# Crowd-Kit: Computational Quality Control for Crowdsourcing
[](https://github.com/Toloka/crowd-kit)
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[![Documentation][docs_badge]][docs_link]
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[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.
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"description": "# Crowd-Kit: Computational Quality Control for Crowdsourcing\n\n[](https://github.com/Toloka/crowd-kit)\n\n[![PyPI Version][pypi_badge]][pypi_link]\n[![GitHub Tests][github_tests_badge]][github_tests_link]\n[![Codecov][codecov_badge]][codecov_link]\n[![Documentation][docs_badge]][docs_link]\n[![Paper][paper_badge]][paper_link]\n\n[pypi_badge]: https://badge.fury.io/py/crowd-kit.svg\n[pypi_link]: https://pypi.python.org/pypi/crowd-kit\n[github_tests_badge]: https://github.com/Toloka/crowd-kit/actions/workflows/tests.yml/badge.svg?branch=main\n[github_tests_link]: https://github.com/Toloka/crowd-kit/actions/workflows/tests.yml\n[codecov_badge]: https://codecov.io/gh/Toloka/crowd-kit/branch/main/graph/badge.svg\n[codecov_link]: https://codecov.io/gh/Toloka/crowd-kit\n[docs_badge]: https://readthedocs.org/projects/crowd-kit/badge/\n[docs_link]: https://crowd-kit.readthedocs.io/\n[paper_badge]: https://joss.theoj.org/papers/10.21105/joss.06227/status.svg\n[paper_link]: https://doi.org/10.21105/joss.06227\n\n**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.\n\nCurrently, Crowd-Kit contains:\n\n* implementations of commonly-used aggregation methods for categorical, pairwise, textual, and segmentation responses;\n* metrics of uncertainty, consistency, and agreement with aggregate;\n* loaders for popular crowdsourced datasets.\n\nAlso, the `learning` subpackage contains PyTorch implementations of deep learning from crowds methods and advanced aggregation algorithms.\n\n## Installing\n\nTo 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]`.\n\nIf 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.\n\n## Getting Started\n\nThis example shows how to use Crowd-Kit for categorical aggregation using the classical Dawid-Skene algorithm.\n\nFirst, let us do all the necessary imports.\n\n````python\nfrom crowdkit.aggregation import DawidSkene\nfrom crowdkit.datasets import load_dataset\n\nimport pandas as pd\n````\n\nThen, you need to read your annotations into Pandas DataFrame with columns `task`, `worker`, `label`. Alternatively, you can download an example dataset:\n\n````python\ndf = pd.read_csv('results.csv') # should contain columns: task, worker, label\n# df, ground_truth = load_dataset('relevance-2') # or download an example dataset\n````\n\nThen, you can aggregate the workers' responses using the `fit_predict` method from the **scikit-learn** library:\n\n````python\naggregated_labels = DawidSkene(n_iter=100).fit_predict(df)\n````\n\n[More usage examples](https://github.com/Toloka/crowd-kit/tree/main/examples)\n\n## Implemented Aggregation Methods\n\nBelow is the list of currently implemented methods, including the already available (\u2705) and in progress (\ud83d\udfe1).\n\n### Categorical Responses\n\n| Method | Status |\n| ------------- | :-------------: |\n| [Majority Vote](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.majority_vote.MajorityVote) | \u2705 |\n| [One-coin Dawid-Skene](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.dawid_skene.OneCoinDawidSkene) | \u2705 |\n| [Dawid-Skene](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.dawid_skene.DawidSkene) | \u2705 |\n| [Gold Majority Vote](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.gold_majority_vote.GoldMajorityVote) | \u2705 |\n| [M-MSR](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.m_msr.MMSR) | \u2705 |\n| [Wawa](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.wawa.Wawa) | \u2705 |\n| [Zero-Based Skill](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.zero_based_skill.ZeroBasedSkill) | \u2705 |\n| [GLAD](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.glad.GLAD) | \u2705 |\n| [KOS](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.kos.KOS) | \u2705 |\n| [MACE](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.classification.mace.MACE) | \u2705 |\n\n### Multi-Label Responses\n\n|Method|Status|\n|-|:-:|\n|[Binary Relevance](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.multilabel.binary_relevance.BinaryRelevance)|\u2705|\n\n### Textual Responses\n\n| Method | Status |\n| ------------- | :-------------: |\n| [RASA](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.embeddings.rasa.RASA) | \u2705 |\n| [HRRASA](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.embeddings.hrrasa.HRRASA) | \u2705 |\n| [ROVER](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.texts.rover.ROVER) | \u2705 |\n\n### Image Segmentation\n\n| Method | Status |\n| ------------------ | :------------------: |\n| [Segmentation MV](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.image_segmentation.segmentation_majority_vote.SegmentationMajorityVote) | \u2705 |\n| [Segmentation RASA](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.image_segmentation.segmentation_rasa.SegmentationRASA) | \u2705 |\n| [Segmentation EM](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.image_segmentation.segmentation_em.SegmentationEM) | \u2705 |\n\n### Pairwise Comparisons\n\n| Method | Status |\n| -------------- | :---------------------: |\n| [Bradley-Terry](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.pairwise.bradley_terry.BradleyTerry) | \u2705 |\n| [Noisy Bradley-Terry](https://toloka.ai/docs/crowd-kit/reference/crowdkit.aggregation.pairwise.noisy_bt.NoisyBradleyTerry) | \u2705 |\n\n### Learning from Crowds\n\n|Method|Status|\n|-|:-:|\n|[CrowdLayer](https://toloka.ai/docs/crowd-kit/reference/crowdkit.learning.crowd_layer.CrowdLayer)|\u2705|\n|[CoNAL](https://toloka.ai/docs/crowd-kit/reference/crowdkit.learning.conal.CoNAL)|\u2705|\n\n## Citation\n\n* Ustalov D., Pavlichenko N., Tseitlin B. 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