topsis-vansh-102303922 0.1.1

Command line implementation of TOPSIS

TOPSIS

A Python implementation of the TOPSIS multi-criteria decision-making (MCDM) method.

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Features

• Implements the standard TOPSIS algorithm
• Accepts CSV input files
• Supports custom weights and impacts
• Outputs TOPSIS score and rank
• Command-line based and easy to integrate

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What is TOPSIS?

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method.
It identifies solutions from a finite set of alternatives based on their distance from an ideal best and an ideal worst solution.

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Input File Format

The input file must be a CSV file with:

• First column: Names/IDs of alternatives (non-numeric)
• Remaining columns: Criteria values (numeric)

Sample Input (Table)

Name	P1	P2	P3	P4	P5
M1	0.71	0.50	3.0	40.4	11.15
M2	0.60	0.36	4.7	41.2	11.72
M3	0.65	0.42	3.2	53.5	14.44
M4	0.84	0.71	3.9	56.8	15.56
M5	0.60	0.36	6.3	50.4	14.42
M6	0.65	0.42	5.0	64.3	17.59
M7	0.94	0.88	4.1	57.4	15.83
M8	0.71	0.50	4.1	37.3	10.65

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Installation

Make sure you have Python 3 installed. Then install the required libraries:

pip install pandas numpy

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Usage

Run the script from the command line:

python topsis.py <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Arguments

• InputDataFile: CSV file containing the dataset
• Weights: Comma-separated numeric weights (e.g., 1,1,1,1,1)
• Impacts: Comma-separated impacts (+ for benefit, - for cost)
• OutputResultFileName: Name of the output CSV file

Example

python topsis.py data.csv 1,1,1,1,1 +,+,+,+,+ result.csv

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Output

The output CSV file will contain:

• Topsis Score: Performance score of each alternative
• Rank: Ranking based on TOPSIS score (higher is better)

Sample Output (Table)

Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.71	0.50	3.0	40.4	11.15	0.1961	8
M2	0.60	0.36	4.7	41.2	11.72	0.2386	7
M3	0.65	0.42	3.2	53.5	14.44	0.2661	5
M4	0.84	0.71	3.9	56.8	15.56	0.5743	2
M5	0.60	0.36	6.3	50.4	14.42	0.4278	4
M6	0.65	0.42	5.0	64.3	17.59	0.4689	3
M7	0.94	0.88	4.1	57.4	15.83	0.6954	1
M8	0.71	0.50	4.1	37.3	10.65	0.2503	6

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Error Handling

The program validates:

• File existence
• Numeric criteria columns
• Correct number of weights and impacts
• Valid impact symbols (+ or -)

Meaningful error messages are shown if validation fails.

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Dependencies

• pandas
• numpy
• sys (standard library)

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