Saravanan666/pypi-org huggingface dataset:
Package: topsis-anubhav-102003049
TOPSISSubmitted By:Anubhav 102003049What 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-anubhav-102003049```


### In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+" result.csv## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)


Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------- | ------------- 
M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.7722 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.2255 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.4388 | 4
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.5238 | 3
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.8113 | 1


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Package: topsis-anubhav-102003551
Project description
CALCO-O-TOPSIS
By: Anubhav GuptaWhat 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-anubhav-102003551==1.0.0UsageArguments Required:
(Assume we have 5  attributes in dataset.)You have to required one .csv file. (102003551-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. (102003551-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
M80.930.863.43710.55INPUTtopsis102003551-data.csv1,1,1,1,1+,-,+,-,+102003551-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-Anupam-102003692
Topsis implemented by Anupam KatochFunctionality 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_Anupam_102003692Examplefrom topsis_Anupam_102003692 import main
ip=sys.argv[1]
wt=sys.argv[2]
im=sys.argv[3]
op=sys.argv[4]
main.find_rank(ip,wt,im,op)Run the following Script.python test.py <inputFile.csv> weights impacts <outputFile.csv>
Package: topsis-Anurag
#TOPSIS PackageTOPSIS stands for Technique for Oder Preference by Similarity to 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. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.This package has been created based on Project 1 of course UCS633.
Anurag Aggarwal COE-4 101703088#In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+"
Package: topsis-anurag-101703088
TOPSIS PackageTOPSIS stands for Technique for Oder Preference by Similarity to 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. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.This package has been created based on Project 1 of course UCS633.
Anurag Aggarwal COE-4 101703088In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+"
Package: topsis-arryuann-102103062
topsis-Arryuann-102103062for: Assignment01-Topsis (UCS633) submitted by: Arryuann Khanna Roll no: 102103062 Group: 3COE3
This Python library helps with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).Installationpip install topsis-Arryuann-102103062How to use it?Open terminal and enter a valid csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, then impacts vector with comma separated signs (+,-) and finally the output file name in csv format.topsis 102103062-data.csv "1,1,1,1" "+,-,+,+" 102103062-result-1.csvExampleIf you have this content inside the 102103062-data.csv-Fund NameP1P2P3P4P5M10.880.773.057.715.59M20.870.764.939.411.48M30.910.835.559.416.66M40.610.373.239.610.95M50.600.365.662.317.22M60.770.596.151.514.74M70.610.374.743.312.25M70.710.506.142.612.48and you ran the command-topsis 102103062-data.csv "1,1,1,1,1" "+,-,+,-,+" 102103062-result-1.csvyou get this as output-Fund Name    P1    P2   P3    P4     P5  Topsis Score  Rank
0        M1  0.88  0.77  3.0  57.7  15.59      0.626255     1
1        M2  0.87  0.76  4.9  39.4  11.48      0.535066     3
2        M3  0.91  0.83  5.5  59.4  16.66      0.460473     4
3        M4  0.61  0.37  3.2  39.6  10.95      0.559417     2
4        M5  0.60  0.36  5.6  62.3  17.22      0.363836     7
5        M6  0.77  0.59  6.1  51.5  14.74      0.353200     8
6        M7  0.61  0.37  4.7  43.3  12.25      0.455437     5
7        M8  0.71  0.50  6.1  42.6  12.48      0.378622     6and also saved in 102103062-result-1.csv in current working directoryLicense© 2024 Arryuann KhannaThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-ARYAN-101903144
#Topsis-Aryan-101903144
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-ARYAN-101903144##Sample Use case
import topsis-ARYAN-10903144 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-aryan-102103073
topsis-aryan-102103073topsis-aryan-102103073 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 manager pip to install topsis-aryan-102103073.pip install topsis-aryan-102103073How to use it?Enter 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 finally enter the name of the output file also followed by .csv extension.sample.csv 1,1,1,1 +,-,+,+ final.csvNoteMake sure the csv does not contain categorical values.LicenseÂ© 2024 aryan kalia
This repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-asharma-3027
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-ashikothari10
A data pre-processing approach applied on a small data to predict the ranks of the model. This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution) approach to select best among many things based on different attributes, such as selecting the best Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy. You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-' where '+' indicates that you need to maximise value of that particular column and '-' indicates that you need to minimise value of that particular column. After passing all these parameters you will get your row rank.
Package: topsis-ashna-102117144
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-AYUSH-101903749
#Topsis-Ayush-101903749
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-AYUSH-101903749##Sample Use case
import topsis-AYUSH-101903749 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-banaj-101916008
TOPSISSubmitted By:Banaj Bedi.Type:Package.Title:TOPSIS method for multiple-criteria decision making (MCDM).Version:1.1.1.Date:2022-02-26.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_banaj_101916008In Command Prompt>> topsis data.csv "1,1,2,1,2" "+,+,-,-,+" 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.Weights (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)The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**View package at:https://pypi.org/project/topsis-banaj-101916008/
Package: topsis-Bhagesh-101903540
Topsis-Bhaegesh-10193540This package is implementation of topsis technique of multi-criteria decision analysis. This package will accept three parameters as input:.csv file which contains the models and parametersstring of weights separated by commas(,)string of requirements (+/-) separated by commas(,)It performs the topsis, calculate the Performance
Score and Rank according to the inputs and store them in the output file.
This program must be run through command line only.What is TOPSISThe Technique for Order of Preference by Similarity to Ideal Solution
(TOPSIS) is a multi-criteria decision analysis method.
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).InstallationTo install this module write the following line in the command prompt.pip install Topsis-Bhagesh-101903540How to use thisTopsis-Bhagesh-101903540 can be run as in the following example:####In Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+"###Example on the Dataset####Input FileMobliePrice($)Storage(GB)Camera(MP)RatingM125016125M22001683M330032164M42753284M522516162In addition to input file we provide impact and weight for each columns from 2nd to last column. Impact must be +ve or -ve.
Weight and Impact must be seperated with commans.####Output File
The output file is steuctured as followsMobliePrice($)Storage(GB)Camera(MP)RatingTopsis ScoreRankM10.110.080.110.150.533M20.090.080.070.090.315M30.130.150.140.120.691M40.120.150.070.120.532M50.10.080.140.060.404For calulating Rank it compare upto six decimal digits but display only upto two decimal points.
The 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.###License
Â© 2021 Bhagesh GuptaThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-bharat
TOPSISA Python package to get topsis (Technique for Order of Preference by Simmilarity to Ideal Solution ).Following query on terminal will provide you the topsis detail.Usages:python topsis.pyExample:ÂFor a dataset having 4 columns of atrributes we can runpass the arguments as follows:python topsis.py myData.csv "1,2,1,1" "+,+,-,+"
Package: topsis-Bhavya
Topsis AnalysisInstallationUse the package managerpipto install topsis-Bhavya.pipinstalltopsis-BhavyaUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsisB -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-bhavya-102103345
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-bhavya-102103345UsagePlease 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 Bhavya BhallaThis repository is licensed under the MIT license.See LICENSE for details.
Package: topsis-Bhumika
Topsis AnalysisInstallationUse the package managerpipto install topsis-Bhumika.pipinstalltopsis-BhumikaUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsisBG -x "dataset-name" -y "w1,w2,w3,w4,..." -z "i1,i2,i3,i4,..."Do not mention the file format, 'csv', as part of dataset-name.w1,w2,w3,w4 represent weights (w), not already normalised will be normalised later in the code.i1,i2,i3,i4.... represent information of benefit positive(1) or negative(0) impact criteria.Note that the first row and first column of dataset is droppedRank 1 item denotes the best choice.LicenseMIT
Package: topsis-by-ravsehaj
pypi_PackagesThis package is implementation of TOPSIS technique of multi-criteria decision analysis. There is a function in this package named rank_data which will accept three parameters: data_filename_path //file which contains the models and parameters list of weights separated by commas(,) list of impacts [+/-] separated by commas(,) You can install this package using following command pip install topsis_by_ravsehaj
Package: topsis-calci
Topsis CalciTopsis Calci is a Python package implementing Topsis method for decision analysis.Installationpip install topsis-calciUsagetopsis input_file weights impacts output_fileORpython topsis.py input_file weights impacts output_fileInputs:-input_file -> "Excel/Csv" file pathweights -> Comma seperated integersimpacts -> Comma seperated +/- impactsoutput_file -> output csv file path"Outputs:-Creates a csv file on "output_file" path containing score and RanksExamples :-ReferExamplesLicenseThis Project is Licensed underBSD-4-Clause
Package: topsis-Chinmay-101903597
#Topsis-Chinmay-101903597
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-CHINMAY-101903597##Sample Use case
import topsis-CHINMAY-101903597 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-chirag-102103278
TopsisTOPSIS( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Chirag Gupta, 102103278, TIET, Patiala.InstallationUse the package manager pip to install.pip install topsis_chirag_102103278UsageEnter 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_chirag_102103278 [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]Exampletopsis_chirag_102103278 sample.csv "1,1,1,1" "-,+,+,+" output.csvSample InputFundP1P2P3P4P5M10.720.5275616.06M20.830.693.73710.56M30.810.66430.48.97M40.770.593.644.112.27M50.820.67734.610.77M60.720.52358.515.69M70.850.724.250.514.07M80.640.41338.510.64Sample OutputFundP1P2P3P4P5TOPSIS ScoreRankM10.720.5275616.060.63391254958009091M20.830.693.73710.560.37516670106188326M30.810.66430.48.970.42688953650638833M40.770.593.644.112.270.36315740310537848M50.820.67734.610.770.63205912133966272M60.720.52358.515.690.36870647672047017M70.850.724.250.514.070.38629568748048645M80.640.41338.510.640.40697618883953394Please Note ThatThe 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 Chirag GuptaThis repository is licensed under MIT License. See LICENSE for details.
Package: topsis-csv-python3
TOPSIS-python3This python code save "ordered.csv" file containing a extra column of Rank.TOPSISTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the altenrative of shortest Euclidean distance from the idael solution, and fartherst distance from the negative-ideal solution.
Package: topsis-daman
topsis-pythonTopsis analysis of a csv file""UCS633 Project Submission""Name - Damanpreet SinghRoll no. - 101703154About 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-damanUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis-daman -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