From d577b8499369bf5edc62db060affc646e8022369 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 19:34:17 +0000 Subject: [PATCH 01/22] read the docs --- .readthedocs.yaml | 13 ++++ README.md | 7 ++- docs/Makefile | 20 ++++++ docs/make.bat | 35 +++++++++++ docs/requirements.txt | 2 + docs/source/Copulas.rst | 5 ++ docs/source/Installation.rst | 19 ++++++ docs/source/Misc.rst | 5 ++ docs/source/Multivariate.rst | 5 ++ docs/source/Univariate.rst | 6 ++ docs/source/conf.py | 39 ++++++++++++ docs/source/index.rst | 117 +++++++++++++++++++++++++++++++++++ pyproject.toml | 3 + 13 files changed, 274 insertions(+), 2 deletions(-) create mode 100644 .readthedocs.yaml create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/requirements.txt create mode 100644 docs/source/Copulas.rst create mode 100644 docs/source/Installation.rst create mode 100644 docs/source/Misc.rst create mode 100644 docs/source/Multivariate.rst create mode 100644 docs/source/Univariate.rst create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..188a2df --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,13 @@ +version: 2 + +build: + os: "ubuntu-22.04" + tools: + python: "3.10" + +python: + install: + - requirements: docs/requirements.txt + +sphinx: + configuration: docs/source/conf.py \ No newline at end of file diff --git a/README.md b/README.md index 119c3cf..a724507 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ MIT license   - build   here. +- We are continuing to expand our library, and are open to suggestions for new models to implement. If you have a model you would like to see implemented, please open an issue on our GitHub page. ## Testing All tests are written using pytest and cover all user accessible code. diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..6247f7e --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..53fc1f3 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,2 @@ +sphinx==7.1.2 +sphinx-rtd-theme==1.3.0rc1 diff --git a/docs/source/Copulas.rst b/docs/source/Copulas.rst new file mode 100644 index 0000000..81619a3 --- /dev/null +++ b/docs/source/Copulas.rst @@ -0,0 +1,5 @@ +.. _copulas: + +############## +Copula Models +############## diff --git a/docs/source/Installation.rst b/docs/source/Installation.rst new file mode 100644 index 0000000..2d551e9 --- /dev/null +++ b/docs/source/Installation.rst @@ -0,0 +1,19 @@ +.. _installation: + +Installation +------------ + +To use SklarPy, first install it using pip: + +.. code-block:: text + + pip install sklarpy + +For Developers +-------------- + +If you wish to add your own significant modifications to SklarPy, you can can clone the current repository using: + +.. code-block:: text + + git clone https://github.com/tfm000/sklarpy diff --git a/docs/source/Misc.rst b/docs/source/Misc.rst new file mode 100644 index 0000000..5089f38 --- /dev/null +++ b/docs/source/Misc.rst @@ -0,0 +1,5 @@ +.. _misc: + +#################### +Miscellaneous Tools +#################### diff --git a/docs/source/Multivariate.rst b/docs/source/Multivariate.rst new file mode 100644 index 0000000..372e116 --- /dev/null +++ b/docs/source/Multivariate.rst @@ -0,0 +1,5 @@ +.. _multivariate: + +############################ +Multivariate Distributions +############################ diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst new file mode 100644 index 0000000..03bd91c --- /dev/null +++ b/docs/source/Univariate.rst @@ -0,0 +1,6 @@ +.. _univariate: + +######################### +Univariate Distributions +######################### + diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..4ab9d24 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,39 @@ +# Configuration file for the Sphinx documentation builder. +import os +import sys + +sys.path.insert(0, os.path.abspath("...")) + +# -- Project information + +project = 'SklarPy' +copyright = '2023, Tyler Mitchell' +author = 'Tyler Mitchell' + +release = '1.0.0' +version = '1.0.0' + +# -- General configuration + +extensions = [ + 'sphinx.ext.duration', + 'sphinx.ext.doctest', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', +] + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3/', None), + 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), +} +intersphinx_disabled_domains = ['std'] + +templates_path = ['_templates'] + +# -- Options for HTML output + +html_theme = 'sphinx_rtd_theme' + +# -- Options for EPUB output +epub_show_urls = 'footnote' diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..782d8d7 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,117 @@ +.. image:: https://github.com/tfm000/sklarpy/blob/main/media/logo.png?raw=true + :alt: SklarPy logo + :scale: 60% + :align: center + +.. raw:: html + + + + + + +

+ + MIT license   + + build   + + downloads   + + maintained +

+ +

+ + mac os + + windows +

+ + +SklarPy (pronounced 'Sky-Lar-Pee' or 'Sky-La-Pie') is an open-source software for probability distribution fitting. +It contains useful tools for fitting Copula, Multivariate and Univariate probability distributions. +In addition to over 100 univariate distributions, we implement many multivariate normal mixture distributions and their copulas, including Gaussian, Student-T, Skewed-T and Generalized Hyperbolic distributions. +Named after Sklar's theorem and Abe Sklar, the American mathematician who proved that multivariate cumulative distribution functions can be expressed in terms of copulas and their marginals. + +This library has many different possible use cases, ranging from machine learning to finance. + +Contents +-------- + +.. toctree:: + :maxdepth: 2 + Installation + Univariate + Multivariate + Copulas + Misc + +Why we are better +----------------- +- Unlike other Python implementations of copulas, we implement more than the Gaussian and Archimedean copulas. A full list of our implementated copula models can be found in the documentation, though it includes many normal mean-variance mixture models as well as Archimedean and non-parametric models. +- We allow for easy parameter fitting of both the univariate marginals and the multivariate copula distribution. +- We allow for easy plotting of all our distributions, allowing you to visualize your models. +- We use scipy.stats as a backend for all our univariate models, meaning as scipy expands and improves their model selection, so will ours! +- We provide multivariate and univariate distributions, in addition to our copula models, meaning SklarPy can act as a one-stop-shop for all probability distribution fitting. A full list of our implemented multivariate distributions can be found in the documentation. +- We are continuing to expand our library, and are open to suggestions for new models to implement. If you have a model you would like to see implemented, please open an issue on our GitHub page. + +Example +-------- + +Here we show a quick example of working with SklarPy. +For more information, see the specific documentaion.:: + + import numpy as np + import pandas as pd + import matplotlib.pyplot as plt + from sklarpy.copulas import gh_copula + + # generating random data + n: int = 1000 + obs: np.ndarray = np.full((n, 2), np.nan) + obs[:, 0] = np.random.normal(3,4, size=(n,)) + obs[:, 1] = obs[:, 0] + 0.5 * np.random.normal(3, 5, size=(n,)) + obvs_df: pd.DataFrame = pd.DataFrame(obs, columns=['Process A', 'Process B']) + + # fitting our copula model + fitted_copula = gh_copula.fit(obvs_df) + + # printing our fitted copula parameters + print(fitted_copula.copula_params.to_dict) + + # printing our fitted marginal distributions + print(fitted_copula.mdists) + + # plotting our fit + fitted_copula.pdf_plot(show=False) + fitted_copula.copula_pdf_plot(show=False) + plt.show() + +This outputs: + +.. code-block:: text + + {'lamb': -10.0, 'chi': 4.227038325195731, 'psi': 10.0, + 'loc': array([[0.], [0.]]), + 'shape': array([[1. , 0.84273015], + [0.84273015, 1.]]), + 'gamma': array([[0.99696041], [0.99913161]])} + + {0: lognorm(0.02, -203.22, 206.18), 1: lognorm(0.04, -110.89, 115.4)} + +.. image:: https://github.com/tfm000/sklarpy/blob/main/media/PDF_Gh_PDF_Plot_Plot.png?raw=true + :alt: GH PDF + :scale: 60% + :align: center + +.. image:: https://github.com/tfm000/sklarpy/blob/main/media/Copula_PDF_Gh_Copula_PDF_Plot_Plot.png?raw=true + :alt: GH Copula PDF + :scale: 60% + :align: center diff --git a/pyproject.toml b/pyproject.toml index 20fe848..406b08d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,6 +66,9 @@ dev = [ "pytest==7.1.2", "tox==3.25.1", ] +docs = [ + "sphinx~=4.2.0", +] [project.urls] # Optional "Homepage" = "https://github.com/tfm000/sklarpy" From c1807e818bb9dbe70e611839c6a45cba92220d01 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 20:17:22 +0000 Subject: [PATCH 02/22] adding to docs --- README.md | 3 +++ docs/source/Univariate.rst | 24 ++++++++++++++++++++++++ docs/source/index.rst | 3 +++ 3 files changed, 30 insertions(+) diff --git a/README.md b/README.md index a724507..2cbc243 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,9 @@ build   + + build   downloads   diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index 03bd91c..f730c7a 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -4,3 +4,27 @@ Univariate Distributions ######################### +This SklarPy package contains many different univariate distributions in addition to objects allowing for easy fitting. +With the exception of a handful of distributions, all univariate distribution objects are wrappers of scipy.stats univariate distributions, with added functionalities for plotting, fitting and saving. + +There is also the UnivariateFitter object, which allows for easy fitting of univariate distributions to data and for determining the best / statistically significant distribution(s). + +.. automodule:: sklarpy.univariate + :members: + :exclude-members: UnivariateFitter + + .. autoclass:: UnivariateFitter + :members: + + .. automethod:: __init__ + + .. automethod:: fit + + .. caution:: + + If 'use_processpoolexecutor' is set to True, the UnivariateFitter object will use the ProcessPoolExecutor to parallelize the fitting process. However, if the code is ran outside 'if __name__ == "__main__":', you may receive a runtime error. + + .. automethod:: plot + + .. automethod:: fitted_distributions + diff --git a/docs/source/index.rst b/docs/source/index.rst index 782d8d7..55a20bf 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,6 +17,9 @@ build   + + build   downloads   From 3fe558fb2ec3cb9cb46a341dd020e0c98e1d4758 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 20:32:17 +0000 Subject: [PATCH 03/22] adding to docs --- docs/source/Univariate.rst | 27 +++++++++++++++++++++++++++ docs/source/conf.py | 2 +- 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index f730c7a..b3f8c09 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -6,9 +6,36 @@ Univariate Distributions This SklarPy package contains many different univariate distributions in addition to objects allowing for easy fitting. With the exception of a handful of distributions, all univariate distribution objects are wrappers of scipy.stats univariate distributions, with added functionalities for plotting, fitting and saving. +This means that the distributions available in SklarPy are the same as those available in your installed version of scipy. There is also the UnivariateFitter object, which allows for easy fitting of univariate distributions to data and for determining the best / statistically significant distribution(s). +Why is my interpreter unable to find univariate distributions? +-------------------------------------------------------------- + +If you try:: + + from sklarpy.univariate import normal + +You will likely find that your interpreter flags an error along the lines of "cannot find reference 'normal' in __init__.py" +Do not worry, this is to be expected an a side effect of the dynamic way SklarPy univariate distributions are created from scipy.stats distributions. +At runtime, *your code will work without any errors*! + +But how do I know which distributions are available? +---------------------------------------------------- +Good question! You can use the following code to print out a list of all available univariate distributions:: + + from sklarpy.univariate import distributions_map + print(distributions_map) + +For scipy version 1.11.4 you should get an output along the lines of: + +.. code-block:: text + + {'all': ('ksone', 'kstwo', 'kstwobign', 'alpha', 'anglit', 'arcsine', 'beta', 'betaprime', 'bradford', 'burr', 'burr12', 'fisk', 'cauchy', 'chi', 'chi2', 'cosine', 'dgamma', 'dweibull', 'expon', 'exponnorm', 'exponweib', 'exponpow', 'fatiguelife', 'foldcauchy', 'f', 'foldnorm', 'weibull_min', 'truncweibull_min', 'weibull_max', 'genlogistic', 'genpareto', 'genexpon', 'genextreme', 'gamma', 'erlang', 'gengamma', 'genhalflogistic', 'genhyperbolic', 'gompertz', 'gumbel_r', 'gumbel_l', 'halfcauchy', 'halflogistic', 'halfnorm', 'hypsecant', 'gausshyper', 'invgamma', 'invgauss', 'geninvgauss', 'norminvgauss', 'invweibull', 'johnsonsb', 'johnsonsu', 'laplace', 'laplace_asymmetric', 'levy', 'levy_l', 'logistic', 'loggamma', 'loglaplace', 'lognorm', 'gibrat', 'maxwell', 'mielke', 'kappa4', 'kappa3', 'moyal', 'nakagami', 'ncx2', 'ncf', 'nct', 'pareto', 'lomax', 'pearson3', 'powerlaw', 'powerlognorm', 'powernorm', 'rdist', 'rayleigh', 'loguniform', 'reciprocal', 'rice', 'recipinvgauss', 'semicircular', 'skewcauchy', 'skewnorm', 'trapezoid', 'trapz', 'triang', 'truncexpon', 'truncnorm', 'truncpareto', 'tukeylambda', 'uniform', 'vonmises', 'vonmises_line', 'wald', 'wrapcauchy', 'gennorm', 'halfgennorm', 'crystalball', 'argus', 'studentized_range', 'rel_breitwigner', 'gh', 'gig', 'ig', 'normal', 'student_t', 'gaussian_kde', 'empirical', 'poisson', 'planck', 'discrete_laplace', 'discrete_uniform', 'geometric', 'discrete_empirical'), 'all continuous': ('ksone', 'kstwo', 'kstwobign', 'alpha', 'anglit', 'arcsine', 'beta', 'betaprime', 'bradford', 'burr', 'burr12', 'fisk', 'cauchy', 'chi', 'chi2', 'cosine', 'dgamma', 'dweibull', 'expon', 'exponnorm', 'exponweib', 'exponpow', 'fatiguelife', 'foldcauchy', 'f', 'foldnorm', 'weibull_min', 'truncweibull_min', 'weibull_max', 'genlogistic', 'genpareto', 'genexpon', 'genextreme', 'gamma', 'erlang', 'gengamma', 'genhalflogistic', 'genhyperbolic', 'gompertz', 'gumbel_r', 'gumbel_l', 'halfcauchy', 'halflogistic', 'halfnorm', 'hypsecant', 'gausshyper', 'invgamma', 'invgauss', 'geninvgauss', 'norminvgauss', 'invweibull', 'johnsonsb', 'johnsonsu', 'laplace', 'laplace_asymmetric', 'levy', 'levy_l', 'logistic', 'loggamma', 'loglaplace', 'lognorm', 'gibrat', 'maxwell', 'mielke', 'kappa4', 'kappa3', 'moyal', 'nakagami', 'ncx2', 'ncf', 'nct', 'pareto', 'lomax', 'pearson3', 'powerlaw', 'powerlognorm', 'powernorm', 'rdist', 'rayleigh', 'loguniform', 'reciprocal', 'rice', 'recipinvgauss', 'semicircular', 'skewcauchy', 'skewnorm', 'trapezoid', 'trapz', 'triang', 'truncexpon', 'truncnorm', 'truncpareto', 'tukeylambda', 'uniform', 'vonmises', 'vonmises_line', 'wald', 'wrapcauchy', 'gennorm', 'halfgennorm', 'crystalball', 'argus', 'studentized_range', 'rel_breitwigner', 'gh', 'gig', 'ig', 'normal', 'student_t', 'gaussian_kde', 'empirical'), 'all discrete': ('poisson', 'planck', 'discrete_laplace', 'discrete_uniform', 'geometric', 'discrete_empirical'), 'all common': ('cauchy', 'chi2', 'expon', 'gamma', 'lognorm', 'powerlaw', 'rayleigh', 'uniform', 'discrete_laplace', 'discrete_uniform', 'geometric', 'poisson'), 'all multimodal': ('arcsine', 'beta'), 'all parametric': ('ksone', 'kstwo', 'kstwobign', 'alpha', 'anglit', 'arcsine', 'beta', 'betaprime', 'bradford', 'burr', 'burr12', 'fisk', 'cauchy', 'chi', 'chi2', 'cosine', 'dgamma', 'dweibull', 'expon', 'exponnorm', 'exponweib', 'exponpow', 'fatiguelife', 'foldcauchy', 'f', 'foldnorm', 'weibull_min', 'truncweibull_min', 'weibull_max', 'genlogistic', 'genpareto', 'genexpon', 'genextreme', 'gamma', 'erlang', 'gengamma', 'genhalflogistic', 'genhyperbolic', 'gompertz', 'gumbel_r', 'gumbel_l', 'halfcauchy', 'halflogistic', 'halfnorm', 'hypsecant', 'gausshyper', 'invgamma', 'invgauss', 'geninvgauss', 'norminvgauss', 'invweibull', 'johnsonsb', 'johnsonsu', 'laplace', 'laplace_asymmetric', 'levy', 'levy_l', 'logistic', 'loggamma', 'loglaplace', 'lognorm', 'gibrat', 'maxwell', 'mielke', 'kappa4', 'kappa3', 'moyal', 'nakagami', 'ncx2', 'ncf', 'nct', 'pareto', 'lomax', 'pearson3', 'powerlaw', 'powerlognorm', 'powernorm', 'rdist', 'rayleigh', 'loguniform', 'reciprocal', 'rice', 'recipinvgauss', 'semicircular', 'skewcauchy', 'skewnorm', 'trapezoid', 'trapz', 'triang', 'truncexpon', 'truncnorm', 'truncpareto', 'tukeylambda', 'uniform', 'vonmises', 'vonmises_line', 'wald', 'wrapcauchy', 'gennorm', 'halfgennorm', 'crystalball', 'argus', 'studentized_range', 'rel_breitwigner', 'gh', 'gig', 'ig', 'normal', 'student_t', 'poisson', 'planck', 'discrete_laplace', 'discrete_uniform', 'geometric'), 'all numerical': ('gaussian_kde', 'empirical', 'discrete_empirical'), 'all continuous parametric': ('ksone', 'kstwo', 'kstwobign', 'alpha', 'anglit', 'arcsine', 'beta', 'betaprime', 'bradford', 'burr', 'burr12', 'fisk', 'cauchy', 'chi', 'chi2', 'cosine', 'dgamma', 'dweibull', 'expon', 'exponnorm', 'exponweib', 'exponpow', 'fatiguelife', 'foldcauchy', 'f', 'foldnorm', 'weibull_min', 'truncweibull_min', 'weibull_max', 'genlogistic', 'genpareto', 'genexpon', 'genextreme', 'gamma', 'erlang', 'gengamma', 'genhalflogistic', 'genhyperbolic', 'gompertz', 'gumbel_r', 'gumbel_l', 'halfcauchy', 'halflogistic', 'halfnorm', 'hypsecant', 'gausshyper', 'invgamma', 'invgauss', 'geninvgauss', 'norminvgauss', 'invweibull', 'johnsonsb', 'johnsonsu', 'laplace', 'laplace_asymmetric', 'levy', 'levy_l', 'logistic', 'loggamma', 'loglaplace', 'lognorm', 'gibrat', 'maxwell', 'mielke', 'kappa4', 'kappa3', 'moyal', 'nakagami', 'ncx2', 'ncf', 'nct', 'pareto', 'lomax', 'pearson3', 'powerlaw', 'powerlognorm', 'powernorm', 'rdist', 'rayleigh', 'loguniform', 'reciprocal', 'rice', 'recipinvgauss', 'semicircular', 'skewcauchy', 'skewnorm', 'trapezoid', 'trapz', 'triang', 'truncexpon', 'truncnorm', 'truncpareto', 'tukeylambda', 'uniform', 'vonmises', 'vonmises_line', 'wald', 'wrapcauchy', 'gennorm', 'halfgennorm', 'crystalball', 'argus', 'studentized_range', 'rel_breitwigner', 'gh', 'gig', 'ig', 'normal', 'student_t'), 'all discrete parametric': ('poisson', 'planck', 'discrete_laplace', 'discrete_uniform', 'geometric'), 'all continuous numerical': ('gaussian_kde', 'empirical'), 'all discrete numerical': ('discrete_empirical',), 'common continuous': ('cauchy', 'chi2', 'expon', 'gamma', 'lognorm', 'powerlaw', 'rayleigh', 'uniform'), 'common discrete': ('discrete_laplace', 'discrete_uniform', 'geometric', 'poisson'), 'continuous multimodal': ('arcsine', 'beta'), 'discrete multimodal': ()} + +So you have a lot to choose from! + .. automodule:: sklarpy.univariate :members: :exclude-members: UnivariateFitter diff --git a/docs/source/conf.py b/docs/source/conf.py index 4ab9d24..750fb68 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -2,7 +2,7 @@ import os import sys -sys.path.insert(0, os.path.abspath("...")) +sys.path.insert(0, os.path.abspath("..")) # -- Project information From 544fdb78494fac6b537eeeb9a68db52759352c33 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 20:53:33 +0000 Subject: [PATCH 04/22] adding to docs --- README.md | 2 ++ docs/source/Univariate.rst | 12 ++++++++++-- docs/source/conf.py | 2 +- 3 files changed, 13 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 2cbc243..29812cd 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,8 @@ Named after Sklar's theorem and Abe Sklar, the American mathematician who proved This library has many different possible use cases, ranging from machine learning to finance. +## Read the Docs +Official SklarPy documentation can be found here. ## Table of contents diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index b3f8c09..9bba1ac 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -17,7 +17,7 @@ If you try:: from sklarpy.univariate import normal -You will likely find that your interpreter flags an error along the lines of "cannot find reference 'normal' in __init__.py" +You will likely find that your interpreter flags an error along the lines of "cannot find reference 'normal' in __init__.py". Do not worry, this is to be expected an a side effect of the dynamic way SklarPy univariate distributions are created from scipy.stats distributions. At runtime, *your code will work without any errors*! @@ -36,7 +36,7 @@ For scipy version 1.11.4 you should get an output along the lines of: So you have a lot to choose from! -.. automodule:: sklarpy.univariate +.. automodule:: sklarpy.univariate.univariate_fitter :members: :exclude-members: UnivariateFitter @@ -51,7 +51,15 @@ So you have a lot to choose from! If 'use_processpoolexecutor' is set to True, the UnivariateFitter object will use the ProcessPoolExecutor to parallelize the fitting process. However, if the code is ran outside 'if __name__ == "__main__":', you may receive a runtime error. + .. automethod:: get_summary + + .. automethod:: get_best + .. automethod:: plot .. automethod:: fitted_distributions + +.. automodule:: sklarpy.univariate._prefit + :members: + :exclude-members: PreFitUnivariateBase, PreFitNumericalUnivariateBase diff --git a/docs/source/conf.py b/docs/source/conf.py index 750fb68..4ab9d24 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -2,7 +2,7 @@ import os import sys -sys.path.insert(0, os.path.abspath("..")) +sys.path.insert(0, os.path.abspath("...")) # -- Project information From c4eda6aefeb456fb99f90223855b7dc3e127d9a7 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 20:56:44 +0000 Subject: [PATCH 05/22] adding to docs --- docs/source/Univariate.rst | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index 9bba1ac..f2a9ecf 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -60,6 +60,11 @@ So you have a lot to choose from! .. automethod:: fitted_distributions -.. automodule:: sklarpy.univariate._prefit +.. automodule:: sklarpy.univariate._prefit_dists :members: :exclude-members: PreFitUnivariateBase, PreFitNumericalUnivariateBase + + +.. automodule:: sklarpy.univariate._fitted_dists + :members: + :exclude-members: FittedUnivariateBase \ No newline at end of file From 131d6ef21f78eee54e543a3b185cf7bb7199594b Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 21:13:20 +0000 Subject: [PATCH 06/22] adding to docs --- docs/source/Univariate.rst | 87 +++++++++++++++++++++++++++++++++++++- 1 file changed, 86 insertions(+), 1 deletion(-) diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index f2a9ecf..b832301 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -67,4 +67,89 @@ So you have a lot to choose from! .. automodule:: sklarpy.univariate._fitted_dists :members: - :exclude-members: FittedUnivariateBase \ No newline at end of file + :exclude-members: FittedUnivariateBase + +Continuous Example +--------------------- +Here we use the normal and gamma distributions, though all methods and attributes are generalized.:: + + import numpy as np + import pandas as pd + + # generating random variables + from sklarpy.univariate import normal + + num_generate: int = 100 + + # generating a 1d array of N(1, 1) random variables + normal_rvs1: np.ndarray = normal.rvs((num_generate,), (1, 1)) + # generating a 1d array of N(2, 3) random variables + normal_rvs2: np.ndarray = normal.rvs((num_generate,), (0, 3)) + rvs = normal_rvs1 * normal_rvs2 + + # fitting a gamma distribution to our product of normal random variables + from sklarpy.univariate import gamma + + fitted_gamma = gamma.fit(rvs) + + # we can easily retrieve the fitted parameters + fitted_gamma_params: tuple = fitted_gamma.params + print(fitted_gamma_params) + +.. code-block:: text: + + (9754.44976841112, -411.8704014945831, 0.042211986922603084) + +We can also print a summary of our fit:: + + summary: pd.DataFrame = fitted_gamma.summary + print(summary) + +.. code-block:: text: + + summary + Parametric/Non-Parametric Parametric + Discrete/Continuous continuous + Distribution gamma + #Params 3 + param0 9754.449768 + param1 -411.870401 + param2 0.042212 + Support (-411.8704014945831, inf) + Fitted Domain (-20.13664960054484, 17.86802768972715) + Cramér-von Mises statistic 3.411862 + Cramér-von Mises p-value 0.0 + Cramér-von Mises @ 10% False + Cramér-von Mises @ 5% False + Cramér-von Mises @ 1% False + Kolmogorov-Smirnov statistic 0.094371 + Kolmogorov-Smirnov p-value 0.0 + Kolmogorov-Smirnov @ 10% False + Kolmogorov-Smirnov @ 5% False + Kolmogorov-Smirnov @ 1% False + Likelihood 0.0 + Log-Likelihood -2846.513514 + AIC 5699.027028 + BIC 5713.750294 + Sum of Squared Error 12.319097 + #Fitted Data Points 1000 + +And plot our fitted distribution:: + + fitted_gamma.plot() + +.. image:: media/univariate_continuous_example_figure1.png + :alt: gamma plot + :align: center + +And save:: + + fitted_gamma.save() + + +We can then easily reload our saved model:: + + from sklarpy import load + + loaded_fitted_gamma = load('gamma.pickle') + From e81562e2e9f7810529e88bff10803560a447a98f Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 21:17:37 +0000 Subject: [PATCH 07/22] adding to docs --- docs/source/Univariate.rst | 2 +- .../univariate_continuous_example_figure1.png | Bin 0 -> 106320 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 media/univariate_continuous_example_figure1.png diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index b832301..04f9e17 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -79,7 +79,7 @@ Here we use the normal and gamma distributions, though all methods and attribute # generating random variables from sklarpy.univariate 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zFj4Ys{k@}i;zEJgK(wLWmaHHQP7Eb>3waxJ0*nK)F_#vKqn}NEO0s{iz z9E%~=?n$%~)}Z83pTGjx0GmbadmDsN6({!)U$X_yIf;@Tm#qY_{K@(HGuHARIQu^+ Date: Tue, 28 Nov 2023 21:22:37 +0000 Subject: [PATCH 08/22] adding to docs --- docs/source/Univariate.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index 04f9e17..6626108 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -138,7 +138,7 @@ And plot our fitted distribution:: fitted_gamma.plot() -.. image:: media/univariate_continuous_example_figure1.png +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/univariate_continuous_example_figure1.png?raw=true :alt: gamma plot :align: center From 48fe89e08b495651e3c85b68de0b66f7a7c0c0a5 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 21:47:56 +0000 Subject: [PATCH 09/22] changing eps --- sklarpy/univariate/_fitted_dists.py | 2 +- sklarpy/univariate/_prefit_dists.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/sklarpy/univariate/_fitted_dists.py b/sklarpy/univariate/_fitted_dists.py index 05866ab..30119e1 100644 --- a/sklarpy/univariate/_fitted_dists.py +++ b/sklarpy/univariate/_fitted_dists.py @@ -393,7 +393,7 @@ def plot(self, xrange: np.ndarray = None, include_empirical: bool = True, "no empirical data to display.") # getting xrange and qrange - eps: float = 10 ** -4 + eps: float = 0.05 prob_bounds: tuple = (eps, 1 - eps) if xrange is None: if not (isinstance(num_to_plot, int) and num_to_plot >= 1): diff --git a/sklarpy/univariate/_prefit_dists.py b/sklarpy/univariate/_prefit_dists.py index addac3a..29d4aad 100644 --- a/sklarpy/univariate/_prefit_dists.py +++ b/sklarpy/univariate/_prefit_dists.py @@ -598,7 +598,7 @@ def plot(self, params: tuple, xrange: np.ndarray = None, "show are all boolean.") # getting xrange and qrange - eps: float = 10 ** -4 + eps: float = 0.05 prob_bounds: tuple = (eps, 1 - eps) if xrange is None: if not (isinstance(num_to_plot, int) and num_to_plot >= 1): From 431198ef12eed410f01d32e65fdc0e25b55cc641 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 21:48:34 +0000 Subject: [PATCH 10/22] adding to docs --- docs/source/Univariate.rst | 113 ++++++++++++++++++ .../univariate_examples/discrete_example.py | 2 +- .../univariate_fitter_example.py | 2 +- media/univariate_discrete_example_figure1.png | Bin 0 -> 96705 bytes media/univariate_fitter_example_figure1.png | Bin 0 -> 100719 bytes 5 files changed, 115 insertions(+), 2 deletions(-) create mode 100644 media/univariate_discrete_example_figure1.png create mode 100644 media/univariate_fitter_example_figure1.png diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index 6626108..c70d86f 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -153,3 +153,116 @@ We can then easily reload our saved model:: loaded_fitted_gamma = load('gamma.pickle') + + +Discrete Example +--------------------- +Here we use the poisson distribution, though all methods and attributes are generalized. +We see this works in exactly the same way as continuous distributions.:: + + import numpy as np + import pandas as pd + + # generating random variables + from sklarpy.univariate import poisson + + num_generate: int = 10000 + poisson_rvs: np.ndarray = poisson.rvs((num_generate, ), (4,)) + rvs_df: pd.DataFrame = pd.DataFrame(poisson_rvs, columns=['rvs'], dtype=int) + + # fitting a poisson distribution to a dataframe of rvs + fitted_poisson = poisson.fit(rvs_df) + + # we can easily retrieve the fitted parameters + fitted_poisson_params: tuple = fitted_poisson.params + print(fitted_poisson_params) + +.. code-block:: text: + + (3.992,) + +We can also print a summary of our fit:: + + summary: pd.DataFrame = fitted_poisson.summary + print(summary) + +.. code-block:: text: + + summary + Parametric/Non-Parametric Parametric + Discrete/Continuous discrete + Distribution poisson + #Params 1 + param0 3.985 + Support (0, inf) + Fitted Domain (0, 12) + chi-square statistic 7.059903 + chi-square p-value 1.0 + chi-square @ 10% True + chi-square @ 5% True + chi-square @ 1% True + Likelihood 0.0 + Log-Likelihood -2100.955867 + AIC 4203.911734 + BIC 4208.819489 + Sum of Squared Error 0.044802 + #Fitted Data Points 1000 + +And plot our fitted distribution:: + + fitted_poisson.plot() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/univariate_discrete_example_figure1.png?raw=true + :alt: poisson plot + :align: center + +And save:: + + fitted_poisson.save() + + +We can then easily reload our saved model:: + + from sklarpy import load + + loaded_fitted_poisson = load('poisson.pickle') + +UnivariateFitter Example +------------------------- +Here we use the UnivariateFitter object to fit a distribution to a dataset.:: + + import numpy as np + + # generating random variables + from sklarpy.univariate import normal + + num_generate: int = 10000 + # generating a 1d array of N(1, 1) random variables + normal_rvs1: np.ndarray = normal.rvs((num_generate,), (1, 1)) + # generating a 1d array of N(2, 3) random variables + normal_rvs2: np.ndarray = normal.rvs((num_generate,), (0, 3)) + rvs = normal_rvs1 * normal_rvs2 + + # applying UnivariateFitter to our product of normal random variables + from sklarpy.univariate import UnivariateFitter + + ufitter: UnivariateFitter = UnivariateFitter(rvs) + ufitter.fit() + + # finding our best fit + best_fit = ufitter.get_best(significant=False) + best_fit.plot() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/univariate_fitter_example_figure1.png?raw=true + :alt: poisson plot + :align: center + +We can also save our UnivariateFitter object:: + + ufitter.save() + +We can then easily reload this:: + from sklarpy import load + + loaded_ufitter = load('UnivariateFitter.pickle') + loaded_best_fit = loaded_ufitter.get_best(significant=False) diff --git a/examples/univariate_examples/discrete_example.py b/examples/univariate_examples/discrete_example.py index e29b1a3..409cc8b 100644 --- a/examples/univariate_examples/discrete_example.py +++ b/examples/univariate_examples/discrete_example.py @@ -8,7 +8,7 @@ # generating random variables from sklarpy.univariate import poisson -num_generate: int = 10000 +num_generate: int = 100 poisson_rvs: np.ndarray = poisson.rvs((num_generate, ), (4,)) rvs_df: pd.DataFrame = pd.DataFrame(poisson_rvs, columns=['rvs'], dtype=int) diff --git a/examples/univariate_examples/univariate_fitter_example.py b/examples/univariate_examples/univariate_fitter_example.py index 9b88fa1..9ceb4c4 100644 --- a/examples/univariate_examples/univariate_fitter_example.py +++ b/examples/univariate_examples/univariate_fitter_example.py @@ -5,7 +5,7 @@ # generating random variables from sklarpy.univariate import normal -num_generate: int = 10000 +num_generate: int = 1000 # generating a 1d array of N(1, 1) random variables normal_rvs1: np.ndarray = normal.rvs((num_generate,), 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ax[3].plot(ppf_values, empirical_ppf_values, color=color, alpha=alpha, label=self.name) + ax[3].set_xlim([ppf_values.min(), ppf_values.max()]) # plotting distribution ax[0].plot(xrange, self.pdf(xrange), color=color, From 28a7cbc9b1fe342a266f73b7a6767e998aa36f60 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 22:02:15 +0000 Subject: [PATCH 12/22] adding to docs --- .../univariate_continuous_example_figure1.png | Bin 106320 -> 101624 bytes media/univariate_discrete_example_figure1.png | Bin 96705 -> 101565 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/media/univariate_continuous_example_figure1.png b/media/univariate_continuous_example_figure1.png index 25547deb33d2373c11606435ffeb578431b561db..fa8310efe5f8a7ade15f33f5967a9b05e7b701ce 100644 GIT binary patch literal 101624 zcmc$`XINF+5+%Ao5k-NcfFcqMo17HMSy5q=C4&M7C1=SQOemtD8v&6lIZMvLfCxwi zk&KcvDmivdJm>SK^PjBvggbz7nFQ@B>VU$Mbe?+NLDHa$JhT*SV(r}BK9dPwh 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z8E9LV-o1UQwV^fp{P(BpBQwzddj01+)r)5M4*b!xTDw5C{#;u>gGT3`qdwmLRvV+D zt|?n>wMG~$vfFafcFIKki$*1Jj(e|K(c3b)z#-F@D4k>w9Z`K(3h%{4?yR}0Ip?}Te1tF2-*i3D`8f% z+x1_69pdf1HK3%kvxSbIQKIgMBqxL5QxjnFS6p(Lks4k$cc)RBBL#Qjs|mElSLf2D zi`HJ?AMnt!vNDIR@vTw2);Hhld!!i4FZpWI7y`?Ng@9?nKz^N!m2};rgYtOLkw2PQ zSghdBLo{HhtD`qK(sP*{1LqZn0Hk8D^o^B$RQ{q+Cd&5g*~6&e(F78Wpn~&C`$GI= z-?}vxb<2gO5~IJEG7KB$;{i(JcXfunD13noxhxuts(D;d+pevfu@kAdh0x^Zru-xj zHHS)a(PLCUR?CLPunM3)6Hd>Kg^v-}BR$f@CCp&~;4;HrbgX{yVnEyoH(C)CKt-p5 z$2ET6J!2shCCu|+uo1C}pEb)oY}mHMF0Hu|bO<>?x}5afWbdtQCUsur4qC7VxKF^| zNqYyGuoq}`iq0(#7RN%d7f@xTp0&Js9?i+48WHlxOuCZ6gMRuXKM5_Izb From b6aabe290ce4166dae32edc795bd1078e42058eb Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 22:19:41 +0000 Subject: [PATCH 13/22] adding to docs --- docs/source/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 4ab9d24..ec43217 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -21,6 +21,8 @@ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', + 'sphinx.ext.napoleon', + 'sphinx.ext.viewcode', ] intersphinx_mapping = { From 63559b1a68d3458a5d6142ed6bf29251bc116918 Mon Sep 17 00:00:00 2001 From: Tyler Date: Tue, 28 Nov 2023 22:31:26 +0000 Subject: [PATCH 14/22] adding to docs --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index ec43217..6dbbbe7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -2,7 +2,7 @@ import os import sys -sys.path.insert(0, os.path.abspath("...")) +sys.path.insert(0, os.path.abspath(os.path.join('..', '..'))) # -- Project information From 97a121eb12c620a5c2ccf9a570a048540acba06d Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 00:28:40 +0000 Subject: [PATCH 15/22] adding to docs --- docs/source/Multivariate.rst | 107 ++++++++++++++++++ docs/source/Univariate.rst | 94 ++++++++++++++- docs/source/mvt_table.csv | 13 +++ .../multivariate_example.py | 10 +- ...C_CDF_Mvt_Shyperbolic_MC_CDF_Plot_Plot.png | Bin 0 -> 194660 bytes media/PDF_Mvt_Shyperbolic_PDF_Plot_Plot.png | Bin 0 -> 173347 bytes media/mvt_shyperbolic_marginal_pair_plot.png | Bin 0 -> 104849 bytes 7 files changed, 214 insertions(+), 10 deletions(-) create mode 100644 docs/source/mvt_table.csv create mode 100644 media/MC_CDF_Mvt_Shyperbolic_MC_CDF_Plot_Plot.png create mode 100644 media/PDF_Mvt_Shyperbolic_PDF_Plot_Plot.png create mode 100644 media/mvt_shyperbolic_marginal_pair_plot.png diff --git a/docs/source/Multivariate.rst b/docs/source/Multivariate.rst index 372e116..debcc34 100644 --- a/docs/source/Multivariate.rst +++ b/docs/source/Multivariate.rst @@ -3,3 +3,110 @@ ############################ Multivariate Distributions ############################ + +This SklarPy package contains many different multivariate distributions. +Unlike univariate distribution, these are not wrappers of scipy objects (with the exceptions of mvt_normal and mvt_student_t). + +All implemented multivariate distributions are able to be fitted to both multivariate numpy and pandas data and contain easy saving and plotting methods. + +What multivariate distributions are implemented? +------------------------------------------------ +Currently, the following multivariate distributions are implemented: + +.. csv-table:: Multivariate Distributions + :file: mvt_table.csv + :header-rows: 1 + +Multivariate Example +--------------------- +Here we use the multivariate normal and multivariate symmetric hyperbolic distributions, though all methods and attributes are generalized.:: + + import numpy as np + import pandas as pd + import matplotlib.pyplot as plt + + # specifying the parameters of the multivariate normal distribution we are + # sampling from + my_mu: np.ndarray = np.array([33, 44], dtype=float) + my_corr: np.ndarray = np.array([[1, 0.7], [0.7, 1]], dtype=float) + my_sig: np.ndarray = np.array([1.3, 2.5]) + my_cov: np.ndarray = np.diag(my_sig) @ my_corr @ np.diag(my_sig) + my_mvn_params: tuple = (my_mu, my_cov) + + # generating multivariate random normal variables + from sklarpy.multivariate import mvt_normal + + rvs: np.ndarray = mvt_normal.rvs(1000, my_mvn_params) + rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Wife Age', 'Husband Age'], + dtype=float) + + # fitting a symmetric hyperbolic dist to our generated data using + # Maximum Likelihood Estimation + from sklarpy.multivariate import mvt_shyperbolic + + fitted_msh = mvt_shyperbolic.fit(rvs_df, method='mle', show_progress=True) + + # printing our fitted parameters + print(fitted_msh.params.to_dict) + print(fitted_msh.params.cov) + + +.. code-block:: text + + {'chi': 6.817911964473556, 'psi': 10.0, 'loc': array([[32.99012429], + [43.91822886]]), 'shape': array([[1.72408489, 2.27711492], + [2.27711492, 6.27443288]])} + + [[1.78702958 2.36025021] + [2.36025021 6.50350643]] + +Printing a summary of our fit:: + + print(fitted_msh.summary()) + +.. code-block:: text + + summary + Distribution mvt_shyperbolic + #Variables 2 + #Params 4 + #Scalar Params 7 + Converged True + Likelihood 0.0 + Log-Likelihood -3664.49604 + AIC 7342.99208 + BIC 7377.346367 + #Fitted Data Points 1000 + +Plotting our fitted distribution:: + + fitted_msh.pdf_plot(show=False) + fitted_msh.mc_cdf_plot(show=False) + fitted_msh.marginal_pairplot(show=False) + plt.show() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/PDF_Mvt_Shyperbolic_PDF_Plot_Plot.png?raw=true + :alt: Symmetric Hyperbolic PDF + :scale: 60% + :align: center + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/MC_CDF_Mvt_Shyperbolic_MC_CDF_Plot_Plot.png?raw=true + :alt: Symmetric Hyperbolic PDF + :scale: 60% + :align: center + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/mvt_shyperbolic_marginal_pair_plot.png?raw=true + :alt: Symmetric Hyperbolic PDF + :scale: 60% + :align: center + +Saving our fitted parameters:: + + fitted_msh.params.save() + +Reloading and fitting to another distribution of the same type:: + + from sklarpy import load + + loaded_msh_params = load('mvt_shyperbolic.pickle') + param_fitted_msh = mvt_shyperbolic.fit(params=loaded_msh_params) diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index c70d86f..a630ae5 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -36,6 +36,41 @@ For scipy version 1.11.4 you should get an output along the lines of: So you have a lot to choose from! +PreFitUnivariateBase +--------------------- +This class and its subclasses contain the following methods / functions: + +- pdf (probability density function) +- cdf (cumulative density function) +- ppf (percent point function / cumulative inverse function) +- support +- ppf_approx (approximate ppf) +- cdf_approx (approximate cdf) +- rvs (random variate generator / sampler) +- logpdf (log of the probability density function) +- likelihood (likelihood function) +- loglikelihood (log of the likelihood function) +- aic (Akaike information criterion) +- bic (Bayesian information criterion) +- sse (Sum of squared errors) +- gof (goodness of fit) +- plot (plotting) +- fit (fitting the distribution to data) + +Many / all of these methods take params as an argument. This is a tuple containing the parameters of the associated scipy.stats distribution object. +ppf_approx and cdf_approx are approximations of the ppf and cdf functions respectively, which may be useful for distributions where the cdf and therefore ppf functions require numerical integration to evaluate. + +FittedUnivariateBase +--------------------- +This class is the fitted version of PreFitUnivariateBase's subclasses. +It implements the same methods as PreFitUnivariateBase, but does not require params as an argument in addition to the following: + +- summary (summary of the distribution fit) +- params (the fitted parameters) +- fitted domain (the domain over which the distribution is fitted) +- fitted_num_data_points (the number of data points used to fit the distribution) +- save (save the fitted distribution to a pickle file) + .. automodule:: sklarpy.univariate.univariate_fitter :members: :exclude-members: UnivariateFitter @@ -96,7 +131,7 @@ Here we use the normal and gamma distributions, though all methods and attribute fitted_gamma_params: tuple = fitted_gamma.params print(fitted_gamma_params) -.. code-block:: text: +.. code-block:: text (9754.44976841112, -411.8704014945831, 0.042211986922603084) @@ -105,7 +140,7 @@ We can also print a summary of our fit:: summary: pd.DataFrame = fitted_gamma.summary print(summary) -.. code-block:: text: +.. code-block:: text summary Parametric/Non-Parametric Parametric @@ -177,7 +212,7 @@ We see this works in exactly the same way as continuous distributions.:: fitted_poisson_params: tuple = fitted_poisson.params print(fitted_poisson_params) -.. code-block:: text: +.. code-block:: text (3.992,) @@ -186,7 +221,7 @@ We can also print a summary of our fit:: summary: pd.DataFrame = fitted_poisson.summary print(summary) -.. code-block:: text: +.. code-block:: text summary Parametric/Non-Parametric Parametric @@ -249,10 +284,58 @@ Here we use the UnivariateFitter object to fit a distribution to a dataset.:: ufitter: UnivariateFitter = UnivariateFitter(rvs) ufitter.fit() - # finding our best fit + # printing out the summary of our fits + from sklarpy import print_full + print_full() + + print(ufitter.get_summary()) + +.. code-block:: text + + Parametric/Non-Parametric Discrete/Continuous Distribution #Params param0 param1 param2 Support Fitted Domain Cramér-von Mises statistic Cramér-von Mises p-value Cramér-von Mises @ 10% Cramér-von Mises @ 5% Cramér-von Mises @ 1% Kolmogorov-Smirnov statistic Kolmogorov-Smirnov p-value Kolmogorov-Smirnov @ 10% Kolmogorov-Smirnov @ 5% Kolmogorov-Smirnov @ 1% Likelihood Log-Likelihood AIC BIC Sum of Squared Error #Fitted Data Points + chi2 Parametric continuous chi2 3 448.683161 -68.423622 0.15222 (-68.42362151895298, inf) (-24.241200503425766, 21.971575538054054) 3.955007 0.0 False False False 0.099469 0.0 False False False 0.0 -2916.834582 5839.669164 5854.39243 12.84073 1000 + powerlaw Parametric continuous powerlaw 3 1.485383 -24.284621 46.256197 (-24.28462141839885, 21.97157553805406) (-24.241200503425766, 21.971575538054054) 53.515366 0.0 False False False 0.393459 0.0 False False False 0.0 -3765.295723 7536.591446 7551.314712 23.1246 1000 + cauchy Parametric continuous cauchy 2 -0.141171 1.744522 NaN (-inf, inf) (-24.241200503425766, 21.971575538054054) 0.223919 0.225566 True True True 0.03747 0.117619 True True True 0.0 -2848.628202 5701.256403 5711.071914 7.057125 1000 + expon Parametric continuous expon 2 -24.241201 24.121323 NaN (-24.241200503425766, inf) (-24.241200503425766, 21.971575538054054) 68.507136 0.0 False False False 0.465333 0.0 False False False 0.0 -4183.09624 8370.19248 8380.007991 24.962541 1000 + lognorm Parametric continuous lognorm 3 0.024195 -185.928209 185.754474 (-185.92820884247777, inf) (-24.241200503425766, 21.971575538054054) 3.726801 0.0 False False False 0.093801 0.0 False False False 0.0 -2910.878606 5827.757211 5842.480477 12.702458 1000 + rayleigh Parametric continuous rayleigh 2 -24.268255 17.360527 NaN (-24.268254515672, inf) (-24.241200503425766, 21.971575538054054) 45.036613 0.0 False False False 0.364332 0.0 False False False 0.0 -3548.608918 7101.217836 7111.033346 21.635708 1000 + gamma Parametric continuous gamma 3 614.186953 -110.593183 0.179857 (-110.5931825074225, inf) (-24.241200503425766, 21.971575538054054) 3.612011 0.0 False False False 0.094024 0.0 False False False 0.0 -2911.657958 5829.315916 5844.039182 12.618159 1000 + uniform Parametric continuous uniform 2 -24.241201 46.212776 NaN (-24.241200503425766, 21.971575538054054) (-24.241200503425766, 21.971575538054054) 43.325309 0.0 False False False 0.328626 0.0 False False False 0.0 -3833.256298 7670.512595 7680.328106 23.507262 1000 + +finding our best fit:: + best_fit = ufitter.get_best(significant=False) + print(best_fit.summary) best_fit.plot() +.. code-block:: text + + summary + Parametric/Non-Parametric Parametric + Discrete/Continuous continuous + Distribution cauchy + #Params 2 + param0 -0.070741 + param1 1.642212 + Support (-inf, inf) + Fitted Domain (-16.627835918238397, 20.41344998969709) + Cramér-von Mises statistic 0.272381 + Cramér-von Mises p-value 0.162046 + Cramér-von Mises @ 10% True + Cramér-von Mises @ 5% True + Cramér-von Mises @ 1% True + Kolmogorov-Smirnov statistic 0.034967 + Kolmogorov-Smirnov p-value 0.169277 + Kolmogorov-Smirnov @ 10% True + Kolmogorov-Smirnov @ 5% True + Kolmogorov-Smirnov @ 1% True + Likelihood 0.0 + Log-Likelihood -2791.769256 + AIC 5587.538511 + BIC 5597.354022 + Sum of Squared Error 9.18869 + #Fitted Data Points 1000 + .. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/univariate_fitter_example_figure1.png?raw=true :alt: poisson plot :align: center @@ -262,6 +345,7 @@ We can also save our UnivariateFitter object:: ufitter.save() We can then easily reload this:: + from sklarpy import load loaded_ufitter = load('UnivariateFitter.pickle') diff --git a/docs/source/mvt_table.csv b/docs/source/mvt_table.csv new file mode 100644 index 0000000..3b41026 --- /dev/null +++ b/docs/source/mvt_table.csv @@ -0,0 +1,13 @@ +Family,Name,SklarPy Model +Normal Mixture,Normal / Gaussian,mvt_normal +Normal Mixture,Student-T,mvt_student_t +Normal Mixture,Skewed-T,mvt_skewed_t +Normal Mixture,Generalized Hyperbolic,mvt_gh +Normal Mixture,Symmetric Generalized Hyperbolic,mvt_sgh +Normal Mixture,Hyperbolic,mvt_hyperbolic +Normal Mixture,Symmetric Hyperbolic,mvt_shyperbolic +Normal Mixture,Normal-Inverse Gaussian (NIG),mvt_nig +Normal Mixture,Symmetric Normal-Inverse Gaussian,mvt_snig +Normal Mixture,Marginal Hyperbolic,mvt_mh +Normal Mixture,Symmetric Marginal Hyperbolic,mvt_smh +Numerical,Gaussian KDE,mvt_gaussian_kde \ No newline at end of file diff --git a/examples/multivariate_examples/multivariate_example.py b/examples/multivariate_examples/multivariate_example.py index ed1093b..60fcd15 100644 --- a/examples/multivariate_examples/multivariate_example.py +++ b/examples/multivariate_examples/multivariate_example.py @@ 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zpfCd{6LqRk8+CUP!A2F=4zh+av64&sVxrHSyzfEtVCy?5qbIk21@04p9yHcW9uQ{L zWEV~-xK4424eG<6_mP$GnVE9K@Rg1|3@ABJF9j0jb@0cVF%;pn!5{hvWWuXguV&}q zNC{exzGAwhy!hoP-DJNL;*&R3T09mPSHn#gi;9Nfx}*4<7$&$nGy^@KPlaqgRU=P6l@w}t zTU?@|qA3vi1KR&Yl=|@4j{6%q?L&jBY$s;8`FU0F=0vW8{9Zw zCgR0GcxBWafej2e_)tJo5r6+3fTge*?yN>r5>2fF^c#sDExC<>xKn}&GiY!Ip%a-S zd|fA@5X`vKFq za&RZJzLbUU+9>=_fBWujsan?O{|jVwg%bb( literal 0 HcmV?d00001 From 8cb2634ec36093962138d9b34389f75d1d675c9e Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 00:58:11 +0000 Subject: [PATCH 16/22] adding to docs --- docs/source/Multivariate.rst | 39 +++++++++++++++++++ docs/source/Univariate.rst | 3 +- .../multivariate_example.py | 8 ++-- 3 files changed, 45 insertions(+), 5 deletions(-) diff --git a/docs/source/Multivariate.rst b/docs/source/Multivariate.rst index debcc34..e92e315 100644 --- a/docs/source/Multivariate.rst +++ b/docs/source/Multivariate.rst @@ -17,6 +17,45 @@ Currently, the following multivariate distributions are implemented: :file: mvt_table.csv :header-rows: 1 +PreFitContinuousMultivariate +---------------------------- +This is the base class for all multivariate distributions. It implements the following methods and attributes: + +- logpdf (log of the probability density function) +- pdf (probability density function) +- cdf (cumulative density function) +- mc_cdf (Monte Carlo approximation of the cumulative density function) +- rvs (random variate generator / sampler) +- likelihood (likelihood function) +- loglikelihood (log of the likelihood function) +- aic (Akaike Information Criterion) +- bic (Bayesian Information Criterion) +- marginal_pairplot (pairplot of the marginal distributions) +- pdf_plot (plot of the probability density function) +- cdf_plot (plot of the cumulative density function) +- mc_cdf_plot (plot of the Monte Carlo approximation of the cumulative density function) +- num_params (The number of parameters in the distribution) +- num_scalar_params (The number of scalar values across all parameters in the distribution) +- fit (fitting the distribution to data) + +mc_cdf is a numerical approximation of the cumulative density function. This is usually necessary for distributions that do not have a closed form cumulative density function, as the numerical integration alternative is computationally expensive. + +num_params is the number of parameter objects in the distribution, i.e. a vector / matrix is counted as 1. +num_scalar_params counts the number of unique scalar values across all parameter objects. + +FittedContinuousMultivariate +---------------------------- +This class is the fitted version of PreFitContinuousMultivariate's subclasses. +It implements the same methods as PreFitContinuousMultivariate, but does not require params as an argument. +It also implements the following additional methods and attributes: + +- params (the fitted parameters) +- num_variables (the number of variables the distribution is fitted too) +- fitted_num_data_points (the number of observations used to fit the distribution) +- converged (whether the fitting algorithm converged) +- summary (a summary of the fitted distribution) +- save (save the fitted distribution object) + Multivariate Example --------------------- Here we use the multivariate normal and multivariate symmetric hyperbolic distributions, though all methods and attributes are generalized.:: diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index a630ae5..e92c88b 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -63,7 +63,8 @@ ppf_approx and cdf_approx are approximations of the ppf and cdf functions respec FittedUnivariateBase --------------------- This class is the fitted version of PreFitUnivariateBase's subclasses. -It implements the same methods as PreFitUnivariateBase, but does not require params as an argument in addition to the following: +It implements the same methods as PreFitUnivariateBase, but does not require params as an argument. +It also implements the following additional methods and attributes: - summary (summary of the distribution fit) - params (the fitted parameters) diff --git a/examples/multivariate_examples/multivariate_example.py b/examples/multivariate_examples/multivariate_example.py index 60fcd15..ad6b928 100644 --- a/examples/multivariate_examples/multivariate_example.py +++ b/examples/multivariate_examples/multivariate_example.py @@ -28,12 +28,12 @@ fitted_msh = mvt_shyperbolic.fit(rvs_df, method='mle', show_progress=True) # printing our fitted parameters -# print(fitted_msh.params.to_dict) -# print(fitted_msh.params.cov) +print(fitted_msh.params.to_dict) +print(fitted_msh.params.cov) # printing a summary of our fit -# print(fitted_msh.summary) -# +print(fitted_msh.summary) + # # can plot fitted_msh.pdf_plot(show=False) fitted_msh.mc_cdf_plot(show=False) From b93b1a299e29a5137534e82085b698d29b627ddb Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 03:25:51 +0000 Subject: [PATCH 17/22] adding to docs --- docs/source/Copulas.rst | 335 ++++++++++++++++++ docs/source/Multivariate.rst | 18 +- docs/source/copula_table.csv | 16 + media/Copula_PDF_Gh_Copula_PDF_Plot_Plot2.png | Bin 0 -> 187902 bytes media/MC_CDF_Gh_MC_CDF_Plot_Plot2.png | Bin 0 -> 187843 bytes media/PDF_Gh_PDF_Plot_Plot2.png | Bin 0 -> 158170 bytes media/mfitter_pairplot.png | Bin 0 -> 228234 bytes 7 files changed, 362 insertions(+), 7 deletions(-) create mode 100644 docs/source/copula_table.csv create mode 100644 media/Copula_PDF_Gh_Copula_PDF_Plot_Plot2.png create mode 100644 media/MC_CDF_Gh_MC_CDF_Plot_Plot2.png create mode 100644 media/PDF_Gh_PDF_Plot_Plot2.png create mode 100644 media/mfitter_pairplot.png diff --git a/docs/source/Copulas.rst b/docs/source/Copulas.rst index 81619a3..2d814b9 100644 --- a/docs/source/Copulas.rst +++ b/docs/source/Copulas.rst @@ -3,3 +3,338 @@ ############## Copula Models ############## + +This SklarPy package contains many different copula models. +Unlike univariate distributions, these are not wrappers of scipy objects. + +All implemented copula models are able to be fitted to both multivariate numpy and pandas data and contain easy saving and plotting methods. + +An important concept to remember when using these models that they are composed of 2 overall parts: +1. The marginal distributions. These are univariate distributions of each random variable. +2. The copula distribution. This multivariate model captures the dependence structure between the variables. + +The overall multivariate joint distribution is created by combining these two parts. + +Which copula models are implemented? +------------------------------------ +Currently, the following copula models are implemented: + +.. csv-table:: Copula Models + :file: copula_table.csv + :header-rows: 1 + +All Normal-Mixture models use the parameterization specified by McNeil, Frey and Embrechts (2005). + +MarginalFitter +-------------- +This class is used to fit multiple univariate distributions to data easily and evaluate their methods. +It implements the following methods and attributes: + +- marginal_logpdf (log of the probability density functions of the marginal distributions) +- marginal_pdfs (the probability density functions of the fitted marginal distributions) +- marginal_cdfs (the cumulative distribution functions of the fitted marginal distributions) +- marginal_ppfs (the percent point functions / inverse cdfs of the fitted marginal distributions) +- marginal_rvs (random variate generators / samplers of the fitted marginal distributions) +- pairplot (pairplot of the fitted marginal distributions) +- marginals (the fitted marginal distributions as a dictionary) +- summary (a summary of the fitted marginal distributions) +- num_variables (the number of variables present in the original dataset) +- fitted (whether the marginal distributions have been fitted to data) +- fit (fitting the marginal distributions to data) + +PreFitCopula +------------- +This is the base class for all copula models. It implements the following methods and attributes: + +- logpdf (log of the probability density function of the overall joint distribution) +- pdf (probability density function of the overall joint distribution) +- cdf (cumulative distribution function of the overall joint distribution) +- mc_cdf (Monte Carlo approximation of the cumulative distribution function of the overall joint distribution) +- rvs (random variate generator / sampler of the overall joint distribution) +- copula_logpdf (log of the probability density function of the copula distribution) +- copula_pdf (probability density function of the copula distribution) +- copula_cdf (cumulative distribution function of the copula distribution) +- copula_mc_cdf (Monte Carlo approximation of the cumulative distribution function of the copula distribution) +- copula_rvs (random variate generator / sampler of the copula distribution) +- num_marginal_params (number of parameters in the marginal distributions) +- num_copula_params (number of parameters in the copula distribution) +- num_scalar_params (number of scalar parameters in the overall joint distribution) +- num_params (number of parameters in the overall joint distribution) +- likelihood (likelihood of the overall joint distribution) +- loglikelihood (log of the likelihood of the overall joint distribution) +- aic (Akaike Information Criterion of the overall joint distribution) +- bic (Bayesian Information Criterion of the overall joint distribution) +- marginal_pairplot (pairplot of the marginal distributions) +- pdf_plot (plot of the probability density function of the overall joint distribution) +- cdf_plot (plot of the cumulative distribution function of the overall joint distribution) +- mc_cdf_plot (plot of the Monte Carlo approximation of the cumulative distribution function of the overall joint distribution) +- copula_pdf_plot (plot of the probability density function of the copula distribution) +- copula_cdf_plot (plot of the cumulative distribution function of the copula distribution) +- copula_mc_cdf_plot (plot of the Monte Carlo approximation of the cumulative distribution function of the copula distribution) +- fit (fitting the overall joint distribution to data) + +mc_cdf and copula_mc_cdf are numerical approximations of their respective cumulative distribution functions. +These are usually necessary as the analytical forms of these functions are often not available and numerical integration is computationally expensive. + +Also note that pdf and cdf plots are only implemented for 2-dimensional distributions. + +FittedCopula +------------ +This class is the fitted version of PreFitCopula's subclasses. +It implements the same methods as PreFitCopula, but does not require copula_params or mdists as arguments. +It also implements the following additional methods and attributes: + +- copula_params (the fitted parameters of the copula distribution) +- mdists (the fitted univariate marginal distributions) +- num_variables (the number of variables the distribution is fitted too) +- fitted_num_data_points (the number of observations used to fit the distribution) +- converged (whether the fitting algorithm converged) +- summary (a summary of the overall fitted distribution) +- save (save the overall fitted distribution object) + +MarginalFitter Example +----------------------- +Generating data and fitting marginal distributions:: + + import numpy as np + import pandas as pd + + # specifying the parameters of the multivariate normal distribution we are + # sampling from + num_generate: int = 1000 + my_mu: np.ndarray = np.array([33, 44], dtype=float) + my_corr: np.ndarray = np.array([[1, 0.7], [0.7, 1]], dtype=float) + my_sig: np.ndarray = np.array([1.3, 2.5]) + my_cov: np.ndarray = np.diag(my_sig) @ my_corr @ np.diag(my_sig) + my_mvn_params: tuple = (my_mu, my_cov) + + # generating multivariate random normal variables + from sklarpy.multivariate import mvt_normal + + rvs: np.ndarray = mvt_normal.rvs(num_generate, my_mvn_params) + rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Wife Age', 'Husband Age' + ], dtype=float) + + # applying MarginalFitter to our random variables + from sklarpy.copulas import MarginalFitter + + mfitter: MarginalFitter = MarginalFitter(rvs_df) + mfitter.fit({'pvalue': 0.01}) + + # printing out a summary of our fits + from sklarpy import print_full + print_full() + + print(mfitter.summary) + + +.. code-block:: text + + Wife Age Husband Age + Parametric/Non-Parametric Parametric Parametric + Discrete/Continuous continuous continuous + Distribution lognorm lognorm + #Params 3 3 + param0 0.000005 0.000001 + param1 -262115.561308 -2097116.799667 + param2 262148.497841 2097160.700641 + Support (-262115.56130758836, inf) (-2097116.7996667635, inf) + Fitted Domain (28.438692411392555, 36.673753788627785) (35.20033323448715, 51.735336956575935) + Cramér-von Mises statistic 0.124954 0.102395 + Cramér-von Mises p-value 0.475847 0.573349 + Cramér-von Mises @ 10% True True + Cramér-von Mises @ 5% True True + Cramér-von Mises @ 1% True True + Kolmogorov-Smirnov statistic 0.032827 0.024709 + Kolmogorov-Smirnov p-value 0.226385 0.56612 + Kolmogorov-Smirnov @ 10% True True + Kolmogorov-Smirnov @ 5% True True + Kolmogorov-Smirnov @ 1% True True + Likelihood 0.0 0.0 + Log-Likelihood -1666.824453 -2382.153726 + AIC 3339.648906 4770.307452 + BIC 3354.372172 4785.030718 + Sum of Squared Error 16.819752 6.322994 + #Fitted Data Points 1000 1000 + +Printing Marginals:: + + print(mfitter.marginals) + +.. code-block:: text + + {0: lognorm(0.0, -262115.56, 262148.5), 1: lognorm(0.0, -2097116.8, 2097160.7)} + +Calculating marginal cdf values:: + + mcdf_values: pd.DataFrame = mfitter.marginal_cdfs() + print(mcdf_values) + +.. code-block:: text + + Wife Age Husband Age + 0 0.446886 0.676438 + 1 0.162115 0.107338 + 2 0.631869 0.461236 + 3 0.182751 0.589056 + 4 0.827908 0.870150 + .. ... ... + 995 0.732827 0.523818 + 996 0.457342 0.372388 + 997 0.319827 0.598163 + 998 0.476477 0.350149 + 999 0.353060 0.323429 + +Producing a pairplot of the marginals:: + + data: np.ndarray = np.full((num_generate, 10), np.NaN) + data[:, :2] = np.random.poisson(4, (num_generate, 2)) + data[:, 2] = np.random.randint(-5, 5, (num_generate,)) + data[:, 3] = data[:, :2].sum(axis=1) + data[:, 4] = data[:, 0] + data[:, 3] + data[:, 5] = np.random.normal(4, 2, (num_generate,)) + data[:, 6] = np.random.gamma(2, 1, (num_generate,)) + data[:, 7:9] = np.random.standard_t(3, (num_generate, 2)) + data[:, 9] = np.random.uniform(0, 1, (num_generate,)) + + mfitter2: MarginalFitter = MarginalFitter(data).fit() + + mfitter2.pairplot() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/mfitter_pairplot.png?raw=true + :alt: MarginalFitter Pair-Plot + :scale: 60% + :align: center + +Copula Example +-------------- +Here we use the generalized hyperbolic copula, though all methods and attributes are generalized.:: + + import numpy as np + import pandas as pd + import matplotlib.pyplot as plt + + # specifying the parameters of the multivariate hyperbolic distribution we are + # generating from + my_loc = np.array([1, -3], dtype=float) + my_shape = np.array([[1, 0.7], [0.7, 1]], dtype=float) + my_chi = 1.7 + my_psi = 4.5 + my_gamma = np.array([2.3, -4.3], dtype=float) + my_params = (my_chi, my_psi, my_loc, my_shape, my_gamma) + + # generating multivariate hyperbolic random variables + from sklarpy.multivariate import mvt_hyperbolic + + num_generate: int = 1000 + rvs: np.ndarray = mvt_hyperbolic.rvs(num_generate, my_params) + rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Process A', 'Process B'], + dtype=float) + + # fitting a generalized hyperbolic copula to our generated data using + # Maximum Likelihood Estimation + from sklarpy.copulas import gh_copula + + fitted_copula = gh_copula.fit( + data=rvs_df, method='mle', + univariate_fitter_options={'significant': False}, show_progress=True) + + # prining our fitted parameters + from sklarpy import print_full + print_full() + + print(fitted_copula.copula_params.to_dict) + +.. code-block:: text + + {'lamb': -10.0, 'chi': 8.460830761870396, 'psi': 10.0, + 'loc': array([[0.], [0.]]), + 'shape': array([[ 1. , -0.5214283], + [-0.5214283, 1. ]]), + 'gamma': array([[0.99848424], [0.94696141]])} + +Printing marginal distributions:: + + print(fitted_copula.mdists) + +.. code-block:: text + + {0: lognorm(0.38, -0.78, 4.02), 1: lognorm(0.0, -1276.15, 1268.45)} + +Printing covariance parameters:: + + print(fitted_copula.copula_params.cov) + +.. code-block:: text + + [[ 0.39404386 -0.18821382] + [-0.18821382 0.3928638 ]] + +Printing a summargy of our joint fit:: + + print(fitted_copula.summary) + +.. code-block:: text + + Joint Distribution gh summary summary + Distribution Joint Distribution mvt_gh lognorm cauchy + #Variables 2 2 NaN NaN + #Params 11 6 3 2 + #Scalar Params 11 6 NaN NaN + Converged True True NaN NaN + Likelihood 0.0 0.0 0.0 0.0 + Log-Likelihood -4298.311941 -1032.490682 -1880.434874 -2561.765741 + AIC 8618.623881 2076.981365 3766.869748 5127.531482 + BIC 8672.609189 2106.427896 3781.593014 5137.346993 + #Fitted Data Points 1000 1000 1000 1000 + Parametric/Non-Parametric NaN NaN Parametric Parametric + Discrete/Continuous NaN NaN continuous continuous + param0 NaN NaN 0.328725 -6.937913 + param1 NaN NaN -1.596967 1.485756 + param2 NaN NaN 4.826054 NaN + Support NaN NaN (-1.5969673012994325, inf) (-inf, inf) + Fitted Domain NaN NaN (0.030085402918948567, 10.416203209871883) (-28.483718062724616, -2.8836636097027206) + Cramér-von Mises statistic NaN NaN 0.055878 3.834238 + Cramér-von Mises p-value NaN NaN 0.840024 0.0 + Cramér-von Mises @ 10% NaN NaN True False + Cramér-von Mises @ 5% NaN NaN True False + Cramér-von Mises @ 1% NaN NaN True False + Kolmogorov-Smirnov statistic NaN NaN 0.018599 0.128949 + Kolmogorov-Smirnov p-value NaN NaN 0.872994 0.0 + Kolmogorov-Smirnov @ 10% NaN NaN True False + Kolmogorov-Smirnov @ 5% NaN NaN True False + Kolmogorov-Smirnov @ 1% NaN NaN True False + Sum of Squared Error NaN NaN 11.475127 8.464622 + +Plotting our fit:: + + fitted_copula.copula_pdf_plot(show=False) + fitted_copula.pdf_plot(show=False) + fitted_copula.mc_cdf_plot(show=False) + plt.show() + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/PDF_Gh_PDF_Plot_Plot2.png?raw=true + :alt: Generalized Hyperbolic PDF + :scale: 60% + :align: center + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/Copula_PDF_Gh_Copula_PDF_Plot_Plot2.png?raw=true + :alt: Generalized Hyperbolic Copula PDF + :scale: 60% + :align: center + +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/MC_CDF_Gh_MC_PDF_Plot_Plot2.png?raw=true + :alt: Generalized Hyperbolic CDF + :scale: 60% + :align: center + +Saving our fitted copula:: + + fitted_copula.save() + +We can then easily reload this object later:: + + from sklarpy import load + + loaded_copula = load('gh.pickle') + print(loaded_copula.summary) \ No newline at end of file diff --git a/docs/source/Multivariate.rst b/docs/source/Multivariate.rst index e92e315..035663d 100644 --- a/docs/source/Multivariate.rst +++ b/docs/source/Multivariate.rst @@ -5,11 +5,11 @@ Multivariate Distributions ############################ This SklarPy package contains many different multivariate distributions. -Unlike univariate distribution, these are not wrappers of scipy objects (with the exceptions of mvt_normal and mvt_student_t). +Unlike univariate distributions, these are not wrappers of scipy objects (with the exceptions of mvt_normal and mvt_student_t). All implemented multivariate distributions are able to be fitted to both multivariate numpy and pandas data and contain easy saving and plotting methods. -What multivariate distributions are implemented? +Which multivariate distributions are implemented? ------------------------------------------------ Currently, the following multivariate distributions are implemented: @@ -17,14 +17,16 @@ Currently, the following multivariate distributions are implemented: :file: mvt_table.csv :header-rows: 1 +All Normal-Mixture models use the parameterization specified by McNeil, Frey and Embrechts (2005). + PreFitContinuousMultivariate ---------------------------- This is the base class for all multivariate distributions. It implements the following methods and attributes: - logpdf (log of the probability density function) - pdf (probability density function) -- cdf (cumulative density function) -- mc_cdf (Monte Carlo approximation of the cumulative density function) +- cdf (cumulative distribution function) +- mc_cdf (Monte Carlo approximation of the cumulative distribution function) - rvs (random variate generator / sampler) - likelihood (likelihood function) - loglikelihood (log of the likelihood function) @@ -32,17 +34,19 @@ This is the base class for all multivariate distributions. It implements the fol - bic (Bayesian Information Criterion) - marginal_pairplot (pairplot of the marginal distributions) - pdf_plot (plot of the probability density function) -- cdf_plot (plot of the cumulative density function) -- mc_cdf_plot (plot of the Monte Carlo approximation of the cumulative density function) +- cdf_plot (plot of the cumulative distribution function) +- mc_cdf_plot (plot of the Monte Carlo approximation of the cumulative distribution function) - num_params (The number of parameters in the distribution) - num_scalar_params (The number of scalar values across all parameters in the distribution) - fit (fitting the distribution to data) -mc_cdf is a numerical approximation of the cumulative density function. This is usually necessary for distributions that do not have a closed form cumulative density function, as the numerical integration alternative is computationally expensive. +mc_cdf is a numerical approximation of the cumulative distribution function. This is usually necessary for distributions that do not have a closed form cumulative density function, as the numerical integration alternative is computationally expensive. num_params is the number of parameter objects in the distribution, i.e. a vector / matrix is counted as 1. num_scalar_params counts the number of unique scalar values across all parameter objects. +Also note that pdf and cdf plots are only implemented for 2-dimensional distributions. + FittedContinuousMultivariate ---------------------------- This class is the fitted version of PreFitContinuousMultivariate's subclasses. diff --git a/docs/source/copula_table.csv b/docs/source/copula_table.csv new file mode 100644 index 0000000..4b60d1b --- /dev/null +++ b/docs/source/copula_table.csv @@ -0,0 +1,16 @@ +Family,Name,Dimensions,SklarPy Model +Normal Mixture,Normal / Gaussian,Multivariate,gaussian_copula +Normal Mixture,Student-T,Multivariate,student_t_copula +Normal Mixture,Skewed-T,Multivariate,skewed_t_copula +Normal Mixture,Generalized Hyperbolic,Multivariate,gh_copula +Normal Mixture,Symmetric Generalized Hyperbolic,Multivariate,sgh_copula +Normal Mixture,Hyperbolic,Multivariate,hyperbolic_copula +Normal Mixture,Symmetric Hyperbolic,Multivariate,shyperbolic_copula +Normal Mixture,Normal-Inverse Gaussian (NIG),Multivariate,nig_copula +Normal Mixture,Symmetric Normal-Inverse Gaussian,Multivariate,snig_copula +Normal Mixture,Marginal Hyperbolic,Multivariate,mh_copula +Normal Mixture,Symmetric Marginal Hyperbolic,Multivariate,smh_copula +Archimedean,Clayton,Multivariate,clayton_copula +Archimedean,Gumbel,Multivariate,gumbel_copula +Archimedean,Frank,Bivariate,frank_copula +Numerical,Gaussian KDE,Multivariate,gaussian_kde_copula \ No newline at end of file diff --git a/media/Copula_PDF_Gh_Copula_PDF_Plot_Plot2.png b/media/Copula_PDF_Gh_Copula_PDF_Plot_Plot2.png new file mode 100644 index 0000000000000000000000000000000000000000..54c42f5792275819eac6d79393fcd99a857bf1ce GIT binary patch literal 187902 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zer{%FCJ86*U~`rS4%5{1G@p$c9~a?WQ?df5IXW)RZ+Re1 zs!)A#YrY*WlcHBNhrn93LD~t%ltno7IaG(D)6y=7m&Lu#R_6E}sHv!Ym2&+C&HUxE z9~l}lffvSMV`Cc}%({Qbvy#NEb3OI35o$`DE9t9OuS#4tbyG#(#xDOP2BVQN zjNDr2#0RU%%*?#?<|Lq`gqI91f#^piR@T=$A(#CzGee}Ttc>bIfV|_k&^~7QL^v1@ zCnu+tfdOLhT)~l%pyRpf(+0Z|9SWS=gl#3b)w{p7neOUXz-GK MqV%}vk-qQ$0>4n1AOHXW literal 0 HcmV?d00001 From ea11cb6dced78e0abf3eef636ee0220143008885 Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 03:29:00 +0000 Subject: [PATCH 18/22] adding to docs --- docs/source/Copulas.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/Copulas.rst b/docs/source/Copulas.rst index 2d814b9..902d529 100644 --- a/docs/source/Copulas.rst +++ b/docs/source/Copulas.rst @@ -323,7 +323,7 @@ Plotting our fit:: :scale: 60% :align: center -.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/MC_CDF_Gh_MC_PDF_Plot_Plot2.png?raw=true +.. image:: https://github.com/tfm000/sklarpy/blob/docs/readthedocs/media/MC_CDF_Gh_MC_CDF_Plot_Plot2.png?raw=true :alt: Generalized Hyperbolic CDF :scale: 60% :align: center From 39475d570bac5a9e929457c9e8d8ffffdf949319 Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 03:46:46 +0000 Subject: [PATCH 19/22] adding to docs --- docs/source/Misc.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/source/Misc.rst b/docs/source/Misc.rst index 5089f38..896a9ce 100644 --- a/docs/source/Misc.rst +++ b/docs/source/Misc.rst @@ -3,3 +3,14 @@ #################### Miscellaneous Tools #################### + +This SklarPy package contains functions / objects which are both implemented across SklarPy and also intended for user use. + +CorrelationMatrix +------------------ + + +.. autoclass:: sklarpy.misc.CorrelationMatrix + :members: + :undoc-members: + :show-inheritance: From 85b13327ce9d7d8bfcaad35632dd19a2af12bb7e Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 04:08:11 +0000 Subject: [PATCH 20/22] adding to docs --- docs/source/Misc.rst | 85 +++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 81 insertions(+), 4 deletions(-) diff --git a/docs/source/Misc.rst b/docs/source/Misc.rst index 896a9ce..07e8b0d 100644 --- a/docs/source/Misc.rst +++ b/docs/source/Misc.rst @@ -8,9 +8,86 @@ This SklarPy package contains functions / objects which are both implemented acr CorrelationMatrix ------------------ +CorrelationMatrix is a SklarPy class which allows the user to estimate correlation and covariance matrices using a number of different estimators. +This code is inspired by the methods described by Xu, Brin (2016) and implements the following estimators: -.. autoclass:: sklarpy.misc.CorrelationMatrix - :members: - :undoc-members: - :show-inheritance: +- pearson +- spearman +- kendall +- pp-kendall +- rm-pearson +- rm-spearman +- rm-kendall +- rm-pp-kendall +- laloux-pearson +- laloux-spearman +- laloux-kendall +- laloux-pp-kendall + +rm stands for the technique described by Rousseeuw and Molenberghs (1993) and laloux for that by Laloux et al. (2000). + +The corr method allows you to calculate correlation matrices, whilst cov allows you to calculate covariance matrices. + +debye +----- +This function allows the user to easily evaluate any member of the Debye function family. + +gradient_1d +------------ +This function allows the user to calculate the numerical first derivative / gradient of a given 1-d function. + +kv +--- +This class allows the user to easily evaluate the Modified Bessel function of the 2nd kind, in addition to its log-values. +Limiting cases of the family parameter, v, and value, z, are also implemented. + +CorrelationMatrix Example +-------------------------- + +Here we calculate both the covariance and correlation matrix estimators:: + + import numpy as np + import pandas as pd + + # specifying the parameters of the multivariate hyperbolic distribution we are + # generating from + my_loc = np.array([1, -3], dtype=float) + my_shape = np.array([[1, 0.7], [0.7, 1]], dtype=float) + my_chi = 1.7 + my_psi = 4.5 + my_gamma = np.array([2.3, -4.3], dtype=float) + my_params = (my_chi, my_psi, my_loc, my_shape, my_gamma) + + # generating multivariate hyperbolic random variables + from sklarpy.multivariate import mvt_hyperbolic + + num_generate: int = 1000 + rvs: np.ndarray = mvt_hyperbolic.rvs(num_generate, my_params) + rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Process A', 'Process B'], + dtype=float) + + # calculating covariance matrix and correlation matrix estimators + from sklarpy.misc import CorrelationMatrix + + cmatrix: CorrelationMatrix = CorrelationMatrix(rvs_df) + +Calculating PP-Kendall Correlation Matrix with Laloux's adjustments:: + + corr_estimator: np.ndarray = cmatrix.corr(method='laloux_pp_kendall') + print(corr_estimator) + +.. code-block:: text + + [[ 1. -0.53750912] + [-0.53750912 1. ]] + +Calculating Spearman's Covariance Matrix:: + + cov_estimator: np.ndarray = cmatrix.cov(method='spearman') + print(cov_estimator) + +.. code-block:: text + + [[ 3.02797258 -2.68535942] + [-2.68535942 8.68778502]] \ No newline at end of file From e12ae043bcbfde8342f3ea7140790e373a74a8bf Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 04:19:20 +0000 Subject: [PATCH 21/22] adding to docs --- docs/source/Univariate.rst | 9 +++++++++ docs/source/univariate_table.csv | 6 ++++++ 2 files changed, 15 insertions(+) create mode 100644 docs/source/univariate_table.csv diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index e92c88b..269bc93 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -36,6 +36,15 @@ For scipy version 1.11.4 you should get an output along the lines of: So you have a lot to choose from! +Name differences between SklarPy and SciPy +------------------------------------------- +Whilst we have generally kept most of the distribution names consistent with SciPy, there are a few notable exceptions. +These are: + +.. csv-table:: Distribution Name Discrepancies + :file: univariate_table.csv + :header-rows: 1 + PreFitUnivariateBase --------------------- This class and its subclasses contain the following methods / functions: diff --git a/docs/source/univariate_table.csv b/docs/source/univariate_table.csv new file mode 100644 index 0000000..2e409cb --- /dev/null +++ b/docs/source/univariate_table.csv @@ -0,0 +1,6 @@ +SciPy Stats,SklarPy Univariate +norm,normal +t,student_t +dlaplace,discrete_laplace +randint,discrete_uniform +geom,geometric \ No newline at end of file From 43111af9db131af255cd063309cd72c74c4c179a Mon Sep 17 00:00:00 2001 From: Tyler Date: Wed, 29 Nov 2023 04:47:40 +0000 Subject: [PATCH 22/22] adding to docs --- docs/source/Copulas.rst | 8 +++++--- docs/source/Multivariate.rst | 7 +++++-- docs/source/Univariate.rst | 9 +++++---- 3 files changed, 15 insertions(+), 9 deletions(-) diff --git a/docs/source/Copulas.rst b/docs/source/Copulas.rst index 902d529..30f740d 100644 --- a/docs/source/Copulas.rst +++ b/docs/source/Copulas.rst @@ -10,10 +10,12 @@ Unlike univariate distributions, these are not wrappers of scipy objects. All implemented copula models are able to be fitted to both multivariate numpy and pandas data and contain easy saving and plotting methods. An important concept to remember when using these models that they are composed of 2 overall parts: + 1. The marginal distributions. These are univariate distributions of each random variable. 2. The copula distribution. This multivariate model captures the dependence structure between the variables. -The overall multivariate joint distribution is created by combining these two parts. +The overall multivariate joint distribution is created by combining these two parts, +which is handled by SklarPy's copula models. Which copula models are implemented? ------------------------------------ @@ -208,7 +210,7 @@ Producing a pairplot of the marginals:: Copula Example -------------- -Here we use the generalized hyperbolic copula, though all methods and attributes are generalized.:: +Here we use the generalized hyperbolic copula, though all methods and attributes are generalized:: import numpy as np import pandas as pd @@ -270,7 +272,7 @@ Printing covariance parameters:: [[ 0.39404386 -0.18821382] [-0.18821382 0.3928638 ]] -Printing a summargy of our joint fit:: +Printing a summary of our joint fit:: print(fitted_copula.summary) diff --git a/docs/source/Multivariate.rst b/docs/source/Multivariate.rst index 035663d..e091343 100644 --- a/docs/source/Multivariate.rst +++ b/docs/source/Multivariate.rst @@ -40,7 +40,9 @@ This is the base class for all multivariate distributions. It implements the fol - num_scalar_params (The number of scalar values across all parameters in the distribution) - fit (fitting the distribution to data) -mc_cdf is a numerical approximation of the cumulative distribution function. This is usually necessary for distributions that do not have a closed form cumulative density function, as the numerical integration alternative is computationally expensive. +mc_cdf is a numerical approximation of the cumulative distribution function. +This is usually necessary for distributions that do not have a closed form cumulative distribution function, +as the numerical integration alternative is computationally expensive. num_params is the number of parameter objects in the distribution, i.e. a vector / matrix is counted as 1. num_scalar_params counts the number of unique scalar values across all parameter objects. @@ -62,7 +64,8 @@ It also implements the following additional methods and attributes: Multivariate Example --------------------- -Here we use the multivariate normal and multivariate symmetric hyperbolic distributions, though all methods and attributes are generalized.:: +Here we use the multivariate normal and multivariate symmetric hyperbolic +distributions, though all methods and attributes are generalized:: import numpy as np import pandas as pd diff --git a/docs/source/Univariate.rst b/docs/source/Univariate.rst index 269bc93..4d79d44 100644 --- a/docs/source/Univariate.rst +++ b/docs/source/Univariate.rst @@ -18,7 +18,7 @@ If you try:: from sklarpy.univariate import normal You will likely find that your interpreter flags an error along the lines of "cannot find reference 'normal' in __init__.py". -Do not worry, this is to be expected an a side effect of the dynamic way SklarPy univariate distributions are created from scipy.stats distributions. +Do not worry, this is to be expected as a side effect of the dynamic way SklarPy univariate distributions are created from scipy.stats distributions. At runtime, *your code will work without any errors*! But how do I know which distributions are available? @@ -50,7 +50,7 @@ PreFitUnivariateBase This class and its subclasses contain the following methods / functions: - pdf (probability density function) -- cdf (cumulative density function) +- cdf (cumulative distribution function) - ppf (percent point function / cumulative inverse function) - support - ppf_approx (approximate ppf) @@ -67,6 +67,7 @@ This class and its subclasses contain the following methods / functions: - fit (fitting the distribution to data) Many / all of these methods take params as an argument. This is a tuple containing the parameters of the associated scipy.stats distribution object. + ppf_approx and cdf_approx are approximations of the ppf and cdf functions respectively, which may be useful for distributions where the cdf and therefore ppf functions require numerical integration to evaluate. FittedUnivariateBase @@ -116,7 +117,7 @@ It also implements the following additional methods and attributes: Continuous Example --------------------- -Here we use the normal and gamma distributions, though all methods and attributes are generalized.:: +Here we use the normal and gamma distributions, though all methods and attributes are generalized:: import numpy as np import pandas as pd @@ -274,7 +275,7 @@ We can then easily reload our saved model:: UnivariateFitter Example ------------------------- -Here we use the UnivariateFitter object to fit a distribution to a dataset.:: +Here we use the UnivariateFitter object to fit a distribution to a dataset:: import numpy as np