hiplot-mm


Namehiplot-mm JSON
Version 0.0.2 PyPI version JSON
download
home_pagehttps://github.com/facebookresearch/hiplot
SummaryHigh dimensional Interactive Plotting tool
upload_time2023-12-09 21:49:39
maintainer
docs_urlNone
authorFacebook AI Research
requires_python>=3.6
license
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            # HiPlot - High dimensional Interactive Plotting [![CircleCI](https://circleci.com/gh/facebookresearch/hiplot/tree/main.svg?style=svg&circle-token=c89b6825078e174cf35bdc18e4ad4a16e28876f9)](https://circleci.com/gh/facebookresearch/hiplot/tree/main)


![Logo](https://raw.githubusercontent.com/facebookresearch/hiplot/main/hiplot/static/logo.png)

[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine)
[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)
[![PyPI download month](https://img.shields.io/pypi/dm/hiplot.svg)](https://pypi.python.org/pypi/hiplot/) [![PyPI version](https://img.shields.io/pypi/v/hiplot.svg)](https://pypi.python.org/pypi/hiplot/) [![docs](https://img.shields.io/badge/docs-passing-brightgreen.svg)](https://facebookresearch.github.io/hiplot/index.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/hiplot/blob/main/examples/HiPlotColabExample.ipynb)


HiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.

### [Try a demo now with sweep data](https://facebookresearch.github.io/hiplot/_static/demo/ml1.csv.html) or [upload your CSV](https://facebookresearch.github.io/hiplot/_static/hiplot_upload.html)  or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/hiplot/blob/main/examples/HiPlotColabExample.ipynb)

There are several modes to HiPlot:
- As a web-server (if your data is a CSV for instance)
- In a jupyter notebook (to visualize python data), or in [Streamlit apps](https://facebookresearch.github.io/hiplot/tuto_streamlit.html)
- In CLI to render standalone HTML


```bash
pip install -U hiplot  # Or for conda users: conda install -c conda-forge hiplot
```

If you have a jupyter notebook, you can get started with something as simple as:

```python
import hiplot as hip
data = [{'dropout':0.1, 'lr': 0.001, 'loss': 10.0, 'optimizer': 'SGD'},
        {'dropout':0.15, 'lr': 0.01, 'loss': 3.5, 'optimizer': 'Adam'},
        {'dropout':0.3, 'lr': 0.1, 'loss': 4.5, 'optimizer': 'Adam'}]
hip.Experiment.from_iterable(data).display()
```

### [See the live result](https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html)
![Result](https://raw.githubusercontent.com/facebookresearch/hiplot/main/assets/notebook.png)

## Links

* Blog post: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/
* Documentation: https://facebookresearch.github.io/hiplot/index.html
* Pypi package: https://pypi.org/project/hiplot/
* Conda package: https://anaconda.org/conda-forge/hiplot
* NPM package: https://www.npmjs.com/package/hiplot
* Examples: https://github.com/facebookresearch/hiplot/tree/main/examples


## Citing

```bibtex
@misc{hiplot,
    author = {Haziza, D. and Rapin, J. and Synnaeve, G.},
    title = {{Hiplot, interactive high-dimensionality plots}},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/facebookresearch/hiplot}},
}
```

## Credits
Inspired by and based on code from [Kai Chang](http://bl.ocks.org/syntagmatic/3150059), [Mike Bostock](http://bl.ocks.org/1341021) and [Jason Davies](http://bl.ocks.org/1341281).

External contributors (*please add your name when you submit your first pull request*):
- [louismartin](https://github.com/louismartin)
- [GoldenCorgi](https://github.com/GoldenCorgi)
- [callistachang](https://github.com/callistachang)


## License
HiPlot is [MIT](LICENSE) licensed, as found in the [LICENSE](LICENSE) file.

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/facebookresearch/hiplot",
    "name": "hiplot-mm",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.6",
    "maintainer_email": "",
    "keywords": "",
    "author": "Facebook AI Research",
    "author_email": "",
    "download_url": "https://files.pythonhosted.org/packages/6d/a8/cf4e432528a81186436d8ef29f3b0799bdc64d2ddee43284e159516f7c6f/hiplot-mm-0.0.2.tar.gz",
    "platform": null,
    "description": "# HiPlot - High dimensional Interactive Plotting [![CircleCI](https://circleci.com/gh/facebookresearch/hiplot/tree/main.svg?style=svg&circle-token=c89b6825078e174cf35bdc18e4ad4a16e28876f9)](https://circleci.com/gh/facebookresearch/hiplot/tree/main)\n\n\n![Logo](https://raw.githubusercontent.com/facebookresearch/hiplot/main/hiplot/static/logo.png)\n\n[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine)\n[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)\n[![PyPI download month](https://img.shields.io/pypi/dm/hiplot.svg)](https://pypi.python.org/pypi/hiplot/) [![PyPI version](https://img.shields.io/pypi/v/hiplot.svg)](https://pypi.python.org/pypi/hiplot/) [![docs](https://img.shields.io/badge/docs-passing-brightgreen.svg)](https://facebookresearch.github.io/hiplot/index.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/hiplot/blob/main/examples/HiPlotColabExample.ipynb)\n\n\nHiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.\n\n### [Try a demo now with sweep data](https://facebookresearch.github.io/hiplot/_static/demo/ml1.csv.html) or [upload your CSV](https://facebookresearch.github.io/hiplot/_static/hiplot_upload.html)  or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/hiplot/blob/main/examples/HiPlotColabExample.ipynb)\n\nThere are several modes to HiPlot:\n- As a web-server (if your data is a CSV for instance)\n- In a jupyter notebook (to visualize python data), or in [Streamlit apps](https://facebookresearch.github.io/hiplot/tuto_streamlit.html)\n- In CLI to render standalone HTML\n\n\n```bash\npip install -U hiplot  # Or for conda users: conda install -c conda-forge hiplot\n```\n\nIf you have a jupyter notebook, you can get started with something as simple as:\n\n```python\nimport hiplot as hip\ndata = [{'dropout':0.1, 'lr': 0.001, 'loss': 10.0, 'optimizer': 'SGD'},\n        {'dropout':0.15, 'lr': 0.01, 'loss': 3.5, 'optimizer': 'Adam'},\n        {'dropout':0.3, 'lr': 0.1, 'loss': 4.5, 'optimizer': 'Adam'}]\nhip.Experiment.from_iterable(data).display()\n```\n\n### [See the live result](https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html)\n![Result](https://raw.githubusercontent.com/facebookresearch/hiplot/main/assets/notebook.png)\n\n## Links\n\n* Blog post: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/\n* Documentation: https://facebookresearch.github.io/hiplot/index.html\n* Pypi package: https://pypi.org/project/hiplot/\n* Conda package: https://anaconda.org/conda-forge/hiplot\n* NPM package: https://www.npmjs.com/package/hiplot\n* Examples: https://github.com/facebookresearch/hiplot/tree/main/examples\n\n\n## Citing\n\n```bibtex\n@misc{hiplot,\n    author = {Haziza, D. and Rapin, J. and Synnaeve, G.},\n    title = {{Hiplot, interactive high-dimensionality plots}},\n    year = {2020},\n    publisher = {GitHub},\n    journal = {GitHub repository},\n    howpublished = {\\url{https://github.com/facebookresearch/hiplot}},\n}\n```\n\n## Credits\nInspired by and based on code from [Kai Chang](http://bl.ocks.org/syntagmatic/3150059), [Mike Bostock](http://bl.ocks.org/1341021) and [Jason Davies](http://bl.ocks.org/1341281).\n\nExternal contributors (*please add your name when you submit your first pull request*):\n- [louismartin](https://github.com/louismartin)\n- [GoldenCorgi](https://github.com/GoldenCorgi)\n- [callistachang](https://github.com/callistachang)\n\n\n## License\nHiPlot is [MIT](LICENSE) licensed, as found in the [LICENSE](LICENSE) file.\n\n\n",
    "bugtrack_url": null,
    "license": "",
    "summary": "High dimensional Interactive Plotting tool",
    "version": "0.0.2",
    "project_urls": {
        "Homepage": "https://github.com/facebookresearch/hiplot"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "8db66b5b4af671f7541e34c5589dcf57179eb7dd16e88a3e227a7845d4a01e16",
                "md5": "d68e56ad7a5c49be390437ebd64565d4",
                "sha256": "bb43b2e70de854667fbbf7bfd7815e83330d53daaff8721495fc73f615073a6b"
            },
            "downloads": -1,
            "filename": "hiplot_mm-0.0.2-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "d68e56ad7a5c49be390437ebd64565d4",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.6",
            "size": 16966125,
            "upload_time": "2023-12-09T21:49:35",
            "upload_time_iso_8601": "2023-12-09T21:49:35.398497Z",
            "url": "https://files.pythonhosted.org/packages/8d/b6/6b5b4af671f7541e34c5589dcf57179eb7dd16e88a3e227a7845d4a01e16/hiplot_mm-0.0.2-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "6da8cf4e432528a81186436d8ef29f3b0799bdc64d2ddee43284e159516f7c6f",
                "md5": "12caa921884371960e91b031d326b28c",
                "sha256": "f45e6d4b079e537ebdcb555285c47bad3ce900c40e371c70f7d4f28471b7dd5e"
            },
            "downloads": -1,
            "filename": "hiplot-mm-0.0.2.tar.gz",
            "has_sig": false,
            "md5_digest": "12caa921884371960e91b031d326b28c",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 881110,
            "upload_time": "2023-12-09T21:49:39",
            "upload_time_iso_8601": "2023-12-09T21:49:39.117321Z",
            "url": "https://files.pythonhosted.org/packages/6d/a8/cf4e432528a81186436d8ef29f3b0799bdc64d2ddee43284e159516f7c6f/hiplot-mm-0.0.2.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-12-09 21:49:39",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "facebookresearch",
    "github_project": "hiplot",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "circle": true,
    "lcname": "hiplot-mm"
}
Facebook AI Research
Elapsed time: 0.43378s