semvec/pypi-packages huggingface dataset:
topsis-Nandini-101903480 1.1
A Python package to rank ML models/choices using topsis technique
License: MIT

This package is implementation of topsis technique of multi-criteria decision analysis.
This package will accept three parameters:
1. data.csv //file which contains the models and parameters
2. string of weights separated by commas(,)
3. string of requirements (+/-) separated by commas(,)
// important
install pandas,sys,operator and math libraries before installing this
//
You can install this package using following command
pip install topsis-101703557
Topsis-NKushal-101903494 0.3.1
A TOPSIS calculator
License: MIT
Keywords: TOPSIS

## Project description

This 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-NKushal-101903494==0.3.1

In the commandline, you can write as - $ python <program_filename> <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 "1,1,1,1,1" "+,+,+,+,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format

License -> MIT

## Change Log
0.2.5 (25/02/2022)
First Release
Topsis-Shruti-101903372 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,UCS538,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-Shruti-101903372==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 "1,1,1,1,1" "+,+,-,+,+" 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 (12/11/2020)
------------------
- First Release
Topsis-Sarthak-101903774 0.3
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

Submitted By: **Sarthak Arora**

***

## 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:

The Topsis-Sarthak-101903774 has a function topsis() that takes 4 parameters, i.e., inputFile, weights, impacts, and outputFile, and returns the resulting dataframe having **Topsis Score** and **Rank** as additional columns. 

Where,

* **inputFile:** Can be either csv file or pandas dataframe. Input file must contain three or more columns, where, First column is the object/variable name like M1, M2, M3, M4, etc. Also, from 2nd to last columns must contain numeric values only.
* **weights:** In the form of string having numerical values separated by commas.
* **impacts:** In the form of string having + or - values separated by commas. Here, + refers to positive impact, whereas, - refers to negative impact.
* **outputFile** (optional): csv file in which output of the function will be stored.


For Example,

### Method 1: By passing csv file as input
```python
>>> import Topsis_Sarthak_101903774 as s
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = s.topsis(inputFile, weights, impacts)
```

<br>

## Sample Input 

### Dataset

Fund Name | P1 | P2 | P3 | P4 | P5
----------|----|----|----|----|----
M1 | 0.65 | 0.42 | 3.3 | 46.3 | 12.67
M2 | 0.81 | 0.66 | 4.9 | 51.4 | 14.44
M3 | 0.87 | 0.76 | 6 | 65.4 | 18.26
M4 | 0.87 | 0.76 | 4.2 | 40.7 | 11.63
M5 | 0.75 | 0.56 | 6.8 | 57.5 | 16.4
M6 | 0.64 | 0.41 | 5.3 | 44.7 | 12.76
M7 | 0.77 | 0.59 | 4.7 | 49.8 | 13.97
M8 | 0.7 | 0.49 | 3.1 | 43.9 | 12.05

### Weights

weights = "1,1,1,2,1"

### Impacts

impacts = "+,+,-,+,+"

<br>

## Sample Output

Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
----------|----|----|----|----|----|--------------|------
M1 | 0.65 | 0.42 | 3.3 | 46.3 | 12.67 | 0.41202513 | 7
M2 | 0.81 | 0.66 | 4.9 | 51.4 | 14.44 | 0.510060544 | 2
M3 | 0.87 | 0.76 | 6 | 65.4 | 18.26 | 0.685105262 | 1
M4 | 0.87 | 0.76 | 4.2 | 40.7 | 11.63 | 0.433129944 | 5
M5 | 0.75 | 0.56 | 6.8 | 57.5 | 16.4 | 0.469643489 | 3
M6 | 0.64 | 0.41 | 5.3 | 44.7 | 12.76 | 0.225789842 | 8
M7 | 0.77 | 0.59 | 4.7 | 49.8 | 13.97 | 0.451566364 | 4
M8 | 0.7 | 0.49 | 3.1 | 43.9 | 12.05 | 0.418005937 | 6 

<br>
Topsis-Aneesh-101903395 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: Topsis,UCS654,101903395

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-101903395==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-Soumil-101903663 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: Topsis,UCS654,101903663

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-101903663==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-Abhinav-101903756 3.0
Topsis Calculation
License: MIT
Keywords: Topsis,TopsisPackage

## Topsis 
## A Multi-Criteria Decision Analysis Method

**Submitted By** - Abhinav Sinha 

**Roll No.** - 101903756

**Group** - 3COE29


It is a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution. 

## Installation
This package requires pandas to be pre-installed and python to run.
Install the dependencies and devDependencies and start the server.
 **To Install and run in cmd line:**
```sh
pip install Topsis-Abhinav-101903756
python
import Topsis_Abhinav_101903756
Topsis_Abhinav_101903756.topsis_cal(Input_File_Name.csv,"1,1,1,1 ...","+,-,+,- ...","resultfile.csv") 
```
## Project description
This package is implementation of topsis technique of multi-criteria decision analysis. This package will accept three parameters:

data.csv //file which contains the models and parameters
string of weights separated by commas(,)
string of requirements (+/-) separated by commas(,) // important install pandas,sys,operator and math libraries before installing this // You can install this package using following command pip install Topsis_Abhinav_101903756

## License
Â© 2022 Abhinav Sinha

This repository is licensed under the MIT license. See LICENSE for details.
Topsis-Shivangi-101903122 1.0.0
A Python package for topsis analysis.
License: MIT

# Topsis-Shivangi-101903122
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-Shivangi-101903122```

## Usage
Step 1: Open the command prompt
Step 2: type 
> python <filename> <dataset> <weights> <impacts> <outputfilename>
TOPSIS-Shobhit-101903095 0.0.1
THIS PACKAGE IS TO IMPLEMENT TOPSIS
License: MIT

# TOPSIS_Shobhit_101903095

With this you can calculate the TOPSIS score and RANK of the data provided in '.csv' format.

- Input file:
  - contain three or more columns
  - First column is the object/variable name.
  - From 2nd to last column contain numeric values only

# Overview

- it calculates the posis score and a rank based on that score

## Usage

i have explained how to make use topsis yourself below

### Getting it

To download TOPSIS use  pip .

    $ pip install TOPSIS_Shobhit_101903095

### Using it

TOPSIS was programmed with ease-of-use in mind. Just, import topsis from TOPSIS_Shobhit_101903095.topsis1 import topsis
    topsis('inputfilename','Weights','Impacts','Outputfilename')

And you are ready to go!

## Topsis

There are 5 steps in this:

- normalized_matrix
- weight_normalized
- ideal_best_worst
- euclidean_distance
- topsis_score

## License

[MIT](https://choosealicense.com/licenses/mit/)

## Pre-requisite

The data should be in csv format and have more than 3 columns in it.

## Result

the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.
topsis-101903576 4.1.0
Python package to implement TOPSIS
License: MIT

This package is implementation of topsis technique of multi-criteria decision analysis.
This package will accept three parameters:
1. data.csv //file which contains the models and parameters
2. string of weights separated by commas(,)
3. string of requirements (+/-) separated by commas(,)
// important
install pandas,sys and math libraries before installing this
//
You can install this package using following command
pip install topsis-101903576
# topsis-101903576
Topsis-Gaurvi-401903031 0.0.2
The package calculates the topsis score and respective rank
License: MIT

# Topsis-Gaurvi-401903031

The above package gives the topsis score and the rank

## Installation

```pip install Topsis-Gaurvi-401903031```

## To use via command line

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

data.csv is the input data file, then enter the weights of the corresponding columns and then impacts for each column
except for name column and then enter the name of the file in which you want to save the output file

## to use in .py file

```from topsis-Gaurvi-401903031 import main```

```main(data.csv, "1,1,1,1,1", "+,+,+,+,+" result.csv)```

## Debugging

 Correct number of parameters (inputFileName, Weights, Impacts, resultFileName). 
 
 Show the appropriate message for wrong inputs.

 Handling of “File not Found” exception

 Input file must contain three or more columns.

 From 2nd to last columns must contain numeric values only (Handling of non-numeric values).

 Number of weights, number of impacts and number of columns (from 2nd to last columns) must be same.

 Impacts must be either +ve or -ve.

 Impacts and weights must be separated by ‘,’ (comma).
Topsis-Ishav-101903773 1.0.0
A Python package implementing TOPSIS technique by Ishav_101903773.
License: MIT
Keywords: topsis,topsis implementation

# TOPSIS-Python

Submitted By: **Ishav Gupta**

***

## 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:

The Topsis-Ishav-101903773 has a function topsis() that takes 4 parameters, i.e., inputFile, weights, impacts, and outputFile, and returns the resulting dataframe having **Topsis Score** and **Rank** as additional columns. 

Where,

* **inputFile:** Can be either csv file or pandas dataframe. Input file must contain three or more columns, where, First column is the object/variable name like M1, M2, M3, M4, etc. Also, from 2nd to last columns must contain numeric values only.
* **weights:** In the form of string having numerical values separated by commas.
* **impacts:** In the form of string having + or - values separated by commas. Here, + refers to positive impact, whereas, - refers to negative impact.
* **outputFile** (optional): csv file in which output of the function will be stored.


For Example,

### Method 1: By passing required csv file as output
```python
>>> import Topsis_Ishav_101903773 as ans
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = ans.topsis(inputFile, weights, impacts,"Any_file_name_here.csv")
```
<br>

### Method 2: By leaving the last argument blank
```python
>>> import Topsis_Ishav_101903773 as ans
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = ans.topsis(inputFile, weights, impacts)  #the output file will be named as 101903773.csv
```

<br>

## Sample Input 

### Dataset

Fund Name | P1 | P2 | P3 | P4 | P5
----------|----|----|----|----|----
M1 | 0.65 | 0.42 | 3.3 | 46.3 | 12.67
M2 | 0.81 | 0.66 | 4.9 | 51.4 | 14.44
M3 | 0.87 | 0.76 | 6 | 65.4 | 18.26
M4 | 0.87 | 0.76 | 4.2 | 40.7 | 11.63
M5 | 0.75 | 0.56 | 6.8 | 57.5 | 16.4
M6 | 0.64 | 0.41 | 5.3 | 44.7 | 12.76
M7 | 0.77 | 0.59 | 4.7 | 49.8 | 13.97
M8 | 0.7 | 0.49 | 3.1 | 43.9 | 12.05

### Weights

weights = "1,1,1,2,1"

### Impacts

impacts = "+,+,-,+,+"

<br>

## Sample Output

Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
----------|----|----|----|----|----|--------------|------
M1 | 0.65 | 0.42 | 3.3 | 46.3 | 12.67 | 0.41202513 | 7
M2 | 0.81 | 0.66 | 4.9 | 51.4 | 14.44 | 0.510060544 | 2
M3 | 0.87 | 0.76 | 6 | 65.4 | 18.26 | 0.685105262 | 1
M4 | 0.87 | 0.76 | 4.2 | 40.7 | 11.63 | 0.433129944 | 5
M5 | 0.75 | 0.56 | 6.8 | 57.5 | 16.4 | 0.469643489 | 3
M6 | 0.64 | 0.41 | 5.3 | 44.7 | 12.76 | 0.225789842 | 8
M7 | 0.77 | 0.59 | 4.7 | 49.8 | 13.97 | 0.451566364 | 4
M8 | 0.7 | 0.49 | 3.1 | 43.9 | 12.05 | 0.418005937 | 6 

<br>
Topsis-AnanyaAga-102083036 0.5.3
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT

Project Description
Topsis Value Calculator
Submitted by: Ananya Agarwal (102083036)

Topsis_AnanyaAga_102083036 is a Python package, implementing Topsis method 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

```
>>> import Topsis_AnanyaAga_102083036 as tp
>>> input_file_name = "102083036-data.csv"
>>> weights = "1,1,0.5,2,1"
>>> impacts = "+,+,-,+,-"
>>> output_file_name = "102083036-result.csv"
>>> output_df = tp.topsis(input_file_name, weights, impacts,output_file_name)
```
Note: Pass the file names within double quotes(" ")

Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. 

License
MIT
Topsis-Bhavesh-101917085 1.0.0
Topsis Implementation
License: MIT

#Bhavesh Sareen

#Description
#Topsis Implementation

##INSTALLATION GUIDE
```pip install Topsis-Bhavesh-101917085```

##USAGE GUIDE
 
#import topsis
#input order ("input_file.csv", "1,1,1,1", "+,-,+,-" , "outfut_file_name)