GitHub - antononcube/Python-Regressionizer: Python package with a class that allows pipeline-like specification and execution of regression workflows. · GitHub

main

Branches Tags

Go to file

Code

NameName

Last commit message

Last commit date

Latest commit History 83 Commits
Regressionizer		Regressionizer
docs		docs
examples		examples
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
setup.py		setup.py
View all files

Repository files navigation

More items

Regressionizer

Python package with a class that allows pipeline-like specification and execution of regression workflows.

Extensive guide is given in the Jupyter notebook "Rapid-specification-of-regression-workflows.ipynb" and the corresponding Markdown document "Rapid-specification-of-regression-workflows.md".

Features summary

The class Regressionizer facilitates rapid specifications of regressions workflows.
- To quickly specify:
  - data rescaling and summary
  - regression computations
  - outliers finding
  - conditional Cumulative Distribution Functions (CDFs) reconstruction
  - plotting of data, fits, residual errors, outliers, CDFs
Regressionizer works with data frames, numpy arrays, lists of numbers, and lists of numeric pairs.

Details and arguments

The curves computed with Quantile Regression are called regression quantiles.
Regressionizer has three regression methods:
- quantile_regression
- quantile_regression_fit
- least_squares_fit
The regression quantiles computed with the methods quantile_regression and quantile_regression_fit correspond to probabilities specified with the argument probs.
The methodquantile_regression computes fits using a B-spline functions basis.
- The basis is specified with the arguments knots and order.
- order is 3 by default.
The methods quantile_regession_fit and least_squares_fit use lists of basis functions to fit with specified with the argument funcs.

Workflows flowchart

The following flowchart summarizes the workflows that are supported by Regressionizer:

Usage examples

Import libraries:

from Regressionizer import *
import numpy as np

Generate random data:

np.random.seed(0)
x = np.linspace(0, 2, 300)
y = np.sin(2 * np.pi * x) + np.random.normal(0, 0.4, x.shape)
data = np.column_stack((x, y)

Compute quantile regression for probabilities [0.2, 0.5, 0.8] and make the corresponding plot:

obj = (Regressionizer(data)
       .quantile_regression(knots=8, probs=[0.2, 0.5, 0.8])
       .plot(title="B-splines fit", template="plotly")
       )