semvec/pypi-packages huggingface dataset:
GeneticAlgorithmFeatureSelection 0.0.3
Feature Selection with Genetic Algorithm
Requires Python: >=3.9

# GeneticAlgorithmFeatureSelection
Feature Selection with Genetic Algorithm published in [pypi](https://pypi.org/project/GeneticAlgorithmFeatureSelection/).

#### Installation
    pip install GeneticAlgorithmFeatureSelection
    
#### Example

The original example code can be found in [test.py](https://github.com/alisharifi2000/GeneticAlgorithmFeatureSelection/blob/main/tests/test.py).
    
    from sklearn.datasets import make_classification
    import pandas as pd
    from genetic_algoirthm.GA import GenticAlgorithmFeatureSelection
    
Define the sample classification dataset
    
    x, y = make_classification(n_features=100, n_samples=2500)

input data must be pandas dataframe. we split target and features.
    
    columns = [f'f_{i}' for i in range(1, 101)]
    features = pd.DataFrame(x, columns=columns)
    target = pd.DataFrame(y, columns=['target'])
    
run feature selection

     GA = GenticAlgorithmFeatureSelection(features=features, target=target, population_size=100, elite_rate=0.5,
                                          fitness_alpha=0.55, tourn_size=25, no_generation=50)
     
     GA.run()
     
see history 
     
    history = GA.history

    for generation, detail in history.items():
        print(f'Generation :{generation}')
        print(f'best score: {detail["best_score"]}')
        print(f'features: {detail["selected_features"]}')
        
find best score and features in last generation
    
    print(f'best score last generation :{GA.best_score}')
    print(f'feature selected in last generation: {GA.selected_features}')
Topsis-102003592-Bhavna 0.5
Evaluation of alternatives based on multiple criteria using TOPSIS method.

## Topsis_102003592_Bhavna

# TOPSIS - Multiple Criteria Decision Making

Submitted By: **Bhavna Goyal - 102003592**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **0.5**.

Date: **21-01-2023**.

Author: **Bhavna Goyal**.

Maintainer: **Bhavna Goyal <bgoyal_be20@thapar.edu>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## 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.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```
>> pip install Topsis_102003592_Bhavna
```

### In Command Prompt

```
>> topsisbhav data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

| Attribute | Price or cost | Storage Space | Camera| Looks |
| -----     | -----------   | ------------- | ---- | -------|
| Mobile 1  |250$           | 16GB          | 12MP | 5      |
| Mobile 2  | 200$          | 16GB          | 8MP  | 3      |
| Mobile 3  | 300$          | 32GB          | 16MP | 4      |
| Mobile 4  |  275$         | 32GB          | 8MP  | 4      |
| Mobile 5  | 225$          | 16GB          | 16MP | 2      |

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>

## Output file (result.csv)

| Attribute | Price or cost | Storage Space | Camera| Looks | Topsis_score | Rank|
| -----     | -----------   | ------------- | ---- | -------| ------------ | ----|
| Mobile 1  |250$           | 16GB          | 12MP | 5      | 0.4228       | 4   |
| Mobile 2  |200$           | 16GB          | 8MP  | 3      | 0.4635       | 3   |
| Mobile 3  |300$           | 32GB          | 16MP | 4      | 0.5097       | 2   |
| Mobile 4  |275$           | 32GB          | 8MP  | 4      | 0.3772       | 5   |
| Mobile 5  |225$           | 16GB          | 16MP | 2      | 0.6871       | 1   |


<br>
The output file contains columns of input file along with two additional columns having *Topsis_score* and *Rank*