semvec/pypi-packages huggingface dataset:
102217211-Ashmeet-topsis 1.1.0
A Python package for TOPSIS calculation
Requires Python: >=3.6

# **TOPSIS Package**  
_A Python package for decision-making using the TOPSIS method._  

---

## **What is TOPSIS?**  
The **Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)** is a multi-criteria decision analysis method.  
It ranks alternatives based on their closeness to the ideal solution and farthest from the worst solution, making it an excellent tool for decision-making problems.  

---

## **Features**  
- Simple and efficient implementation of the TOPSIS algorithm.  
- Accepts a decision matrix via a CSV file.  
- Supports customizable weights and impacts for criteria.  
- Outputs the TOPSIS scores and ranks into an easy-to-read CSV file.  

---

## **Installation**  
You can install the package from PyPI using:  

```bash
pip install 102217211-Ashmeet-topsis==1.1.0
```

---

## **How to Use**  

### **Run the Package**  

You can run the TOPSIS package directly from the command line using:  

```bash
python -m 102217211_Ashmeet_topsis <input_file> <weights> <impacts> <output_file>
```

### **Parameters**  
- `<input_file>`: Path to the CSV file containing the decision matrix.  
- `<weights>`: Comma-separated weights for each criterion (e.g., `0.2,0.1,0.43,0.3`).  
- `<impacts>`: Comma-separated impacts for each criterion (e.g., `+,+,-,-`, where `+` indicates a benefit criterion and `-` indicates a cost criterion).  
- `<output_file>`: Path to the output CSV file where the results will be saved.  

---

## **Example Usage**  

### Input CSV (`input.csv`)  
```bash
Fund Name,P1,P2,P3,P4,P5
M1,0.91,0.83,6,53,15.19
M2,0.88,0.77,4.1,61.1,16.71
M3,0.67,0.45,3.5,59.4,16.01
M4,0.83,0.69,4.8,44.9,12.81
M5,0.74,0.55,6.7,66.3,18.57
M6,0.6,0.36,4,37.8,10.69
M7,0.72,0.52,4.4,40.7,11.59
M8,0.73,0.53,4.4,66.8,18.12
```

### Command  
```bash
python -m 102217211_Ashmeet_topsis "input.csv" "0.2,0.1,0.4,0.3,0.1" "-,+,+,-,+" "output.csv"
```

### Output CSV (`output.csv`) 
```bash 
Fund Name,P1,P2,P3,P4,P5,TOPSIS Score,Rank
M1,0.91,0.83,6,53,15.19,0.7521,2
M2,0.88,0.77,4.1,61.1,16.71,0.8256,1
M3,0.67,0.45,3.5,59.4,16.01,0.4823,6
M4,0.83,0.69,4.8,44.9,12.81,0.6332,4
M5,0.74,0.55,6.7,66.3,18.57,0.6789,3
M6,0.6,0.36,4,37.8,10.69,0.3145,8
M7,0.72,0.52,4.4,40.7,11.59,0.4012,7
M8,0.73,0.53,4.4,66.8,18.12,0.5798,5
```

---

## **How It Works**  
1. **Normalization**: The decision matrix is normalized to bring all criteria onto a comparable scale.  
2. **Weighting**: Each criterion is multiplied by its corresponding weight.  
3. **Ideal Solutions**: Calculates the ideal best and worst solutions based on the impacts.  
4. **Distance Calculation**: Computes the distance of each alternative from the ideal best and worst solutions.  
5. **Ranking**: Scores and ranks alternatives based on their relative closeness to the ideal solution.  

---

## **License**  
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.  

---
Topsis-102396003 1.0.0
A Python implementation of the TOPSIS decision-making method
Requires Python: >=3.6

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)

TOPSIS is a Python library for implementing the Technique for Order of Preference by Similarity to Ideal Solution, a multi-criteria decision-making method. This library allows users to rank alternatives based on given criteria, weights, and impacts.

Installation

You can install the library via pip:

pip install Topsis-102396003

Usage

Command-line Usage

The library can be used directly from the command line. Here's the general syntax:

python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>

Example

Create an input file, data.csv:

Name,C1,C2,C3,C4
A,250,16,12,5
B,200,32,8,3
C,300,24,10,4
D,275,20,11,4
E,225,28,9,2

Run the command:

python topsis.py data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv

The output result.csv will contain:

Name,C1,C2,C3,C4,Topsis Score,Rank
A,250,16,12,5,0.64,2
B,200,32,8,3,0.43,4
C,300,24,10,4,0.78,1
D,275,20,11,4,0.56,3
E,225,28,9,2,0.36,5

Python Library Usage

Importing the Library

You can also use the library directly in Python:

from topsis import Topsis

# Input data
data = "data.csv"
weights = [0.25, 0.25, 0.25, 0.25]
impacts = ["+", "+", "-", "+"]
result_file = "result.csv"

# Perform TOPSIS
Topsis(data, weights, impacts, result_file)
print(f"Results saved in {result_file}")

Parameters

InputDataFile: A CSV file with the first column as the name of the alternatives and the rest as numeric criteria.

Weights: A comma-separated string of numeric weights (e.g., "0.25,0.25,0.25,0.25").

Impacts: A comma-separated string of impacts for each criterion, either + (positive impact) or - (negative impact).

ResultFileName: The name of the output CSV file where results will be saved.

Features

Validate input file format and values.

Handle missing or invalid values gracefully.

Normalize and weight criteria.

Calculate TOPSIS scores and rankings.

Limitations

All criteria must be numeric.

The number of weights and impacts must match the number of criteria.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Feel free to open issues or create pull requests to improve the library!
Topsis-Rijul-102217207 1.0.0
A Python package for implementing the TOPSIS decision-making method
Requires Python: >=3.6

# Topsis-<FirstName>-<RollNumber>
        
        A Python package to implement the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision-making method.
        
        ## Installation
        
        ```bash
        pip install Topsis-Rijul-102217207
        
        from topsis import topsis
        
        # Example usage
        topsis.topsis('<InputFile>', '<Weights>', '<Impacts>', '<ResultFile>')
        
        python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
Topsis-Mandar-102203163 1.0
A Python Package which creates topsis rankings on the basis of performance scores
Requires Python: >=3.6

## TOPSIS

Python package which allows one to modify a data set by adding TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) scores and ranks to it.

## Setup 
Install the package using the following command: 
# pip install Topsis_Mandar_102203163

In order to use the package include the following in your python program : 
# import topsis as tp

## Features 
The package currently consists of the following function: 
# topsis(inputFileName : str, weights : str, impacts : str, resultFileName : str) 

- inputFileName : name of your input (.csv) file. For example : "train.csv"
- weights : weights to be applied to each feature of the data set. The number of weights must be equal to the number of features. It must also contain the weights separated by commas in a string. For example, for a data set consisting of 4 features, weights = "1,2,1,1"
- impacts : ideal value for each feature of the data set. The number of impacts must be equal to the number of features. If the value is "+", then a higher value is preferable in the feature. Similarly, if the value if --s "-", then a lower value is more preferable in the feature. Its value must contain the values separated by commas in a string. For example, for a data set consisting of 5 features, impacts = "+,-,+,+"
- resultFileName : File name in which the updated data set containing the respective TOPSIS scores and Ranks must be saved. For example : "train-result.csv".