dimsmauls/code4ml-annotated-rated

Code4ML Annotated Subset with SonarQube and MLScent Ratings

The 7,947 manually annotated Python snippets from the Code4ML corpus (Zenodo record 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

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, 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:

@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, 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.