semvec/pypi-packages huggingface dataset:
TOPSIS-Sezalpreet-101803216 0.2
This is python program for implementation of topsis
License: MIT

**_TOPSIS_** is an acronym that stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ 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.

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems.[5][6] Compensatory methods such as TOPSIS allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion. This provides a more realistic form of modelling than non-compensatory methods, which include or exclude alternative solutions based on hard cut-offs. An example of application on nuclear power plants is provided in.
TOPSIS-Shreya-101803595 0.0.1
This Python Package is used to implement the TOPSIS Algorithm
License: MIT
Keywords: TOPSIS

Submitted By: **Shreya Gupta - 101803595 - COE27**

***

## 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 use this package:

TOPSIS-Shreya-101803595  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```
<br>

### In Python IDLE:
```
>>> import pandas as pd
>>> from packageTopsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>>final = "final.csv"
>>> topsis(d,w,im,final)
```

<br>

## Sample dataset

The decision matrix (`a`) 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 | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

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

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

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<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.


Change Log
==========

1.0.1 (13/11/2020)
--------------------
- First Release
TOPSIS-Arsh-101803614 0.0.1
This Python Package is used to implement the TOPSIS Algorithm
License: MIT
Keywords: TOPSIS

Submitted By: **Arsh Mittal - 101803614 - COE28**

***

## 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 use this package:

TOPSIS-Arsh-101803614  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```
<br>

### In Python IDLE:
```
>>> import pandas as pd
>>> from packageTopsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>>final = "final.csv"
>>> topsis(d,w,im,final)
```

<br>

## Sample dataset

The decision matrix (`a`) 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 | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

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

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

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<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.


Change Log
==========

0.0.1 (13/11/2020)
--------------------
- First Release