TOPSIS: A Python Implementation

This project provides an implementation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a widely-used decision-making method. The algorithm evaluates and ranks alternatives based on multiple attributes, considering the importance of each attribute (weights) and its impact type (benefit or cost).

Features

• Validation of Inputs: Ensures proper formatting of input data, weights, and impacts for consistent processing.
• Comprehensive Error Handling: Manages issues like missing files, invalid data, and incorrect input parameters.
• Output Results: Generates a CSV file that includes original data, calculated scores, and rankings.

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How to Run

1. Clone the repository and navigate to the project directory.

2. Ensure Python and the required dependencies are installed.

3. Execute the program using the following command:

   python -m topsis_shamma_102253003.topsis <InputDataSet.csv> <Weights> <Impacts> <Result.csv>

Example Usage

• Input File: A CSV file containing the dataset where each column represents an attribute, and each row represents an alternative.
• Weights: Specify the importance of each attribute as a list (e.g., [1, 2, 3, 4, 5]).
• Impacts: Define whether each attribute is a benefit (1) or a cost (0) (e.g., [1, 0, 1, 1, 0]).
• Output File: A CSV file with calculated TOPSIS scores and rankings.

Example Weights

[1, 1, 1, 1, 1]

Example Impacts

[1, 0, 1, 0, 1]

• 1 indicates a benefit
• 0 indicates a cost

Output

The resulting file includes:

• Original data
• Calculated TOPSIS scores
• Rankings based on the scores

Additional Resources

The package is available on PyPI. You can access it through the following link:

TOPSIS Package on PyPI