tinybig 0.2.1
tinybig library for deep function learning
License: MIT License
Requires Python: >=3.10
Keywords: tinybig, rpn, deep function learning, data transformation function, data interdependence function, parameter reconciliation function, remainder function, reconciled polynomial network

<p align="center">
  <a href="https://www.tinybig.org">
    <img src="https://raw.githubusercontent.com/jwzhanggy/tinyBIG/main/docs/assets/img/tinybig.png" alt="function_data" style="max-width: 100%; height: auto;">
  </a>
</p>

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### Introduction

`tinybig` is a Python library developed by the IFM Lab for deep function learning model designing and building.

* List of RPN Papers: 
    * RPN 1 (July 2024): https://arxiv.org/abs/2407.04819
    * RPN 2 (November 2024): https://arxiv.org/abs/2411.11162
    * RPN 3 (To be released ...)
* `tinybig` based Applications:
    * TBD
* Official Website: https://www.tinybig.org/
* PyPI: https://pypi.org/project/tinybig/
* IFM Lab: https://www.ifmlab.org/index.html
* Project Description in Chinese: 
    * [RPN 1 项目中文介绍](docs/中文简介/RPN_1/README.md)
    * [RPN 2 项目中文介绍](docs/中文简介/RPN_2/README.md)

### Citation

If you find `tinybig` library and RPN papers useful in your work, please cite the RPN papers as follows:
```
@article{Zhang2024RPN_version1,
    title={RPN: Reconciled Polynomial Network Towards Unifying PGMs, Kernel SVMs, MLP and KAN},
    author={Jiawei Zhang},
    year={2024},
    eprint={2407.04819},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@article{Zhang2024RPN_version2,
    title={RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer},
    author={Jiawei Zhang},
    year={2024},
    eprint={2411.11162},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

### Installation

You can install `tinybig` either via `pip` or directly from the github source code.

#### Install via Pip

```shell
pip install tinybig
```

#### Install from Source

```shell
git clone https://github.com/jwzhanggy/tinyBIG.git
```

After entering the downloaded source code directory, tinybig can be installed with the following command:

```shell
python setup.py install
```

If you don't have `setuptools` installed locally, please consider to first install `setuptools`:
```shell
pip install setuptools 
```

### Install Dependency

Please download the [requirements.txt](https://github.com/jwzhanggy/tinyBIG/blob/main/requirements.txt) file, and install all the dependency packages:
```shell
pip install -r requirements.txt
```

### Verification

If you have successfully installed both `tinybig` and the dependency packages, now you can use `tinybig` in your projects.

To ensure that `tinybig` was installed correctly, we can verify the installation by running the sample python code as follows:

```python
>>> import torch
>>> import tinybig as tb
>>> expansion_func = tb.expansion.taylor_expansion()
>>> expansion_func(torch.Tensor([[1, 2]]))
```
The output should be something like:
```python
tensor([[1., 2., 1., 2., 2., 4.]])
```

### Tutorials

|                                         Tutorial ID                                         |            Tutorial Title            |    Last Update    |
|:-------------------------------------------------------------------------------------------:|:------------------------------------:|:-----------------:|
|                  [Tutorial 0](https://www.tinybig.org/guides/quick_start/)                  |         Quickstart Tutorial          |   July 6, 2024    |
|     [Tutorial 1](https://www.tinybig.org/tutorials/beginner/module/expansion_function/)     |       Data Expansion Functions       |   July 7, 2024    |
|  [Tutorial 2](https://www.tinybig.org/tutorials/beginner/module/reconciliation_function/)   |  Parameter Reconciliation Functions  | November 28, 2024 |
|  [Tutorial 3](https://www.tinybig.org/tutorials/beginner/module/interdependence_function/)  |    Data Interdependence Functions    | December 1, 2024  |
| [Tutorial 4](https://www.tinybig.org/tutorials/beginner/module/interdependence_function_2/) | Structural Interdependence Functions | December 10, 2024 |

### Examples

|                              Example ID                               |                   Example Title                    | Released Date  |
|:---------------------------------------------------------------------:|:--------------------------------------------------:|:--------------:|
|        [Example 0](https://www.tinybig.org/examples/text/kan/)        |           Failure of KAN on Sparse Data            |  July 9, 2024  |
|  [Example 1](https://www.tinybig.org/examples/function/elementary/)   |         Elementary Function Approximation          |  July 7, 2024  |
|   [Example 2](https://www.tinybig.org/examples/function/composite/)   |          Composite Function Approximation          |  July 8, 2024  |
|    [Example 3](https://www.tinybig.org/examples/function/feynman/)    |           Feynman Function Approximation           |  July 8, 2024  |
|      [Example 4](https://www.tinybig.org/examples/image/mnist/)       | MNIST Classification with Identity Reconciliation  |  July 8, 2024  |
| [Example 5](https://www.tinybig.org/examples/image/mnist_dual_lphm/)  | MNIST Classification with Dual LPHM Reconciliation |  July 8, 2024  |
|     [Example 6](https://www.tinybig.org/examples/image/cifar10/)      |          CIFAR10 Image Object Recognition          |  July 8, 2024  |
|       [Example 7](https://www.tinybig.org/examples/text/imdb/)        |             IMDB Review Classification             |  July 9, 2024  |
|      [Example 8](https://www.tinybig.org/examples/text/agnews/)       |            AGNews Topic Classification             |  July 9, 2024  |
|       [Example 9](https://www.tinybig.org/examples/text/sst2/)        |           SST-2 Sentiment Classification           |  July 9, 2024  |
|     [Example 10](https://www.tinybig.org/examples/tabular/iris/)      |    Iris Species Inference (Naive Probabilistic)    |  July 9, 2024  |
|   [Example 11](https://www.tinybig.org/examples/tabular/diabetes/)    |      Diabetes Diagnosis (Comb. Probabilistic)      |  July 9, 2024  |
|   [Example 12](https://www.tinybig.org/examples/tabular/banknote/)    |   Banknote Authentication (Comb. Probabilistic)	   |  July 9, 2024  |

### Library Organizations

| Components                                                                           | Descriptions                                                                                        |
|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|
| [`tinybig`](https://www.tinybig.org/documentations/tinybig/)                         | a deep function learning library like torch.nn, deeply integrated with autograd                     |
| [`tinybig.model`](https://www.tinybig.org/documentations/model/)                     | a library providing the RPN models for addressing various deep function learning tasks              |
| [`tinybig.module`](https://www.tinybig.org/documentations/module/)                   | a library providing the basic building blocks for RPN model designing and implementation            |
| [`tinybig.layer`](https://www.tinybig.org/documentations/layer/)                     | a library providing the implemented layers for RPN model designing and implementation               |
| [`tinybig.head`](https://www.tinybig.org/documentations/head/)                       | a library providing the implemented heads for RPN model designing and implementation                |
| [`tinybig.config`](https://www.tinybig.org/documentations/config/)                   | a library providing model component instantiation from textual configuration descriptions           |
| [`tinybig.expansion`](https://www.tinybig.org/documentations/expansion/)             | a library providing the "data expansion functions" for effective data expansions                    |
| [`tinybig.compression`](https://www.tinybig.org/documentations/compression/)         | a library providing the "data compression functions" for effective data compression                 |
| [`tinybig.transformation`](https://www.tinybig.org/documentations/transformation/)   | a library providing the "data transformation functions" for effective data transformation           |
| [`tinybig.reconciliation`](https://www.tinybig.org/documentations/reconciliation/)   | a library providing the "parameter reconciliation functions" for parameter efficient learning       |
| [`tinybig.remainder`](https://www.tinybig.org/documentations/remainder/)             | a library providing the "remainder functions" for complementary information addition                |
| [`tinybig.interdependence`](https://www.tinybig.org/documentations/interdependence/) | a library providing the "interdependence functions" for data interdependence relationships modeling |
| [`tinybig.fusion`](https://www.tinybig.org/documentations/fusion/)                   | a library providing the "fusionn functions" for multi-source/channel/head data integration          |
| [`tinybig.koala`](https://www.tinybig.org/documentations/koala/)                     | a library providing the functions from mathematics, statistics and other interdisciplinary sciences |
| [`tinybig.data`](https://www.tinybig.org/documentations/data/)                       | a library providing multi-modal datasets for solving various deep function learning tasks           |
| [`tinybig.output`](https://www.tinybig.org/documentations/output/)                   | a library providing the processing method interfaces for output processing, saving and loading      |
| [`tinybig.loss`](https://www.tinybig.org/documentations/loss/)                       | a library providing the loss functions for model introduced error computation in learning           |
| [`tinybig.metric`](https://www.tinybig.org/documentations/metric/)                      | a library providing the metrics that can be used for model performance evaluation                   |
| [`tinybig.optimizer`](https://www.tinybig.org/documentations/optimizer/)                      | a library providing the optimizers that can be used for model parameter optimization in training    |
| [`tinybig.learner`](https://www.tinybig.org/documentations/learner/)                      | a library providing the learner that can be used for model effective and efficient training         |
| [`tinybig.visual`](https://www.tinybig.org/documentations/visual/)                          | a library of utility functions for data, model and learning process visualization and rendering     |
| [`tinybig.util`](https://www.tinybig.org/documentations/util/)                          | a library of utility functions for RPN model design, implementation and learning                    | 
| [`tinybig.zootopia`](https://www.tinybig.org/documentations/zootopia/)                          | a library of models developed with the functions for concrete AI applications                       | 


### License & Copyright

Copyright © 2024 [IFM Lab](https://www.ifmlab.org/). All rights reserved.

* `tinybig` source code is published under the terms of the MIT License. 
* `tinybig`'s documentation and the RPN papers are licensed under a Creative Commons Attribution-Share Alike 4.0 Unported License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)).