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https://github.com/Toloka/crowd-kit

Control the quality of your labeled data with the Python tools you already know.
https://github.com/Toloka/crowd-kit

aggregations annotation crowd crowdsourcing data-mining data-science labeling python quality-control toloka truth-inference

Last synced: about 1 month ago
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Control the quality of your labeled data with the Python tools you already know.

Host: GitHub
URL: https://github.com/Toloka/crowd-kit
Owner: Toloka
License: other
Created: 2021-03-01T23:02:08.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-04-13T12:50:20.000Z (5 months ago)
Last Synced: 2024-04-14T02:39:49.923Z (5 months ago)
Topics: aggregations, annotation, crowd, crowdsourcing, data-mining, data-science, labeling, python, quality-control, toloka, truth-inference
Language: Python
Homepage: https://crowd-kit.readthedocs.io/
Size: 1.32 MB
Stars: 191
Watchers: 13
Forks: 15
Open Issues: 4
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
- Authors: AUTHORS

Awesome Lists containing this project

README

        # Crowd-Kit: Computational Quality Control for Crowdsourcing

[![Crowd-Kit](https://tlk.s3.yandex.net/crowd-kit/Crowd-Kit-GitHub.png)](https://github.com/Toloka/crowd-kit)

[![PyPI Version][pypi_badge]][pypi_link]

[![GitHub Tests][github_tests_badge]][github_tests_link]

[![Codecov][codecov_badge]][codecov_link]

[![Documentation][docs_badge]][docs_link]

[![Paper][paper_badge]][paper_link]

[pypi_badge]: https://badge.fury.io/py/crowd-kit.svg

[pypi_link]: https://pypi.python.org/pypi/crowd-kit

[github_tests_badge]: https://github.com/Toloka/crowd-kit/actions/workflows/tests.yml/badge.svg?branch=main

[github_tests_link]: https://github.com/Toloka/crowd-kit/actions/workflows/tests.yml

[codecov_badge]: https://codecov.io/gh/Toloka/crowd-kit/branch/main/graph/badge.svg

[codecov_link]: https://codecov.io/gh/Toloka/crowd-kit

[docs_badge]: https://readthedocs.org/projects/crowd-kit/badge/

[docs_link]: https://crowd-kit.readthedocs.io/

[paper_badge]: https://joss.theoj.org/papers/10.21105/joss.06227/status.svg

[paper_link]: https://doi.org/10.21105/joss.06227

**Crowd-Kit** is a powerful Python library that implements commonly-used aggregation methods for crowdsourced annotation and offers the relevant metrics and datasets. We strive to implement functionality that simplifies working with crowdsourced data.

Currently, Crowd-Kit contains:

* implementations of commonly-used aggregation methods for categorical, pairwise, textual, and segmentation responses;

* metrics of uncertainty, consistency, and agreement with aggregate;

* loaders for popular crowdsourced datasets.

Also, the `learning` subpackage contains PyTorch implementations of deep learning from crowds methods and advanced aggregation algorithms.

## Installing

To install Crowd-Kit, run the following command: `pip install crowd-kit`. If you also want to use the `learning` subpackage, type `pip install crowd-kit[learning]`.

If you are interested in contributing to Crowd-Kit, use [Pipenv](https://pipenv.pypa.io/en/latest/) to install the library with its dependencies: `pipenv install --dev`. We use [pytest](https://pytest.org/) for testing.

## Getting Started

This example shows how to use Crowd-Kit for categorical aggregation using the classical Dawid-Skene algorithm.

First, let us do all the necessary imports.

````python

from crowdkit.aggregation import DawidSkene

from crowdkit.datasets import load_dataset

import pandas as pd

````

Then, you need to read your annotations into Pandas DataFrame with columns `task`, `worker`, `label`. Alternatively, you can download an example dataset:

````python

df = pd.read_csv('results.csv')  # should contain columns: task, worker, label

# df, ground_truth = load_dataset('relevance-2')  # or download an example dataset

````

Then, you can aggregate the workers' responses using the `fit_predict` method from the **scikit-learn** library:

````python

aggregated_labels = DawidSkene(n_iter=100).fit_predict(df)

````

[More usage examples](https://github.com/Toloka/crowd-kit/tree/main/examples)

## Implemented Aggregation Methods

Below is the list of currently implemented methods, including the already available (✅) and in progress (🟡).

### Categorical Responses

| Method | Status |

| ------------- | :-------------: |

| [Majority Vote](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.majority_vote.MajorityVote) | ✅ |

| [One-coin Dawid-Skene](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.dawid_skene.OneCoinDawidSkene) | ✅ |

| [Dawid-Skene](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.dawid_skene.DawidSkene) | ✅ |

| [Gold Majority Vote](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.gold_majority_vote.GoldMajorityVote) | ✅ |

| [M-MSR](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.m_msr.MMSR) | ✅ |

| [Wawa](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.wawa.Wawa) | ✅ |

| [Zero-Based Skill](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.zero_based_skill.ZeroBasedSkill) | ✅ |

| [GLAD](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.glad.GLAD) | ✅ |

| [KOS](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.kos.KOS) | ✅ |

| [MACE](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.mace.MACE) | ✅ |

### Multi-Label Responses

|Method|Status|

|-|:-:|

|[Binary Relevance](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.multilabel.binary_relevance.BinaryRelevance)|✅|

### Textual Responses

| Method | Status |

| ------------- | :-------------: |

| [RASA](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.embeddings.rasa.RASA) | ✅ |

| [HRRASA](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.embeddings.hrrasa.HRRASA) | ✅ |

| [ROVER](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.texts.rover.ROVER) | ✅ |

### Image Segmentation

| Method | Status |

| ------------------ | :------------------: |

| [Segmentation MV](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.image_segmentation.segmentation_majority_vote.SegmentationMajorityVote) | ✅ |

| [Segmentation RASA](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.image_segmentation.segmentation_rasa.SegmentationRASA) | ✅ |

| [Segmentation EM](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.image_segmentation.segmentation_em.SegmentationEM) | ✅ |

### Pairwise Comparisons

| Method | Status |

| -------------- | :---------------------: |

| [Bradley-Terry](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.pairwise.bradley_terry.BradleyTerry) | ✅ |

| [Noisy Bradley-Terry](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.pairwise.noisy_bt.NoisyBradleyTerry) | ✅ |

### Learning from Crowds

|Method|Status|

|-|:-:|

|[CrowdLayer](https://toloka.ai/docs/crowd-kit/reference/crowdkit.learning.crowd_layer.CrowdLayer)|✅|

|[CoNAL](https://toloka.ai/docs/crowd-kit/reference/crowdkit.learning.conal.CoNAL)|✅|

## Citation

* Ustalov D., Pavlichenko N., Tseitlin B. (2024). [Learning from Crowds with Crowd-Kit](https://doi.org/10.21105/joss.06227). Journal of Open Source Software, 9(96), 6227

```bibtex

@article{CrowdKit,

  author    = {Ustalov, Dmitry and Pavlichenko, Nikita and Tseitlin, Boris},

  title     = {{Learning from Crowds with Crowd-Kit}},

  year      = {2024},

  journal   = {Journal of Open Source Software},

  volume    = {9},

  number    = {96},

  pages     = {6227},

  publisher = {The Open Journal},

  doi       = {10.21105/joss.06227},

  issn      = {2475-9066},

  eprint    = {2109.08584},

  eprinttype = {arxiv},

  eprintclass = {cs.HC},

  language  = {english},

}

```

## Support and Contributions

Please use [GitHub Issues](https://github.com/Toloka/crowd-kit/issues) to seek support and submit feature requests. We accept contributions to Crowd-Kit via GitHub as according to our guidelines in [CONTRIBUTING.md](CONTRIBUTING.md).

## License

© Crowd-Kit team authors, 2020–2024. Licensed under the Apache License, Version 2.0. See LICENSE file for more details.