# 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`:
------
## 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()
```
------
## 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).
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"description": "# Regressionizer\n\nPython package with a class that allows pipeline-like specification and execution of regression workflows.\n\nExtensive guide is given in the Jupyter notebook \n[\"Rapid-specification-of-regression-workflows.ipynb\"](https://github.com/antononcube/Python-Regressionizer/blob/main/docs/Rapid-specification-of-regression-workflows.ipynb) \nand the corresponding Markdown document\n[\"Rapid-specification-of-regression-workflows.md\"](https://github.com/antononcube/Python-Regressionizer/blob/main/docs/Rapid-specification-of-regression-workflows.md).\n\n------\n\n## Features summary \n\n- The class `Regressionizer` facilitates rapid specifications of regressions workflows.\n - To quickly specify: \n - data rescaling and summary\n - regression computations\n - outliers finding\n - conditional Cumulative Distribution Functions (CDFs) reconstruction\n - plotting of data, fits, residual errors, outliers, CDFs \n\n- `Regressionizer` works with data frames, numpy arrays, lists of numbers, and lists of numeric pairs.\n\n\n### Details and arguments\n\n- The curves computed with Quantile Regression are called **regression quantiles**.\n\n- `Regressionizer` has three regression methods:\n - `quantile_regression`\n - `quantile_regression_fit`\n - `least_squares_fit`\n \n- The regression quantiles computed with the methods `quantile_regression` and `quantile_regression_fit` \n correspond to probabilities specified with the argument `probs`.\n\n- The method`quantile_regression` computes fits using a B-spline functions basis. \n - The basis is specified with the arguments `knots` and `order`.\n - `order` is 3 by default. \n\n- The methods `quantile_regession_fit` and `least_squares_fit` use lists of basis functions to fit with \n specified with the argument `funcs`.\n\n### Workflows flowchart\n\nThe following flowchart summarizes the workflows that are supported by `Regressionizer`:\n\n\n\n\n------\n\n## Usage examples \n\nImport libraries:\n\n```python\nfrom Regressionizer import *\nimport numpy as np\n```\n\nGenerate random data:\n\n```python\nnp.random.seed(0)\nx = np.linspace(0, 2, 300)\ny = np.sin(2 * np.pi * x) + np.random.normal(0, 0.4, x.shape)\ndata = np.column_stack((x, y)\n```\n\nCompute quantile regression for probabilities `[0.2, 0.5, 0.8]` and make the corresponding plot:\n\n```python\nobj = (Regressionizer(data)\n .quantile_regression(knots=8, probs=[0.2, 0.5, 0.8])\n .plot(title=\"B-splines fit\", template=\"plotly\")\n )\n```\n\nShow the plot obtained above:\n\n```python\nobj.take_value().show()\n```\n\n\n\n------\n\n## References\n\n### Articles, books\n\n[RK1] Roger Koenker, \n[Quantile Regression](https://books.google.com/books/about/Quantile_Regression.html?id=hdkt7V4NXsgC), \nCambridge University Press, 2005.\n\n[RK2] Roger Koenker,\n[\"Quantile Regression in R: a vignette\"](https://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf),\n(2006),\n[CRAN](https://cran.r-project.org/).\n\n[AA1] Anton Antonov,\n[\"A monad for Quantile Regression workflows\"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-Quantile-Regression-workflows.md),\n(2018),\n[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).\n\n### Packages, paclets\n\n[RKp1] Roger Koenker,\n[`quantreg`](https://cran.r-project.org/web/packages/quantreg/index.html),\n[CRAN](https://cran.r-project.org/).\n\n[AAp1] Anton Antonov,\n[Quantile Regression WL paclet](https://github.com/antononcube/WL-QuantileRegression-paclet),\n(2014-2023),\n[GitHub/antononcube](https://github.com/antononcube).\n\n[AAp2] Anton Antonov,\n[Monadic Quantile Regression WL paclet](https://github.com/antononcube/WL-MonadicQuantileRegression-paclet),\n(2018-2024),\n[GitHub/antononcube](https://github.com/antononcube).\n\n[AAp3] Anton Antonov,\n[`QuantileRegression`](https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression),\n(2019),\n[Wolfram Function Repository](https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression).\n\n### Repositories\n\n[AAr1] Anton Antonov,\n[DSL::English::QuantileRegressionWorkflows in Raku](https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows),\n(2020),\n[GitHub/antononcube](https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows).\n\n### Videos\n\n[AAv1] Anton Antonov,\n[\"Boston useR! QuantileRegression Workflows 2019-04-18\"](https://www.youtube.com/watch?v=a_Dk25xarvE),\n(2019),\n[Anton Antonov at YouTube](https://www.youtube.com/@AAA4Prediction).\n\n[AAv2] Anton Antonov,\n[\"useR! 2020: How to simplify Machine Learning workflows specifications\"](https://www.youtube.com/watch?v=b9Uu7gRF5KY),\n(2020),\n[R Consortium at YouTube](https://www.youtube.com/channel/UC_R5smHVXRYGhZYDJsnXTwg).\n",
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