USER MANUAL

TOPSIS – Arihan (102303750)

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1. Overview

topsis-arihan-102303750 is a Python command-line tool that implements the
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

TOPSIS is a Multi-Criteria Decision Making (MCDM) technique used to rank a set of alternatives based on multiple evaluation criteria.
The alternative closest to the ideal best solution and farthest from the ideal worst solution is ranked highest.

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2. System Requirements

• Python version 3.7 or higher
• Operating System: Windows / macOS / Linux
• Python packages: numpy, pandas

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3. Installation

Install the package from PyPI using pip:

pip install topsis-arihan-102303750

After installation, the command topsis becomes available in the terminal.

4. Command-Line Usage

4.1 General Syntax

Enter CSV filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-)

topsis <InputDataFile> <Weights> <Impacts> <OutputFile>

Example

topsis data.csv "1,1,1,1,2" "+,+,-,+,+" output.csv

4.2 Description of Parameters

InputDataFile
CSV file containing the alternatives and criteria.

Weights
Comma-separated numeric values representing the importance of each criterion.

Impacts
Comma-separated symbols indicating the nature of each criterion:
+ → Benefit criterion (higher value is better)
- → Cost criterion (lower value is better)

OutputFile
Name of the CSV file where results will be saved.

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5. Example Usage

5.1 Input File (data.csv)

Fund Name,P1,P2,P3,P4,P5
M1,0.84,0.71,6.7,42.1,12.59
M2,0.91,0.83,7.0,31.7,10.11
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
M8,0.93,0.86,3.4,37.0,10.55

The first column is treated as an identifier (Fund Name).
All remaining columns must contain numeric criteria.

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5.2 Weights and Impacts Used

Weights:
1,1,1,1,2

Impacts:
+,+,-,+,+

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5.3 Command Executed

topsis data.csv "1,1,1,1,2" "+,+,-,+,+" output.csv

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6. Output Description

The output file contains:

• Original data
• Topsis Score (closeness coefficient)
• Rank (1 indicates the best alternative)

6.1 Output File (output.csv)

Fund Name,P1,P2,P3,P4,P5,Topsis Score,Rank
M1,0.84,0.71,6.7,42.1,12.59,0.38,6
M2,0.91,0.83,7.0,31.7,10.11,0.31,8
M3,0.79,0.62,4.8,46.7,13.23,0.48,3
M4,0.78,0.61,6.4,42.4,12.55,0.34,7
M5,0.94,0.88,3.6,62.2,16.91,0.98,1
M6,0.88,0.77,6.5,51.5,14.91,0.59,2
M7,0.66,0.44,5.3,48.9,13.83,0.44,5
M8,0.93,0.86,3.4,37.0,10.55,0.46,4

7. Assumptions and Constraints

• Input CSV must contain only numeric values after the first column.
• Number of weights and impacts must match the number of criteria.
• Higher TOPSIS score implies better ranking.
• Missing or categorical values are not supported.

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8. Error Handling

The program performs validation and displays appropriate error messages for:

• Incorrect number of command-line arguments.
• File not found.
• Non-numeric values in criteria columns.
• Mismatch in number of criteria, weights, and impacts.
• Invalid impact symbols (only + and - allowed).