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Feature Select PyPackage
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`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
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- Multiple optimization algorithms to work with.
- Works with most class based Machine Learning models over a range of libraries.
- Compatible with all platforms.
.. _`Feature Select` : https://github.com/himanshu-dutta/featureselect/
Quickstart
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Install the latest Feature Select with ::
pip install featureselect
Usage
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.. code:: python
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.
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* Epoch: 5 | Accuracy: 0.972.
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* Epoch: 6 | Accuracy: 0.972.
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* Epoch: 7 | Accuracy: 0.972.
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* Epoch: 8 | Accuracy: 0.972.
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* Epoch: 9 | Accuracy: 0.986.
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* Epoch: 10 | Accuracy: 0.986.
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(0.9859154929577465, array([ 2, 4, 5, 6, 9, 11, 12, 13, 14, 17, 19, 20, 21, 24, 26, 29, 32]))
Note
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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.
