topsis-vidyt-102303747 1.0.1

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) implementation for multi-criteria decision making

TOPSIS Implementation

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision-making algorithm that ranks alternatives based on their closeness to an ideal solution.

Installation

pip install topsis-vidyt-102303747

Usage

Command Line

topsis <InputDataFile> <Weights> <Impacts> <OutputResultFile>

Arguments:

• InputDataFile: CSV or XLSX file with data (first column: identifiers, rest: numeric criteria)
• Weights: Comma-separated numeric weights (e.g., "0.2,0.2,0.2,0.2,0.2")
• Impacts: Comma-separated impacts: '+' for benefit, '-' for cost (e.g., "+,+,+,-,+")
• OutputResultFile: Output CSV file name

Example:

topsis data.xlsx "0.2,0.2,0.2,0.2,0.2" "+,+,+,+,+" output.csv

Python Code

import numpy as np
from topsis_package import topsis

# Data: 8 alternatives, 5 criteria
data = np.array([
    [0.67, 0.45, 6.5, 42.0, 12.56],
    [0.60, 0.36, 3.3, 53.3, 14.47],
    [0.82, 0.67, 3.6, 38.0, 17.1],
    [0.60, 0.36, 3.5, 60.9, 18.42],
    [0.76, 0.58, 4.8, 43.0, 12.29],
    [0.69, 0.48, 6.6, 48.7, 14.12],
    [0.79, 0.62, 4.8, 59.2, 16.35],
    [0.84, 0.71, 6.5, 34.5, 10.64],
])

weights = [0.2, 0.2, 0.2, 0.2, 0.2]
impacts = ["+", "+", "+", "+", "+"]

result = topsis(data, weights, impacts)
print("Scores:", result.scores)
print("Ranks:", result.ranks)

Input File Format

CSV Example (data.csv):

Fund_Name,P1,P2,P3,P4,P5
M1,0.67,0.45,6.5,42.0,12.56
M2,0.60,0.36,3.3,53.3,14.47
M3,0.82,0.67,3.6,38.0,17.1

XLSX Example:

Same structure, saved as .xlsx file.

Output Format

Output CSV includes original columns plus:

• Topsis Score: Closeness coefficient (0 to 1, higher is better)
• Rank: 1-based ranking (1 is best)

Example Output (output.csv):

Fund_Name,P1,P2,P3,P4,P5,Topsis Score,Rank
M1,0.67,0.45,6.5,42.0,12.56,0.650000,2
M2,0.60,0.36,3.3,53.3,14.47,0.480000,5
M3,0.82,0.67,3.6,38.0,17.1,0.720000,1

Validation Rules

The program validates:

• ✓ Correct number of CLI arguments
• ✓ Weights are numeric and comma-separated
• ✓ Impacts are '+' or '-' only
• ✓ Input file exists (File not found handling)
• ✓ Input file has ≥3 columns (ID + ≥2 criteria)
• ✓ All criteria columns contain numeric values only
• ✓ Number of weights = number of impacts = number of criteria columns
• ✓ Input file has at least one data row

TOPSIS Algorithm Steps

1. Normalize the decision matrix using vector normalization
2. Weight each normalized column
3. Determine ideal best and worst based on impact type (+ or -)
4. Calculate distances from each alternative to ideal best/worst
5. Compute closeness coefficient = distance_to_worst / (distance_to_best + distance_to_worst)
6. Rank alternatives by descending score (1 = best)

Error Handling

The program provides clear error messages:

Error: Input file not found
Error: Weights must be numeric and separated by commas
Error: Impacts must be either '+' or '-' and separated by commas
Error: Number of weights, impacts, and criteria columns must match
Error: Non-numeric value found in row X (criteria columns must be numeric)

Requirements

• Python ≥ 3.7
• numpy
• pandas
• openpyxl (for XLSX support)

License

MIT License