Topsis-Agam-102016068 1.0.0

Topsis package for MCDM problems

Topsis-Agam-102016068

for: Project-1 (UCS654) submitted by: Agamjot Singh Roll no: 102016068 Group: 3CS11

What is TOPSIS?

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

Topsis-Agam-102016068 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).

Installation

Use the package manager pip to install Topsis-Agam-102016068.

>> pip install Topsis-Agam-102016068

Usage

Enter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-), further followed by the filename having .csv extension to store result

>> topsis data.csv "1,1,1,1" "+,-,+,+" res.csv

or vectors can be entered without " "

>> topsis data.csv 1,1,1,1 +,-,+,+ res.csv

But the second representation does not provide for inadvertent spaces between vector values. So, if the input string contains spaces, make sure to enclose it between double quotes (" ").

To view usage help, use

topsis /h

Example

data.csv

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

Model	Correlation	R2	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

Weights (weights) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts.

Input:

topsis data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv

Output file (result.csv)

Model	Correlation	R2	RMSE	Accuracy	Score	Rank
M1	0.79	0.62	1.25	60.89	0.7722	2
M2	0.66	0.44	2.89	63.07	0.2255	5
M3	0.56	0.31	1.57	62.87	0.4388	4
M4	0.82	0.67	2.68	70.19	0.5238	3
M5	0.75	0.56	1.3	80.39	0.8113	1

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

Other notes

• The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in data.csv.
• Make sure the csv does not contain categorical values

License

MIT