Feature Select PyPackage

Feature Select is a simple yet effective solution to select features from a numeric dataset, which yields the best results, given a Machine Learning algorithm.

• GitHub repo: https://github.com/himanshu-dutta/featureselect/
• Free software: MIT license

Features

• Multiple optimization algorithms to work with.
• Works with most class based Machine Learning models over a range of libraries.
• Compatible with all platforms.

Quickstart

Install the latest Feature Select with

pip install featureselect

Usage

from featureselect import DEOptimizer, SAOptimizer, GAOptimizer, PSOptimizer
from sklearn.tree import DecisionTreeClassifier
import pandas as pd

# loading a dataset
dataset = pd.read_csv("dataset.csv", header=None)
dataset[34] = dataset[34].apply(lambda x: 1 if x == "g" else 0)
dataset = dataset.dropna()
X, y = dataset.iloc[:, :-1].to_numpy(), dataset.iloc[:, -1].to_numpy()

# best_accuracy, index_of_best_features = GAOptimizer((X, y), DecisionTreeClassifier, epochs = 10, threshold=0.6, verbose=1, max_depth=3)
# best_accuracy, index_of_best_features = SAOptimizer((X, y), DecisionTreeClassifier, epochs = 10, threshold=0.6, verbose=True, max_depth=3)
# best_accuracy, index_of_best_features = PSOptimizer((X, y), DecisionTreeClassifier, epochs = 10, verbose=1, max_depth=3)


best_accuracy, index_of_best_features = DEOptimizer((X, y), DecisionTreeClassifier, epochs = 10, threshold=0.6, verbose=1, max_depth=3)

#############
#   Output
#############
Initial Accuracy: 0.887.
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*  Epoch:  1 | Accuracy: 0.958.
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*  Epoch:  2 | Accuracy: 0.958.
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*  Epoch:  3 | Accuracy: 0.958.
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*  Epoch:  4 | Accuracy: 0.958.
----------------------------------
*  Epoch:  5 | Accuracy: 0.972.
----------------------------------
*  Epoch:  6 | Accuracy: 0.972.
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*  Epoch:  7 | Accuracy: 0.972.
----------------------------------
*  Epoch:  8 | Accuracy: 0.972.
----------------------------------
*  Epoch:  9 | Accuracy: 0.986.
----------------------------------
*  Epoch: 10 | Accuracy: 0.986.
----------------------------------
(0.9859154929577465, array([ 2,  4,  5,  6,  9, 11, 12, 13, 14, 17, 19, 20, 21, 24, 26, 29, 32]))

Note

The project is still in developement phase and will be expanded and made better over time. Any contribution to it is welcomed. Stable release would be made available soon.