TOPSIS-Purnima-101803523 1.0.2

A Python package to implement TOPSIS.

TOPSIS

Submitted By: Purnima Lal | 101803523

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What is TOPSIS?

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method.

How to install this package:

>> pip install TOPSIS-Purnima-101803523

After installation, in Command Prompt/Terminal in pwd/current dir:

>> topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Weights (weights) may not be normalised but will be normalised in the code. Note: To avoid errors - Input file must contain three or more columns. 2nd to last columns must contain numeric values only. Number of weights, number of impacts and number of columns (from 2 nd to last columns) must be same. Impacts must be either +ve or -ve. Impacts and weights must be separated by ‘,’ (comma).

InputDataFile (data.csv) - an example

The decision matrix should be constructed with each row representing a Model alternative and each column representing a criterion like Correlation, R², Root Mean Squared Error, Accuracy, etc.

Model	Corr	Rseq	RMSE	Accuracy
M1	0.79	0.62	1.25	60.89
M2	0.66	0.44	2.89	63.07
M3	0.56	0.31	1.57	62.87
M4	0.82	0.67	2.68	70.19
M5	0.75	0.56	1.3	80.39

Output file (result.csv) -

Based on the above input file and setting weights as "1,2,1,1" and impacts as "+,-,-,+".

Model	Corr	Rseq	RMSE	Accuracy	Topsis Score	Rank
M1	0.79	0.62	1.25	60.89	0.423744391359611	4
M2	0.66	0.44	2.89	63.07	0.0.467426368298297	3
M3	0.56	0.31	1.57	62.87	0.760230957034903	1
M4	0.82	0.67	2.68	70.19	0.207772533881566	5
M5	0.75	0.56	1.3	80.39	0.504864457803718	2

The output file contains columns of input file along with two additional columns having Topsis Score and Rank.