Topsis-Lakshita-102303505

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) - A Python package for multi-criteria decision analysis.

What is TOPSIS?

TOPSIS is a multi-criteria decision analysis method that identifies the alternative closest to the ideal solution and farthest from the negative-ideal solution. The ideal solution maximizes benefit criteria and minimizes cost criteria.

Installation

pip install Topsis-Lakshita-102303505

Usage

Command Line

topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Examples

# Example 1: Equal weights, all positive impacts
topsis data.csv "1,1,1,1,1" "+,+,+,+,+" output.csv

# Example 2: Different weights, mixed impacts
topsis data.csv "2,1,3,1,2" "+,+,-,+,+" results.csv

# Example 3: Using Excel file
topsis data.xlsx "1,1,1,2" "+,+,-,+" output.csv

Python API

from Topsis_Lakshita_102303505 import topsis_analysis

result_df = topsis_analysis(
    input_file='data.csv',
    weights='1,1,1,2',
    impacts='+,+,-,+',
    output_file='output.csv'
)

print(result_df)

Input File Format

The input file (CSV or Excel) must have:

• At least 3 columns: 1 name column + 2 or more criteria columns
• First column: Alternative names (text)
• Remaining columns: Numeric criteria values only

Example:

Model	Price	Storage	Camera	Looks
M1	250	16	12	5
M2	200	16	8	3
M3	300	32	16	4

Parameters

• Weights: Comma-separated numeric values (e.g., "1,1,1,2")

  • Must match the number of criteria columns

• Impacts: Comma-separated + or - signs (e.g., "+,+,-,+")

  • + for beneficial criteria (higher is better)
  • - for cost criteria (lower is better)
  • Must match the number of criteria columns

Output

The output CSV file contains all original columns plus:

• Topsis Score: Performance score (0-1 range, higher is better)
• Rank: Integer ranking (1 = best alternative)

Example Output:

Model	Price	Storage	Camera	Looks	Topsis Score	Rank
M3	300	32	16	4	0.691	1
M1	250	16	12	5	0.534	2
M2	200	16	8	3	0.308	3

Error Handling

The package validates:

• ✅ File existence
• ✅ Minimum 3 columns
• ✅ Numeric values in criteria columns
• ✅ Matching counts of weights, impacts, and criteria
• ✅ Valid impact signs (+ or -)
• ✅ Proper comma separation

Algorithm Steps

1. Normalize the decision matrix using vector normalization
2. Apply weights to normalized values
3. Identify ideal solutions (best and worst)
4. Calculate Euclidean distances from ideal solutions
5. Compute performance scores
6. Rank alternatives based on scores

Dependencies

• pandas >= 1.0.0
• numpy >= 1.18.0
• openpyxl >= 3.0.0 (for Excel support)

License

MIT License - See LICENSE file for details

Author

Lakshita Gupta
Email: lakshitagupta0518@gmail.com
Roll Number: 102303505

Links

• PyPI: https://pypi.org/project/Topsis-Lakshita-102303505/
• GitHub: https://github.com/Lakshita018/topsis

Version

1.0.0 (2026-01-20)

• Initial release
• TOPSIS algorithm implementation
• CSV and Excel file support
• Command-line interface
• Python API
• Comprehensive error handling

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For detailed documentation, examples, and troubleshooting, visit the GitHub repository.