| --- |
| license: cc-by-4.0 |
| pretty_name: Code4ML Annotated Subset with SonarQube and MLScent Ratings |
| language: |
| - en |
| tags: |
| - code |
| - python |
| - machine-learning |
| - code4ml |
| - kaggle |
| - code-quality |
| - static-analysis |
| - sonarqube |
| task_categories: |
| - text-generation |
| - text-classification |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: code4ml_annotated.csv |
| - config_name: training |
| data_files: |
| - split: train |
| path: code4ml_annotated_training.csv |
| --- |
| |
| # Code4ML Annotated Subset with SonarQube and MLScent Ratings |
|
|
| The 7,947 manually annotated Python snippets from the **Code4ML** corpus |
| ([Zenodo record 7312803](https://zenodo.org/records/7312803)), joined with their task-class |
| names and annotated with structural code-quality ratings from **SonarQube** and **MLScent**. |
|
|
| Code4ML contains 2,599,355 code cells from 23,103 Kaggle notebooks. Only this annotated |
| subset carries a human-assigned task label, so it is the subset usable for building |
| instruction–code pairs. This repository holds that subset in one file. Each snippet was scanned on its own, so |
| the ratings describe the snippet, not its whole notebook. |
|
|
| ## Dataset summary |
|
|
| | | | |
| |---|---| |
| | Records | 7,947 | |
| | Source file | Code4ML `markup_data.csv`, labels from `vertices.csv` | |
| | Task classes | 12 classes, 70 subclasses | |
| | Format | single comma-delimited CSV with header | |
| | Language | Python | |
|
|
| ## Columns |
|
|
| | column | type | description | |
| |---|---|---| |
| | `id` | int | row index from Code4ML `markup_data.csv` | |
| | `code_block` | string | the Python snippet (one notebook cell, may span multiple lines) | |
| | `too_long` | string | `Yes`/`No` flag from the Code4ML annotators | |
| | `marks` | int | annotator confidence, 1 (lowest) to 5 (highest) | |
| | `graph_vertex_id` | int | task-label id from Code4ML `vertices.csv` | |
| | `graph_vertex_class` | string | task class, e.g. `Data_Transform`, `Model_Train` | |
| | `graph_vertex_subclass` | string | task subclass, e.g. `choose_model_class` | |
| | `kernel_id` | int or empty | Kaggle notebook id, recovered by matching `code_block` text against Code4ML `code_blocks.csv`; empty when the text appears in more than one notebook or none | |
| | `sqale_rating` | float | SonarQube SQALE rating (1.0 = A … 5.0 = E) | |
| | `maintainability_rating` | string | SonarQube maintainability grade `A`–`E` | |
| | `code_smells` | int | SonarQube code smells | |
| | `complexity` | int | SonarQube cyclomatic complexity | |
| | `cognitive_complexity` | int | SonarQube cognitive complexity | |
| | `bugs` | int | SonarQube bugs | |
| | `sqale_index` | int | SonarQube technical debt (minutes) | |
| | `ncloc` | int or empty | non-comment lines of code; empty when SonarQube did not analyse the snippet | |
| | `mlscent_analysis_status` | string | `ok`, `no_ml_import` (no ML library import in the snippet or its recovered notebook imports), or `invalid_python` | |
| | `mlscent_smell_count` | int | number of MLScent findings | |
| | `mlscent_unique_smell_count` | int | number of distinct MLScent smell types | |
| | `mlscent_findings_json` | JSON string | all MLScent findings | |
| | `mlscent_ml_rating` | string | `A`–`E` from ML-specific findings only, by severity; `N/A` when MLScent did not analyse the snippet | |
| | `mlscent_ml_rating_basis` | string | findings and severities behind `mlscent_ml_rating` | |
| | `mlscent_ml_findings_json` | JSON string | ML-specific findings used for the rating | |
| | `mlscent_ignored_findings_json` | JSON string | finding types not used for the rating | |
| | `context_line_count` | int | lines of recovered notebook ML imports prepended for MLScent analysis | |
| | `combined_rating` | string | worse of `maintainability_rating` and `mlscent_ml_rating`; `N/A` when either is missing | |
|
|
| ## Statistics |
|
|
| Task classes: |
|
|
| | `graph_vertex_class` | Snippets | |
| |---|---:| |
| | Data_Transform | 2,387 | |
| | Debug | 1,446 | |
| | Visualization | 862 | |
| | Model_Train | 816 | |
| | Data_Extraction | 517 | |
| | Environment | 441 | |
| | Other | 372 | |
| | Model_Evaluation | 354 | |
| | Data_Export | 350 | |
| | EDA | 231 | |
| | Hyperparam_Tuning | 160 | |
| | Model_Interpretation | 11 | |
| |
| Annotator confidence (`marks`): |
| |
| | 1 | 2 | 3 | 4 | 5 | |
| |---:|---:|---:|---:|---:| |
| | 44 | 210 | 903 | 1,419 | 5,371 | |
| |
| Other properties: |
| |
| | Property | Value | |
| |---|---| |
| | `too_long` = `Yes` | 1,360 | |
| | Duplicate `code_block` values | 108 | |
| | Lines per snippet (median / p75 / max) | 2 / 6 / 582 | |
|
|
| Ratings: |
|
|
| | Rating | `maintainability_rating` | `mlscent_ml_rating` | `combined_rating` | |
| |---|---:|---:|---:| |
| | A | 7,816 | 3,045 | 2,996 | |
| | B | 84 | 952 | 962 | |
| | C | 40 | 56 | 89 | |
| | D | 7 | 111 | 117 | |
| | E | 0 | 8 | 8 | |
| | N/A | — | 3,775 | 3,775 | |
|
|
| | Rating property | Value | |
| |---|---| |
| | `kernel_id` recovered | 4,373 | |
| | `ncloc` empty (not analysed by SonarQube) | 1,003 | |
| | `mlscent_analysis_status` = `ok` / `no_ml_import` / `invalid_python` | 4,172 / 3,551 / 224 | |
|
|
| No rows were removed or modified; duplicates and short snippets are kept as in the source. |
|
|
| ## How the ratings were produced |
|
|
| 1. **SonarQube** Community Edition, default Python profile, through a FastAPI wrapper around |
| `sonar-scanner` (`/scan-batch`). |
| 2. **MLScent** (`ml-code-smell-detector==0.1.2`), with recovered notebook ML imports |
| prepended as context, through a local rating service (`/rate-batch`). |
| 3. The data was split into two halves, each rated by its own SonarQube + MLScent instance |
| pair, then merged back by `id`. |
|
|
| ## Training file |
|
|
| `code4ml_annotated_training.csv` holds 3,063 instruction–code pairs built from |
| `code4ml_annotated.csv` for fine-tuning: |
|
|
| | Step | Rule | Rows dropped | |
| |---|---|---:| |
| | 1 | Drop task classes without ML logic: `Debug`, `Environment`, `Other` | 2,259 | |
| | 2 | Drop `combined_rating` = N/A | 2,502 | |
| | 2 | Drop `combined_rating` = D or E (quality floor) | 121 | |
| | 3 | Drop duplicate `code_block` | 0 | |
| | 4 | Turn the subclass label into an instruction, e.g. `train_model` → "Write Python code to train the model." | — | |
| | 5 | Drop snippets whose 8-grams overlap a DS-1000 reference solution by ≥ 50% | 2 | |
| | 6 | Split 80/10/10 per task class with seed 42 | — | |
|
|
| | Column | Description | |
| |---|---| |
| | `id` | row `id` in `code4ml_annotated.csv` | |
| | `split` | `train` (2,451), `validation` (308), or `test` (304) | |
| | `graph_vertex_class`, `graph_vertex_subclass` | Code4ML task label | |
| | `instruction` | instruction sentence built from the subclass label | |
| | `code` | the snippet | |
| | `combined_rating` | `A` (2,122), `B` (868), or `C` (73) | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("dimsmauls/code4ml-annotated-rated", split="train") |
| print(ds[0]["graph_vertex_class"], ds[0]["combined_rating"], ds[0]["code_block"]) |
| |
| train = load_dataset("dimsmauls/code4ml-annotated-rated", "training", split="train") |
| train_set = train.filter(lambda row: row["split"] == "train") |
| ``` |
|
|
| ## Provenance & attribution |
|
|
| - **Source corpus:** Code4ML, [Zenodo record 7312803](https://zenodo.org/records/7312803), |
| released under CC-BY-4.0. |
| - **Original code:** the snippets are Kaggle notebook cells, each published under the |
| **Apache-2.0** license. See `NOTICE.txt`. |
| - **This derivative** joins `markup_data.csv` with the class names from `vertices.csv` and adds |
| SonarQube and MLScent rating columns. No code was changed. |
|
|
| ## Citation |
|
|
| Please cite the original Code4ML work: |
|
|
| ```bibtex |
| @article{drozdova2023code4ml, |
| title={Code4ML: a Large-scale Dataset of Annotated Machine Learning Code}, |
| author={Drozdova, Anastasia and Trofimova, Ekaterina and Guseva, Polina and Scherbakova, Anna and Ustyuzhanin, Andrey}, |
| journal={PeerJ Computer Science}, |
| year={2023}, |
| doi={10.7717/peerj-cs.1230} |
| } |
| ``` |
|
|
| ## License |
|
|
| Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/), the same license |
| as Code4ML. The underlying `code_block` snippets are Kaggle notebook code under |
| **Apache-2.0**; that attribution is preserved in `NOTICE.txt`. |
|
|
