Welcome to Pywedge documentation

What is Pywedge?

Pywedge is a pip installable Python package that intends to,

  1. Make multiple interactive charts in a single line of code, to enable the user to quickly read through the charts and can make informed choices in pre-processing steps

  2. Interactively preprocess the data by taking the user’s preferred choice of pre-processing techniques,

  3. Make a baseline model summary, which can return ten various baseline models & predict the standout test data from selected baseline model.

  4. Interactively select hyperparameters in a widget style tab, track the hyperparameters using MLFlow & predict on standout data.

Pywedge intends to help the user by quickly making charts, preprocessing the data and to rightly point out the best performing baseline model for the given dataset so that the user can spend quality time tuning such a model algorithm.

Installation

pip install pywedge

For JupyterLab, please run the following commands in anaconda prompt to enable required JupyterLab extensions to display interactive chart widget,

conda install -c conda-forge nodejs

jupyter labextension install @jupyter-widgets/jupyterlab-manager

jupyter labextension install jupyterlab-plotly@4.14.1

jupyter labextension install @jupyter-widgets/jupyterlab-manager plotlywidget@4.14.1

Mlflow is not a hard requirement in Pywedge, this is mainly to keep the pywedge light weight for the benefit of trying pywedge in web apps like Heroku. So mlflow has to be separately installed to track the hyperparameters,

pip install mlflow

Please note: Pywedge is available only in Jupyter Notebook & JupyterLab environments

Pywedge Features

  • Makes 8 different types of interactive charts with interactive axis selection widgets

  • Interactive pre-processing & 10 different baseline models

    • Missing values imputation for numeric & categorical columns
    • Standardization
    • Feature importance
    • Class oversampling using SMOTE
    • Computes 10 different baseline models

  • Interactive Hyperparameter tuning & tracking hyperparameters using integrated MLFlow

    • Classification / Regression Hyperparameters tuning

      • Available baseline estimators for interactive hyperparameter tuning as of now, more baseline estimators will be added soon for interactive hyperparameter tunings
      Classification Regression
      Logistic Regression Linear Regression
      Decision Tree Classifier Decision Tree Regressor
      Random Forest Classifier Random Forest Regressor
      AdaBoost Classifier AdaBoost Regressor
      ExtraTrees Classifier ExtraTrees Regressor
      KNN Classifier KNN Regressor