# TOPSIS
Submitted By: **Sarthak Tiwari | 102183051**
***
## What is TOPSIS?
**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
## How to install this package:
```bash
>> pip install -e .[dev]
```
## After installation, in Command Prompt/Terminal in pwd/current dir:
```
>> topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
Weights (`weights`) may not be normalised but will be normalised in the code.
**Note:** To avoid errors -
Input file must contain three or more columns.
2nd to last columns must contain numeric values only.
Number of weights, number of impacts and number of columns (from 2 nd to last columns) must
be same.
Impacts must be either +ve or -ve.
Impacts and weights must be separated by ‘,’ (comma).
## InputDataFile (data.csv) - an example
The decision matrix should be constructed with each row representing a Model alternative and each column representing a criterion like Correlation, R<sup>2</sup>, Root Mean Squared Error, Accuracy, etc.
Model | Corr | Rseq | 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
## Output file (result.csv) -
Based on the above input file and setting weights as "1,2,1,1" and impacts as "+,-,-,+".
Model | Corr | Rseq | RMSE | Accuracy | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------- | -------------
M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.423744391359611 | 4
M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.0.467426368298297 | 3
M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.760230957034903 | 1
M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.207772533881566 | 5
M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.504864457803718 | 2
The output file contains columns of input file along with two additional columns having Topsis Score and Rank.
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"description": "# TOPSIS\r\n\r\nSubmitted By: **Sarthak Tiwari | 102183051**\r\n\r\n***\r\n\r\n## What is TOPSIS?\r\n\r\n**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution \r\n(TOPSIS) originated in the 1980s as a multi-criteria decision making method.\r\n\r\n## How to install this package:\r\n```bash\r\n>> pip install -e .[dev]\r\n```\r\n\r\n## After installation, in Command Prompt/Terminal in pwd/current dir:\r\n```\r\n>> topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>\r\n```\r\n\r\nWeights (`weights`) may not be normalised but will be normalised in the code.\r\n**Note:** To avoid errors - \r\nInput file must contain three or more columns.\r\n2nd to last columns must contain numeric values only.\r\nNumber of weights, number of impacts and number of columns (from 2 nd to last columns) must\r\nbe same.\r\nImpacts must be either +ve or -ve.\r\nImpacts and weights must be separated by \u00e2\u20ac\u02dc,\u00e2\u20ac\u2122 (comma).\r\n\r\n## InputDataFile (data.csv) - an example\r\n\r\nThe decision matrix should be constructed with each row representing a Model alternative and each column representing a criterion like Correlation, R<sup>2</sup>, Root Mean Squared Error, Accuracy, etc. \r\n\r\nModel | Corr | Rseq | RMSE | Accuracy\r\n------------ | ------------- | ------------ | ------------- | ------------\r\nM1 |\t0.79 | 0.62\t| 1.25 | 60.89\r\nM2 | 0.66 | 0.44\t| 2.89 | 63.07\r\nM3 |\t0.56 | 0.31\t| 1.57 | 62.87\r\nM4 |\t0.82 | 0.67\t| 2.68 | 70.19\r\nM5 |\t0.75 | 0.56\t| 1.3 | 80.39\r\n\r\n## Output file (result.csv) - \r\nBased on the above input file and setting weights as \"1,2,1,1\" and impacts as \"+,-,-,+\".\r\n\r\nModel | Corr | Rseq | RMSE | Accuracy | Topsis Score | Rank\r\n------------ | ------------- | ------------ | ------------- | ------------ | ------------- | ------------- \r\nM1 |\t0.79 | 0.62\t| 1.25 | 60.89 | 0.423744391359611 | 4\r\nM2 | 0.66 | 0.44\t| 2.89 | 63.07 | 0.0.467426368298297 | 3\r\nM3 |\t0.56 | 0.31\t| 1.57 | 62.87 | 0.760230957034903 | 1\r\nM4 |\t0.82 | 0.67\t| 2.68 | 70.19 | 0.207772533881566 | 5\r\nM5 |\t0.75 | 0.56\t| 1.3\t | 80.39 | 0.504864457803718 | 2\r\n\r\nThe output file contains columns of input file along with two additional columns having Topsis Score and Rank.\r\n\r\n",
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