Topsis-SaiVarsha-102217040 1.0.1

A Python package to implement TOPSIS

TOPSIS-Python

TOPSIS-Python is a Python package for performing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) evaluation method, a popular multi-criteria decision-making (MCDM) technique. This package simplifies the process of ranking alternatives based on multiple criteria by identifying the option closest to the ideal solution and farthest from the nadir solution.

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Installation

You can install the package directly from PyPI using pip:

pip install Topsis-SaiVarsha-102217040

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Usage

To use the package, you need a dataset containing alternatives (rows) and their criteria (columns), along with the weights and beneficial/non-beneficial indicators for the criteria.

Command-Line Interface

After installation, the package provides a CLI for quick use:

topsis --input input_file.csv --weights 1,2,3,4 --impacts +,+,-,+ --output output_file.csv

Python API

You can also use the package programmatically:

from topsis import topsis_evaluate

# Example data
data = [
    [250, 16, 12, 5],
    [200, 16, 8, 3],
    [300, 32, 16, 4],
    [275, 32, 8, 4],
    [225, 16, 16, 2]
]
weights = [0.25, 0.25, 0.25, 0.25]
impacts = ['+', '+', '-', '+']

# Perform TOPSIS
rankings = topsis_evaluate(data, weights, impacts)
print(rankings)

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Example

Input File

The input file should be a CSV file containing the following structure:

Fund Name	P1	P2	P3	P4	P5
M1	0.84	0.71	6.7	42.1	12.59
M2	0.91	0.83	7	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	10.55

Python API Example

from topsis import topsis_evaluate

# Example dataset
data = [
    [0.84, 0.71, 6.7, 42.1, 12.59],
    [0.91, 0.83, 7, 31.7, 10.11],
    [0.79, 0.62, 4.8, 46.7, 13.23],
    [0.78, 0.61, 6.4, 42.4, 12.55],
    [0.94, 0.88, 3.6, 62.2, 16.91],
    [0.88, 0.77, 6.5, 51.5, 14.91],
    [0.66, 0.44, 5.3, 48.9, 13.83],
    [0.93, 0.86, 3.4, 37, 10.55]
]
weights = [0.25, 0.25, 0.25, 0.25, 0.25]
impacts = ['+', '+', '-', '+', '+']

# Perform TOPSIS
rankings = topsis_evaluate(data, weights, impacts)
print("Rankings:", rankings)

Output File

The output file will append two columns to the original dataset:

• Performance Score
• Rank

Fund Name	P1	P2	P3	P4	P5	Performance Score	Rank
M1	0.84	0.71	6.7	42.1	12.59	0.5346	3
M2	0.91	0.83	7	31.7	10.11	0.3084	5
M3	0.79	0.62	4.8	46.7	13.23	0.6912	1
M4	0.78	0.61	6.4	42.4	12.55	0.5346	2
M5	0.94	0.88	3.6	62.2	16.91	0.4038	4
M6	0.88	0.77	6.5	51.5	14.91	0.4912	6
M7	0.66	0.44	5.3	48.9	13.83	0.4238	7
M8	0.93	0.86	3.4	37	10.55	0.3756	8

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Input Format

• CSV File:
  • First row: Criteria names.
  • First column: Alternative names.
  • Remaining cells: Criteria values for each alternative.
• Weights:
  • A list of non-negative values representing the importance of each criterion.
• Impacts:
  • A list of + or - for each criterion indicating whether it is beneficial or non-beneficial.

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Output Format

The output is a ranked list of alternatives based on their closeness to the ideal solution. The results include:

1. Performance Score: Closeness of each alternative to the ideal solution.
2. Rank: Rank of each alternative based on the performance score.

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License

This project is licensed under the MIT License.

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