Saravanan666/pypi-org huggingface dataset:
Package: topsis-python-souravdlboy
topsis-pythonPackage Description :Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) ALGORITHM.Motivation :This is a part of project - I made for UCS633 - Data analytics and visualization at TIET.@Author : Sourav Kumar@Roll no. : 101883068Algorithm :STEP 1 :Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria.Apply any preprocessing if required.STEP 2 :The matrix is then normalised using the norm.STEP 3 :Calculate the weighted normalised decision matrix.STEP 4 :Determine the worst alternative and the best alternative.STEP 5 :Calculate the L2-distance between the target alternative i and the worst condition.STEP 6 :Calculate the similarity to the worst condition.STEP 7 :Rank the alternatives according to final performance scores.Getting started Locally :Run On Terminalpython -m topsis.topsis <filename.csv> <weights> <impacts>ex. python python -m topsis.topsis topsis.csv 0.25,0.25,0.25,0.25 -,+,+,+Run In IDLEfrom topsis import topsist = topsis.topsis('filepath', [list of weights], [list of impacts])t.topsis_main()Run on JupyterOpen terminal (cmd)jupyter notebookCreate a new python3 file.If file <filename.csv> doesn't exists, then make sure to upload the file to jupyter env.from topsis import topsist = topsis.topsis('filepath', [list of weights], [list of impacts])t.topsis_main()topsis_main()has been specifically designed to inhibit leakeage of inbuilt functions.topsis_main(debug=True)use this to display all the intermediate matrices.Make sure thatfilename.csvis in same directory where package is installed.PAPER :Find the research paper atarxiv.OUTPUT :Prints out overall ml models ranked and the best model / alternative.
Package: topsis-RajatGupta-101703427
Ranking System Using TopsisProject 1 : UCS633Submitted By:Rajat Gupta 101703427InstallationUse the package managerpipto install Ranking system.pipinstalltopsis-RajatGupta-101703427How to use this package:topsis-RajatGupta-101703427 can be run as done below:In Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+"In Python IDLE:>>>importpandasaspd>>>importtopsis>>>data=pd.read_csv('data.csv').values>>>data=data[:,1:]>>>w=[1,1,1,1]>>>impacts=["+","+","-","+"]>>>topsis.topsis(data,w,impacts)Sample datasetThe 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 list is not already normalised will be normalised later in the code.Information of benefit positive(+) or negative(-) impact criteria should be provided inimpacts.OutputModel   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Package: topsis-rohitThapar-102003482
TOPSISTitle:TOPSIS method for multiple-criteria decision making (MCDM).Version:1.0.0.Author:Rohit Thapar.Description:Evaluation of alternatives based on multiple criteria using TOPSIS method.How to install this package:>> pip install topsis-rohitThapar-102003482In Command Prompt>> topsis data.csv "1,1,1,1,1" "+,+,-,+,-" output.csvInput file (data.csv)Fund Name          P1        P2        P3        P4        P50  M1           0.395818  0.419886  0.221712  0.485574  0.4670841  M2           0.271291  0.196656  0.221712  0.485574  0.46242  M3           0.413607  0.457091  0.286079  0.45586   0.4467873  M4           0.302422  0.244491  0.479183  0.28917   0.3106954  M5           0.271291  0.196656  0.479183  0.226118  0.2529285  M6           0.413607  0.457091  0.221712  0.226118  0.2347136  M7           0.413607  0.457091  0.286079  0.28917   0.2971647  M8           0.302422  0.244491  0.479183  0.226118  0.253969Output file (result.csv)Fund Name          P1        P2        P3        P4        P5    Topsis Score    Rank0  M1           0.395818  0.419886  0.221712  0.485574  0.467084       0.924217        1
1  M2           0.271291  0.196656  0.221712  0.485574  0.4624         0.591977        3
2  M3           0.413607  0.457091  0.286079  0.45586   0.446787       0.864965        2
3  M4           0.302422  0.244491  0.479183  0.28917   0.310695       0.208741        6
4  M5           0.271291  0.196656  0.479183  0.226118  0.252928       0.0340168       8
5  M6           0.413607  0.457091  0.221712  0.226118  0.234713       0.530087        5
6  M7           0.413607  0.457091  0.286079  0.28917   0.297164       0.577036        4
7  M8           0.302422  0.244491  0.479183  0.226118  0.253969       0.110191        7The output file contains columns of input file along with two additional columns having Topsis_score and Rank
Package: topsis-rupanshijain-102017010
topsis_rupanshi_102017010TopsisPy is a Python Package implementingTopsismethod used for multi-criteria decision analysis.
Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.InstallationInstall the Package using the command -$pipinstalltopsis_rupanshi_102017010Usagepython-mtopsis_rupanshijain_102017010.topsis102017010<InputDataFile><Weights><Impacts><ResultFileName>ExampleFund NameP1P2P3P4P5M10.940.883.840.811.61M20.770.595.645.413.09M30.80.64757.716.54M40.670.453.161.616.46M50.890.795.967.718.82M60.660.446.262.117.35M70.730.536.634.510.59M80.910.833.255.315.06weights : [1, 1, 1, 2, 1]impacts : [+, +, -, +, +]input:102017010.csvpython3-mtopsis_rupanshijain_102017010.topsis102017010102017010.csv"1,1,1,2,1""+,+,-,+,+"102017010-result.csvoutput:102017010-result.csvFund NameP1P2P3P4P5Performance ScoreRankM10.940.883.840.811.610.471233758800886006M20.770.595.645.413.090.336332541233781007M30.80.647.057.716.540.52178720088903505M40.670.453.161.616.460.62398388691950703M50.890.795.967.718.820.73229856185186001M60.660.446.262.117.350.53541776437505904M70.730.536.634.510.590.099857695142785508M80.910.833.255.315.060.7069438294400742Other notesThe 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 sample.csv.Make sure the csv does not contain categorical valuesLicenseMITFree Software, Hell Yeah!
Package: topsis-sahil-102103134
Topsis Python PackageMade By
Sahil Manchanda (Roll No. 102103134)DescriptionThe Topsis Python Package is a Python library that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is a multi-criteria decision-making method used to determine the best alternative among a set of alternatives based on their performance on multiple criteria.UsageUse the following command to perform TOPSIS analysis on a dataset:topsis data.csv "1,0,1,0,1" "+,-,+,-,+"-data.csv: Path to the input CSV file containing the dataset.-"1,0,1,0,1": Weights for each criterion separated by commas.-"+,-,+,-,+": Impacts for each criterion (either+for maximizing or-for minimizing).The output will be saved in a file namedoutput.csv.
Package: topsis-sanchita-102003177
Topsis-Sanchita-102003177Installationpip install topsis-sanchita-102003177How to use it?Open terminal and type topsis-sanchita-102003177 and then inputtopsis-sanchita-102003177 <script_name> <input_data_file_name> <wt> <imp> <result_file_name>LicenseThis repository is licensed under the MIT license.
Package: topsis-sanjana-102103040
For:Assignment-1 (UCS654)Submitted by:Sanjana SinhaRoll No:102103040Group:3COE2Topsis_Sanjana_102103040Topsis_Sanjana_102103040 is a Python library for dealing with Multiple Criteria Decision Making (MCDM) problems by applying Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).InstallationInstall Topsis_Sanjana_102103040 using Pypi via pip.$pipinstalltopsis_sanjana_102103040UsageYou can use this package in python as :fromtopsis_sanjana_102103040importtopsisinputFile="sample.csv"weights="1,1,1,1"impacts="-,+,+,+"resultFile="result.csv"topsis(inputFile,weights,impacts,resultFile)ORYou can use this package via commandline as :$topsis[InputDataFileas.csv][Weightsasastring][Impactsasastring][ResultFileNameas.csv]For Example :$topsissample.csv"1,1,1,1""-,+,+,+"result.csvPlease Note That :The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So the csv  MUST follow the format as shown in sample.csv shown in theExamplesection.The input data file MUST contain three or more columns.The second to last columns of the data file MUST containNUMERICvalues.The number of weights, impacts and columns (second to last) MUST beSAME.Impacts MUST either be '+' or '-'.Impacts and Weights MUST be separated by â€˜,â€™ (comma).Examplesample.csvA csv file that contains data for mobile models and their features.ModelPrice (in $)Storage Space (in GB)Camera (in MP)Looks (1 to 5)M125016125M22001683M330032164M42753284M522516162Weights : 0.25,0.25,0.25,0.25Impacts : -,+,+,+input :$ topsis sample.csv "0.25,0.25,0.25,0.25" "-,+,+,+" result.csvresult.csvA csv file that contains the same data as sample.csv with two additional columns 'Topsis Score' and 'Rank'.ModelPrice (in $)Storage Space (in GB)Camera (in MP)Looks (1 to 5)Topsis ScoreRankM12501612553.433M2200168330.845M33003216469.161M4275328453.472M52251616240.14LicenseMIT LicenseCopyright (c) 2024 Sanjana SinhaPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: topsis-sarpreet-101803165
topsis module
It takes a input data file,weights,impacts,output file name and creats a output file wth topsis score and rank of each row of input file,along wih the content of the input file.Installation
"pip install topsis-sarpreet-101803165"How to use it?
Open terminal and type topsis and then input the datafile,weights,impacts and the utput file.
"python"
"from topsis_sarpreet.t import topsis"
"topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")"License
Â© 2020 Sarpreet TiwanaThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-sarvagyjain-102003553
Project description
TOPSIS-ANALYSIS
By: Sarvagy JainWhat 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.Installationpipinstalltopsis-sarvagyjain-102003553==1.0.1UsageArguments Required:
(Assumne we have 5  attributes in dataset.)You have to required one .csv file. (102003553-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+,-,+])
Pass the name of the file with you want to put on .csv file. (102003553-result-1.csv)Enter csv filename followed by .csv extension, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output fileExamplesample.csvFundNameP1P2P3P4P5
M10.840.716.742.112.59
M20.910.83731.710.11
M30.790.624.846.713.23
M40.780.616.442.412.55
M50.940.883.662.216.91
M60.880.776.551.514.91
M70.660.445.348.913.83
M80.930.863.43710.55INPUTtopsis102003553-data.csv1,1,1,1,1+,-,+,-,+102003553-result-1.csvOUTPUTFundNameP1P2P3P4P5TopsisScoreRank
M10.3510774370.3444005880.4214336610.3225390840.3359922880.5945517252M20.3803338910.4026091380.4403038250.242861970.2698079450.5662461793M30.3301799710.3007441750.3019226230.3577808840.3530721180.4853941236M40.3260004780.2958934630.4025634970.3248374620.3349247980.6127758821M50.392872370.42686270.2264419670.4765304280.4512811420.3615509188M60.3677954110.3735048630.4088535510.3945549360.3979066730.5387640665M70.2758465580.213431350.3333728960.3746356580.3690844590.5604586214M80.3886928770.4171612750.2138618580.2834666530.2815503280.389662937
Package: topsis-Shantam-102017142
#topsis-Shantam-102017142
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-Shantam-102017142##Sample Use case
import topsis-Shantam-102017142 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-shiti-102003101
TOPSISSubmitted By:Shiti-Garg 102003101What 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-shiti-102003101In 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-shreshth-101803503
TOPSIS Python PackageMade byShreshth AroraThis is a package made for implementation of TOPSIS inUCS538course atThapar Institute of Engineering and Technology.This Repository will be activily maintained and more features will be added to it as time progress. The below is the impmentation of the using the python package in it's current state.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.It 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.Install from pippip install topsis-shreshth-101803503Run in Command Prompttopsis data.csv "1,1,3.5,0.34"  "-,+,+,-" result.csvDataset used for testingModelCorrelationR2RMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39Final Result is stored in csv format by name of your choice
Package: topsis-ShreyaGarg-101953015
Topsis-Shreya-101953015This is a python package for rank calculation using topsis.Steps for InstallationStep 1:Install the package usingpip install Topsis-Shreya-101953015Step 2:Import the packagetp =import("Topsis-Shreya-101953015"))Step 3:Run the package in command prompt in the same format as given in the following example:tp.topsis('101953015-data.csv',"1,1,1,1,1","+,-,+,-,+",'101953015-result.csv')Result gets stored in 101953015-result.csvSubject to constraints:(1) Impacts and weights must be separated by commas.(2) Impacts must be either +ve or -ve.(3) Command prompt should include 4 paramters namely - input csv file, weights, impacts and name of the output csv file.LicenseÂ© 2022 Shreya GargThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-shubham-dhanda
topsis_package
This package inpired when we stuck in such a situation where we have to choose one  within multiple choice varying by some fetures or properties.
This package suggest to with which one you should go based upon your weight on features and which feature you want to maximize or minmize.use this package as:from  topsis_shubham_dhanda import topsis
import pandas as pd'-' -----you want to minimize it
'+' ---- you want to maximize it#you have to define index column
df=pd.read_csv('topsis.csv', index_col="")
s=topsis(df,[.25,.25,.25,.25],['-','+','+','+'])
print("You should Choose item at:"+str(s.choose()))
Package: topsis-sidharth-102017016
Topsis-Sidharth-102017016It calculates topsis for a given data in csv file.Installationpip install topsis-sidharth-102017016How to useSample command : topsis input.csv "1,1,1,1,1" "+,-,+,-,+" output.csvLicense© 2023 Sidharth BahlThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-Simar-102117028
TopsisTOPSIS( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Simarjit Kaur Khangura, 102117028, TIET, Patiala.InstallationUse the package manager pip to install.pip install topsis-Simar-102117028UsageEnter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and enter the output file name followed by .csv extension.topsis-simar-102117028 [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]LicenseÃ‚Â© 2024 Simarjit Kaur KhanguraThis repository is licensed under MIT License. See LICENSE for details.
Package: topsis-simran-101803100
TOPSISUse it to compare results or parameters of different models and rank them accordinglyDocumentationCommands:import topsis_simran_101803100 as tptp.topsis("filename","weights array","impacts array","resultfilename")Note:File should be of CSV type containing all the parameter for different files.All the data should be of numeric type.TOPSIS score will be stored in the resultfilename.csvProvdie weights and impact array in form of listwts => [1,1,1,1], impacts => ['-','+','+','+']Eg.ModelCorrRseqRmseAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87tp.topsis('data.csv',[1,1,1,1],['-','+','+','+'],'result.csv')RESULTModelCorrRseqRmseAccuracyp_scorerankM10.790.621.2560.890.6243851M20.660.442.8963.070.2898483M30.560.311.5762.870.3985572
Package: topsis-Srishti-101903715
This package is implementation of topsis technique of multi-criteria decision analysis.
This package will accept three parameters:data.csv //file which contains the models and parametersstring of weights separated by commas(,)string of requirements (+/-) separated by commas(,)
// important
install pandas,sys,operator and math libraries before installing this
//
You can install this package using following command
pip install topsis-101703557
Package: topsis-sukham-102003334
Topsis_Sukham_102003334This 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-sukham-102003334In 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-sunidhi
# topsis_pckg
It is a Python package to find out the best value among the different data using the mathematical calculations.
## InstallationUse the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.`bash pip installtopsis-sunidhi`## About
TOPSIS is an acronym that stands for ‘Technique of Order Preference Similarity to the Ideal Solution’ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to thoseTo get started quickly, just use the following:`bash topsis filename weights impacts result `make ensure the weights and impacts should be in “”eg: “1,1,1,1” and “+,-,+,-”## Pre-requisite
The data should be enclosed in the csv file. There must be more than 2 columns## Result
the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.
Package: topsis-taanisha-10210323
Topsis Python PackageMade By
Taanisha Agarwal(Roll No. 102103123)DescriptionThe Topsis Python Package is a Python library that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is a multi-criteria decision-making method used to determine the best alternative among a set of alternatives based on their performance on multiple criteria.UsageUse the following command to perform TOPSIS analysis on a dataset:topsis data.csv "1,0,1,0,1" "+,-,+,-,+"-data.csv: Path to the input CSV file containing the dataset.-"1,0,1,0,1": Weights for each criterion separated by commas.-"+,-,+,-,+": Impacts for each criterion (either+for maximizing or-for minimizing).The output will be saved in a file namedoutput.csv.
Package: topsis-tanisha-101803042
TOPSISTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision making method.
The method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.Package UsageTo install the package:pip install topsis_tanisha_101803042This is a package focusing on finding the topsis score and rank of a dataframe(csv file)with only numerical values.Python IDLE or any Editorimport topsis_tanisha_101803042.topsisf = "input_csv_filepath"w = "1,1,1,1"im = "+,+,+,-"r = "result_csv_filepath"topsis_tanisha_101803042.topsis.rank(f,w,im,r)The final result can be seen in the result file given as r.
Package: topsis-Vinay-101803108
TOPSISPerform TOPSIS on different models to rank them depending on the weights and impact of each parameters.DocumentationCommands:import topsis_vinay_101803108 as toptop.topsis("filename","wts list","impacts list","resultfile")Note:File should be of CSV type containing all the parameter for different files.Provdie weights and impact array in form of listwts => [1,1,1,1], impacts => ['-','+','+','+']Eg.After Analysingtop.topsis('file.csv',[1,1,0,5,1],['+','+','-','+'],'out.csv')ModelCorrRseqRmseAccuracyT_scoreRankM10.790.621.2560.890.323582M20.660.442.8963.070.298713M30.560.311.5762.870.592831