Regressionizer 0.1.9
Regression workflows package based on Least Squares Regression and Quantile Regression.
Requires Python: >=3.7
Keywords: regression, quantile, quantile regression, linear, linear regression, workflow

# 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"](https://github.com/antononcube/Python-Regressionizer/blob/main/docs/Rapid-specification-of-regression-workflows.ipynb) 
and the corresponding Markdown document
["Rapid-specification-of-regression-workflows.md"](https://github.com/antononcube/Python-Regressionizer/blob/main/docs/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 method`quantile_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`:

![](https://raw.githubusercontent.com/antononcube/Python-Regressionizer/main/docs/img/Quantile-regression-workflow-extended.jpg)


------

## Usage examples 

Import libraries:

```python
from Regressionizer import *
import numpy as np
```

Generate random data:

```python
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:

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

Show the plot obtained above:

```python
obj.take_value().show()
```

![](https://raw.githubusercontent.com/antononcube/Python-Regressionizer/main/docs/img/random-data-B-spline-rqs.png)

------

## References

### Articles, books

[RK1] Roger Koenker, 
[Quantile Regression](https://books.google.com/books/about/Quantile_Regression.html?id=hdkt7V4NXsgC), 
Cambridge University Press, 2005.

[RK2] Roger Koenker,
["Quantile Regression in R: a vignette"](https://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf),
(2006),
[CRAN](https://cran.r-project.org/).

[AA1] Anton Antonov,
["A monad for Quantile Regression workflows"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-Quantile-Regression-workflows.md),
(2018),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).

### Packages, paclets

[RKp1] Roger Koenker,
[`quantreg`](https://cran.r-project.org/web/packages/quantreg/index.html),
[CRAN](https://cran.r-project.org/).

[AAp1] Anton Antonov,
[Quantile Regression WL paclet](https://github.com/antononcube/WL-QuantileRegression-paclet),
(2014-2023),
[GitHub/antononcube](https://github.com/antononcube).

[AAp2] Anton Antonov,
[Monadic Quantile Regression WL paclet](https://github.com/antononcube/WL-MonadicQuantileRegression-paclet),
(2018-2024),
[GitHub/antononcube](https://github.com/antononcube).

[AAp3] Anton Antonov,
[`QuantileRegression`](https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression),
(2019),
[Wolfram Function Repository](https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression).

### Repositories

[AAr1] Anton Antonov,
[DSL::English::QuantileRegressionWorkflows in Raku](https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows),
(2020),
[GitHub/antononcube](https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows).

### Videos

[AAv1] Anton Antonov,
["Boston useR! QuantileRegression Workflows 2019-04-18"](https://www.youtube.com/watch?v=a_Dk25xarvE),
(2019),
[Anton Antonov at YouTube](https://www.youtube.com/@AAA4Prediction).

[AAv2] Anton Antonov,
["useR! 2020: How to simplify Machine Learning workflows specifications"](https://www.youtube.com/watch?v=b9Uu7gRF5KY),
(2020),
[R Consortium at YouTube](https://www.youtube.com/channel/UC_R5smHVXRYGhZYDJsnXTwg).