# TOPSIS Analysis Tool
This project implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision making. The tool takes a decision matrix, applies weights and impacts for each criterion, and ranks the alternatives based on their relative performance. This python package was built under acronym topsis-varun-102217105 as University Student Project.
## Features
- Supports decision matrix input via CSV or Excel files (.xlsx, .xls).
- Allows customizable weights and impacts for each criterion.
- Outputs ranked results in a CSV file.
---
## Installation
1. Clone this repository or download the script file.
2. Ensure you have Python installed (version 3.6 or later).
3. Install the required dependencies using pip:
```bash
pip install numpy pandas openpyxl
```
---
## Usage
The script accepts command-line arguments to specify the input file, weights, impacts, and output file.
### Command
```bash
python topsis.py <input_file> <weights> <impacts> <output_file>
```
### Parameters
- **`<input_file>`**: Path to the input decision matrix file. Accepts `.csv`, `.xlsx`, or `.xls` files.
- **`<weights>`**: Comma-separated weights for each criterion (e.g., `0.3,0.4,0.3`).
- **`<impacts>`**: Comma-separated impacts for each criterion (`+` for beneficial, `-` for non-beneficial, e.g., `+,+,-`).
- **`<output_file>`**: Path to save the output CSV file containing ranks.
### Example
#### Input File (`data.csv`):
| Alternatives | Criterion 1 | Criterion 2 | Criterion 3 |
|--------------|-------------|-------------|-------------|
| A1 | 250 | 16 | 12 |
| A2 | 200 | 20 | 10 |
| A3 | 300 | 18 | 15 |
#### Command:
```bash
python topsis.py data.csv "0.3,0.4,0.3" "+,+,-" output.csv
```
#### Output File (`output.csv`):
| Alternatives | Criterion 1 | Criterion 2 | Criterion 3 | Rank |
|--------------|-------------|-------------|-------------|------|
| A1 | 250 | 16 | 12 | 2 |
| A2 | 200 | 20 | 10 | 3 |
| A3 | 300 | 18 | 15 | 1 |
---
## Functions
### `topsis(data, weights, impacts)`
Performs TOPSIS analysis and returns the ranks of the alternatives.
#### Parameters:
- **`data`**: 2D list or numpy array representing the decision matrix.
- **`weights`**: List of weights for each criterion.
- **`impacts`**: List of impacts (`+` or `-`) for each criterion.
#### Returns:
- List of ranks for the alternatives.
---
### `excel_to_csv(excel_file, csv_output)`
Converts an Excel file to a CSV file.
#### Parameters:
- **`excel_file`**: Path to the Excel file.
- **`csv_output`**: Path to save the converted CSV file.
#### Returns:
- The path to the converted CSV file.
---
## Notes
- Ensure that the weights and impacts match the number of criteria in the decision matrix.
- The decision matrix should not include the alternative names in the criteria columns (e.g., the first column in the input file should contain names like A1, A2, etc.).
---
## License
This project is licensed under the MIT License.
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"description": "# TOPSIS Analysis Tool\r\n\r\nThis project implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision making. The tool takes a decision matrix, applies weights and impacts for each criterion, and ranks the alternatives based on their relative performance. This python package was built under acronym topsis-varun-102217105 as University Student Project.\r\n\r\n## Features\r\n- Supports decision matrix input via CSV or Excel files (.xlsx, .xls).\r\n- Allows customizable weights and impacts for each criterion.\r\n- Outputs ranked results in a CSV file.\r\n\r\n---\r\n\r\n## Installation\r\n1. Clone this repository or download the script file.\r\n2. Ensure you have Python installed (version 3.6 or later).\r\n3. Install the required dependencies using pip:\r\n\r\n```bash\r\npip install numpy pandas openpyxl\r\n```\r\n\r\n---\r\n\r\n## Usage\r\n\r\nThe script accepts command-line arguments to specify the input file, weights, impacts, and output file.\r\n\r\n### Command\r\n```bash\r\npython topsis.py <input_file> <weights> <impacts> <output_file>\r\n```\r\n\r\n### Parameters\r\n- **`<input_file>`**: Path to the input decision matrix file. Accepts `.csv`, `.xlsx`, or `.xls` files.\r\n- **`<weights>`**: Comma-separated weights for each criterion (e.g., `0.3,0.4,0.3`).\r\n- **`<impacts>`**: Comma-separated impacts for each criterion (`+` for beneficial, `-` for non-beneficial, e.g., `+,+,-`).\r\n- **`<output_file>`**: Path to save the output CSV file containing ranks.\r\n\r\n### Example\r\n#### Input File (`data.csv`):\r\n| Alternatives | Criterion 1 | Criterion 2 | Criterion 3 |\r\n|--------------|-------------|-------------|-------------|\r\n| A1 | 250 | 16 | 12 |\r\n| A2 | 200 | 20 | 10 |\r\n| A3 | 300 | 18 | 15 |\r\n\r\n#### Command:\r\n```bash\r\npython topsis.py data.csv \"0.3,0.4,0.3\" \"+,+,-\" output.csv\r\n```\r\n\r\n#### Output File (`output.csv`):\r\n| Alternatives | Criterion 1 | Criterion 2 | Criterion 3 | Rank |\r\n|--------------|-------------|-------------|-------------|------|\r\n| A1 | 250 | 16 | 12 | 2 |\r\n| A2 | 200 | 20 | 10 | 3 |\r\n| A3 | 300 | 18 | 15 | 1 |\r\n\r\n---\r\n\r\n## Functions\r\n\r\n### `topsis(data, weights, impacts)`\r\nPerforms TOPSIS analysis and returns the ranks of the alternatives.\r\n\r\n#### Parameters:\r\n- **`data`**: 2D list or numpy array representing the decision matrix.\r\n- **`weights`**: List of weights for each criterion.\r\n- **`impacts`**: List of impacts (`+` or `-`) for each criterion.\r\n\r\n#### Returns:\r\n- List of ranks for the alternatives.\r\n\r\n---\r\n\r\n### `excel_to_csv(excel_file, csv_output)`\r\nConverts an Excel file to a CSV file.\r\n\r\n#### Parameters:\r\n- **`excel_file`**: Path to the Excel file.\r\n- **`csv_output`**: Path to save the converted CSV file.\r\n\r\n#### Returns:\r\n- The path to the converted CSV file.\r\n\r\n---\r\n\r\n## Notes\r\n- Ensure that the weights and impacts match the number of criteria in the decision matrix.\r\n- The decision matrix should not include the alternative names in the criteria columns (e.g., the first column in the input file should contain names like A1, A2, etc.).\r\n\r\n---\r\n\r\n## License\r\nThis project is licensed under the MIT License.\r\n",
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