# TOPSIS Package in Python
Submitted by: Kartik Madan
Roll no: 102003565
---
## TOPSIS
TOPSIS is an acronym that stands for Technique of Order Preference Similarity to the Ideal Solution and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.
---
## How to use
The package topsis-kartik-102003565 can be run though the command line as follows:
```
>> pip install topsis-kartik-102003565
```
```
>>python topsis data.csv "1,1,1,1" "+,+,-,+" result
```
## Sample Input
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.
<table><thead><tr><th>Fund Name</th><th>P1</th><th>P2</th><th>P3</th><th>P4</th></tr></thead><tbody><tr><td>M1</td><td>250</td><td>16</td><td>12</td><td>5</td></tr><tr><td>M2</td><td>200</td><td>16</td><td>8</td><td>3</td></tr><tr><td>M3</td><td>300</td><td>32</td><td>16</td><td>4</td></tr><tr><td>M4</td><td>275</td><td>32</td><td>8</td><td>4</td></tr><tr><td>M5</td><td>225</td><td>16</td><td>16</td><td>2</td></tr></tbody></table>
<br>
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>
## Sample Output
The output that generated from the following input will be:
<table><thead><tr><th>Model</th><th align="right">P1</th><th align="center">P2</th><th>P3</th><th>P4</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">250</td><td align="center">16</td><td>12</td><td>5</td><td>0.534277</td><td>3</td></tr><tr><td>M2</td><td align="right">200</td><td align="center">16</td><td>8</td><td>3</td><td>0.308368</td><td>5</td></tr><tr><td>M3</td><td align="right">300</td><td align="center">32</td><td>16</td><td>4</td><td>0.691632</td><td>1</td></tr><tr><td>M4</td><td align="right">275</td><td align="center">32</td><td>8</td><td>4</td><td>0.534737</td><td>2</td></tr><tr><td>M5</td><td align="right">225</td><td align="center">16</td><td>16</td><td>2</td><td>0.401046</td><td>4</td></tr></tbody></table>
<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** .
Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.
---
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"description": "# TOPSIS Package in Python\n\nSubmitted by: Kartik Madan\n\nRoll no: 102003565\n\n---\n\n## TOPSIS\n\nTOPSIS is an acronym that stands for Technique of Order Preference Similarity to the Ideal Solution and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.\n\n---\n\n## How to use\n\nThe package topsis-kartik-102003565 can be run though the command line as follows:\n\n```\n>> pip install topsis-kartik-102003565\n```\n\n```\n>>python topsis data.csv \"1,1,1,1\" \"+,+,-,+\" result\n```\n\n## Sample Input\n\nThe 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.\n\n<table><thead><tr><th>Fund Name</th><th>P1</th><th>P2</th><th>P3</th><th>P4</th></tr></thead><tbody><tr><td>M1</td><td>250</td><td>16</td><td>12</td><td>5</td></tr><tr><td>M2</td><td>200</td><td>16</td><td>8</td><td>3</td></tr><tr><td>M3</td><td>300</td><td>32</td><td>16</td><td>4</td></tr><tr><td>M4</td><td>275</td><td>32</td><td>8</td><td>4</td></tr><tr><td>M5</td><td>225</td><td>16</td><td>16</td><td>2</td></tr></tbody></table>\n\n<br>\nWeights `weights` is not already normalised will be normalised later in the code.\n\nInformation of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.\n<br>\n\n## Sample Output\n\nThe output that generated from the following input will be:\n\n<table><thead><tr><th>Model</th><th align=\"right\">P1</th><th align=\"center\">P2</th><th>P3</th><th>P4</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align=\"right\">250</td><td align=\"center\">16</td><td>12</td><td>5</td><td>0.534277</td><td>3</td></tr><tr><td>M2</td><td align=\"right\">200</td><td align=\"center\">16</td><td>8</td><td>3</td><td>0.308368</td><td>5</td></tr><tr><td>M3</td><td align=\"right\">300</td><td align=\"center\">32</td><td>16</td><td>4</td><td>0.691632</td><td>1</td></tr><tr><td>M4</td><td align=\"right\">275</td><td align=\"center\">32</td><td>8</td><td>4</td><td>0.534737</td><td>2</td></tr><tr><td>M5</td><td align=\"right\">225</td><td align=\"center\">16</td><td>16</td><td>2</td><td>0.401046</td><td>4</td></tr></tbody></table>\n\n<br>\nThe output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** .\nHere the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.\n\n---\n",
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