Topsis-Naman-101903304 1.0.2

A Python package implementing TOPSIS technique.

TOPSIS implementation in Python

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. More details at wikipedia.

Installation

Use the package manager pip to install this package.

pip install Topsis-Naman-101903304

How to use this package ?

In Terminal

$ topsis data.csv "1,1,1,1,2" "+,+,-,+,+" output.csv

In Python:

from topsis import TOPSIS

filepath = "input.csv"
weights  = "1,1,1,1,2"
impacts  = "+,-,+,-,+"
output   = "output.csv"

topsis = TOPSIS(filepath, impacts, weights, output)

# Method 1: Stepwise

topsis.readCSV()
topsis.normalize()
topsis.weight_assignment()
topsis.find_ibw()
topsis.euclidean_distance()
topsis.performance_score()
topsis.find_rank()
topsis.storeCSV(output)

# Method 2: Automated

topsis.auto()


"""
Attributs provided under TOPSIS :
filepath    : Input file path.
filename    : Extracted filename from filepath
impacts     : given impacts
weights     : given weithts
output      : output file name
odf         : output data
df          : modified dataframe
sp          : S+
sn          : S-
scores      : performance score
ideal_worst : V+
ideal_best  : V-

Usage:
    topsis = TOPSIS(filepath, impacts, weights, output)
    topsis.df
"""

Sample dataset

Fund Name	P1	P2	P3	P4	P5
M1	0.92	0.71	4.5	43	12.59
M2	0.71	0.83	4.4	41.9	10.11
M3	0.77	0.62	3.5	33.2	13.2
M4	0.92	0.61	4.4	50.9	12.55
M5	0.7	0.88	6.7	43.7	16.91
M6	0.64	0.77	6.9	64.5	14.91
M7	0.68	0.44	4.5	31.1	13.83
M8	0.6	0.86	3	36.4	10.55

Output

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.92	0.71	4.5	43.0	12.59	0.606157764635227	6.0
M2	0.71	0.83	4.4	41.9	10.11	0.630939331184659	3.0
M3	0.77	0.62	3.5	33.2	13.23	0.6376673741860752	2.0
M4	0.92	0.61	4.4	50.9	12.55	0.44683746237145194	7.0
M5	0.7	0.88	6.7	43.7	16.91	0.6223296058794716	4.0
M6	0.64	0.77	6.9	64.5	14.91	0.36651530625461226	8.0
M7	0.68	0.44	4.5	31.1	13.83	0.6381151861152682	1.0
M8	0.6	0.86	3.0	36.4	10.55	0.6124418308455085	5.0

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