Saravanan666/pypi-org huggingface dataset:
Package: topsis-lovish-102117148
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-lovish-102117148UsagePlease 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-mannat-102117185
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-manvendra-102103799
OverviewThis is a Python package that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives by evaluating them based on multiple criteria.InstallationYou can install the package using pip:pip install topsis-manvendra-102103799USAGEfrom topsis-manvendra-102103799 import topsis#Example data (replace this with your actual data)data = {
'Alternative1': [1, 2, 3, 4],
'Alternative2': [4, 3, 2, 1],
# Add more alternatives and their values
}#Criteria weights (replace this with your actual weights)weights = [0.25, 0.25, 0.25, 0.25]#Criteria impacts ('+' or '-' for each criterion)impacts = ['+', '+', '+', '-']#Perform TOPSIS analysisresult = topsis(data, weights, impacts)#Display the rankingprint("Ranking:", result)Parametersdata: A dictionary where keys are alternative names, and values are lists representing the performance values for each criterion.weights: A list of weights corresponding to the importance of each criterion.impacts: A list of impacts ('+' or '-') corresponding to the desired effect of each criterion.LicenseThis package is distributed under the MIT License - see the LICENSE file for details.
Package: topsis-mnvu05
Topsis
Topsis (Technique for Order of Preference by Similarity to Ideal Solution) is a technique used for choosing best out of available choices when the best choice is dependent on more than one parameter. So, basically it is a technique for multi-criteria decision making (MCDM). So, this is a package which helps you to apply this technique to a dataset. The dataset should have all columns having numerical values. You have to pass the weights and impacts of each column in different strings while running the command. Impacts should contain either '+' sign or '-' sign where '+' denotes that the more the value for that column, more ideal it will be, and '-' denotes that the less the value for that column, more ideal it will be. And weights and impacts should be comma separated.How to make it work :
Command for running the package will look like :
python topsismnvu.py <dataset_name>  
e.g. python topsismnvu.py dataset.csv "0.25,0.25,0.25,0.25" "+,-,+,+"
Package: topsis-NamanJain-101803065
TOPSIS-PythonBy:Naman Jain - 101803065pypi:https://pypi.org/project/topsis-NamanJain-101803065/1.0.0/git:https://github.com/namanjain323232/topsisWhat 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. More details atwikipedia.How to use this package?pip install topsis-NamanJain-101803065In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+" 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    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-Nandini-101903480
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: topsisnandinise102017121
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 -$pipinstalltopsisnandinise102017121You may use the Package via commandline as$topsis[pathofcsv][weightsasstring][impactsasstring][pathofresultcsvasstring]
Package: topsis-naveen
Package for implementing topsis functionalityNaveen Budhwal  
COE-16
Roll number: 101703362DescriptionPackage for implementing the topsis functionality easily.Installationpip install topsis_naveenCommand Line Argumentstopsis_naveen <dataset>.csv "<weigths> "<levels>"First argument is the input csv filename containing the dataset.
The second argument is the weigths assigned to each column.
The third argument is a string of +,- denoting whether the particular column/feature needs to be increased(+) or decreased(-)Use via py scriptfrom topsis_naveen import topsis
topsis('<input_filename>.csv', '<Weights>', '<Levels>')
Package: topsis-navkiran
TOPSIS implementation in python for multi-criteria decision makingClass project for DATA ANALYSIS AND VISUALISATION 2020 - UCS633 Thapar University, Patiala
Navkiran Singh  

COE17
Roll number: 101703365Update: Added windows command line supportOutput is a dataframe with 3 columnsAlternativesserial numberCorresponding performanceScoreor closeness to ideal solutionRankInstallationpip install topsis_navkiranNote the name has an underscore not a hyphen. If installation gives error or package is not found after installing, install as sudo.Recommended - test it out in a virtual environment.Upgradepip install topsis_navkiran --upgradeTo use via command linetopsis_navkiran_cli data.csv 25,25,25,25 -+++First argument after run.py is filename with .csv extension. The .csv file is assumed to have a structure similar to one provided in topsis_navkiran/data.csvThat is, the .csv file should have a header with column names and first column should only list alternatives and not attribute values.To use in .py scriptfrom topsis_navkiran import topsis
"""
decision_matrix is 2D numpy array, weights is a 1D array and impacts is a string of the form +-+-- 
where + indicates benefit and - indicates cost
"""
output_dataframe = topsis(decision_matrix,weights,impacts)Debugging and Exception HandlingThe program has several assert statements which raise errors with helpful description in the following cases:Wrong dimensions of decision matrix (not2D), weights (not1D)Length of weights and impacts don't matchWeights or impacts don't match number of attributesFor command line, number of arguments is less than 3 requiredFile extension must be .csvBased off on a similar package for TOPSIS in R
Package: topsis-neeraj
Topsis AnalysisInstallationUse the package managerpipto install topsis.pipinstalltopsis-neerajUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis -n "dataset-name" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."Do not mention the file format, 'csv', as part of dataset-name.
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-nishant-76
TOPSIS implementation in python for multi-criteria decision makingThis package has been created based on Project 1 of course UCS633(DATA ANALYSIS AND VISUALISATION ) Thapar University, Patiala
Nishant Goel  
COE17
Roll number: 101703376Output is a list with rankings of different objects.also includesBest_performance_objectInstallationpip install topsis_nishant_76Recommended - test it out in a virtual environment.Upgradepip install topsis_nishant_76 --upgradeTo use via command linetopsis_cli mydata.csv '1,2,1,1' '-,+,+,+'First argument is filename with .csv extension. The .csv file is assumed to have a structure similar to one provided in topsis_nishant_76/mydata.csvThat is, the .csv file should have a header with column names and first column should only list alternatives and not attribute values.To use in .py scriptfrom topsis_nishant_76 import topsis_nish
"""
decision_matrix is 2D numpy array, weights is a string seperated with ',' and impacts is a string of the form '+,-,+,-' 
where + indicates benefit and - indicates cost
"""
topsis_nish('mydata.csv','1,2,1,1','+,-,+,+')
Package: topsis-nitanshjain-102017025
topsis_nitanshjain_102017025What is TOPSISTechnique 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.Installationpip install topsis-nitanshjain-102017025Input csv formatInput file contain three or more columnsFirst column is the object/variable nameFrom 2nd to last columns contain numeric values onlyHow to use itPython Filefrom topsis.topsis_nitanshjain_102017025 import solve_topsis
solve_topsis()Command Prompttopsis <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>Example:topsis topsis.py inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csvNote: The weights and impacts should be ',' seperated, input file should be in pwd.Functions, Parameters and Return Valuesfunction = solve_topsis()
parameters = No input parameters
return values = Creates a csv file with the topsis rank and performance scoreSample input dataModelP1P2P3P4P5M10.620.383.833.89.65M20.750.565.750.314.33M30.950.906.565.618.49M40.610.376.243.612.70M50.600.366.461.217.14M60.760.585.368.018.66M70.660.446.247.213.63M80.800.645.737.111.06Sample output dataModelP1P2P3P4P5Performance ScoreTopsis RankM10.620.383.833.89.650.3172721858M20.750.565.750.314.330.4520688714M30.950.906.565.618.490.6890373071M40.610.376.243.612.700.3403839037M50.600.366.461.217.140.3672063766M60.760.585.368.018.660.4813509013M70.660.446.247.213.630.3729999725M80.800.645.737.111.060.512266352
Package: topsispack-NG
A library capable of implementing TOPSISPROJECT 1, UCS633 - Data Analysis and Visualization
Nikhil Gupta  
COE17
Roll number: 101703371Output is the best allternative out of list of all allternatives. Other ranks are streamed to the csv file.Best Attribute: Mobile 3Installationpip install topsispack_NGNote the name has an underscore not a hyphen. If installation gives error or package is not found after installing, install as sudo.Recommended - test it out in a virtual environment.To use via command lineTopcli myData.csv "1,1,1,1" "-,+,+,+"First argument after nikcli is the location of the .csv file. The weights and impacts should be passed as strings in double quotes with each weight or impact separated by a comma (',').To use in .py scriptfrom toplib.models import topsis
topsis(['myData.csv',"1,1,1,1","-,+,+,+"])The argument passed should be a list.Can email me for any issues or suggestions
Package: topsis-pckg-bansalz1208
topsis_pckgit is a Python package to find out the best value among the different data using the mathematical calculations.InstallationUse the package managerpipto install this package.pipinstalltopsis_pckg##Pre-requisite
The data should be enclosed in the csv file.##Result
the result.csv is saved in the project folder with extra 2 columns with topsis score and rank.ContributingPull 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-ppruthi-101883058
Ranking System Using TopsisProject 1 : UCS633Submitted By:Pritpal Singh Pruthi 101883058pypi:https://pypi.org/project/topsis-ppruthi-101883058/InstallationUse the package managerpipto install Ranking system.pipinstalltopsis-ppruthi-101883058How to use this package:topsis-ppruthi-101883058 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.LicenseMIT
Package: topsis-prabhnoor-102003560
Project description
TOPSIS-ANALYSIS
By: Prabhnoor Singh GhotraWhat 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-prabhnoor-102003560==1.0.0UsageArguments Required:
(Assumne we have 3 attributes in dataset.)You have to required one .csv file. (102003560-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. (102003560-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
M80.930.863.43710.55INPUTtopsis102003560-data.csv1,1,1,1,1+,-,+,-,+102003560-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: topsispragati102017122
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 topsispragati102017122You may use the Package via commandline as$topsis[pathofcsv][weightsasstring][impactsasstring][pathofresultcsvasstring]
Package: topsis-priyank-raheja-101703404
Topsis AnalysisInstallationUse the package managerpipto install topsis-asharma-3027.pipinstalltopsis-asharma-3027UsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis-asharma-3027 -a "dataset-name" -b "w1,w2,w3,w4,..." -c "i1,i2,i3,i4,..."Do not mention the file format, 'csv', as part of dataset-name.
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 droppedThe item attributed rank 1 is the best choice.LicenseMIT
Package: topsis-Pulkit-102103239
TopsisTOPSIS( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Pulkit Sanan, 102103239, TIET, Patiala.InstallationUse the package manager pip to install.pip install topsis_Pulkit_102103239UsageEnter 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]Exampletopsis_Pulkit_102103239 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).License© 2024 Pulkit SananThis reopsitory is licensed under MIT License. See LICENSE for details.
Package: topsis-pulkit-102103267
TOPSISAuthor:Pulkit Arora.Type:Package.Title:TOPSIS method for multiple-criteria decision making (MCDM).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.csvLicenseÂ© 2024 Pulkit AroraThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsispy
TopsisPyTopsisPy is a Python Package implementingTopsismethod 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 StudentsAnd offcourse, PyTopsis code is Open Source.How to use?Install the Package using the command -$pipinstalltopsispyAnd using the in python as ->>>importtopsispyastp>>>a=[...[250,16,12,5],...[200,16,8,3],...[300,32,16,4],...[275,32,8,4],...[225,16,16,2]...]>>>w=[0.25,0.25,0.25,0.25]>>>sign=[-1,1,1,1]>>>tp.topsis(a,w,sign)This will return a tuple withthe index of winning data pointarray containing scores of all data points
As for above -(2,array([0.53427686,0.30836777,0.69163223,0.53473658,0.40104612]))DevelopmentWant to contribute? Great!
Use your Python skills and creativity for easy decision making for the world.LicenseMITFree Software, Hell Yeah!
Package: topsis-pypck
TOPSIS implementation in pythonProject1 for DATA ANALYSIS AND VISUALISATION(UCS633)
Nishchay Mahajan COE18
Roll number: 101703377Output is a dataframe with 3 columnsAlternativesserial numberCorresponding performanceScoreor closeness to ideal solutionRankInstallationpip install topsis-pypckUpgradepip install topsis-pypck --upgradeTo use via command linepython3 topsis.py file.csv "0.25,0.25,0.25,0.25" "-,+,+,+"To use in python IDLE>>>from topsis_pypck.topsis import topsis
>>> f= "file.csv"
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>>topsis(f,w,im)OutputModel     Score    Rank
-------  --------  ------
      1  0.534277       3
      2  0.308368       5
      3  0.691632       1
      4  0.534737       2
      5  0.401046       4