semvec/pypi-packages huggingface dataset:
shifterator 0.3.0
Interpretable data visualizations for understanding how texts differ at the word level
Requires Python: ~=3.6
Keywords: natural language processing,sentiment analysis,information theory,computational social socience,digital humanities,text analysis,text as data,data visualization,data viz

# Shifterator

The Shifterator package provides functionality for constructing **word shift graphs**, vertical bart charts that quantify *which* words contribute to a pairwise difference between two texts and *how* they contribute. By allowing you to look at changes in how words are used, word shifts help you to conduct analyses of sentiment, entropy, and divergence that are fundamentally more interpretable.

<p align="center">
  <img src ="docs/figs/shift_sentiment_detailed_full.png" width="400"/>
</p>


## Install

Python code to produce shift graphs can be downloaded via pip.

`pip install shifterator`

## Documentation

[The documentation](https://shifterator.readthedocs.io/en/latest/) details how to create various kinds of word shift graphs with Shifterator, and includes a detailed cookbook for how to interpret, visualize, and work with word shifts.

See the following paper for more details on word shifts, and please cite it if you use them in your work:

> Gallagher, R. J., Frank, M. R., Mitchell, Lewis, Schwartz, A. J., Reagan, A. J., Danforth, C. M., Dodds, P. S. (2021). [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-021-00260-3). *EPJ Data Science*, 10(4).
lightonml 1.4.3
LightOn technologies for Large scale Machine Learning with Optical Processing Unit

# <img src="https://cloud.lighton.ai/wp-content/uploads/2020/01/LightOnCloud.png" width=80/> LightOnML library

[![Twitter Follow](https://img.shields.io/twitter/follow/LightOnIO.svg?style=social)](https://twitter.com/LightOnIO) [![PyPi Version](https://img.shields.io/pypi/v/lightonml.svg)](https://pypi.python.org/pypi/lightonml/) [![Python Versions](https://img.shields.io/pypi/pyversions/lightonml.svg)](https://pypi.python.org/pypi/lightonml/)

LightOnML is a high level machine learning-oriented API that allows to perform random projections on
LightOn’s optical processing units (OPUs). LightOn’s OPUs are available through [LightOn’s Cloud service](https://cloud.lighton.ai).

## Features

 * Run large-scale non-linear and linear random projections using LightOn’s Aurora OPUs
 * Simulate these projections on any machine without access to an OPU
 * Encode input data in a binary form using various encoders, for OPU input

## Installation

`lightonml` doesn't require access to an OPU for some functionalities, but for performing
 computations on an OPU you'll need one. Otherwise, a simulated OPU can be used.

To install, use `pip`:

    pip install lightonml

Optional dependencies are :
* `torch`, required for the encoder classes, and the PyTorch `OPUMap`.
* `scikit-learn`, required for using the corresponding `OPUMap` to work.

## Documentation, examples and help

Main documentation can be found at the [API docs website](https://docs.lighton.ai).

Check the examples directory in the repo, if you don't have access to an OPU you can run the code locally with a simulated OPU

For getting help on the LightOn Cloud service check the [Community website](https://community.lighton.ai/)

For help on the library itself, you can use issues on this repository.

## <img src="https://cloud.lighton.ai/wp-content/uploads/2020/01/cropped-lightOnCloud-1-2.png" width=120/> Access to Optical Processing Units

To request access to LightOn Cloud and try our photonic co-processor, please visit: https://cloud.lighton.ai/

For researchers, we also have a LightOn Cloud for Research program, please visit https://cloud.lighton.ai/lighton-research/ for more information.