Metadata-Version: 2.1
Name: autooptimizer
Version: 0.5.1
Summary: AutoOptimizer is a python package for optimize ML algorithms.
Home-page: https://github.com/mrb987/autooptimizer
Author: MohammadReza Barghi
Author-email: info@genesiscube.ir
License: MIT
Description: Machine Learning algorithm optimizer for sklearn and evaluation Metrics for Regression Model.
        AutoOptimizer provides tools to automatically optimize machine learning model for a dataset with very little user intervention.
        
        It refers to techniques that allow semi-sophisticated machine learning practitioners and non-experts 
        to discover a good predictive model pipeline for their machine learning algorithm task quickly,
        with very little intervention other than providing a dataset.
        
        
        #Prerequisites:
        
        jupyterlab(contains all sub packages except mlxtend) or: {sklearn,matplotlib,mlxtend,numpy}	
        
        
        #Usage:
        
        
        Scikit learn supervised and unsupervised learning models using python.
        
        
        {DBSCAN, KMeans, MeanShift,  LogisticRegression, KNeighborsClassifier, SupportVectorClassifier, DecisionTree}
        
        
        #Running for example:
        
        
        from autooptimizer.dbscan import dbscan
        
        
        from autooptimizer.kmeans import kmeans
        
        
        from autooptimizer.meanshift import meanshift
        
        
        from autooptimizer.logreg import logreg
        
        
        from autooptimizer.knn import knn
        
        
        from autooptimizer.svc import svc
        
        
        from autooptimizer.decisiontree import decisiontree
        
        
        dbscan(x)
        
        
        kmeans(x)
        
        
        meanshift(x)
        
        
        logreg(x,y)
        
        
        knn(x,y)
        
        
        svc(x,y)
        
        
        decisiontree(x,y)
        
        
        'x' should be your independent variable or feature's values and 'y' is target variable or dependent variable.
        The output of the program is the maximum possible accuracy with the appropriate parameters to use in model.
        
        #Metrics
        
        {root_mean_squared_error, root_mean_squared_log_error, root_mean_squared_precentage_error,
        symmetric_mean_absolute_precentage_error, mean_bias_error, relative_squared_error, root_relative_squared_error
        relative_absolute_error, median_absolute_percentage_error, mean_absolute_percentage_error}
        
        
        from autooptimizer.metrics import root_mean_squared_error
        
        
        root_mean_squared_error(true, predicted)
        
        
        #Contact and Contributing:
        Please share your good ideas with us. 
        Simply letting us know how we can improve the programm to serve you better.
        Thanks for contributing with the programm.
        
        >>>https://github.com/mrb987/autooptimizer
        >>>info@genesiscube.ir
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
