| Name | ComputationalHypergraphDiscovery JSON |
| Version |
1.0.4
JSON |
| download |
| home_page | |
| Summary | Implements the Computational Hypergraph Discovery algorithm |
| upload_time | 2024-03-12 21:06:28 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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| keywords |
machine learning
data science
applied maths
|
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# Computational Hypergraph Discovery: A Gaussian process framework for connecting the dots
[](https://opensource.org/license/apache-2-0/)
[](https://www.python.org/downloads/release/python-3114/)
[](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery)
This is the source code for the paper ["Computational Hypergraph Discovery: A Gaussian process framework for connecting the dots"](https://arxiv.org/abs/2311.17007).
Please see the [companion blog post](https://theobourdais.github.io/posts/2023/11/CHD/) for a gentle introduction to the method and the code. See the repo [here](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery) for full documentation and examples.
## Installation
The code is written in Python 3 and requires the following packages:
- matplotlib
- NumPy
- scipy
- scikit-learn
- networkx
You can install using pip:
```bash
pip install ComputationalHypergraphDiscovery
```
## Quick start
Graph discovery takes very little time. The following code runs the method on the example dataset provided in the repo. The dataset is a 2D array of shape (n_samples, n_features) where each row is a sample and each column is a feature. After fitting the model, the graph is stored in the `GraphDiscovery` object, specifically its graph `G` attribute. The graph is a `networkx` object, which can be easily plotted using `.plot_graph()`.
>You can find the Sachs dataset in the repo, at this [link](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery/blob/main/examples/SachsData.csv).
```python
import ComputationalHypergraphDiscovery as CHD
import pandas as pd
df=pd.read_csv('path_to\SachsData.csv')
kernels=CHD.Modes.LinearMode()+CHD.Modes.QuadraticMode()
graph_discovery = CHD.GraphDiscovery.from_dataframe(df,mode_kernels=kernels)
graph_discovery.fit()
graph_discovery.plot_graph()
```
## Available modifications of the base algorithm
The code gives an easy-to-use interface to manipulate the graph discovery method. It is designed to be modular and flexible. The main changes you can make are
- **Kernels and modes**: You can decide what type of function will be used to link the nodes. The code provides a set of kernels, but you can easily add your own. The interface is designed to resemble the scikit-learn API, and you can use any kernel from scikit-learn.
- **Decision logics**: In order to identify the edges of the graph, we need to decide whether certain connections are significant. The code provides indicators (like the level of noise), and the user specifies how to interpret them. The code provides a set of decision logic, but you can define your own.
- **Clustering**: If a set of nodes is highly dependent, it is possible to merge them into a cluster of nodes. This gives greater readability and prevents the graph discovery method from missing other connections.
- **Possible edges**: If you know that specific nodes cannot be connected, you can specify it to the algorithm. By default, all edges are possible.
Full documentation is available [here](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery).
## Acknowledgements
Copyright 2023 by the California Institute of Technology. ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged. This software may be subject to U.S. export control laws. By accepting this software, the user agrees to comply with all applicable U.S. export laws and regulations. User has the responsibility to obtain export licenses, or other export authority as may be required before exporting such information to foreign countries or providing access to foreign persons.
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"author_email": "Theo Bourdais <tbourdai@caltech.edu>, Houman Owhadi <owhadi@caltech.edu>, Pau Batlle Franch <pbatllef@caltech.edu>, Ricardo Baptista <rsb@caltech.edu>, Xianjin Yang <yxjmath@caltech.edu>",
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"description": "\n# Computational Hypergraph Discovery: A Gaussian process framework for connecting the dots\n\n[](https://opensource.org/license/apache-2-0/)\n[](https://www.python.org/downloads/release/python-3114/)\n[](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery)\n\nThis is the source code for the paper [\"Computational Hypergraph Discovery: A Gaussian process framework for connecting the dots\"](https://arxiv.org/abs/2311.17007). \n\nPlease see the [companion blog post](https://theobourdais.github.io/posts/2023/11/CHD/) for a gentle introduction to the method and the code. See the repo [here](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery) for full documentation and examples.\n\n\n## Installation \n\nThe code is written in Python 3 and requires the following packages:\n- matplotlib\n- NumPy\n- scipy\n- scikit-learn\n- networkx\n\nYou can install using pip:\n\n```bash\npip install ComputationalHypergraphDiscovery\n```\n\n\n## Quick start\n\nGraph discovery takes very little time. The following code runs the method on the example dataset provided in the repo. The dataset is a 2D array of shape (n_samples, n_features) where each row is a sample and each column is a feature. After fitting the model, the graph is stored in the `GraphDiscovery` object, specifically its graph `G` attribute. The graph is a `networkx` object, which can be easily plotted using `.plot_graph()`.\n\n>You can find the Sachs dataset in the repo, at this [link](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery/blob/main/examples/SachsData.csv).\n\n```python\nimport ComputationalHypergraphDiscovery as CHD\nimport pandas as pd\ndf=pd.read_csv('path_to\\SachsData.csv')\nkernels=CHD.Modes.LinearMode()+CHD.Modes.QuadraticMode()\ngraph_discovery = CHD.GraphDiscovery.from_dataframe(df,mode_kernels=kernels)\ngraph_discovery.fit()\ngraph_discovery.plot_graph()\n```\n\n## Available modifications of the base algorithm\n\nThe code gives an easy-to-use interface to manipulate the graph discovery method. It is designed to be modular and flexible. The main changes you can make are\n- **Kernels and modes**: You can decide what type of function will be used to link the nodes. The code provides a set of kernels, but you can easily add your own. The interface is designed to resemble the scikit-learn API, and you can use any kernel from scikit-learn. \n- **Decision logics**: In order to identify the edges of the graph, we need to decide whether certain connections are significant. The code provides indicators (like the level of noise), and the user specifies how to interpret them. The code provides a set of decision logic, but you can define your own. \n- **Clustering**: If a set of nodes is highly dependent, it is possible to merge them into a cluster of nodes. This gives greater readability and prevents the graph discovery method from missing other connections. \n- **Possible edges**: If you know that specific nodes cannot be connected, you can specify it to the algorithm. By default, all edges are possible. \n\n\nFull documentation is available [here](https://github.com/TheoBourdais/ComputationalHypergraphDiscovery). \n\n## Acknowledgements\n\nCopyright 2023 by the California Institute of Technology. ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged. This software may be subject to U.S. export control laws. By accepting this software, the user agrees to comply with all applicable U.S. export laws and regulations. User has the responsibility to obtain export licenses, or other export authority as may be required before exporting such information to foreign countries or providing access to foreign persons.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright [2023] [California Institute of Technology] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
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