chore: import upstream snapshot with attribution
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wehub-resource-sync
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# coding: utf-8
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import logging
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import numpy as np
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import pytest
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import lightgbm as lgb
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def test_register_logger(tmp_path):
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logger = logging.getLogger("LightGBM")
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter("%(levelname)s | %(message)s")
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log_filename = tmp_path / "LightGBM_test_logger.log"
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file_handler = logging.FileHandler(log_filename, mode="w", encoding="utf-8")
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file_handler.setLevel(logging.DEBUG)
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file_handler.setFormatter(formatter)
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logger.addHandler(file_handler)
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def dummy_metric(_, __):
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logger.debug("In dummy_metric")
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return "dummy_metric", 1, True
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lgb.register_logger(logger)
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X = np.array([[1, 2, 3], [1, 2, 4], [1, 2, 4], [1, 2, 3]], dtype=np.float32)
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y = np.array([0, 1, 1, 0])
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lgb_train = lgb.Dataset(X, y, categorical_feature=[1])
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lgb_valid = lgb.Dataset(X, y, categorical_feature=[1]) # different object for early-stopping
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eval_records = {}
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callbacks = [lgb.record_evaluation(eval_records), lgb.log_evaluation(2), lgb.early_stopping(10)]
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lgb.train(
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{"objective": "binary", "metric": ["auc", "binary_error"], "verbose": 1},
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lgb_train,
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num_boost_round=10,
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feval=dummy_metric,
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valid_sets=[lgb_valid],
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callbacks=callbacks,
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)
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lgb.plot_metric(eval_records)
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expected_log = r"""
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INFO | [LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.
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INFO | [LightGBM] [Info] Number of positive: 2, number of negative: 2
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INFO | [LightGBM] [Info] Total Bins 0
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INFO | [LightGBM] [Info] Number of data points in the train set: 4, number of used features: 0
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INFO | [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | Training until validation scores don't improve for 10 rounds
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [2] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [4] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [6] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [8] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
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DEBUG | In dummy_metric
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INFO | [10] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1
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INFO | Did not meet early stopping. Best iteration is:
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[1] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1
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WARNING | More than one metric available, picking one to plot.
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""".strip()
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gpu_lines = [
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"INFO | [LightGBM] [Info] This is the GPU trainer",
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"INFO | [LightGBM] [Info] Using GPU Device:",
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"INFO | [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...",
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"INFO | [LightGBM] [Info] GPU programs have been built",
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"INFO | [LightGBM] [Warning] GPU acceleration is disabled because no non-trivial dense features can be found",
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"INFO | [LightGBM] [Warning] Using sparse features with CUDA is currently not supported.",
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"INFO | [LightGBM] [Warning] CUDA currently requires double precision calculations.",
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"INFO | [LightGBM] [Info] LightGBM using CUDA trainer with DP float!!",
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]
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cuda_lines = [
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"INFO | [LightGBM] [Warning] Metric auc is not implemented in cuda version. Fall back to evaluation on CPU.",
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"INFO | [LightGBM] [Warning] Metric binary_error is not implemented in cuda version. Fall back to evaluation on CPU.",
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]
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with open(log_filename, "rt", encoding="utf-8") as f:
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actual_log = f.read().strip()
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actual_log_wo_gpu_stuff = []
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for line in actual_log.split("\n"):
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if not any(line.startswith(gpu_or_cuda_line) for gpu_or_cuda_line in gpu_lines + cuda_lines):
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actual_log_wo_gpu_stuff.append(line)
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assert "\n".join(actual_log_wo_gpu_stuff) == expected_log
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def test_register_invalid_logger():
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class LoggerWithoutInfoMethod:
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def warning(self, msg: str) -> None:
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print(msg)
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class LoggerWithoutWarningMethod:
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def info(self, msg: str) -> None:
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print(msg)
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class LoggerWithAttributeNotCallable:
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def __init__(self):
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self.info = 1
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self.warning = 2
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expected_error_message = "Logger must provide 'info' and 'warning' method"
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with pytest.raises(TypeError, match=expected_error_message):
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lgb.register_logger(LoggerWithoutInfoMethod())
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with pytest.raises(TypeError, match=expected_error_message):
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lgb.register_logger(LoggerWithoutWarningMethod())
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with pytest.raises(TypeError, match=expected_error_message):
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lgb.register_logger(LoggerWithAttributeNotCallable())
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def test_register_custom_logger():
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logged_messages = []
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class CustomLogger:
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def custom_info(self, msg: str) -> None:
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logged_messages.append(msg)
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def custom_warning(self, msg: str) -> None:
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logged_messages.append(msg)
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custom_logger = CustomLogger()
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lgb.register_logger(custom_logger, info_method_name="custom_info", warning_method_name="custom_warning")
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lgb.basic._log_info("info message")
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lgb.basic._log_warning("warning message")
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expected_log = ["info message", "warning message"]
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assert logged_messages == expected_log
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logged_messages = []
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X = np.array([[1, 2, 3], [1, 2, 4], [1, 2, 4], [1, 2, 3]], dtype=np.float32)
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y = np.array([0, 1, 1, 0])
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lgb_data = lgb.Dataset(X, y, categorical_feature=[1])
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lgb.train(
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{"objective": "binary", "metric": "auc"},
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lgb_data,
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num_boost_round=10,
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valid_sets=[lgb_data],
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)
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assert logged_messages, "custom logger was not called"
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