kennethleungty / StatsAssume

Automating Assumption Checks for Regression Models (Work in Progress, Currently Paused)

Automating Assumption Checks for Regression Models (WORK IN PROGRESS)

Features • Download • Usage • Motivation • Contributing • Upcoming

Features

StatsAssume automates the assumption checks of regression models (e.g., linear and logistic regression) on your data and displays the results in an elegant dashboard. 

• Automatically detects regression task (and relevant assumption checks) based on the target variable of dataset.

• Automatically executes statistical tests and visual plots of assumption checks relevant to the regression task.

• Generates clear visual output of results in a beautiful dashboard (built on Jupyter-Dash).

• Displays insightful information on assumption concepts and possible fixes for assumption violations.

• Able to automatically encode categorical variables to create dataset suitable for regression modelling (unless specified otherwise).

Download

pip install statsassume

Usage

Quickstart

from statsassume import Check
from statsassume.datasets import load_data

df = load_data('Fish_processed')  # Get toy dataset (pre-processed)

assume = Check(df, target='Weight')  # Initiate Check class and define target variable
assume.report()  # Run assumption checks and generate dashboard report

NOTE: Data should ideally be pre-processed before running StatsAssume assumption checks.

Toy datasets available in StatsAssume can be found HERE

Comprehensive Usage

• While pre-processing should ideally be performed prior, StatsAssume comes with automatic encoding of categorical variables so that we can quickly commence model runs and assumption checks
• Here's how to put the Check class (core object of StatsAssume) to its best use:

df = load_data('Fish')  # Get toy dataset (raw)

assume = Check(df=df, 
               target='Weight',
               task='linear regression',
               predictors=['Height', 'Width', 'Length1', 'Species'],
               keep=True,
               categorical_features=['Species'],
               categorical_encoder='ohe',
               mode='inline')

Attributes

• df: pd.DataFrame
  Dataset (in pandas DataFrame format)

• target: str
  Column name of target (dependent) variable

• task: str
  Type of regression task to be performed. Options include: 'linear regression'(More tasks to come soon). If None specified, task will be automatically determined based on target variable.

• predictors: list
  List of column names of predictor (independent) features. If None specified, all columns other than target will be regarded as predictors

• keep: bool
  If True, variables in predictors list will be kept as predictor variables, and other non-target variables will be dropped. If False, variables in predictors list will be dropped, and other non-target variables will be retained. Default is True.

• categorical_features: list
  List of column names deemed categorical, so that appropriate encoding can be performed. If None specified, the categorical variables will be automatically detected and encoded into numerical format for regression modelling. Default is None.

• categorical_encoding: str
  Type of encoding technique to be performed on categorical variables. Options include: ohe (i.e. one-hot encoding) and ord (i.e. ordinal encoding). Default is ohe.

• mode: str
  Type of display for dashboard report. Options include inline (displayed as output directly in Jupyter notebook), external (displayed in a new full-screen browser tab), or jupyterlab (displayed in separate tab right inside JupyterLab). Default is inline.

Notes

• Only df and target attributes are compulsory

Motivation

• Tedious to perform assumption checks manually
• Lack of rigour and consistency in references and notebooks online

Contributing

1. Have a look at the existing Issues and Pull Requests that you would like to help with.
2. Clone repo and create a new branch: $ git checkout https://github.com/kennethleungty/statsassume -b name_of_new_branch.
3. Make changes and test
4. Submit Pull Request with comprehensive description of changes

If you would like to request a feature or report a bug, please create a GitHub Issue.

See full contribution guide →

Upcoming

• Assumption checks for Logistic Regression (meanwhile, take a look at this article on logistic regression assumptions)