topsis-lovish-102117148 1.0.2

It gives the ranking to models as per the TOPSIS score.Please view the instructions so as to run the package smoothly in your terminal.

TOPSIS PACKAGE - Assignment 1

I have developed a command line python program to implement the TOPSIS. TOPSIS (technique for order performance by similarity to ideal solution) is a useful technique in dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real world

Installation

pip install topsis-lovish-102117148

Usage

Please provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.

py -m topsis.__main__ [input_file_name.csv] [weight as string] [impact as string] [result_file_name.csv]

Example Usage

The below example is for the data have 5 columns. py -m topsis.__main__ "C:\User\...." "1,1,2,0.5,0.75" "+,+,-,-,-" "C:\User\....."

Example Dataset

Fund Name	P1	P2	P3	P4	P5
M1	0.78	0.61	5.5	34.7	10.4
M2	0.88	0.77	5	58.4	16.26
M3	0.61	0.37	5.9	39.9	11.7
M4	0.76	0.58	4.2	57.7	15.81
M5	0.84	0.71	3.2	48	13.19
M6	0.76	0.58	4	68.8	18.54
M7	0.81	0.66	6.5	38.2	11.54
M8	0.81	0.66	3.2	32.8	9.37

Output Dataset

Fund Name	P1	P2	P3	P4	P5	TOPSIS Score	Rank
M1	0.78	0.61	5.5	34.7	10.4	0.5303740545041122	4
M2	0.88	0.77	5	58.4	16.26	0.5372510220778413	3
M3	0.61	0.37	5.9	39.9	11.7	0.4715707210914604	8
M4	0.76	0.58	4.2	57.7	15.81	0.5099483054760279	6
M5	0.84	0.71	3.2	48	13.19	0.57723478293325	1
M6	0.76	0.58	4	68.8	18.54	0.49447887833737925	7
M7	0.81	0.66	6.5	38.2	11.54	0.5244107252631429	5
M8	0.81	0.66	3.2	32.8	9.37	0.5576533672285703	2

Important Points

1. There should be only numeric columns except the first column i.e. Fund Name.
2. Input file must contain atleast three columns.