Saravanan666/pypi-org huggingface dataset:
Package: topsis-101703557
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-101703572
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 and math libraries before installing this
//
You can install this package using following command
pip install topsis-101703550
Package: topsis-101803128
TOPSIS-PythonAssignment 6: UCS538Submitted By:DIVYAM JAIN-101803128What 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-Divyam-101803128 can be run as in the following example:Python Scriptfrom topsis_101803128.topsis import CalcTopsisScore
filename = "input.csv"
weight = "1,1,1,2"
impact = "+,+,-,+"
CalcTopsisScore(filename, weight, impact )Sample 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.OutputModelCorrelationR2RMSEAccuracyScoreRankM10.790.621.2560.890.772212M20.660.442.8963.070.2255995M30.560.311.5762.870.4388974M40.820.672.6870.190.5238783M50.750.561.380.390.8113891The 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.LicenseÂ© 2020 Divyam JainThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-101803398
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.How to Installpip install topsis-101803398How to use it?Open terminal and typepython topsisalong with input file path, weight array, impact array and name of output file. for eg.python topsis test.csv "1,1,2,3,1" "+,-,+,-,-" output.csvLicenseÂ©  2022 Harshit Verma
This repository is licensed under the MIT license. See LICENSE for details
Package: topsis-101803545
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 and math libraries before installing this
//
You can install this package using following command
pip install topsis-101803545
Package: topsis-101883071
This package is implementation of topsis technique of multi-criteria decision analysis.
This packages will work on command line interfaceUse command to runpython <input.csv>input.csv is your input csv fileweights is  string in which each digit represent weight of corresponding column(eg "1,1,1,1")impacts are impacts of column (eg "-,+,+,+" )You can install this package using following command
pip install topsis_101883071Author-Yogesh
Batch:Coe18
Roll no:101883071
Course:Data Analytics and Visualization
Package: topsis-101903570
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 and math libraries before installing this
//
You can install this package using following command
pip install topsis-101903576topsis-101903570
Package: topsis-101903576
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 and math libraries before installing this
//
You can install this package using following command
pip install topsis-101903576topsis-101903576
Package: topsis-10191117-1yash
TOPSIS-PythonSubmitted By:YASHWARDHAN RATHORE 101917117ASSIGNMENT 4What 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-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:In Command Prompt to run the code:>> topsis data.csv "1,1,1,1" "+,+,-,+"Sample datasetThe decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.M1|	0.8	|0.64	|3.5	|37.5	|10.61
M2|	0.86	|0.74	|3.4	|42.2	|11.8
M3|	0.69	|0.48	|5.7	|70	|19.22
M4|	0.65	|0.42	|5.7	|65.5	|18.07
M5|	0.9	|0.81	|6.6	|39.1	|11.85
M6|	0.76	|0.58	|4	|53.5	|14.71
M7|	0.69	|0.48	|6.2	|51.3	|14.67
M8|	0.65	|0.42	|6	|50.2	|14.32OutputRow_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2The 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-10191117pip1-yash
TOPSIS-PythonSubmitted By:YASHWARDHAN RATHORE 101917117ASSIGNMENT 4What 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-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:In Command Prompt to run the code:>> topsis data.csv "1,1,1,1" "+,+,-,+"Sample datasetThe decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.Fund NameP1P2P3P4P5M10.80.643.537.510.61M20.860.743.442.211.8M30.690.485.77019.22M40.650.425.765.518.07M50.90.816.639.111.85M60.760.58453.514.71M70.690.486.251.314.67M80.650.42650.214.32OutputRow_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2The 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-10191117pip-yash
TOPSIS-PythonSubmitted By:YASHWARDHAN RATHORE 101917117ASSIGNMENT 4What 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-YASHWARDHAN_RATHORE_101917117  can be run as in the following example:In Command Prompt to run the code:>> topsis data.csv "1,1,1,1" "+,+,-,+"Sample datasetThe decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.ModelCorrelationR2RMSEAccuracyM10.80.643.537.5M20.860.743.442.2M30.690.485.770M40.650.425.765.5M50.90.816.639.1M60.760.58453.5M70.690.486.251.3M80.650.42650.2OutputRow_NO	Performance_score	Rank
1	0.436737	7
2	0.389937	8
3	0.56565	4
4	0.590487	3
5	0.522924	5
6	0.4513	6
7	0.637889	1
8	0.635536	2The 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-101917140
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-101953010
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-101953010
Package: topsis-102003083-Utkarsh
TOPSIS-PythonSubmitted By:Utkarsh ChaudharysWhat 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-102003083_Utkarsh  can be run as in the following example:In Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+" final.csvIn Python IDLE:>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> f = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im,f)Sample 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    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-102003451
This package is implementation of topsis technique of multi-criteria decision analysis. This package will accept three parameters:1.data.csv //file which contains the models and parameters
2.string of weights separated by commas(,)
3.string of requirements (+/-) separated by commas(,) // important install pandas,sys and math libraries before installing this // You can install this package using following command pip install topsis-101903451To run the file and enter the following command on CLI
python 102003451.py data.csv "1,1,1,1,1" "+,+,+,-,+" result.csv
Package: topsis-102003527
TOPSISAuthor:Aarushi Abrol.Type:Package.Title:TOPSIS method for multiple-criteria decision making (MCDM).Version:1.1.0Date:2022-1-22Maintainer:Aarushi Abrolaabrol_be20@thapar.edu.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 installIn Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csvLicense© 2022 Aarushi AbrolThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-102017134
topsis_102017134for:Project-1 (UCS654)submitted by:Soumya SrivastavaRoll no:102017134Group:3CS6topsis-102017134 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).InstallationUse the package managerpipto install topsis_102017134.pipinstalltopsis-102017134UsageEnter csv filename followed by.csvextentsion, then enter theweightsvector with vector values separated by commas, followed by theimpactsvector with comma separated signs(+,-), and lastly theoutputFileName.topsisSolvesample.csv"1,1,1,1""+,-,+,+""outputFile.csv"Examplesample.csvA csv file showing data for different mobile handsets having varying features.ModelStorage space(in gb)Camera(in MP)Price(in $)Looks(out of 5)M116122505M21682003M332163004M43282754M516162252weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]impacts vector = [ + , + , - , + ]input:topsisSolvesample.csv"0.25,0.25,0.25,0.25""+,+,-,+""outputFile.csv"output:P-Score  Rank
1  0.534277     3
2  0.308368     5
3  0.691632     1
4  0.534737     2
5  0.401046     4LicenseMITGithub Repository
Package: topsis-102103043
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 metric separated by commas(,)string of impacts (+/-) separated by commas(,)
// important
install pandas,sys and math libraries before installing this
//
You can install this package using following command
pip install topsis-102103043
Package: topsis-102103357
IntroductionTOPSIS (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.InstallationUse the package manager pip to install.pip install topsis-102103357UsageEnter 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-102103357 [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]Sample InputFund NameP1P2P3P4P5M10.840.716.742.112.59M20.910.83731.710.11M30.790.624.846.713.23M40.780.616.442.412.55M50.940.883.662.216.91M60.880.776.551.514.91M70.660.445.348.913.83M80.930.863.43710.55topsis-102103357 data.csv "1,1,1,1,1" "+,-,+,-,+" output.csvSample OutputResults saved to output.csvFund NameP1P2P3P4P5PerformanceRankM10.840.716.742.112.590.4042684698091455.0M20.910.83731.710.110.6992978255036121.0M30.790.624.846.713.230.3335817419280518.0M40.780.616.442.412.550.3649680172900416.0M50.940.883.662.216.910.5348316886498162.0M60.880.776.551.514.910.4396930125401454.0M70.660.445.348.913.830.5263567203734823.0M80.930.863.43710.550.3419723560979687.0The best model is M2LicenseThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-102103411
TopsisTopsis( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Harshit Goyal, 102103411, TIET, Patiala.InstallationUse the package manager pip to install Topsis
pip install Topsis_harshit_102103411UsageEnter 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.python [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]Examplepython sample.csv "1,1,1,1" "-,+,+,+" output.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 the Example section.
The input data file MUST contain three or more columns.
The second to last columns of the data file MUST contain NUMERIC values.
The number of weights, impacts and columns (second to last) MUST be SAME.
Impacts MUST either be '+' or '-'.
Impacts and Weights MUST be separated by , (comma).LicenseÂ© 2024 Harshit GoyalThis reopsitory is licensed under MIT License. See LICENSE for details.
Package: topsis-102103596
Topsis-102103596Library for calculating topsis score and rank for a Multi Criteria Decision Making problem.UsageInstallationpipinstalltopsis-102103596UsageContains a single functioncal_topsis_score(df,w,i,out_file):where:df -> pd.DataFrame

w -> list of weights (only numeric columns are considered, rest are ignored)

i -> impact list ("+" for columns that are to be maximized, "-" for columns to be minimized, for example ["-","+","+","+"])

out_file -> csv file in which output Dataset will be storedScreenshotsInput file: data.csvOutput file: out.csv
Package: topsis-102117154-tanishq-dublish
Topsis Analysis PackageTopsis_TanishqDublish_102117154OverviewThe Topsis_TanishqDublish_102117154 package is a comprehensive Python tool for performing Topsis (Technique for Order of Preference by Similarity to Ideal Solution) analysis on datasets. Topsis is a multi-criteria decision-making method that evaluates and ranks alternatives based on their proximity to an ideal solution.FeaturesEfficient Topsis Analysis: The package efficiently calculates Topsis scores and ranks for decision-making alternatives given a dataset.Command-Line Interface: Use the command-line interface to run Topsis analysis easily with specified parameters.Error Handling: The package includes robust error handling to provide clear feedback on incorrect inputs or missing files.INSTALLATIONpip install Topsis_TanishqDublish_102117154USAGEtopsisExampletopsis data.xlsx "1,1,1,2" "+,+,-,+" 101556-result.csvRequirementsPython 3.xpandas libraryContributionContributions are welcome! Feel free to open issues, submit pull requests, or suggest improvements.AcknowledgmentsSpecial thanks to the Topsis analysis community for their valuable contributions.
Package: topsis-102197016
topsis-102197016for:Project-1 (UCS654)submitted by:Mukul ShardaRoll no:102197016Group:3CS-4topsis-102197016 is a Python library which implements the TOPSIS algorithm used for Multiple Criteria Decision Making(MCDM).Know more about TopsisInstallationUse the package managerpipto install topsis-102197016.pipinstalltopsis-102197016UsageEnter filename followed by.csvor.xlsxextentsion, then enter theweightsvector with vector values separated by commas, followed by theimpactsvector with comma separated signs(+,-), make sure to pass the file where you want to store your results.topsissample.csv"1,1,1,1""+,-,+,+"output.csvExamplesample.csvA csv file showing data for different mobile handsets having varying features.ModelStorage space(in gb)Camera(in MP)Price(in $)Looks(out of 5)M116122505M21682003M332163004M43282754M516162252weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]impacts vector = [ + , + , - , + ]input:topsissample.csv"0.25,0.25,0.25,0.25""+,+,-,+"output.csvOther notesMake sure the csv does not contain categorical values.Make sure your dataset has atleast 3 rows and 3 columns.The length of weights vector and impact vector must equal the number of columns in dataset.While writing weights vector or impact vectors, please make sure not to put extra spaces.
Package: topsis-17188
Topsis Python PackageThis is a Python Package implementing Topsis Multi-Criteria Decison Making method.It is a simple implementation using
- pandas
- numpyIt stores the output as a csv file with user-specified name.Output contains the original data with two additional columns i.e. Topsis Score and Rank based on the score.InstallationTo install the packagepip install topsis_17188UsageTo use the package from the command line (Data file and result file name should contain .csv extension)topsis [data file name] [weights as string seperated by ','] [impacts as string seperated by ','] [result data file name]For exampletopsis data.csv "1,1,1,1,1" "+,+,+,-,+" result.csvNotesThe data should only contain numerical data.
Package: topsis2
TOPSISTechnique for Order of Preference by Similarity to Ideal SolutionCitationIf you use this package, please refercite.InstallationINSTALLING VIA PIPinbuilt Python package management system, pip.You can can install, update, or delete the topsis2.installpipinstalltopsis2updatepip install --upgrade topsis2uninstallpip uninstall topsis2UsageUsing this TOPSIS implementation is straightforward as importing it and writing only two lines. The important thing is the decision matrix in the type of pandas data frame.The decision matrix would be some data frame as the following example.After building your decision matrix, you need to define the criteria types (benefit or cost). To have the type, you can define a list as the impact. For instance, we assume that the first two criteria are benefit criteria and the last is the cost.impact=['+','+','-']The ultimate step is assigning the weight array.weight=np.array([0.1,0.7,0.2])After having the three parameters, the model produces the ranking matrix.importnumpyasnpimportpandasaspdfromtopsisimporttopsisarray=np.random.randint(10,size=(5,3))decision_matrix=pd.DataFrame(array,columns=['criterion_'+str(i)foriinrange(1,4)],index=['option_'+str(i)foriinrange(1,6)])impact=['+','+','-']weight=np.array([0.1,0.7,0.2])tp=topsis(decision_matrix=decision_matrix,weight=weight,impact=impact)tp.rank()RequirementsThis package takes advantage of the following libraries, which had already imported to the TOPSIS package:scipynumpypandasKeywordsTOPSIS,MCDM,MADMVersion0.0.2Updated2022-05-19Date-released2022-05-18More about TOPSISTo read more about the TOPSIS method and the logic behind the model, please referhere.
Package: topsis-3037
TOPSIS_102103037TOPSIS method for multiple-criteria decision making (MCDM)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.INSTALLATION>> pip install TOPSIS_Mayank_102103037USAGE>> 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**LicenseByMayank