Table of Contents
About The Project
A very elegant and simple library to evaluate models.
This module builds the BarPlot, BoxPlot, CountPlot, DistPlot, HeatMap, PairPlot and ViolinPlot only with one line of code.A folder is created 'Plots' where the pdf files of all the plots are stored.Along with this, a pdf file will be generated 'FinalPlots.pdf' which contains all the plots with which EDA can be performed easily.
This module will evaulate the Classification problems and Regression problems with 12 and 7 algorithms respectively.
The Classification algorithms are KNN,LogisticRegression,DecisionTreeClassifier, RandomForestClassifier, SupportVectorClassifier, QuadraticDiscriminantSnalysis, SGDClassifier, AdaBoost, CalibratedClassifier, MultinomialNB, BernoulliNB, GaussianNB.
The Regression algorithms are LinearRegression, PolynomialRegression, RidgeRegression, LassoRegression, SupportVectorRegressor, GradientBoostingRegression, PLSRegression.
We also have implmented the Adjusted R Squared method as the Regression Metric Evaluation.
In Classification , Highest Accuracy is Highlighted in Yellow colour.
In Regression Model , Least Error is Highlighted in Yellow colour.
Installation
- Clone the repo
git clone https://github.com/Anand-gokul/pyevals.git- Install using pip or pip3
pip3 install pyevals
(or)
pip install pyevals
Usage
import pyevals
# For Exploratory Data Analysis (or) For building the plots
pyevals.BuildPlots(data,CategoricalFeatures,ContinuousFeatures)
'''CategoricalFeatures and the ContinuousFeatures are the lists of the Categorical
and Continuous Features of the dataset respectively. '''
# For Classification
Object = pyevals.build(x_train,x_test,y_train,y_test,'classification')
Object.evaluate()
# For Regression
Object = pyevals.build(x_train,x_test,y_train,y_test,'regression')
Object.evaluate()
Future Work
In this version we are only providing the reports and the plots as many as possible.We are working on improviing the plots for better EDA.We will try to implement hyperparameter optimization techniques to get the better results. We will also try to implement other algorithms in classification and regression soon.
Contact
Sai Gokul Krishna Reddy Talla - @Krish - gokulkrishna.talla@gmail.com
Ananda Datta Sai Phanindra Tangirala - @Anand - tangiralaphanindra@gmail.com
Anirudh Palaparthi - @anirudh8889 - aniruddhapnbb@gmail.com
Project Link: https://github.com/Anand-Gokul/pyevals
