semvec/pypi-packages huggingface dataset:
slurmworm 1.0.0
A simple listener to monitor slurm jobs

# slurmworm
![alt text](https://raw.githubusercontent.com/grburgess/slurmworm/main/logo.png)

This is a rather simple tool to monitor SLURM emails and alert you via a telegram bot about your jobs.

**Note!** This is under heavy development!

## Installation

* install [imapclient](https://github.com/mjs/imapclient) directly from the github repo *not* pip
* clone this repo and run 
```bash
python setup install
```
* create a directory ```~/.config/slurmworm```
* in this directory you need to place two files. 
* * one is the telegram bot info for the *your* bot that you must [create](https://firstwarning.net/vanilla/discussion/4/create-telegram-bot-and-get-bots-token-and-the-groups-chat-id) named ```access.yml```
* * the other is a UNIX conf file with your IMAP email info named ```imap_monitor.ini``` which identifies the email account to which you will send slurm messages

## Telegram bot setup
If you read the above guide to create your bot you will have nearly everything you need. You then need to get that info into your ```access.yml``` file:
```yaml
token:
  '<your bot ID token>'

chat_id:
  '<the group ID where your bot will send messages>'

```

You should have your bot chat to a group you have created. 


## email setup
This is what should go in ```imap_monitor.ini```:

```ini
[imap]
host = <your imap server>
username = <username>
password = <password>
ssl = True
folder = INBOX

[path]
download = <where mail will be temporarily stored>
```

## Running
After you have installed and set everything up, just type
```bash
slurmworm
```
in a shell and it will run and monitor your email. 

It is likely best to have this running in a tmux shell you can disconnect from.

May all your jobs exit 0!
TOPSIS-SHIVANSH-101803103 0.0.1
Python Package for TOPSIS

# TOPSIS Package in Python

Submitted by : Shivansh Kumar
Roll no : 101803103

UCS538

* * *
## What Is TOPSIS

TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those

* * *

## How To Use

The package TOPSIS-shivansh-101803103 can be run though the command line as follows:
```
>> pip install TOPSIS-shivansh-101803103
```
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```
> **Note:**
> 
> *   Usages: 
>     **topsis data.csv "1,1,1,2" "+,+,-,+" result.csv**
> *   **Input File**:
>     
>     > *   Input file contain three or more columns.
>     > *   First column is the object/variable name (e.g. M1, M2, M3, M4...).
>     > *   From 2nd to last columns contain numeric values only.
> *   **Output File**:
>     
>     > *   Result file contains all the columns of input file and two additional columns having ***Topsis Score*** and ***Rank***
> *   The output is created in the form of csv file and stored and also it is displayed.
> *  The impacts given in the command line should be either â€˜+' or â€˜â€“â€™ depending if you want to maximise the column parameter or minimise it.

## Sample Input

Here is a sample set of data which can be used for the following package:

<table><thead><tr><th>Model</th><th>Correlation</th><th>R2</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output of this sample input

The output that will be generated from the following input data will be:

<table><thead><tr><th>Model</th><th align="right">Correlation</th><th align="center">R2</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133014134258</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212591829692779</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407845677613051</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153239500747</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828266585193581</td><td>1.0</td></tr></tbody></table>

Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.

* * *
ripe-algorithm 0.1.6
Ripe algorithm
Requires Python: >=3.6

# RIPE
Implementation of a rule based prediction algorithm called RIPE (Rule Induction Partitioning Estimate). RIPE is a deterministic and interpretable algorithm, for regression problem. It has been presented at the International Conference on Machine Learning and Data Mining in Pattern Recognition 2018 (MLDM 18). The paper is available in arXiv https://arxiv.org/abs/1807.04602.

## Getting Started
These instructions will get you a copy of the project up and running on your
local machine for development and testing purposes. See deployment for notes
on how to deploy the project on a live system.

### Prerequisites
RIPE is developed in Python version 2.7. It requires some usual packages
- NumPy (post 1.13.0)
- Scikit-Learn (post 0.19.0)
- Pandas (post 0.16.0)
- SciPy (post 1.0.0) 
- Matplotlib (post 2.0.2) 
- Seaborn (post 0.8.1)

See **requirements.txt**.
```
sudo pip install package_name
```
To install a specific version
```
sudo pip install package_name==version
```

### Installing

The latest version can be installed from the master branch using pip:
```
pip install git+git://github.com/VMargot/RIPE.git
```
Another option is to clone the repository and install using ```python setup.py
install``` or ```python setup.py develop```.

## Usage
RIPE has been developed to be used as a regressor from the package scikit-learn.

### Training
```
from sklearn import datasets
iris = datasets.load_iris()
X, y = iris.data, iris.target

ripe = RIPE.Learning()
ripe.fit(X, y)
```

### Predict
```
ripe.predict(X)
```

### Score
```
ripe.score(X,y)
```

### Inspect rules:
To have the Pandas DataFrame of the selected rules
```
ripe.selected_rs.to_df()
```
Or, one can use
```
ripe.make_selected_df()
```
To draw the distance between selected rules
```
ripe.plot_dist()
```
To draw the count of occurrence of variables in the selected rules
```
ripe.plot_counter_variables()
```

## Notes
This implementation is in progress. If you find a bug, or something witch could
be improve don't hesitate to contact me.

## Authors
* **Vincent Margot**

See also the list of [contributors](https://github.com/VMargot/RIPE/contributors)
who participated in this project.

## License

This project is licensed under the GNU v3.0 - see the [LICENSE.md](LICENSE.md)
file for details