,12 @@ def cv(params, train_set, num_boost_round=100, params['objective'] = 'none' for alias in _ConfigAliases.get("num_iterations"): if alias in params: - warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias)) + _log_warning("Found `{}` in params. Will use it instead of argument".format(alias)) num_boost_round = params.pop(alias) params["num_iterations"] = num_boost_round for alias in _ConfigAliases.get("early_stopping_round"): if alias in params: - warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias)) + _log_warning("Found `{}` in params. Will use it instead of argument".format(alias)) early_stopping_rounds = params.pop(alias) params["early_stopping_round"] = early_stopping_rounds first_metric_only = params.get('first_metric_only', False) diff --git a/python-package/lightgbm/plotting.py b/python-package/lightgbm/plotting.py index 03dbc1e86818..ac1bc9c3b565 100644 --- a/python-package/lightgbm/plotting.py +++ b/python-package/lightgbm/plotting.py @@ -1,12 +1,11 @@ # coding: utf-8 """Plotting library.""" -import warnings from copy import deepcopy from io import BytesIO import numpy as np -from .basic import Booster +from .basic import Booster, _log_warning from .compat import MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED from .sklearn import LGBMModel @@ -326,8 +325,7 @@ def plot_metric(booster, metric=None, dataset_names=None, num_metric = len(metrics_for_one) if metric is None: if num_metric > 1: - msg = "More than one metric available, picking one to plot." - warnings.warn(msg, stacklevel=2) + _log_warning("More than one metric available, picking one to plot.") metric, results = metrics_for_one.popitem() else: if metric not in metrics_for_one: diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py index 06be101e2e8f..9fa930c906f0 100644 --- a/python-package/lightgbm/sklearn.py +++ b/python-package/lightgbm/sklearn.py @@ -1,13 +1,12 @@ # coding: utf-8 """Scikit-learn wrapper interface for LightGBM.""" import copy -import warnings from inspect import signature import numpy as np -from .basic import Dataset, LightGBMError, _ConfigAliases +from .basic import Dataset, LightGBMError, _ConfigAliases, _log_warning from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase, LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckSampleWeight, @@ -931,9 +930,9 @@ def predict_proba(self, X, raw_score=False, start_iteration=0, num_iteration=Non """ result = super().predict(X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs) if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib): - warnings.warn("Cannot compute class probabilities or labels " - "due to the usage of customized objective function.\n" - "Returning raw scores instead.") + _log_warning("Cannot compute class probabilities or labels " + "due to the usage of customized objective function.\n" + "Returning raw scores instead.") return result elif self._n_classes > 2 or raw_score or pred_leaf or pred_contrib: return result diff --git a/tests/python_package_test/test_utilities.py b/tests/python_package_test/test_utilities.py new file mode 100644 index 000000000000..ecb8046cadf4 --- /dev/null +++ b/tests/python_package_test/test_utilities.py @@ -0,0 +1,93 @@ +# coding: utf-8 +import logging + +import numpy as np +import lightgbm as lgb + + +def test_register_logger(tmp_path): + logger = logging.getLogger("LightGBM") + logger.setLevel(logging.DEBUG) + formatter = logging.Formatter('%(levelname)s | %(message)s') + log_filename = str(tmp_path / "LightGBM_test_logger.log") + file_handler = logging.FileHandler(log_filename, mode="w", encoding="utf-8") + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + def dummy_metric(_, __): + logger.debug('In dummy_metric') + return 'dummy_metric', 1, True + + lgb.register_logger(logger) + + X = np.array([[1, 2, 3], + [1, 2, 4], + [1, 2, 4], + [1, 2, 3]], + dtype=np.float32) + y = np.array([0, 1, 1, 0]) + lgb_data = lgb.Dataset(X, y) + + eval_records = {} + lgb.train({'objective': 'binary', 'metric': ['auc', 'binary_error']}, + lgb_data, num_boost_round=10, feval=dummy_metric, + valid_sets=[lgb_data], evals_result=eval_records, + categorical_feature=[1], early_stopping_rounds=4, verbose_eval=2) + + lgb.plot_metric(eval_records) + + expected_log = r""" +WARNING | categorical_feature in Dataset is overridden. +New categorical_feature is [1] +INFO | [LightGBM] [Warning] There are no meaningful features, as all feature values are constant. +INFO | [LightGBM] [Info] Number of positive: 2, number of negative: 2 +INFO | [LightGBM] [Info] Total Bins 0 +INFO | [LightGBM] [Info] Number of data points in the train set: 4, number of used features: 0 +INFO | [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | Training until validation scores don't improve for 4 rounds +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [2] training's auc: 0.5 training's binary_error: 0.5 training's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [4] training's auc: 0.5 training's binary_error: 0.5 training's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [6] training's auc: 0.5 training's binary_error: 0.5 training's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [8] training's auc: 0.5 training's binary_error: 0.5 training's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [10] training's auc: 0.5 training's binary_error: 0.5 training's dummy_metric: 1 +INFO | Did not meet early stopping. Best iteration is: +[1] training's auc: 0.5 training's binary_error: 0.5 training's dummy_metric: 1 +WARNING | More than one metric available, picking one to plot. +""".strip() + + gpu_lines = [ + "INFO | [LightGBM] [Info] This is the GPU trainer", + "INFO | [LightGBM] [Info] Using GPU Device:", + "INFO | [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...", + "INFO | [LightGBM] [Info] GPU programs have been built", + "INFO | [LightGBM] [Warning] GPU acceleration is disabled because no non-trivial dense features can be found" + ] + with open(log_filename, "rt", encoding="utf-8") as f: + actual_log = f.read().strip() + actual_log_wo_gpu_stuff = [] + for line in actual_log.split("\n"): + if not any(line.startswith(gpu_line) for gpu_line in gpu_lines): + actual_log_wo_gpu_stuff.append(line) + + assert "\n".join(actual_log_wo_gpu_stuff) == expected_log