semvec/pypi-packages huggingface dataset:
GeneticAlgorithmFeaturesSelection 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-Gurneet-Kaur-102003494 1.0
Topsis

# Title:Multiple Criteria Decision Making using TOPSIS

## What is TOPSIS:
TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method. It is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993.

## How to get output?
output is taken by running the command on terminal as : python "sourcefile.py" "input.csv" "weights(separateed by ',')" "impact(separated by ','))" use impact as '+' for maximizing the feature and '-' for minimizing. On your terminal run the command:
python source_file_name.py input_file.csv "1,1,0,1" "+,-,+,+" output_file_name.csv Output will be stored in your present working directory as a csv file.

## Constraints
Number of columns in input file(other than the name of the entity) should be equal to length of impacts and length of weights.

Input file should not have blank values

Number of Columns in Input files should not be less than 3

There should not be any csv file with same name as your output file in present directory

Number of Arguments should be equal to the number as specified above

## INPUT FILE(EXAMPLE:DATA.CSV):
Argument used to pass the path of the input file which conatins a dataset having different fields and to perform the topsis mathematical operations

## WEIGHTS(EXAMPLE:"0.25,0.25,0.25,0.25")
The weights to assigned to the different parameters in the dataset should be passed in the argument.It must be seperated by ','.

## IMPACTS(EXAMPLE:"-,+,+,+"):
The impacts are passed to consider which parameters have a positive impact on the decision and which one have the negative impact.Only '+' and '-' values should be passed and should be seperated with ',' only

## OUTPUT FILE(EXAMPLE:RESULT.CSV):
This argument is used to pass the path of the result file where we want the rank and score to be stored