semvec/pypi-packages huggingface dataset:
ExKMC 0.0.3
Expanding Explainable K-Means Clustering
Requires Python: >=3.0

# ExKMC

This repository is the official implementation of [ExKMC: Expanding Explainable k-Means Clustering](https://arxiv.org/pdf/2006.02399.pdf). 

We study algorithms for k-means clustering, focusing on a trade-off between explainability and accuracy. 
We partition a dataset into k clusters via a small decision tree. 
This enables us to explain each cluster assignment by a short sequence of single-feature thresholds. 
While larger trees produce more accurate clusterings, they also require more complex explanations. 
To allow flexibility, we develop a new explainable k-means clustering algorithm, ExKMC, that takes an additional parameter k' &#8805; k and outputs a decision tree with k' leaves. 
We use a new surrogate cost to efficiently expand the tree and to label the leaves with one of k clusters. 
We prove that as k' increases, the surrogate cost is non-increasing, and hence, we trade explainability for accuracy.

<img src="https://raw.githubusercontent.com/navefr/ExKMC/master/images/example.PNG">


## Installation

The package is on [PyPI](https://pypi.org/project/ExKMC/). Simply run:
```
pip install ExKMC
```

## Usage

```python
from ExKMC.Tree import Tree
from sklearn.datasets import make_blobs

# Create dataset
n = 100
d = 10
k = 3
X, _ = make_blobs(n, d, k, cluster_std=3.0)

# Initialize tree with up to 6 leaves, predicting 3 clusters
tree = Tree(k=k, max_leaves=2*k) 

# Construct the tree, and return cluster labels
prediction = tree.fit_predict(X)

# Tree plot saved to filename
tree.plot('filename')
```

## Notebooks
Usage examples:
* [Expand tree for Gaussians](notebooks/Example.ipynb)
* [Expand tree for text data](notebooks/Newsgroups%20example.ipynb)

## Citation
If you use ExKMC in your research we would appreciate a citation to the appropriate paper(s):

* For IMM base tree you can read our [ICML 2020 paper](https://arxiv.org/pdf/2002.12538.pdf).
   ```bash
   @article{dasgupta2020explainable,
     title={Explainable $k$-Means and $k$-Medians Clustering},
     author={Dasgupta, Sanjoy and Frost, Nave and Moshkovitz, Michal and Rashtchian, Cyrus},
     journal={arXiv preprint arXiv:2002.12538},
     year={2020}
   }
   ```
* For ExKMC expansion you can read our [paper](https://arxiv.org/pdf/2006.02399.pdf).
   ```bash
   @article{frost2020exkmc,
     title={ExKMC: Expanding Explainable $k$-Means Clustering},
     author={Frost, Nave and Moshkovitz, Michal and Rashtchian, Cyrus},
     journal={arXiv preprint arXiv:2006.02399},
     year={2020}
   }
   ```
## Contact
* [Nave Frost](mailto:navefrost@mail.tau.ac.il)
* [Michal Moshkovitz](https://sites.google.com/view/michal-moshkovitz)
* [Cyrus Rashtchian](https://sites.google.com/site/cyrusrashtchian/)
Scmepy 0.0.1
Um conversor de varias unidades de medida
Requires Python: >=3.6

Scmepy
======

#### Este é um pacote que faz conversões de várias unidades de medidas.
#### dentre elas:
#### 1. celcius em fahrenheit;
#### 2. fahrenheit em celcius;
#### 3. kelvin em celcius;
#### 4. celcius em kelvin;
#### 5. quilometros em metros por segundo;
#### 6. metros por segundo em quilometros;
#### 7. milhas em quilometros:
#### 8. quilometros em  milhas;
#### 9. graus em  radianos:
#### 10. radianos em graus;
#### 11. polegadas em centimetros;
#### 12. centimetros em polegadas;
#### 13. metros cubicos em litros;
#### 14. litros em metros cubicos;
#### 15. quilograms em libras;
#### 16. libras em quilogramas;
#### 17. jardas em metros;
#### 18. metros em jardas;
#### 19. metros quadrados em acres;
#### 20. acres em metros quadrados;
#### 21. metros quadrados em hectares;
#### 22. hectares em metros quadrados;
aowniereweas 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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st-material-table 0.0.4
Streamlit Custom Components in React
Requires Python: >=3.6

<h1 align="center">
  <br>
  <a href="https://pypi.org/project/st-material-table/"><img src="https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/240/twitter/282/classical-building_1f3db-fe0f.png" alt="mgt2001" width="50"></a>
  <br>
  Streamlit Material-UI Table
  <br>
</h1>

<h4 align="center">Streamlit Custom Components in React.</h4>

<p align="center">
  <a href="https://pypi.org/project/st-material-table/">
    <img src="https://img.shields.io/pypi/v/st-material-table.svg?maxAge=3600"
         alt="View package on PyPI">
  </a>
</p>

<p align="center">
  <a href="#how-to-use">How to Use</a> •
  <a href="#contribution">Contributions</a>
</p>

I've read through [the documentation](https://docs.streamlit.io/en/stable/publish_streamlit_components.html) and searched through other amazing components shown [here](https://streamlit.io/components), but couldn't get exactly what I need. Just yet.

The ultimate goal is to replicate [those Material-UI tables](https://material-ui.com/components/tables/#custom-pagination-actions). 

## How to Use

```sh
pip install st_material_table
```

## Contributions

The project is set up under MIT license. Feel free to play around.