Saravanan666/pypi-org huggingface dataset:
Package: topsis-debolinaghosh-102103365
TOPSIS PackageTOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a Python package that provides an implementation of the TOPSIS method for multi-criteria decision analysis. This package allows users to evaluate and rank alternative solutions based on a set of criteria.Table of ContentsIntroductionInstallationUsageCommand Line InterfaceExamplesContributingLicenseIntroductionMulti-criteria decision analysis (MCDA) is a method for making decisions when there are multiple criteria to consider. TOPSIS is one of the widely used MCDA techniques that helps in ranking alternatives based on their proximity to the ideal solution and their distance to the anti-ideal solution.This package provides a Python implementation of the TOPSIS method, both as a Python module and a command line tool.InstallationYou can install TOPSIS using pip:pipinstalltopsis-package


TouseTOPSISinyourPythoncode,importthetopsismodule:

python
Copycode
fromtopsis_packageimporttopsis# Your decision matrixdata={...}# Your weights and impactsweights=[...]impacts=[...]# Run TOPSISresult_df=topsis(data,weights,impacts)# Display the resultsprint(result_df)Command LineTOPSIS also provides a command line interface for convenient usage:topsisinput.csv<weights><impacts>result.csvinput.csv: Path to the input CSV file containing the decision matrix.weights: Weights for each criterion separated by commas.impacts: Impacts for each criterion (+ or -) separated by commas.result.csv: Path to the output CSV file where the results will be saved.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: topsis-dev-102117155
TOPSIS PACKAGE - Assignment 1I 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 worldInstallationpip install topsis-dev-102117155UsagePlease 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 UsageThe 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 DatasetFund NameP1P2P3P4P5M10.780.615.534.710.4M20.880.77558.416.26M30.610.375.939.911.7M40.760.584.257.715.81M50.840.713.24813.19M60.760.58468.818.54M70.810.666.538.211.54M80.810.663.232.89.37Output DatasetFund NameP1P2P3P4P5TOPSIS ScoreRankM10.780.615.534.710.40.53037405450411224M20.880.77558.416.260.53725102207784133M30.610.375.939.911.70.47157072109146048M40.760.584.257.715.810.50994830547602796M50.840.713.24813.190.577234782933251M60.760.58468.818.540.494478878337379257M70.810.666.538.211.540.52441072526314295M80.810.663.232.89.370.55765336722857032Important PointsThere should be only numeric columns except the first column i.e. Fund Name.Input file must contain atleast three columns.CopyrightsLicense© 2024 Dev AhujaThis repository is licensed under the MIT license.See LICENSE for details.
Package: topsis-dev-102117160
TOPSIS PACKAGE - Assignment 1I 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 worldInstallationpip install topsis-Dev-102117160UsagePlease 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 UsageThe 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 DatasetFund NameP1P2P3P4P5M10.780.615.534.710.4M20.880.77558.416.26M30.610.375.939.911.7M40.760.584.257.715.81M50.840.713.24813.19M60.760.58468.818.54M70.810.666.538.211.54M80.810.663.232.89.37Output DatasetFund NameP1P2P3P4P5TOPSIS ScoreRankM10.780.615.534.710.40.53037405450411224M20.880.77558.416.260.53725102207784133M30.610.375.939.911.70.47157072109146048M40.760.584.257.715.810.50994830547602796M50.840.713.24813.190.577234782933251M60.760.58468.818.540.494478878337379257M70.810.666.538.211.540.52441072526314295M80.810.663.232.89.370.55765336722857032Important PointsThere should be only numeric columns except the first column i.e. Fund Name.Input file must contain atleast three columns.
Package: topsis-Devyansh-102003604
topsis_Devyansh-102003604TOPSISSubmitted By:Devyansh Bansal - 102003604.Type:Package.Title:TOPSIS method for multiple-criteria decision making (MCDM).Version:1.0.0.Date:2023-01-22.Author:Devyansh Bansal.Maintainer:Devyansh Bansaldevyanshbansal@gmail.com.Description:Evaluation of alternatives based on multiple criteria using TOPSIS method..What is TOPSIS?Technique forOrderPreference bySimilarity toIdealSolution
(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.How to install this package:>> pip install topsis-Devyansh-102003604In Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csvInput file (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.ModelCorrelationR2RMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39Weights (weights) is not already normalised will be normalised later in the code.Information of benefit positive(+) or negative(-) impact criteria should be provided inimpacts.Output file (result.csv)ModelCorrelationR2RMSEAccuracyTopsis_scoreRankM10.790.621.2560.890.77222M20.660.442.8963.070.22555M30.560.311.5762.870.43884M40.820.672.6870.190.52383M50.750.561.380.390.81131The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Package: topsis-Dhanvi-101903427
TOPSIS-Python
Source code for TOPSIS optimization algorithm in python.What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method.TOPSIS chooses the alternative
of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.
TOPSIS is a way to allocate the ranks on basis of the weights and impact of the given factors.Weights mean how much a given factor should be taken into consideration
(default weight = 1 for all factors).Impact means that a given factor has a positive or negative impact.How to install this package?
pip install topsis-Dhanvi-101903427==0.0.1To run from command prompt
python <inputdata.csv> <weights> <impacts> <outputfile.csv>
e.g. python input.csv “1,1,1,2,0.5” “+,+,-,+,-” output.csvSample Dataset
Model   P1      P2      P3      P4      P5
M1      0.77    0.59    6.2     58.7    16.57
M2      0.6     0.36    3.2     46      12.54
M3      0.68    0.46    4.3     51.5    14.24
M4      0.95    0.9     4.9     38.9    11.41
M5      0.61    0.37    4.7     62.6    17.07
M6      0.63    0.4     4.1     50      13.78
M7      0.69    0.48    5.1     44.1    12.59
M8      0.8     0.64    4.2     51.3    14.24Output
Model   P1      P2      P3      P4      P5      Topsis Score    Rank
M1      0.77    0.59    6.2     58.7    16.57   0.49740         3
M2      0.6     0.36    3.2     46      12.54   0.361325        6
M3      0.68    0.46    4.3     51.5    14.24   0.402184        5
M4      0.95    0.9     4.9     38.9    11.41   0.533495        2
M5      0.61    0.37    4.7     62.6    17.07   0.465008        4
M6      0.63    0.4     4.1     50      13.78   0.360213        7
M7      0.69    0.48    5.1     44.1    12.59   0.266374        8
M8      0.8     0.64    4.2     51.3    14.24   0.548612        1The model with highest topsis score is given the best ranking and should be chosen as the ideal solution.Applications
1.Mobile selection on basis of multiple criteria like looks,storage,camera etc
2.University selection based on multiple factors.
Package: topsis-dhruv-102003697
TOPSIS-PythonProject 1 : UCS654Submitted By:Dhruv Singla 102003697What is TOPSISTechnique forOrderPreference bySimilarity toIdealSolution (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 atwikipedia.How to use this package:TOPSIS-DHRUV-102003697  can be run as in the following example:In Command Prompt>> topsis 102003697-data.csv "1,1,1,1,1" "+,+,-,+,+" 102003697-result.csvSample datasetThe decision matrix (a) 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.ModelCorrelationR2RMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39Weights (w) is not already normalised will be normalised later in the code.Information of benefit positive(+) or negative(-) impact criteria should be provided inI.OutputModel   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
Package: topsis-dilmanpreet-101903506
topsis_dilmanpreet_101903506TOPSISSubmitted By:Dilmanpreet Singh.Roll Number:101903506.Type:Package.Title:TOPSIS for multiple-criteria decision making (MCDM).Version:1.0.Date:2022-2-24.Description:Evaluation of alternatives based on multiple criteria using TOPSIS method..What is TOPSIS?TOPSIS orTechnique forOrderPreference bySimilarity toIdealSolution is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.How to install this package:>> pip install topsis-dilmanpreet-101903506In Command Prompt>> topsis data.csv "1,1,2,1,1" "+,-,+,+,-" result.csvProcessFirst we create an evaluation matrix which consists of m alternatives and n criterias, with the intersection of each alternative and criteria. Then we move to the preprocessing phase. We then normalize the matrix using norm. Weighted normalised decision matrix is then calculated. We then determine the best and worst alternatives. After that, we calculate the euclidean distance between the target alternative and the worst condition. Finally, the similarity to the worst condition checked and the alternatives are ranked according to the final performance scores, awarding lower rank to higher performance score.Input file (data.csv)Output -- (result.csv)The output file contains columns of input file along with two additional columns for Topsis_score and Rank
Package: topsis-drishti-102117158
TOPSIS PACKAGE - Assignment 1I 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 worldInstallationpip install topsis-Dev-102117160UsagePlease 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 UsageThe 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 DatasetFund NameP1P2P3P4P5M10.780.615.534.710.4M20.880.77558.416.26M30.610.375.939.911.7M40.760.584.257.715.81M50.840.713.24813.19M60.760.58468.818.54M70.810.666.538.211.54M80.810.663.232.89.37Output DatasetFund NameP1P2P3P4P5TOPSIS ScoreRankM10.780.615.534.710.40.53037405450411224M20.880.77558.416.260.53725102207784133M30.610.375.939.911.70.47157072109146048M40.760.584.257.715.810.50994830547602796M50.840.713.24813.190.577234782933251M60.760.58468.818.540.494478878337379257M70.810.666.538.211.540.52441072526314295M80.810.663.232.89.370.55765336722857032Important PointsThere should be only numeric columns except the first column i.e. Fund Name.Input file must contain atleast three columns.
Package: topsis-Eshita-102003522
#Project description
##TOPSIS
What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making (MCDM) method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.##How to Install this Package?
pip install Topsis-Eshita-102003522##How to Run this Package?
topsisEg. topsis /Users/Eshita/Desktop/102003522-data.csv "1,1,1,1,1" "+,+,-,+,+" /Users/Eshita/Desktop/result.csv##Constraints Applied
Number of parameters should be correct i.e. 5.
Print error message if input file doesn't exist.
The impacts and weights should be comma separated.
Impacts should only have +ve or -ve symbols.
Number of columns in the input csv file should be more or equal to 3.
The 2nd to last columns should be in numeric data type.
Number of weights, impacts and columns should be equal.
Input File
Fund Name	P1	P2	P3	P4	P5
M1	0.75	0.56	6.3	51.1	14.68
M2	0.82	0.67	4.2	41.2	11.72
M3	0.89	0.79	6.5	40.2	12.1
M4	0.92	0.85	5.8	49.7	14.32
M6	0.72	0.52	5.3	61.1	16.91
M7	0.69	0.48	3.6	57.9	15.67
M8	0.92	0.85	5.7	31.2	9.67
Output File
Fund Name	P1	P2	P3	P4	P5	TOPSIS Score	Rank
M1	0.32	0.29	0.39	0.36	0.37	0.3655	8
M2	0.35	0.34	0.26	0.29	0.29	0.55	2
M3	0.38	0.41	0.41	0.28	0.30	0.48	5
M4	0.39	0.44	0.36	0.35	0.36	0.57	1
M5	0.33	0.30	0.41	0.39	0.39	0.39	7
M6	0.31	0.27	0.33	0.43	0.42	0.44	6
M7	0.29	0.25	0.22	0.41	0.39	0.50	4
M8	0.39	0.44	0.36	0.22	0.24	0.53	3
License
MIT##Written By
Name : Eshita Arora
Roll No. : 102003522
Package: topsis-experimental
topsis_experimental: TOPSIS Analysis Python PackageOverviewtopsis_experimental is a Python package that implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision-making (MCDM). It provides a straightforward and efficient way to evaluate and rank a set of alternatives based on conflicting criteria.Key FeaturesUser-friendly command-line interface for easy execution.
Supports both numerical and categorical data.
Handles equal weights and impacts, as well as positive and negative impacts.
Generates a TOPSIS score for each alternative, indicating its similarity to the ideal solution.
Provides ranking of alternatives based on their TOPSIS scores.
InstallationTo install topsis_experimental using pip:pipinstalltopsis_experimentalYou can use topsis_experimental from the command line as follows:topsis_experimentalinput.csvweightsimpactsoutput.csvwhere:input.csv: The input CSV file containing the decision matrix (alternatives and criteria).
weights: Comma-separated list of weights for each criterion (numerical, equal to the number of criteria).
impacts: Comma-separated list of impacts for each criterion (+ for positive, - for negative).
output.csv: The output CSV file containing the TOPSIS scores and rankings.
ExampleAssume you have a CSV file data.csv with the following structure:Fund Name,P1,P2,P3,P4,P5
M1,0.84,0.71,6.7,42.1,12.59
M2,0.91,0.83,7.0,31.7,10.11
...
And you want to perform TOPSIS with weights 1,1,1,2,1 and all positive impacts (+++-+).Run the following command:topsis_experimentaldata.csv"1,1,1,2,1""+,+,+,-,+"results.csvThis will create a file results.csv with the TOPSIS scores and rankings.Additional NotesThe weights and impacts must be separated by commas and have the same number of elements as the number of criteria.
For more advanced usage, see the source code or consider creating a custom Python script.
Package: topsisGod
Topsis Score Calculation packagepip install topsisGodfrom topsisGod import topsisInput: Three parametersdata: data should contain only integer fields so preprocess the data and temporarily remove the model namesWeights: weight of each attribute, considered to be an integer listImpact: Considered to be a character listWeight = [1, 1, 1, 1]Impact = ['+', '-', '-', '+']Output: Returns the score of each rowTo calculate the rank based on the score, append the score list as:score = topsis(data, w, i)from topsisGod import rerankrerank(data,score)Github link :GitHub
Package: topsis-gs-102003220
Topsis Implimentation by GurtaranIt takes a csv file, weigths of all columns, impacts as 1 for positive and -1 for negative and output file name as an input and give the final topsis implimented file out.Installationpip install topsis-gs-102003220How to use it?Open terminal and type file path, weights, impact as 1 or -1 and output file name.License© 2023 Gurtaran SinghThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-gurleen-102003138
TopsisBy Gurleen Kaur, 102003138This package can be used for implementation of Multiple criteria decision making using Topsis Algorithm. This is a python library for dealing with Multi-Criteria Decision Making (MCDM) problems by using techniques for order of preference by similarity to ideal solution (TOPSIS).Installationpipinstalltopsis-gurleen-102003138How to useThis package will accept four arguments during file execution:dataset.csv (file path which contains the dataset)string of weights separated by commas(,)string of impacts (+/-) separated by commas(,)result.csv (file path where the result needs to be stored)Pre-requirementsinstall pandas and math libraries before installing this packagepipinstallpandas
pipinstallmathYou can install this package using following commandpipinstalltopsis_gurleen_102003138LicenseÂ© 2023 Gurleen Kaur
This repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-Gurpreet-Singh-101803019
Topsis_Gurpreet_101803019This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)How to use this package:pip install topsis-Gurpreet-Singh-101803019==0.0.1In Command Prompt>> topsispy data.csv "1,1,1,1" "+,+,-,+" out.csvSample datasetModelCorrelationR2RMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39OutputThis will be in our Output csv fileModelCorrelationR2RMSEAccuracyTOPSIS SCORERankM10.790.621.2560.890.772.0M20.660.442.8963.070.235.0M30.560.311.5762.870.444.0M40.820.672.6870.190.523.0M50.750.561.380.390.811.0
Package: topsis-gurtaran-102003220
Topsis Implimentation by GurtaranIt takes a csv file, weigths of all columns, impacts as 1 for positive and -1 for negative and output file name as an input and give the final topsis implimented file out.Installationpip install topsis-gurtaran-102003220How to use it?Open terminal and type file path, weights, impact as 1 or -1 and output file name.License© 2023 Gurtaran SinghThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-harsh-102003675
Topsis implemented by Harsh PabaFunctionality of the packageOutputs the performance score and rank corresponding to each row of the dataset.UsageMake sure you have Python installed in your system.Run the following command in terminal.pip install topsis-harsh-102003675Examplefrom topsis_harsh_102003675 import main
inputFile=sys.argv[1]
weight=sys.argv[2]
impact=sys.argv[3]
outputFile=sys.argv[4]
main.find_rank(inputFile,weight,impact,outputFile)Run the following Script.python test.py <inputFile.csv> weights impacts <outputFile.csv>NoteRank 1 corresponds to minimum performance score and so on.
Package: topsisharsh102017133
Topsis Python PackageTopsisPy is a Python Package implementing Topsis method used for multi-criteria decision analysis. Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.TopsisPy intends to make the process of TOPSIS simple in python. Key features of the package are -Easy to useNumpy basedIdeal for StudentsInstallationInstall the Package using the command -bash
$ pip install topsisharsh102017133You may use the Package via commandline as$topsis[pathofcsv][weightsasstring][impactsasstring][pathofresultcsvasstring]
Package: topsis-Hemakshi-101903145
#Topsis-HEMAKSHI-101903145
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-HEMAKSHI-101903145##Sample Use case
import topsis-HEMAKSHI-10903145 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-hiten
topsis-pythonTopsis analysis of a csv fileUCS633 Project SubmissionName- Hiten GargRoll no.- 101703234Group- COE-11About TopsisThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).Installationpipinstalltopsis-hitenUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis-hiten -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize.
Size of w and i is equal to number of features.Note that the first row and first column of dataset is droppedRank 1 signifies best decisionContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: topsis-hitesh
topsis-pythonTopsis analysis of a csv file""UCS633 Project Submission""Name - Hitesh GuptaRoll no. - 101703235About TopsisThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).Installationpipinstalltopsis-hiteshUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis-hitesh -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize.
Size of w and i is equal to number of features.Note that the first row and first column of dataset is droppedRank 1 signifies best decisionContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT