Topsis-Chahat-102203637 0.1.0

A Python package to implement the TOPSIS method for multi-criteria decision analysis.

Topsis Package

A Python package to calculate TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) scores for decision-making.

Installation

Install the package from PyPI using pip:

pip install Topsis-Chahat-102203637

Usage

Run the TOPSIS algorithm via the command line using the topsis-cli tool.

topsis-cli <input_file.csv> <weights> <impacts> <output_file.csv>

Arguments

• <input_file.csv>: Path to the input CSV file with alternatives and criteria.
• : Comma-separated list of weights for each criterion (e.g., 0.3, 0.4, 0.3).
• : Comma-separated list of impacts for each criterion (+ for positive, - for negative).
• <output_file.csv>: Path for saving the output with TOPSIS scores.

Example

topsis-cli input.csv 0.3,0.4,0.3 +,+,- output.csv

Example Input File (input.csv):

Model	P1	P2	P3
Model1	25	35	30
Model2	30	40	35
Model3	20	30	25

Sample Output:

Output File (output.csv):

Model	P1	P2	P3	Topsis Score	Rank
Model1	25	35	30	0.72	1
Model2	30	40	35	0.85	2
Model3	20	30	25	0.60	3

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How It Works:

1. Normalization:
   The input data is normalized using the root sum of squares method for each column.

2. Weight Normalization:
   The normalized data is then weighted according to the provided weights.

3. Best and Worst Values:
   The best and worst values for each criterion are determined based on the given impacts.

4. Performance Calculation:
   The Euclidean distance to the best and worst values is computed, and a performance score is generated.

5. Ranking:
   The alternatives are ranked based on their performance scores.