Package: featransform
Featransform - Automated Feature Engineering Framework for Supervised Machine LearningFramework ContextualizationTheFeatransformproject constitutes an objective and integrated proposition to automate feature engineering through the integration of various approachs of input pattern recognition known in Machine Learning such as dimensionality reduction, anomaly detection, clustering approaches and datetime feature constrution. This package provides an ensemble of diverse applications of each specific approach, aggregating and generating them all as added feature engineered features based on the original input features.In order to avoid generation of noisy data for predictive consumption, after the engineered features ensemble are concatenated with the original features, a backwards wrapper feature selection also known as backward elimination is implemented to iteratively remove features based on evaluation of relevance, maintaining only valuable columns available for future models performance improvement purposes.The architecture design includes three main sections, these being: data preprocessing, diverse feature engineering ensembles and optimized feature selection validation.This project aims at providing the following application capabilities:General applicability on tabular datasets: The developed feature engineering procedures are applicable on any data table associated with any Supervised ML scopes, based on input data columns to be built up on.Improvement of predictive results: The application of theFeatransformaims at improve the predictive performance of future applied Machine Learning models through added feature construction, increased pattern recognition and optimization of existing input features.Continuos integration: After the train data is fitted, the created object can be saved and implemented in future data with the same structure.Main Development ToolsMajor frameworks used to built this project:PandasSklearnXGBoostOptunaWhere to get itBinary installer for the latest released version is available at the Python Package Index(PyPI).The source code is currently hosted on GitHub at:https://github.com/TsLu1s/FeatransformInstallationTo install this package from Pypi repository run the following command:pip install featransformUsage ExampleFeatransform - Automated Feature Engineering PipelineIn order to be able to apply the automated feature engineeringfeatransformpipeline you need first to import the package.
The following needed step is to load a dataset and define your to be predicted target column name into the variabletarget.
You can customize thefit_engineeringmethod by altering the following running pipeline parameters:validation_split: Division ratio in which the feature engineering methods will be evaluated within the loaded Dataset (range: [0.05, 0.45]).optimize_iters: Number of iterations generated for backwards feature selection optimization.configs: Nested dictionary in which are contained all methods specific parameters configurations. Feel free to customize each method as you see fit (customization example shown bellow);Relevant Note:Although functional,Featransformpipeline is not optimized for big data purposes yet.importpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromfeatransform.pipelineimportFeatransformfromfeatransform.parametersimportconfigurationsimportwarningswarnings.filterwarnings("ignore",category=Warning)# -> For a clean consoledata=pd.read_csv('csv_directory_path',encoding='latin',delimiter=',')# Dataframe Loading Exampletrain,test=train_test_split(data,train_size=0.8)train,test=train.reset_index(drop=True),test.reset_index(drop=True)# -> Required# Load and Customize Parametersconfigs=configurations()print(configs)configs['Unsupervised']['Isolation_Forest']['n_estimators']=300configs['Clustering']['KMeans']['n_clusters']=3configs['DimensionalityReduction']['UMAP']['n_components']=6## Fit Dataft=Featransform(validation_split=0.30,# validation_split:float, optimize_iters:intoptimize_iters=10,configs=configs)ft.fit_engineering(X=train,# X:pd.DataFrame, target:str="Target_Column"target="Target_Column_Name")## Transform Datatrain=atl.transform(X=train)test=atl.transform(X=test)# Export Featransform Metadataimportpickleoutput=open("ft_eng.pkl",'wb')pickle.dump(ft,output)Further ImplementationsFurther automated and customizable feature engineering ensemble methods applications can be checked here:Featransform ExamplesLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactLuis Santos - LinkedIn