Repository Analysis

lightgbm-org/LightGBM

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

5.8 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of lightgbm-org/LightGBM, a C++ project with 18,560 GitHub stars. SynthScan v2.0 examined 112,580 lines of code across 350 source files, recording 653 pattern matches distributed across 13 syntactic categories. The overall adjusted score of 5.8 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

5.8
Adjusted Score
5.8
Raw Score
100%
Time Factor
2026-07-12
Last Push
18.6K
Stars
C++
Language
112.6K
Lines of Code
350
Files
653
Pattern Hits
2026-07-14
Scan Date
0.01
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 4MEDIUM 13LOW 636

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 653 distinct pattern matches across 13 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers264 hits · 220 pts
SeverityFileLineSnippetContext
LOW.ci/parameter-generator.py199def gen_parameter_description(CODE
LOWtests/python_package_test/test_polars.py24def generate_simple_polars_frame() -> pl.DataFrame:CODE
LOWtests/python_package_test/test_polars.py32def generate_nullable_polars_frame(dtype: Any) -> pl.DataFrame:CODE
LOWtests/python_package_test/test_polars.py43def generate_dummy_polars_frame() -> pl.DataFrame:CODE
LOWtests/python_package_test/test_polars.py52def generate_random_polars_frame(CODE
LOWtests/python_package_test/test_polars.py69def generate_random_polars_series(CODE
LOWtests/python_package_test/test_polars.py119def test_dataset_construct_fuzzy(tmp_path, polars_frame_fn, dataset_params):CODE
LOWtests/python_package_test/test_polars.py131def test_dataset_construct_fuzzy_boolean(tmp_path):CODE
LOWtests/python_package_test/test_polars.py147def test_dataset_construct_fields_fuzzy():CODE
LOWtests/python_package_test/test_polars.py175def test_dataset_construct_labels(polars_type):CODE
LOWtests/python_package_test/test_polars.py185def test_dataset_construct_labels_boolean():CODE
LOWtests/python_package_test/test_polars.py198def test_dataset_construct_weights_none():CODE
LOWtests/python_package_test/test_polars.py208def test_dataset_construct_weights(polars_type):CODE
LOWtests/python_package_test/test_polars.py222def test_dataset_construct_groups(polars_type):CODE
LOWtests/python_package_test/test_polars.py236def test_dataset_construct_init_scores_array(polars_type):CODE
LOWtests/python_package_test/test_polars.py246def test_dataset_construct_init_scores_table():CODE
LOWtests/python_package_test/test_polars.py266def assert_equal_predict_polars_pandas(booster: lgb.Booster, data: pl.DataFrame):CODE
LOWtests/python_package_test/test_polars.py308def test_predict_binary_classification():CODE
LOWtests/python_package_test/test_polars.py323def test_predict_multiclass_classification():CODE
LOWtests/python_package_test/test_polars.py354def test_polars_feature_name_auto():CODE
LOWtests/python_package_test/test_polars.py366def test_polars_feature_name_manual():CODE
LOWtests/python_package_test/test_polars.py379def test_get_data_polars_frame():CODE
LOWtests/python_package_test/test_polars.py393def test_get_data_polars_frame_subset(rng):CODE
LOWtests/python_package_test/test_engine.py2436def test_refit_with_one_tree_regression():CODE
LOWtests/python_package_test/test_engine.py2445def test_refit_with_one_tree_binary_classification():CODE
LOWtests/python_package_test/test_engine.py2454def test_refit_with_one_tree_multiclass_classification():CODE
LOWtests/python_package_test/test_engine.py2463def test_refit_dataset_params(rng):CODE
LOWtests/python_package_test/test_engine.py2539def test_constant_features_regression():CODE
LOWtests/python_package_test/test_engine.py2546def test_constant_features_binary():CODE
LOWtests/python_package_test/test_engine.py2552def test_constant_features_multiclass():CODE
LOWtests/python_package_test/test_engine.py2558def test_constant_features_multiclassova():CODE
LOWtests/python_package_test/test_engine.py4817def test_train_and_cv_raise_informative_error_for_train_set_of_wrong_type():CODE
LOWtests/python_package_test/test_engine.py4825def test_train_and_cv_raise_informative_error_for_impossible_num_boost_round(num_boost_round):CODE
LOWtests/python_package_test/test_engine.py4834def test_train_raises_informative_error_if_any_valid_sets_are_not_dataset_objects():CODE
LOWtests/python_package_test/test_engine.py164def test_weighted_percentile_inside_label_range(objective):CODE
LOWtests/python_package_test/test_engine.py201def test_missing_value_handle():CODE
LOWtests/python_package_test/test_engine.py221def test_missing_value_handle_more_na():CODE
LOWtests/python_package_test/test_engine.py241def test_missing_value_handle_na():CODE
LOWtests/python_package_test/test_engine.py272def test_missing_value_handle_zero():CODE
LOWtests/python_package_test/test_engine.py303def test_missing_value_handle_none():CODE
LOWtests/python_package_test/test_engine.py398def test_categorical_handle_na(use_quantized_grad):CODE
LOWtests/python_package_test/test_engine.py448def test_categorical_non_zero_inputs(use_quantized_grad):CODE
LOWtests/python_package_test/test_engine.py527def test_multiclass_prediction_early_stopping():CODE
LOWtests/python_package_test/test_engine.py697def test_ranking_prediction_early_stopping():CODE
LOWtests/python_package_test/test_engine.py780def test_ranking_with_position_information_with_file(tmp_path):CODE
LOWtests/python_package_test/test_engine.py831def test_ranking_with_position_information_with_dataset_constructor(tmp_path):CODE
LOWtests/python_package_test/test_engine.py934def test_early_stopping_ignores_training_set(use_valid):CODE
LOWtests/python_package_test/test_engine.py972def test_early_stopping_via_global_params(first_metric_only):CODE
LOWtests/python_package_test/test_engine.py1000def test_early_stopping_is_not_enabled_for_non_positive_stopping_rounds(early_stopping_round):CODE
LOWtests/python_package_test/test_engine.py1050def test_early_stopping_min_delta(first_only, single_metric, greater_is_better):CODE
LOWtests/python_package_test/test_engine.py1129def test_early_stopping_min_delta_via_global_params(early_stopping_min_delta):CODE
LOWtests/python_package_test/test_engine.py1151def test_early_stopping_can_be_triggered_via_custom_callback():CODE
LOWtests/python_package_test/test_engine.py1154 def _early_stop_after_seventh_iteration(env):CODE
LOWtests/python_package_test/test_engine.py1199def test_continue_train_reused_dataset():CODE
LOWtests/python_package_test/test_engine.py1231def test_continue_train_multiclass():CODE
LOWtests/python_package_test/test_engine.py1319def test_cv_works_with_init_model(tmp_path):CODE
LOWtests/python_package_test/test_engine.py1493def test_feature_name_with_non_ascii(rng, tmp_path):CODE
LOWtests/python_package_test/test_engine.py1510def test_parameters_are_loaded_from_model_file(tmp_path, capsys, rng):CODE
LOWtests/python_package_test/test_engine.py1560def test_string_serialized_params_retrieval(rng):CODE
LOWtests/python_package_test/test_engine.py1604def test_save_load_copy_pickle(tmp_path):CODE
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Over-Commented Block219 hits · 194 pts
SeverityFileLineSnippetContext
LOWCMakeLists.txt801 # with clang, libomp doesn't ship with the compiler and might be supplied separatelyCOMMENT
LOWCMakeLists.txt821 # This can't be easily avoided by forcing R-package builds in LightGBM to use R's libomp.dylibCOMMENT
LOWbuild_r.R1# For macOS users who have decided to use gccCOMMENT
LOWbuild-python.sh1#!/bin/shCOMMENT
LOWbuild-python.sh21# sh ./build-python.sh install --precompileCOMMENT
LOWbuild-python.sh41# --gpuCOMMENT
LOWbuild-cran-package.sh1#!/bin/shCOMMENT
LOW.ci/check-workflow-status.sh1#!/bin/bashCOMMENT
LOW.ci/set-commit-status.sh1#!/bin/bashCOMMENT
LOW.ci/append-comment.sh1#!/bin/bashCOMMENT
LOW.ci/install-r-deps.R1# Install R dependencies, using only base R.COMMENT
LOW.ci/check-dynamic-dependencies.sh1#!/bin/bashCOMMENT
LOW.ci/rerun-workflow.sh1#!/bin/bashCOMMENT
LOW.ci/conda-envs/ci-core.txt1# [description]COMMENT
LOWR-package/demo/basic_walkthrough.R81# Since we do not have this file with us, the following line is just for illustrationCOMMENT
LOWR-package/demo/categorical_features_rules.R1# Here we are going to try training a model with categorical featuresCOMMENT
LOWR-package/demo/categorical_features_rules.R21# $ duration : int 79 220 185 199 226 141 341 151 57 313 ...COMMENT
LOWR-package/demo/categorical_features_rules.R41# $ marital : num 1 2 1 3 3 2 2 2 1 1 ...COMMENT
LOWR-package/demo/efficient_many_training.R1# Efficient training means training without giving up too much RAMCOMMENT
LOWR-package/tests/testthat/helper.R1# ref for this file:COMMENT
LOWR-package/tests/testthat/test_basic.R2281 )COMMENT
LOWR-package/R/lgb.convert_with_rules.R61#'COMMENT
LOWR-package/R/lgb.convert_with_rules.R81#' new_iris <- lgb.convert_with_rules(data = iris)COMMENT
LOWR-package/R/lgb.convert_with_rules.R101#'COMMENT
LOWR-package/R/lgb.interpret.R1#' @name lgb.interpretCOMMENT
LOWR-package/R/lgb.interpret.R21#' \dontshow{data.table::setDTthreads(1L)}COMMENT
LOWR-package/R/lgb.interpret.R41#' )COMMENT
LOWR-package/R/utils.R161 params$metric <- as.list(unique(unlist(params$metric)))COMMENT
LOWR-package/R/utils.R181# For example, "num_iterations" can also be provided to lgb.train()COMMENT
LOWR-package/R/multithreading.R1#' @name setLGBMThreadsCOMMENT
LOWR-package/R/multithreading.R21#' @exportCOMMENT
LOWR-package/R/lgb.restore_handle.R1#' @name lgb.restore_handleCOMMENT
LOWR-package/R/lgb.restore_handle.R21#' \dontshow{setLGBMthreads(2L)}COMMENT
LOWR-package/R/lgb.plot.importance.R1#' @name lgb.plot.importanceCOMMENT
LOWR-package/R/lgb.plot.importance.R21#' \donttest{COMMENT
LOWR-package/R/lgb.model.dt.tree.R1#' @name lgb.model.dt.treeCOMMENT
LOWR-package/R/lgb.model.dt.tree.R21#' for a leaf, it simply labels it as \code{"NA"}}COMMENT
LOWR-package/R/lgb.model.dt.tree.R41#' dtrain <- lgb.Dataset(train$data, label = train$label)COMMENT
LOWR-package/R/lgb.train.R1#' @name lgb.trainCOMMENT
LOWR-package/R/lgb.train.R21#' train <- agaricus.trainCOMMENT
LOWR-package/R/lightgbm.R1#' @name lgb_shared_paramsCOMMENT
LOWR-package/R/lightgbm.R21#' The "metric" section of the documentation}COMMENT
LOWR-package/R/lightgbm.R41#' \item{\bold{c. list}:COMMENT
LOWR-package/R/lightgbm.R61#' validation set does not improve for several consecutive iterations.COMMENT
LOWR-package/R/lightgbm.R81#' de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will onlyCOMMENT
LOWR-package/R/lightgbm.R101#' than \code{\link{lgb.train}}.COMMENT
LOWR-package/R/lightgbm.R121#' \code{label}).COMMENT
LOWR-package/R/lightgbm.R141#' If passing \code{NULL} (the default), will try to use the number of physical cores in theCOMMENT
LOWR-package/R/lightgbm.R261 what = lgb.trainCOMMENT
LOWR-package/R/lightgbm.R281#' https://archive.ics.uci.edu/ml/datasets/MushroomCOMMENT
LOWR-package/R/lightgbm.R301#' \item{\code{label}: the label for each record}COMMENT
LOWR-package/R/lightgbm.R321#' UCI Machine Learning Repository.COMMENT
LOWR-package/R/lgb.make_serializable.R1#' @name lgb.make_serializableCOMMENT
LOWR-package/R/lgb.Dataset.R1#' @name lgb_shared_dataset_paramsCOMMENT
LOWR-package/R/lgb.Dataset.R761#' @title Construct \code{lgb.Dataset} objectCOMMENT
LOWR-package/R/lgb.Dataset.R781#' @param categorical_feature categorical features. This can either be a character vector of featureCOMMENT
LOWR-package/R/lgb.Dataset.R841#' a character representing a path to a text file (CSV, TSV, or LibSVM),COMMENT
LOWR-package/R/lgb.Dataset.R861#'COMMENT
LOWR-package/R/lgb.Dataset.R881#' , row.names = FALSECOMMENT
LOWR-package/R/lgb.Dataset.R921COMMENT
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Redundant / Tautological Comments34 hits · 58 pts
SeverityFileLineSnippetContext
LOWR-package/R/lgb.Predictor.R27 # Check if model file is a booster handle alreadyCOMMENT
LOWR-package/R/lgb.Predictor.R74 # Check if number of iterations is existing - if not, then set it to -1 (use all)COMMENT
LOWR-package/R/lgb.Predictor.R78 # Check if start iterations is existing - if not, then set it to 0 (start from the first iteration)COMMENT
LOWR-package/R/lgb.Predictor.R83 # Check if data is a file name and not a matrixCOMMENT
LOWR-package/R/lgb.Predictor.R243 # Check if data is a matrixCOMMENT
LOWR-package/R/lgb.Predictor.R424 # Check if data is a dgCMatrix (sparse matrix, column compressed format)COMMENT
LOWR-package/R/lgb.Predictor.R450 # Check if number of rows is strange (not a multiple of the dataset rows)COMMENT
LOWR-package/R/lgb.model.dt.tree.R147 # Check if split index is not null in leafCOMMENT
LOWR-package/R/lightgbm.R226 # Set data to a temporary variableCOMMENT
LOWR-package/R/lgb.Dataset.R162 # Check if more categorical features were output over the feature spaceCOMMENT
LOWR-package/R/lgb.Dataset.R366 # Check if dgCMatrix (sparse matrix column compressed)COMMENT
LOWR-package/R/lgb.Dataset.R417 # Check if dgCMatrix (sparse matrix column compressed)COMMENT
LOWR-package/R/lgb.Dataset.R466 # Check if attribute key is in the known attribute listCOMMENT
LOWR-package/R/lgb.Dataset.R517 # Check if attribute key is in the known attribute listCOMMENT
LOWR-package/R/lgb.Dataset.R1026 # Check if invalid element listCOMMENT
LOWR-package/R/lgb.Dataset.R1134 # Check if dataset is not a datasetCOMMENT
LOWR-package/R/lgb.importance.R71 # Check if relative values are requestedCOMMENT
LOWR-package/R/lgb.Booster.R840 # Check if there are evaluation metricsCOMMENT
LOWR-package/R/lgb.Booster.R843 # Check if evaluation metric is a functionCOMMENT
LOWR-package/R/lgb.Booster.R850 # Check if data to assess is existing differentlyCOMMENT
LOWR-package/R/lgb.Booster.R205 # Check if objective is emptyCOMMENT
LOWR-package/R/lgb.Booster.R403 # Check if evaluation was not doneCOMMENT
LOWR-package/R/lgb.Booster.R745 # Check if current iteration was already predictedCOMMENT
LOWR-package/R/lgb.Booster.R1544 # Check if evaluation result is existingCOMMENT
LOWR-package/R/lgb.Booster.R1560 # Check if error is requestedCOMMENT
LOWR-package/R/callback.R79 # Check if period is at least 1 or moreCOMMENT
LOWR-package/R/callback.R85 # Check if iteration matches moduoCOMMENT
LOWR-package/R/callback.R91 # Check if message is existingCOMMENT
LOWR-package/R/callback.R135 # Check if evaluation record existsCOMMENT
LOWR-package/R/callback.R206 # Check if verbose or notCOMMENT
LOWR-package/R/callback.R264 # Check if score is betterCOMMENT
LOWR-package/R/callback.R278 # Check if early stopping is requiredCOMMENT
LOWR-package/src/install.libs.R175# Check if Windows installation (for gcc vs Visual Studio)COMMENT
LOWpython-package/lightgbm/basic.py3027 # Check if the weight contains values other than oneCOMMENT
Deep Nesting43 hits · 34 pts
SeverityFileLineSnippetContext
LOW.ci/parameter-generator.py16CODE
LOW.ci/parameter-generator.py109CODE
LOW.ci/parameter-generator.py199CODE
LOW.ci/parameter-generator.py263CODE
LOWtests/python_package_test/test_engine.py135CODE
LOWtests/python_package_test/test_engine.py724CODE
LOWtests/python_package_test/test_engine.py726CODE
LOWtests/python_package_test/test_basic.py768CODE
LOWtests/python_package_test/test_consistency.py13CODE
LOWtests/python_package_test/test_consistency.py49CODE
LOWtests/python_package_test/utils.py162CODE
LOWtests/python_package_test/utils.py175CODE
LOWtests/python_package_test/test_dask.py153CODE
LOWtests/python_package_test/test_dask.py328CODE
LOWtests/python_package_test/test_dask.py820CODE
LOWtests/python_package_test/test_sklearn.py68CODE
LOWtests/python_package_test/test_sklearn.py1743CODE
LOWtests/python_package_test/test_sklearn.py2084CODE
LOWpython-package/lightgbm/callback.py326CODE
LOWpython-package/lightgbm/callback.py405CODE
LOWpython-package/lightgbm/plotting.py460CODE
LOWpython-package/lightgbm/plotting.py482CODE
LOWpython-package/lightgbm/engine.py109CODE
LOWpython-package/lightgbm/engine.py522CODE
LOWpython-package/lightgbm/dask.py196CODE
LOWpython-package/lightgbm/dask.py442CODE
LOWpython-package/lightgbm/dask.py926CODE
LOWpython-package/lightgbm/basic.py348CODE
LOWpython-package/lightgbm/basic.py476CODE
LOWpython-package/lightgbm/basic.py813CODE
LOWpython-package/lightgbm/basic.py1026CODE
LOWpython-package/lightgbm/basic.py1994CODE
LOWpython-package/lightgbm/basic.py2037CODE
LOWpython-package/lightgbm/basic.py2454CODE
LOWpython-package/lightgbm/basic.py2663CODE
LOWpython-package/lightgbm/basic.py2848CODE
LOWpython-package/lightgbm/basic.py3204CODE
LOWpython-package/lightgbm/basic.py3375CODE
LOWpython-package/lightgbm/basic.py3498CODE
LOWpython-package/lightgbm/basic.py5118CODE
LOWpython-package/lightgbm/sklearn.py869CODE
LOWpython-package/lightgbm/sklearn.py975CODE
LOWpython-package/lightgbm/sklearn.py1595CODE
Unused Imports33 hits · 30 pts
SeverityFileLineSnippetContext
LOWpython-package/lightgbm/compat.py170CODE
LOWpython-package/lightgbm/compat.py171CODE
LOWpython-package/lightgbm/compat.py175CODE
LOWpython-package/lightgbm/compat.py177CODE
LOWpython-package/lightgbm/__init__.py11CODE
LOWpython-package/lightgbm/__init__.py11CODE
LOWpython-package/lightgbm/__init__.py11CODE
LOWpython-package/lightgbm/__init__.py11CODE
LOWpython-package/lightgbm/__init__.py12CODE
LOWpython-package/lightgbm/__init__.py12CODE
LOWpython-package/lightgbm/__init__.py12CODE
LOWpython-package/lightgbm/__init__.py12CODE
LOWpython-package/lightgbm/__init__.py12CODE
LOWpython-package/lightgbm/__init__.py13CODE
LOWpython-package/lightgbm/__init__.py13CODE
LOWpython-package/lightgbm/__init__.py13CODE
LOWpython-package/lightgbm/__init__.py16CODE
LOWpython-package/lightgbm/__init__.py16CODE
LOWpython-package/lightgbm/__init__.py16CODE
LOWpython-package/lightgbm/__init__.py16CODE
LOWpython-package/lightgbm/__init__.py20CODE
LOWpython-package/lightgbm/__init__.py20CODE
LOWpython-package/lightgbm/__init__.py20CODE
LOWpython-package/lightgbm/__init__.py20CODE
LOWpython-package/lightgbm/__init__.py20CODE
LOWpython-package/lightgbm/__init__.py24CODE
LOWpython-package/lightgbm/__init__.py24CODE
LOWpython-package/lightgbm/__init__.py24CODE
LOWpython-package/lightgbm/dask.py52CODE
LOWpython-package/lightgbm/basic.py35CODE
LOWpython-package/lightgbm/basic.py35CODE
LOWpython-package/lightgbm/sklearn.py56CODE
LOWdocs/conf.py112CODE
AI Structural Patterns24 hits · 24 pts
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LOWtests/python_package_test/utils.py38CODE
LOWpython-package/lightgbm/plotting.py37CODE
LOWpython-package/lightgbm/plotting.py176CODE
LOWpython-package/lightgbm/plotting.py294CODE
LOWpython-package/lightgbm/plotting.py749CODE
LOWpython-package/lightgbm/engine.py626CODE
LOWpython-package/lightgbm/dask.py442CODE
LOWpython-package/lightgbm/dask.py1102CODE
LOWpython-package/lightgbm/dask.py1180CODE
LOWpython-package/lightgbm/dask.py1245CODE
LOWpython-package/lightgbm/dask.py1392CODE
LOWpython-package/lightgbm/dask.py1457CODE
LOWpython-package/lightgbm/dask.py1569CODE
LOWpython-package/lightgbm/dask.py1634CODE
LOWpython-package/lightgbm/basic.py1703CODE
LOWpython-package/lightgbm/basic.py4702CODE
LOWpython-package/lightgbm/sklearn.py538CODE
LOWpython-package/lightgbm/sklearn.py975CODE
LOWpython-package/lightgbm/sklearn.py1414CODE
LOWpython-package/lightgbm/sklearn.py1476CODE
LOWpython-package/lightgbm/sklearn.py1529CODE
LOWpython-package/lightgbm/sklearn.py1595CODE
LOWpython-package/lightgbm/sklearn.py1844CODE
LOWpython-package/lightgbm/sklearn.py1893CODE
Cross-Language Confusion3 hits · 22 pts
SeverityFileLineSnippetContext
HIGHdocs/conf.py275 sh build-cran-package.sh || exit 1CODE
HIGHdocs/conf.py276 R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1CODE
HIGHdocs/conf.py277 Rscript .ci/build-docs.R || exit 1CODE
Self-Referential Comments7 hits · 21 pts
SeverityFileLineSnippetContext
MEDIUMR-package/demo/categorical_features_rules.R69# Creating the LightGBM dataset with categorical featuresCOMMENT
MEDIUMR-package/R/lgb.train.R165 # Create the predictor setCOMMENT
MEDIUMR-package/R/lgb.cv.R186 # Create the predictor setCOMMENT
MEDIUMR-package/R/lgb.cv.R541 # Create a vector of integers from 1:k as many times as possible withoutCOMMENT
MEDIUMR-package/inst/make-r-def.R2# Create a definition file (.def) from a .dll file, using objdump.COMMENT
MEDIUMpython-package/lightgbm/basic.py3850 # Create the node record, and populate universal data membersCOMMENT
MEDIUMdocs/conf.py7# This file is execfile()d with the current directory set to itsCOMMENT
AI Slop Vocabulary8 hits · 16 pts
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LOWbuild-python.sh345 # avoid trying to recompile, just use hatchling and copy in relevant filesCOMMENT
LOWR-package/demo/basic_walkthrough.R140# To load it in, simply call lgb.DatasetCOMMENT
MEDIUMtests/python_package_test/test_sklearn.py1415 # Verify that eval_metric is robust to receiving a list with NoneCOMMENT
MEDIUMtests/python_package_test/test_sklearn.py359 y = y.astype(str) # utilize label encoder at it's max powerCODE
MEDIUMtests/python_package_test/test_sklearn.py387 y = y.astype(str) # utilize label encoder at it's max powerCODE
MEDIUMtests/python_package_test/test_sklearn.py417 y = y.astype(str) # utilize label encoder at it's max powerCODE
LOWpython-package/lightgbm/basic.py2731 # If the data is Arrow, we can just pass it to CCOMMENT
MEDIUMsrc/io/dataset_loader.cpp54 // support to get header from parser config, so could utilize following label name to id mapping logic.COMMENT
Structural Annotation Overuse6 hits · 12 pts
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LOWMAINTAINING.md31### Step 1: Put up a Release PRCOMMENT
LOWMAINTAINING.md40### Step 2: Merge the Release PRCOMMENT
LOWMAINTAINING.md46### Step 3: Wait for a New CI Run on `main`COMMENT
LOWMAINTAINING.md52### Step 4: Create a ReleaseCOMMENT
LOWMAINTAINING.md63### Step 5: Upload ArtifactsCOMMENT
LOWMAINTAINING.md83### Step 6: Complete All Other Post-merge Release StepsCOMMENT
Modern Structural Boilerplate8 hits · 8 pts
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LOWtests/distributed/_test_distributed.py84 def _set_ports(self) -> None:CODE
LOWpython-package/lightgbm/callback.py21__all__ = [CODE
LOWpython-package/lightgbm/plotting.py15__all__ = [CODE
LOWpython-package/lightgbm/__init__.py33__all__ = [CODE
LOWpython-package/lightgbm/engine.py29__all__ = [CODE
LOWpython-package/lightgbm/dask.py44__all__ = [CODE
LOWpython-package/lightgbm/basic.py38__all__ = [CODE
LOWpython-package/lightgbm/sklearn.py59__all__ = [CODE
AI Response Leakage1 hit · 8 pts
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HIGHR-package/demo/basic_walkthrough.R4# In this example, we are aiming to predict whether a mushroom is edibleCOMMENT
Slop Phrases3 hits · 6 pts
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LOWR-package/R/lgb.Booster.R1122#' different parameters or prediction type, so make sure to check that the output is whatCOMMENT
LOWinclude/LightGBM/config.h1098 // desc = **Note**: don't forget to allow this port in firewall settings before trainingCOMMENT
MEDIUMsrc/treelearner/parallel_tree_learner.h124* When #data is large and #feature is large, you can use this to have better speed-upCOMMENT