semvec/pypi-packages huggingface dataset:
Topsis-Arnav-101953016 1.0.0
Implements topsis on the given input data file and generates topsis score and ranks accordingly.
License: MIT

# Topsis-Arnav-101953016

Topsis implementation in python. Used as a command line tool to generate topsis score and ranks of the given input data file along with weights and impacts.

## Installation

`pip install Topsis-Arnav-101953016`

## How to use it?

Usage: topsis <input_filename.csv> <"Weights"> <"Impacts"> <output_filename.csv>

## License

Â© 2022 Arnav Gaur

This repository is licensed under the MIT license. See LICENSE for details.
topsis-ShreyaGarg-101953015 1.0.1
A topsis rank calculator.
License: MIT

# Topsis-Shreya-101953015
This is a python package for rank calculation using topsis.

## Steps for Installation
**Step 1:** Install the package using
```pip install Topsis-Shreya-101953015```

**Step 2:** Import the package
```tp =import("Topsis-Shreya-101953015"))```

**Step 3:** Run the package in command prompt in the same format as given in the following example:
```tp.topsis('101953015-data.csv',"1,1,1,1,1","+,-,+,-,+",'101953015-result.csv')```

_Result gets stored in 101953015-result.csv_

## Subject to constraints:

(1) Impacts and weights must be separated by commas.

(2) Impacts must be either +ve or -ve.

(3) Command prompt should include 4 paramters namely - input csv file, weights, impacts and name of the output csv file.

## License

Â© 2022 Shreya Garg

This repository is licensed under the MIT license. See LICENSE for details.
Topsis-RounakTaneja-101903784 5.0.0
It gives a csv file that includes the topsis result
License: MIT

# Topsis Result Calculator
It runs on terminal and takes a csv file , weights of column, impacts and name of the output file as input and gives an output csv file that contains topsis performance score and the rank of a particular entity.

## Installation
On your terminal run the command below:<br>
```pip install Topsis-RounakTaneja-101903784```

## Importing and using in source file
use the package in source file as follows:<br>

```
tbj=__import__("topsis")
tbj.get_topsis_result()
```

## How to get output?
output is taken by running the command on terminal as : python "sourcefile.py" "input.csv" "weights(separateed by ',')" "impact(separated by ','))" 
use impact as '+' for maximizing the feature and '-' for minimizing.
On your terminal run the command:<br>
```python source_file_name.py input_file.csv "1,1,0,1" "+,-,+,+" output_file_name.csv```
<br>
It will display the input arguments and the input data on terminal screen  and Output will be stored in your present working directory as a csv file.

## Constraints
### Number of columns in input file(other than the name of the entity) should be equal to length of impacts and length of weights.
### Input file should not have blank values
### Number of Columns in Input files should not be less than 3
### There should not be any csv file with same name as your output file in present directory
### Number of Arguments should be equal to the number as specified above
Topsis-Gurjot-101917198 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,UCS654,TIET

Topsis Value Calculator

CalcTopsis is a Python package implementing Topsis method sed for multi-criteria decision analysis.
Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide your input attributes and it will give you the results


## Installation

$ pip install TOPSIS-101917198==0.0.1

In the commandline, you can write as -
    $ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv, command will be like
    $ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format

License -> MIT

Change Log
==========

0.0.1 (27/02/2022)
------------------
- First Release
Topsis-Gursimar-101917046 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,UCS654,101917046

Topsis Value Calculator

CalcTopsis is a Python package implementing Topsis method sed for multi-criteria decision analysis.
Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide your input attributes and it will give you the results


## Installation

$ pip install TOPSIS-101917046==0.0.1

In the commandline, you can write as -
    $ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv, command will be like
    $ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format

License -> MIT

Change Log
==========

0.0.1 (27/02/2022)
------------------
- First Release
Topsis-Rishika-101953022 1.0.1
A Python package for topsis analysis.
License: MIT

# Topsis-Rishika-101953022
It takes dataset, weights and impacts associated to the columns of dataset and output file to perform topsis on input dataset.

## Installation
```pip install Topsis-Rishika-101953022```

## Usage
Step 1: Open the command prompt
Step 2: type 
> python <filename> <dataset> <weights> <impacts> <outputfilename>
topsis-Hemakshi-101903145 1.0.0
Multiple criteria decision making (MCDM) using TOPSIS
License: MIT

#Topsis-HEMAKSHI-101903145
Multiple criteria decision making (MCDM) using TOPSIS

## Installation
```pip install topsis-HEMAKSHI-101903145```

##Sample Use case
import topsis-HEMAKSHI-10903145 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Topsis-Ritwik-101917131 1.2.0
Package to calculate Topsis Score and Rank them accordingly
License: MIT
Keywords: Python,Topsis,UCS654

## Topsis_Ritwik

# TOPSIS

Created By: **Ritwik Khanna**.
Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.2.0**.

Author: **Ritwik Khanna**.

Maintainer: **Ritwik Khanna <khannaritwik.rk@gmail.com>>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```
>> pip install Topsis-Ritwik-101917131
```

### In Command Prompt

```
>> python topsis.py data.csv "1,1,1.5,1,2" "+,+,-,+,-" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

| Model |  P1  |  P2  |  P3  |  P4  |  P5   |
| ----- | ---- | ---- | ---- | ---- | ----  |
| M1    | 0.84 | 0.71 | 6.7  | 42.1 | 12.59 |
| M2    | 0.91 | 0.83 | 7.0  | 31.7 | 10.11 |
| M3    | 0.79 | 0.62 | 4.8  | 46.7 | 13.23 |
| M4    | 0.78 | 0.61 | 6.4  | 42.4 | 12.55 |
| M5    | 0.94 | 0.88 | 3.6  | 62.2 | 16.91 |
| M6    | 0.88 | 0.77 | 6.5  | 51.5 | 14.91 |
| M7    | 0.66 | 0.44 | 5.3  | 48.9 | 13.83 |
| M8    | 0.93 | 0.86 | 3.4  | 37.0 | 10.55 |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

| Model |  P1  |  P2  |  P3  |  P4  |  P5   |  Topsis Score  | Rank |
| ----- | ---- | ---- | ---- | ---- | ----  |  ------------  | ---- |
| M1    | 0.84 | 0.71 | 6.7  | 42.1 | 12.59 |     0.4295     |  5   |
| M2    | 0.91 | 0.83 | 7.0  | 31.7 | 10.11 |     0.5055     |  4   |
| M3    | 0.79 | 0.62 | 4.8  | 46.7 | 13.23 |     0.5358     |  3   |
| M4    | 0.78 | 0.61 | 6.4  | 42.4 | 12.55 |     0.4185     |  6   |
| M5    | 0.94 | 0.88 | 3.6  | 62.2 | 16.91 |     0.5616     |  2   |
| M6    | 0.88 | 0.77 | 6.5  | 51.5 | 14.91 |     0.3983     |  8   |
| M7    | 0.66 | 0.44 | 5.3  | 48.9 | 13.83 |     0.4134     |  7   |
| M8    | 0.93 | 0.86 | 3.4  | 37.0 | 10.55 |     0.7332     |  1   |









<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Topsis-Ishaan-101903137 1.1.3
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

Submitted By: **Ishaan 101903137**

---

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

Topsis-Ishaan-101903137 can be run as in the following example:

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" output_file.csv
```

<br>

### In Python IDLE:

```
>>> import pandas as pd
>>> from topsis_py.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im,"output_file.csv")
```

<br>
<br>

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.