Metadata-Version: 2.1
Name: regdata
Version: 1.0.0
Summary: A collection of regression datasets
Home-page: https://github.com/patel-zeel/regdata
Author: Zeel B Patel
Author-email: patel_zeel@iitgn.ac.in
License: MIT
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# RegData

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A collection of regression datasets.

## Install
```bash
pip install regdata
```
## Quick example

```python
import regdata as rd
rd.set_backend('torch') # numpy, tf (numpy is default)
X, y, X_test = rd.Step().get_data() # Loads step function dataset
```

## Features

* Simple API for quick benchmarking on various datasets.
* Get data in any framework: ```torch```, ```tensorflow``` or ```numpy``` by setting a global backend.
* Scale ```X``` and/or ```y``` data with ```MinMaxScaler``` or ```StandardScaler```.
* Get ```y``` in squeezed ```(n,)``` or unsqueezed ```(n,1)``` format.
* Perform only mean normalization on ```y```.

## Plot datasets to have a quick glance

```python
import regdata as rd
rd.Olympic().plot()
```

Checkout all plots [here](https://nbviewer.jupyter.org/github/patel-zeel/regdata/blob/main/notebooks/visualize.ipynb).

## Datasets

```python
from regdata import (
    DellaGattaGene,
    MotorcycleHelmet,
    Olympic,
    SineJump1D,
    Smooth1D,
    Step
)
```

## References

* [DellaGattaGene](http://inverseprobability.com/talks/notes/deep-gaussian-processes.html)
* [MotorcycleHelmet](http://inverseprobability.com/talks/notes/deep-gaussian-processes.html)
* [Olympic](http://inverseprobability.com/talks/notes/deep-gaussian-processes.html)
* [SineJump1D](https://github.com/jmetzen/gp_extras/blob/master/examples/plot_gpr_lls.py)
* [Smooth1D](http://www.stat.cmu.edu/~kass/papers/bars.pdf) - Example 2
* [Step](http://inverseprobability.com/talks/notes/deep-gaussian-processes.html)


