Metadata-Version: 2.1
Name: autooptimizer
Version: 0.4.4
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
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
Requires-Dist: sklearn
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: mlxtend

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}	

#Installation:
Github: https://github.com/mrb987/autooptimizer.git
pip install autooptimizer

#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
from autooptimizer.metrics import root_mean_squared_error
from autooptimizer.metrics import root_mean_squared_log_error
from autooptimizer.metrics import root_mean_squared_precentage_error
from autooptimizer.metrics import symmetric_mean_absolute_precentage_error
from autooptimizer.metrics import mean_bias_error
from autooptimizer.metrics import relative_squared_error
from autooptimizer.metrics import root_relative_squared_error
from autooptimizer.metrics import relative_absolute_error
from autooptimizer.metrics import median_absolute_percentage_error
from autooptimizer.metrics import mean_absolute_percentage_error

root_mean_squared_error(true, predicted)
root_mean_squared_log_error(true, predicted)
root_mean_squared_precentage_error(true, predicted)
symmetric_mean_absolute_precentage_error(true, predicted)
mean_bias_error(true, predicted)
relative_squared_error(true, predicted)
root_relative_squared_error(true, predicted)
relative_absolute_error(true, predicted)
median_absolute_percentage_error(true, predicted)
mean_absolute_percentage_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

