Metadata-Version: 2.4
Name: ISLP
Version: 0.5.0
Summary: Library for ISLP labs
Author-email: Trevor Hastie <hastie@stanford.edu>, Gareth James <gareth@emory.edu>, Jonathan Taylor <jonathan.taylor@stanford.edu>, Rob Tibshirani <tibs@stanford.edu>, Daniela Witten <dwitten@uw.edu>
Maintainer-email: Jonathan Taylor <jonathan.taylor@stanford.edu>
License: Redistribution and use in source and binary forms, with or without
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Project-URL: Homepage, https://github.com/intro-stat-learning/ISLP
Project-URL: Bug Reports, https://github.com/intro-stat-learning/ISLP/issues
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Project-URL: Source, https://github.com/pypa/sampleproject/
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.7.1
Requires-Dist: scipy>=0.9
Requires-Dist: pandas>=1.5
Requires-Dist: lxml
Requires-Dist: scikit-learn>=1.2
Requires-Dist: joblib
Requires-Dist: statsmodels>=0.13
Requires-Dist: lifelines
Requires-Dist: pygam>=0.10
Requires-Dist: torch
Requires-Dist: pytorch_lightning
Requires-Dist: torchmetrics
Provides-Extra: doc
Requires-Dist: Sphinx>=3.0; extra == "doc"
Provides-Extra: test
Requires-Dist: torchinfo; extra == "test"
Requires-Dist: torchvision; extra == "test"

# ISLP
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[![All Contributors](https://img.shields.io/badge/all_contributors-3-orange.svg?style=flat-square)](#contributors-)
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This package collects data sets and various helper functions
for ISLP.

## Install instructions

### Mac OS X / Linux

We generally recommend creating a Python environment to isolate any code                                                                                                                     │
from other dependencies. This is good practice. You can choose between `uv` (recommended) or `conda`.                                                                                        │

To create a `uv` environment in a Mac OS X or Linux environment run:

```
uv venv 
```

To run python code in this environment, you must activate it:

```
source .venv/bin/activate
```

### Installing `ISLP`

Having completed the steps above, we use `pip` to install the `ISLP` package:

```
uv pip install ISLP
```

### Jupyter

If JupyterLab is not already installed, run the following after having activated your `uv` environment:

```
uv pip install jupyterlab
```

## Anaconda App

If not using `uv` but `conda` instead, create a `Python` environment called `islp` in the
Anaconda app. This can be done by selecting `Environments` on the left
hand side of the app's screen. After creating the environment, open a
terminal within that environment by clicking on the "Play" button.

The correspnding installation commands for `ISLP` and Jupyter are:

```
pip install ISLP jupyterlab
```

### Torch requirements

The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements
are included in the requirements for `ISLP` with the exception of those needed
for the labs which are included in the [requirements for the labs](https://github.com/intro-stat-learning/ISLP_labs/blob/main/requirements.txt) which
will be set to the correct versions when a specific git tag is checked out.


## Documentation

See the [docs](https://intro-stat-learning.github.io/ISLP/labs.html) for the latest documentation.

## Authors

- Jonathan Taylor
- Trevor Hastie
- Gareth James
- Robert Tibshirani
- Daniela Witten




## Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

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<table>
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    <tr>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/danielawitten"><img src="https://avatars.githubusercontent.com/u/12654191?v=4?s=100" width="100px;" alt="danielawitten"/><br /><sub><b>danielawitten</b></sub></a><br /><a href="https://github.com/intro-stat-learning/ISLP/commits?author=danielawitten" title="Code">💻</a> <a href="#content-danielawitten" title="Content">🖋</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://web.stanford.edu/~hastie/"><img src="https://avatars.githubusercontent.com/u/13293253?v=4?s=100" width="100px;" alt="trevorhastie"/><br /><sub><b>trevorhastie</b></sub></a><br /><a href="https://github.com/intro-stat-learning/ISLP/commits?author=trevorhastie" title="Code">💻</a> <a href="#content-trevorhastie" title="Content">🖋</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/tibshirani"><img src="https://avatars.githubusercontent.com/u/2848609?v=4?s=100" width="100px;" alt="tibshirani"/><br /><sub><b>tibshirani</b></sub></a><br /><a href="https://github.com/intro-stat-learning/ISLP/commits?author=tibshirani" title="Code">💻</a> <a href="#content-tibshirani" title="Content">🖋</a></td>
    </tr>
  </tbody>
</table>

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This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
