TOPSIS-Shruti-101803512 1

A Python pip package to apply topsis approach to rank the entries in a dataset

TOPSIS_Shruti_101803512

What is TOPSIS ?

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method

How to use the package

TOPSIS_Shruti_101803512 package should be used as:

In command prompt

>> pip install TOPSIS_Shruti_101803512

>> python >> import TOPSIS_Shruti_101803512 as t >> t.topsis_evaluation(,,,) >> t.topsis_evaluation("data.csv","1,1,1,2","+,+,-,+","result.csv")

Some instructions:

• Input file must contain three or more columns. • From 2nd to last columns must contain numeric values only (Handling of non-numeric values) • Number of weights, number of impacts and number of columns (from 2nd to last columns) must be same. • Impacts must be either +ve or -ve. • Impacts and weights must be separated by ‘,’ (comma).

Example:

The dataset upon which topsis is to be performed is taken as folows. It will be in the form of a csv file.

Sample Input

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

The following output with performance score and rank will be produced in a result(csv file) file and printed as output. 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

OUTPUT

Model	Corr	Rseq	RMSE	Accuracy	Topsis Score	Rank
M1	0.79	0.62	1.25	60.89	0.6391330141342587	2
M2	0.66	0.44	2.89	63.07	0.21259182969277918	5
M3	0.56	0.31	1.57	62.87	0.4078456776130516	4
M4	0.82	0.67	2.68	70.19	0.5191532395007472	3
M5	0.75	0.56	1.3	80.39	0.8282665851935813	1