Topsis-Abhinav-102067004 0.1.2

Implementation of topsis algorithm for multiple criteria decision making

---

---

---

Topsis-Abhinav-102067004

| This library has been created as a part of the assignment for UCS654. This package implements Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS) for solving Multiple Criteria Decision Making(MCDM) problems.

Installation

| This package can be installed using pip package manager

pip install Topsis-Abhinav-102067004

Usage

| The library provides support for a 'topsis' command which can be invoked through the command line | Input Arguments:

• file: a file with '.csv' extension, which should only have numeric values for all features, beyond the second column. This file should have minimum three columns, otherwise an error would occur

• weights: A numeric array, with weights value for each feature column

• impacts: A character array with '+' corresponding to features with positive impact and '-' corresponding to features with negative positive impact

• output file name: name of the output csv file having TOPSIS score and rank columns appended to the input file

| Output: | The result table will be printed to the console and a csv file will be created with the output file name

Example

| file.csv

Fund Name	P1	P2	P3	P4	P5
M1	0.67	0.45	5.1	66.4	18.16
M2	0.8	0.64	4.9	46.7	13.26
M3	0.68	0.46	3.6	34.9	9.91
M4	0.84	0.71	4.7	36.8	10.76
M5	0.68	0.46	4.7	51.1	14.24
M6	0.63	0.4	5.3	54.5	15.21
M7	0.8	0.64	3.7	67.3	18.11
M8	0.79	0.62	3.3	66.6	17.83

| Weights = [1,1,1,1,1] | Impacts = ['+', '+', '+', '+', '-' ]

topsis input.csv 1,1,1,1,1 +,+,+,+,- output.csv

Output

| To console:

Results:

Fund Name P1 P2 P3 P4 P5 Topsis Score Rank

0 M1 0.67 0.45 5.1 66.4 18.16 0.484608 5.0

1 M2 0.8 0.64 4.9 46.7 13.26 0.605915 1.0

2 M3 0.68 0.46 3.6 34.9 9.91 0.396911 8.0

3 M4 0.84 0.71 4.7 36.8 10.76 0.596375 2.0

4 M5 0.68 0.46 4.7 51.1 14.24 0.440537 7.0

5 M6 0.63 0.4 5.3 54.5 15.21 0.445909 6.0

6 M7 0.8 0.64 3.7 67.3 18.11 0.53706 3.0

7 M8 0.79 0.62 3.3 66.6 17.83 0.507571 4.0

| outputfilename.csv:

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.67	0.45	5.1	66.4	18.16	0.484607532	5
M2	0.8	0.64	4.9	46.7	13.26	0.605914538	1
M3	0.68	0.46	3.6	34.9	9.91	0.396910752	8
M4	0.84	0.71	4.7	36.8	10.76	0.596374527	2
M5	0.68	0.46	4.7	51.1	14.24	0.44053701	7
M6	0.63	0.4	5.3	54.5	15.21	0.445909442	6
M7	0.8	0.64	3.7	67.3	18.11	0.537060234	3
M8	0.79	0.62	3.3	66.6	17.83	0.507571147	4

License

MIT