# TOPSIS
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a decision-making technique applied in order to rank potential solutions on the basis of multiple criteria.
## Installation
This package requires [Python](https://www.python.org/downloads/) v3.5+ to run.
Use ```pip``` to install:
```
pip install topsis-amrita-102017017
```
## OS Compatibility
It should work on any Python implementation and operating system and is compatible with Python version 3.5 and upwards.
## Usage
Run ```topsis``` in the input file's directory as follows:
```
topsis <input_file_name> <weights> <impacts> <output_file_name>
```
For example,
```
topsis data.csv 1,1,1,1 +,-,+,- result.csv
```
Use quotation marks while including spaces in any argument:
```
topsis data.csv "1, 1, 1, 1" "+, -, +, -" result.csv
```
- Input and output file format should be .CSV
- First column in the input file should be the object name
- Input file must have at least 2 criteria, and all criterion values should be numeric
- Weights must be numeric and comma-separated. For example, ```0.25,0.25,1.0,0.25``` or ```"0.25,0.25,1.0,0.25"```.
- Impacts must be comma-separated with ```+``` for criteria that are to be maximised, and ```-``` for criteria that are to be minimised. For example, ```+,-,+,-``` or ```"+, -, +, -"```
## Example
Consider **input.csv**:
|Model|Corr|R2|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|
If we run the following command:
```
topsis input.csv "1, 1, 1, 1" "+, +, -, +" result.csv
```
we get a file named **result.csv** in the directory with an additional 2 columns containing the TOPSIS score and the rank of each object:
|Model|Corr|R2|RMSE|Accuracy|TOPSIS Score|Rank|
|---|---|---|---|---|---|---|
|M1|0.79|0.62|1.25|60.89|0.7722097345612788|2.0|
|M2|0.66|0.44|2.89|63.07|0.22559875426413367|5.0|
|M3|0.56|0.31|1.57|62.87|0.43889731728018605|4.0|
|M4|0.82|0.67|2.68|70.19|0.5238778712729114|3.0|
|M5|0.75|0.56|1.3|80.39|0.8113887082429979|1.0|
## License
MIT
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"description": "# TOPSIS\r\nTOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a decision-making technique applied in order to rank potential solutions on the basis of multiple criteria.\r\n\r\n## Installation\r\nThis package requires [Python](https://www.python.org/downloads/) v3.5+ to run.\r\nUse ```pip``` to install:\r\n```\r\npip install topsis-amrita-102017017\r\n```\r\n\r\n## OS Compatibility\r\nIt should work on any Python implementation and operating system and is compatible with Python version 3.5 and upwards.\r\n\r\n## Usage\r\nRun ```topsis``` in the input file's directory as follows:\r\n```\r\ntopsis <input_file_name> <weights> <impacts> <output_file_name>\r\n```\r\nFor example,\r\n```\r\ntopsis data.csv 1,1,1,1 +,-,+,- result.csv\r\n```\r\nUse quotation marks while including spaces in any argument:\r\n```\r\ntopsis data.csv \"1, 1, 1, 1\" \"+, -, +, -\" result.csv\r\n```\r\n\r\n- Input and output file format should be .CSV\r\n- First column in the input file should be the object name\r\n- Input file must have at least 2 criteria, and all criterion values should be numeric\r\n- Weights must be numeric and comma-separated. For example, ```0.25,0.25,1.0,0.25``` or ```\"0.25,0.25,1.0,0.25\"```.\r\n- Impacts must be comma-separated with ```+``` for criteria that are to be maximised, and ```-``` for criteria that are to be minimised. For example, ```+,-,+,-``` or ```\"+, -, +, -\"```\r\n\r\n## Example\r\nConsider **input.csv**:\r\n|Model|Corr|R2|RMSE|Accuracy|\r\n|---|---|---|---|---|\r\n|M1|0.79|0.62|1.25|60.89|\r\n|M2|0.66|0.44|2.89|63.07|\r\n|M3|0.56|0.31|1.57|62.87|\r\n|M4|0.82|0.67|2.68|70.19|\r\n|M5|0.75|0.56|1.3|80.39|\r\n\r\nIf we run the following command:\r\n```\r\ntopsis input.csv \"1, 1, 1, 1\" \"+, +, -, +\" result.csv\r\n```\r\nwe get a file named **result.csv** in the directory with an additional 2 columns containing the TOPSIS score and the rank of each object:\r\n|Model|Corr|R2|RMSE|Accuracy|TOPSIS Score|Rank|\r\n|---|---|---|---|---|---|---|\r\n|M1|0.79|0.62|1.25|60.89|0.7722097345612788|2.0|\r\n|M2|0.66|0.44|2.89|63.07|0.22559875426413367|5.0|\r\n|M3|0.56|0.31|1.57|62.87|0.43889731728018605|4.0|\r\n|M4|0.82|0.67|2.68|70.19|0.5238778712729114|3.0|\r\n|M5|0.75|0.56|1.3|80.39|0.8113887082429979|1.0|\r\n\r\n## License\r\nMIT\r\n",
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