TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
TOPSIS is a Python library for implementing the Technique for Order of Preference by Similarity to Ideal Solution, a multi-criteria decision-making method. This library allows users to rank alternatives based on given criteria, weights, and impacts.
Installation
You can install the library via pip:
pip install topsis-Mridul
Usage
Command-line Usage
The library can be used directly from the command line. Here's the general syntax:
python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example
Create an input file, data.csv:
Name,C1,C2,C3,C4
A,250,16,12,5
B,200,32,8,3
C,300,24,10,4
D,275,20,11,4
E,225,28,9,2
Run the command:
python topsis.py data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv
The output result.csv will contain:
Name,C1,C2,C3,C4,Topsis Score,Rank
A,250,16,12,5,0.64,2
B,200,32,8,3,0.43,4
C,300,24,10,4,0.78,1
D,275,20,11,4,0.56,3
E,225,28,9,2,0.36,5
Python Library Usage
Importing the Library
You can also use the library directly in Python:
from topsis import Topsis
# Input data
data = "data.csv"
weights = [0.25, 0.25, 0.25, 0.25]
impacts = ["+", "+", "-", "+"]
result_file = "result.csv"
# Perform TOPSIS
Topsis(data, weights, impacts, result_file)
print(f"Results saved in {result_file}")
Parameters
InputDataFile: A CSV file with the first column as the name of the alternatives and the rest as numeric criteria.
Weights: A comma-separated string of numeric weights (e.g., "0.25,0.25,0.25,0.25").
Impacts: A comma-separated string of impacts for each criterion, either + (positive impact) or - (negative impact).
ResultFileName: The name of the output CSV file where results will be saved.
Features
Validate input file format and values.
Handle missing or invalid values gracefully.
Normalize and weight criteria.
Calculate TOPSIS scores and rankings.
Limitations
All criteria must be numeric.
The number of weights and impacts must match the number of criteria.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Contributing
Feel free to open issues or create pull requests to improve the library!
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"description": "TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)\r\n\r\nTOPSIS is a Python library for implementing the Technique for Order of Preference by Similarity to Ideal Solution, a multi-criteria decision-making method. This library allows users to rank alternatives based on given criteria, weights, and impacts.\r\n\r\nInstallation\r\n\r\nYou can install the library via pip:\r\n\r\npip install topsis-Mridul\r\n\r\nUsage\r\n\r\nCommand-line Usage\r\n\r\nThe library can be used directly from the command line. Here's the general syntax:\r\n\r\npython <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>\r\n\r\nExample\r\n\r\nCreate an input file, data.csv:\r\n\r\nName,C1,C2,C3,C4\r\nA,250,16,12,5\r\nB,200,32,8,3\r\nC,300,24,10,4\r\nD,275,20,11,4\r\nE,225,28,9,2\r\n\r\nRun the command:\r\n\r\npython topsis.py data.csv \"0.25,0.25,0.25,0.25\" \"+,+,-,+\" result.csv\r\n\r\nThe output result.csv will contain:\r\n\r\nName,C1,C2,C3,C4,Topsis Score,Rank\r\nA,250,16,12,5,0.64,2\r\nB,200,32,8,3,0.43,4\r\nC,300,24,10,4,0.78,1\r\nD,275,20,11,4,0.56,3\r\nE,225,28,9,2,0.36,5\r\n\r\nPython Library Usage\r\n\r\nImporting the Library\r\n\r\nYou can also use the library directly in Python:\r\n\r\nfrom topsis import Topsis\r\n\r\n# Input data\r\ndata = \"data.csv\"\r\nweights = [0.25, 0.25, 0.25, 0.25]\r\nimpacts = [\"+\", \"+\", \"-\", \"+\"]\r\nresult_file = \"result.csv\"\r\n\r\n# Perform TOPSIS\r\nTopsis(data, weights, impacts, result_file)\r\nprint(f\"Results saved in {result_file}\")\r\n\r\nParameters\r\n\r\nInputDataFile: A CSV file with the first column as the name of the alternatives and the rest as numeric criteria.\r\n\r\nWeights: A comma-separated string of numeric weights (e.g., \"0.25,0.25,0.25,0.25\").\r\n\r\nImpacts: A comma-separated string of impacts for each criterion, either + (positive impact) or - (negative impact).\r\n\r\nResultFileName: The name of the output CSV file where results will be saved.\r\n\r\nFeatures\r\n\r\nValidate input file format and values.\r\n\r\nHandle missing or invalid values gracefully.\r\n\r\nNormalize and weight criteria.\r\n\r\nCalculate TOPSIS scores and rankings.\r\n\r\nLimitations\r\n\r\nAll criteria must be numeric.\r\n\r\nThe number of weights and impacts must match the number of criteria.\r\n\r\nLicense\r\n\r\nThis project is licensed under the MIT License. See the LICENSE file for details.\r\n\r\nContributing\r\n\r\nFeel free to open issues or create pull requests to improve the library!\r\n\r\n\r\n",
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