Topsis-Nikhil-102317179 0.0.1

A Python implementation of TOPSIS method

Topsis-Nikhil-102317179

A Python implementation of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method with full command-line support and PyPI packaging.

This project demonstrates:

• Implementation of the TOPSIS algorithm
• Command-line interface using sys.argv
• Input validation and error handling
• Packaging using modern pyproject.toml
• Building distribution files
• Publishing to PyPI

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1. Project Overview

TOPSIS is a multi-criteria decision-making (MCDM) technique used to rank alternatives based on their distance from an ideal best and ideal worst solution.

The implemented workflow:

1. Normalize the decision matrix
2. Multiply by weights
3. Determine ideal best and worst
4. Compute Euclidean distance from ideal solutions
5. Calculate performance score
6. Rank alternatives

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2. Command Line Usage

After installation:

topsis <input.csv> "<weights>" "<impacts>" <output.csv>

Example:

topsis input.csv "0.2,0.2,0.2,0.2,0.2" "+,-,+,-,-" output.csv

Input Requirements

• First column must contain alternative names
• Remaining columns must be numeric
• At least 3 columns required
• Weights must be comma-separated
• Impacts must be + or -
• Number of weights and impacts must match criteria columns

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3. Example Input File

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

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4. Example Output

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M6	0.88	0.77	6.5	51.5	14.91	0.738148132	1
M5	0.94	0.88	3.6	62.2	16.91	0.641885815	2
M1	0.84	0.71	6.7	42.1	12.59	0.56369233	3
M2	0.91	0.83	7	31.7	10.11	0.513032103	4
M4	0.78	0.61	6.4	42.4	12.55	0.491956082	5
M3	0.79	0.62	4.8	46.7	13.23	0.439177283	6
M8	0.93	0.86	3.4	37	10.55	0.408498677	7
M7	0.66	0.44	5.3	48.9	13.83	0.407389532	8

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5. Project Structure

Topsis-Nikhil-102317179/
│
├── src/
│   └── TopsisNikhil102317179/
│       ├── __init__.py
│       └── topsis.py
│
├── README.md
├── LICENSE
├── pyproject.toml
├── setup.cfg

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6. Packaging Configuration

pyproject.toml

Key configuration:

• Uses setuptools
• Defines dependencies: pandas, numpy
• Registers CLI entry point
• Uses src layout

Important section:

[project.scripts]
topsis = "TopsisNikhil102317179.topsis:main"

[tool.setuptools.packages.find]
where = ["src"]

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7. Development Pipeline

7.1 Algorithm Flow

flowchart TD
    A[Read CSV File] --> B[Validate Inputs]
    B --> C[Normalize Matrix]
    C --> D[Apply Weights]
    D --> E[Determine Ideal Best/Worst]
    E --> F[Compute Distances]
    F --> G[Calculate Score]
    G --> H[Rank Alternatives]
    H --> I[Write Output CSV]

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7.2 Packaging and Distribution Flow

flowchart TD
    A[Write topsis.py] --> B[Create src Layout]
    B --> C[Add pyproject.toml]
    C --> D[Add setup.cfg]
    D --> E[Build Package]
    E --> F[Generate dist Files]
    F --> G[Install Locally]
    G --> H[Test CLI Command]
    H --> I[Upload to PyPI]

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8. Building the Package Locally

Install build tool:

pip install build

Build:

python -m build

This generates:

dist/
    topsis_nikhil_102317179-0.0.1.tar.gz
    topsis_nikhil_102317179-0.0.1-py3-none-any.whl

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9. Installing Locally

pip install dist/topsis_nikhil_102317179-0.0.1-py3-none-any.whl

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10. Uploading to PyPI

Install twine:

pip install twine

Upload:

twine upload dist/*

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11. Installing from PyPI

After publication:

pip install Topsis-Nikhil-102317179

Then run:

topsis input.csv "0.2,0.2,0.2,0.2,0.2" "+,-,+,-,-" output.csv

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12. Dependencies

• pandas
• numpy
• Python >= 3.8

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13. License

MIT License

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14. Conclusion

This project demonstrates the complete lifecycle of:

• Algorithm implementation
• CLI development
• Modern Python packaging
• Local build testing
• Distribution through PyPI
• Reproducible installation

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15. Part II – Web Service using Streamlit

In addition to the command-line interface, this project also includes a web-based implementation of TOPSIS using Streamlit.

This web application allows users to:

• Upload a CSV file
• Enter weights
• Enter impacts
• Provide an email ID
• Generate the TOPSIS result file

The web interface validates all inputs before processing.

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15.1 Functional Requirements

The web service ensures:

• User must upload an input CSV file
• Number of weights must equal number of criteria columns
• Number of impacts must equal number of criteria columns
• Impacts must be either + or -
• Weights must be comma-separated numeric values
• Email ID format must be valid
• Result file is generated only after successful validation

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15.2 Web Application Flow

flowchart TD
    A[User Uploads CSV] --> B[Enter Weights]
    B --> C[Enter Impacts]
    C --> D[Enter Email ID]
    D --> E[Validate All Inputs]
    E -->|Valid| F[Run TOPSIS Algorithm]
    F --> G[Generate Result File]
    G --> H[Display / Send Result]
    E -->|Invalid| I[Show Error Message]

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15.3 Running the Web Application Locally

Activate virtual environment:

.venv\Scripts\activate

Install required dependencies:

pip install streamlit pandas numpy

Run the application:

streamlit run app.py

After running, open:

http://localhost:8501

The web interface will be available in the browser.

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15.4 Web Project Structure

Topsis-Nikhil-102317179/
│
├── app.py
├── src/
│   └── TopsisNikhil102317179/
│       ├── __init__.py
│       └── topsis.py
│
├── README.md
├── pyproject.toml
├── setup.cfg

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15.5 Integration with Core Algorithm

The Streamlit application reuses the same TOPSIS implementation from:

TopsisNikhil102317179/topsis.py

This ensures:

• No duplication of logic
• Consistent ranking results
• Single source of truth for algorithm implementation

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15.6 Deployment (Optional)

The Streamlit application can be deployed using:

• Streamlit Community Cloud
• Render
• Railway
• Any cloud VM running Python

Basic deployment steps:

1. Push project to GitHub
2. Connect repository to Streamlit Cloud
3. Set entry file as app.py
4. Deploy

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This completes the second part of the assignment by providing a user-friendly web interface for the TOPSIS algorithm.

The repository serves as both an academic submission and a reproducible packaging reference.