# Topsis_Taranpreet_102017050
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 world.
## Installation
Install the Package using the command -
```s
$ pip install Topsis_Taranpreet_102017050
```
## Usage
```s
python -m Topsis_Taranpreet_102017050.Topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
## Example
| Fund Name | P1 | P2 | P3 | P4 | P5 |
|--------------|---------|---------|--------|---------|----------|
| M1 | 0.94 | 0.88 | 6.1 | 40.1 | 12.01 |
| M2 | 0.85 | 0.72 | 5 | 52.9 | 14.87 |
| M3 | 0.84 | 0.71 | 4.5 | 43.2 | 12.31 |
| M4 | 0.73 | 0.53 | 6.7 | 43.8 | 12.94 |
| M5 | 0.88 | 0.77 | 6.5 | 31.7 | 9.96 |
| M6 | 0.6 | 0.36 | 4.4 | 31.2 | 9.14 |
| M7 | 0.6 | 0.36 | 4.4 | 48 | 13.34 |
| M8 | 0.92 | 0.85 | 5.5 | 55.2 | 15.62 |
**Input Method:**
The Input data file is data.csv, output file is result.csv and for the Weights : [2, 2, 3, 3, 4] & Impacts : [-, +, -, +, -], run the following command:
```s
python -m Topsis_Taranpreet_102017050.Topsis data.csv "2,2,3,3,4" "-,+,-,+,-" result.csv
```
**Output generated:**
The output file, result.csv will be as follows:
| Fund Name | P1 | P2 | P3 | P4 | P5 | Performance Score | Rank |
|--------------|---------|---------|--------|---------|----------|------------------------|---------|
| M1 | 0.94 | 0.88 | 6.1 | 40.1 | 12.01 | 0.522346027 | 3 |
| M2 | 0.85 | 0.72 | 5 | 52.9 | 14.87 | 0.508871391 | 4 |
| M3 | 0.84 | 0.71 | 4.5 | 43.2 | 12.31 | 0.580231695 | 1 |
| M4 | 0.73 | 0.53 | 6.7 | 43.8 | 12.94 | 0.390517293 | 8 |
| M5 | 0.88 | 0.77 | 6.5 | 31.7 | 9.96 | 0.503787007 | 5 |
| M6 | 0.6 | 0.36 | 4.4 | 31.2 | 9.14 | 0.531336089 | 2 |
| M7 | 0.6 | 0.36 | 4.4 | 48 | 13.34 | 0.493355187 | 7 |
| M8 | 0.92 | 0.85 | 5.5 | 55.2 | 15.62 | 0.50091679 | 6 |
## Points to note:
- To remove the indices and headers, the library implicitly removes the first column and row respectively. Kindly, make sure the csv follows the format as shown in sample.csv.
- The csv should not contain categorical values.
- The csv should have atleast more than 3 columns.
- The number of Impacts an Weights should be equal to the number of feature columns.
- For maximizing a column, the impact is shown by "+" and for minimizing, "-".
- The weights should be positive and numerical.
- Separate the weights and columns by comma (,).
- Please follow the format to run the program as given in the sample command.
## License
MIT
**Thank You! Keep using and sharing feedbacks!**
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"description": "# Topsis_Taranpreet_102017050\r\nTOPSIS (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.\r\n\r\n## Installation\r\nInstall the Package using the command - \r\n```s\r\n$ pip install Topsis_Taranpreet_102017050\r\n```\r\n\r\n## Usage\r\n```s\r\npython -m Topsis_Taranpreet_102017050.Topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>\r\n```\r\n\r\n## Example\r\n\r\n| Fund Name | P1 | P2 | P3 | P4 | P5 |\r\n|--------------|---------|---------|--------|---------|----------|\r\n| M1 | 0.94 | 0.88 | 6.1 | 40.1 | 12.01 |\r\n| M2 | 0.85 | 0.72 | 5 | 52.9 | 14.87 | \r\n| M3 | 0.84 | 0.71 | 4.5 | 43.2 | 12.31 |\r\n| M4 | 0.73 | 0.53 | 6.7 | 43.8 | 12.94 |\r\n| M5 | 0.88 | 0.77 | 6.5 | 31.7 | 9.96 |\r\n| M6 | 0.6 | 0.36 | 4.4 | 31.2 | 9.14 |\r\n| M7 | 0.6 | 0.36 | 4.4 | 48 | 13.34 |\r\n| M8 | 0.92 | 0.85 | 5.5 | 55.2 | 15.62 |\r\n\r\n**Input Method:** \r\nThe Input data file is data.csv, output file is result.csv and for the Weights : [2, 2, 3, 3, 4] & Impacts : [-, +, -, +, -], run the following command:\r\n\r\n```s\r\npython -m Topsis_Taranpreet_102017050.Topsis data.csv \"2,2,3,3,4\" \"-,+,-,+,-\" result.csv\r\n```\r\n\r\n**Output generated:** \r\nThe output file, result.csv will be as follows:\r\n\r\n| Fund Name | P1 | P2 | P3 | P4 | P5 | Performance Score | Rank |\r\n|--------------|---------|---------|--------|---------|----------|------------------------|---------|\r\n| M1 | 0.94 | 0.88 | 6.1 | 40.1 | 12.01 | 0.522346027 | 3 |\r\n| M2 | 0.85 | 0.72 | 5 | 52.9 | 14.87 | 0.508871391 | 4 |\r\n| M3 | 0.84 | 0.71 | 4.5 | 43.2 | 12.31 | 0.580231695 | 1 |\r\n| M4 | 0.73 | 0.53 | 6.7 | 43.8 | 12.94 | 0.390517293 | 8 |\r\n| M5 | 0.88 | 0.77 | 6.5 | 31.7 | 9.96 | 0.503787007 | 5 |\r\n| M6 | 0.6 | 0.36 | 4.4 | 31.2 | 9.14 | 0.531336089 | 2 |\r\n| M7 | 0.6 | 0.36 | 4.4 | 48 | 13.34 | 0.493355187 | 7 |\r\n| M8 | 0.92 | 0.85 | 5.5 | 55.2 | 15.62 | 0.50091679 | 6 |\r\n\r\n## Points to note:\r\n- To remove the indices and headers, the library implicitly removes the first column and row respectively. Kindly, make sure the csv follows the format as shown in sample.csv.\r\n- The csv should not contain categorical values.\r\n- The csv should have atleast more than 3 columns.\r\n- The number of Impacts an Weights should be equal to the number of feature columns.\r\n- For maximizing a column, the impact is shown by \"+\" and for minimizing, \"-\".\r\n- The weights should be positive and numerical.\r\n- Separate the weights and columns by comma (,). \r\n- Please follow the format to run the program as given in the sample command. \r\n\r\n## License\r\n\r\nMIT\r\n\r\n**Thank You! Keep using and sharing feedbacks!**\r\n",
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