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akaruineko/
git-commits-sorter

joblib

text-classification

mit

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git-commits-sorter

Classify raw git commit messages into categories (bugfix, feature, chore, docs, refactor, etc.) using a hybrid ML pipeline: TF-IDF + metadata → SVM / RF / CatBoost.

Features

Hybrid model — combines TF-IDF on the message text with numeric features (files changed, additions, deletions, test/doc/src counts) and categorical features (file extensions, directories)
Multiple classifiers — LinearSVC, LogisticRegression, RandomForest, CatBoost, XGBoost
CPU & GPU — --gpu flag enables cuML / CatBoost GPU / XGBoost GPU automatically
Auto-labeled dataset — 83% of raw commits automatically labeled via conventional commit prefixes, gitmoji, and keyword heuristics
Resumable scraping — GitHub commit scraper with exponential backoff, progress persistence, and connection error resilience

Pipeline

parser.py          →  GitHub API → commits.jsonl  (resilient scraping)
build_dataset.py   →  commits.jsonl → dataset.jsonl  (auto-label + feature extraction)
train.py           →  dataset.jsonl → model.joblib  (train & save)
predict.py         →  model.joblib → label  (inference CLI)
commitsorter.py    →  CommitClassifier class  (library API)

Usage

CLI

bash

# single prediction
python predict.py "fix broad phase crash" 3 45 12

# positional args: message [files_count] [additions] [deletions]
python predict.py "bump lodash to 2.1"

# batch via stdin
echo '{"text":"fix crash","files_count":2}
{"text":"add login page","files_count":5,"additions":200}' | python predict.py --stdin

Library

python

from commitsorter import CommitClassifier

clf = CommitClassifier("model.joblib")

label, scores = clf.sort("fix renderer crash", files_count=2, additions=10)
# → ("bugfix", [("bugfix", 0.77), ("chore", 0.11), ...])

results = clf.sort_batch([
    {"text": "update dependencies", "files_count": 1},
    {"text": "add user auth", "files_count": 5, "additions": 200},
])

Training

bash

# single model (default: SVM)
python train.py svm
python train.py lr
python train.py rf
python train.py cb
python train.py xgb

# tournament — compare all models
python train.py --all

# GPU acceleration (requires cuML / CUDA)
python train.py svm --gpu
python train.py --all --gpu

Dataset features

Each training sample:

Field	Type	Description
`text`	string	Commit message (prefix stripped)
`files_count`	int	Number of files changed
`additions`	int	Lines added
`deletions`	int	Lines deleted
`changed_tests`	int	Test files touched
`changed_docs`	int	Doc files touched
`changed_source`	int	Source files touched
`has_tests`	bool	Any file in a test directory
`has_docs`	bool	Any file in a docs directory
`extensions`	[string]	Unique file extensions
`directories`	[string]	Unique top-level directories
`label`	string	Target class

Model: TF-IDF + metadata

commit message ──→ TF-IDF(10k unigrams+bigrams)
file metadata  ──→ StandardScaler
extensions     ──→ OneHotEncoder
directories    ──→ OneHotEncoder
                        ↓
                 ColumnTransformer
                        ↓
                 LinearSVC / RF / CatBoost / XGBoost

Best result: LinearSVC at 79% accuracy on held-out test set.

Supported labels

Label	Heuristics
`bugfix`	`fix:`, `:bug:`, "crash", "prevent"
`feature`	`feat:`, `:sparkles:`, "add", "implement"
`chore`	`chore:`, "bump", "update", "lint"
`docs`	`docs:`, file-only docs/, `.md`
`refactor`	`refactor:`, "cleanup", "remove", "rename"
`test`	`test:`, file-only spec/ or test/
`build`	`build:`, "upgrade", dependency changes
`ci`	`ci:`, CI config changes
`perf`	`perf:`, "optimize"
`style`	`style:`, "format", "prettier"
`revert`	`revert:`

Data scraping

bash

# needs GITHUB_TOKEN in .env
python parser.py

Collects commits from configured repos into commits.jsonl. Resumes on interrupt via progress.json.

akaruineko/git-commits-sorter

git-commits-sorter

Features

Pipeline

Usage

CLI

Library

Training

Dataset features

Model: TF-IDF + metadata

Supported labels

Data scraping

akaruineko/
git-commits-sorter