Metadata-Version: 1.1
Name: featureselect
Version: 0.0.4
Summary: An elegant and effectice solution to get best set of features from a numerical dataset!
Home-page: https://github.com/himanshu-dutta/featureselect
Author: Himanshu Dutta
Author-email: meet.himanshu.dutta@gmail.com
License: UNKNOWN
Description: =========================
        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.
        
        .. _`Feature Select` : https://github.com/himanshu-dutta/featureselect/
        
        Quickstart
        ----------
        
        Install the latest Feature Select with ::
        
            pip install featureselect
        
        
        Usage
        -----
        
        .. 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.
           ----------------------------------
           *  Epoch:  1 | Accuracy: 0.958.
           ----------------------------------
           *  Epoch:  2 | Accuracy: 0.958.
           ----------------------------------
           *  Epoch:  3 | Accuracy: 0.958.
           ----------------------------------
           *  Epoch:  4 | Accuracy: 0.958.
           ----------------------------------
           *  Epoch:  5 | Accuracy: 0.972.
           ----------------------------------
           *  Epoch:  6 | Accuracy: 0.972.
           ----------------------------------
           *  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.
        
Keywords: featureselect
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
