is one of"
- @echo " html to make standalone HTML files"
+ @echo " all generate the complete webpage"
+ @echo " html make only the HTML files from the existing rst sources"
+ @echo " linkcheck check all external links for integrity"
clean:
- -rm -rf _build/*
+ rm -rf $(BUILDDIR)/html/*
+ rm -rf $(BUILDDIR)/doctrees
+ rm -rf $(BUILDDIR)/linkcheck
+ rm -rf api/generated
+ rm -rf gallery
+ rm -rf tutorials
+ rm -rf sample_data
+ rm -rf benchmarks
+ rm -rf .ipynb_checkpoints
-html:
- $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) "$(SOURCEDIR)" "$(BUILDDIR)"
+api:
+ @echo
+ @echo "Building API docs."
+ @echo
+ $(SPHINXAUTOGEN) -i -t _templates -o api/generated api/index.rst
+
+html: api
+ @echo
+ @echo "Building HTML files."
@echo
- @echo "Build finished. The HTML pages are in build/html."
+ $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+ @echo
+ @echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+linkcheck: api
+ $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+ @echo
+ @echo "Link check complete; look for any errors in the above output " \
+ "or in $(BUILDDIR)/linkcheck/output.txt."
+
+serve:
+ cd $(BUILDDIR)/html && python -m http.server 8001
diff --git a/docs/README.md b/docs/README.md
index 7c8a7ac5c..8b83ec08d 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,8 +1,6 @@
# Building Docs
-We currently use Sphinx
-
-If you only want to get the documentation, this can be found at [https://hyppo.neurodata.io](https://hyppo.neurodata.io).
+We currently use Sphinx. If you only want to get the documentation, this can be found at [https://hyppo.neurodata.io](https://hyppo.neurodata.io).
## Python Dependencies
@@ -10,7 +8,30 @@ You will need to install all the dependencies as defined in `requirements.txt` f
pip3 install -r requirements.txt
-in the `docs/` directory.
+in the `docs/` directory. This is of course in addition to the package requirements. Here are the documentation requirements:
+
+ sphinxcontrib-rawfiles==0.1.1
+ nbsphinx==0.8.0
+ ipython==7.19.0
+ ipykernel==5.4.3
+ sphinx==3.3
+ pydata-sphinx-theme==0.7.1
+ sphinx-gallery==0.8.2
+ numpydoc==0.7.0
+ myst_parser
+ matplotlib
+ seaborn
+ sphinxcontrib-bibtex>=2.2.0
+ docutils==0.17.1
+ sphinx_sitemap
+
+and make sure that you install the package dependencies as well:
+
+ numpy>=1.17
+ scipy>=1.4.0
+ numba>=0.46
+ scikit-learn>=0.22
+ joblib>=0.17.0
## Pandoc dependency
diff --git a/docs/_static/custom.css b/docs/_static/custom.css
new file mode 100644
index 000000000..e2f666ba3
--- /dev/null
+++ b/docs/_static/custom.css
@@ -0,0 +1,9 @@
+/* Disable the sphinx-gallery download note at the top */
+.sphx-glr-download-link-note {
+ display: none;
+ }
+
+ .sphx-glr-timing {
+ border-top: 1px solid #dddddd;
+ padding: 1.8rem 0;
+ }
\ No newline at end of file
diff --git a/docs/_templates/autosummary/base.rst b/docs/_templates/autosummary/base.rst
new file mode 100644
index 000000000..d079c27d7
--- /dev/null
+++ b/docs/_templates/autosummary/base.rst
@@ -0,0 +1,9 @@
+{{ fullname | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ objname }}
+
+.. raw:: html
+
+
\ No newline at end of file
diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst
new file mode 100644
index 000000000..ee22c0489
--- /dev/null
+++ b/docs/_templates/autosummary/class.rst
@@ -0,0 +1,33 @@
+{{ name | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+.. rubric:: Methods Summary
+
+.. autosummary::
+ {% for item in members %}
+ {% if item in ['__call__'] %}
+ {{ objname }}.{{ item }}
+ {% endif %}
+ {% endfor %}
+ {% for item in methods %}
+ {% if item != '__init__' %}
+ {{ objname }}.{{ item }}
+ {% endif %}
+ {% endfor %}
+
+----
+
+{% for item in methods %}
+{% if item != '__init__' %}
+.. automethod:: {{ objname }}.{{ item }}
+{% endif %}
+{% endfor %}
+
+.. include:: backreferences/{{ fullname }}.examples
+
+.. raw:: html
+
+
diff --git a/docs/_templates/autosummary/exception.rst b/docs/_templates/autosummary/exception.rst
new file mode 100644
index 000000000..a3ee4ec8e
--- /dev/null
+++ b/docs/_templates/autosummary/exception.rst
@@ -0,0 +1,10 @@
+{{ fullname | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autoexception:: {{ objname }}
+
+
+.. raw:: html
+
+
diff --git a/docs/_templates/autosummary/function.rst b/docs/_templates/autosummary/function.rst
new file mode 100644
index 000000000..0da691cb3
--- /dev/null
+++ b/docs/_templates/autosummary/function.rst
@@ -0,0 +1,11 @@
+{{ name | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autofunction:: {{ objname }}
+
+.. include:: backreferences/{{ fullname }}.examples
+
+.. raw:: html
+
+
diff --git a/docs/_templates/autosummary/module.rst b/docs/_templates/autosummary/module.rst
new file mode 100644
index 000000000..3a4a175ec
--- /dev/null
+++ b/docs/_templates/autosummary/module.rst
@@ -0,0 +1,54 @@
+.. raw:: html
+
+
+
+``{{ fullname }}``
+{% for i in range(fullname|length + 15) %}-{% endfor %}
+
+.. raw:: html
+
+
+ {% if hasdoc(pagename) %}
+ {% if pagename.startswith("api/generated") or suffix == ".ipynb" %}
+ {% set title = "Suggested improvement for " + pagename %}
+ {% set body = "Please describe what could be improved about this page or the typo/mistake that you found:" %}
+ Improve this page
+ {% elif pagename.split("/")[0] in galleries %}
+ {% set gallery_path = gallery_dir[pagename.split("/")[0]] %}
+ {% set example_script = pagename|replace(pagename.split("/")[0], gallery_dir[pagename.split("/")[0]]) %}
+ {% if pagename.split("/")[-1] == "index" %}
+ {% set example_script = example_script|replace("index", "README.txt") %}
+ {% else %}
+ {% set example_script = example_script + ".py" %}
+ {% endif %}
+ Improve this page
+ {% else %}
+ Improve this page
+ {% endif %}
+ {% endif %}
+ {% if suffix == ".ipynb" %}
+ Download notebook
+ {% endif %}
+
+{% endblock %}
\ No newline at end of file
diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
new file mode 100644
index 000000000..9532076f6
--- /dev/null
+++ b/docs/_templates/layout.html
@@ -0,0 +1,32 @@
+{# Import the theme's layout. #}
+{% extends "!layout.html" %}
+
+{% block htmltitle %}
+ {% if title == '' or title == 'Home' %}
+ {{ docstitle|e }}
+ {% else %}
+ {{ title|striptags|e }}{{ titlesuffix }}
+ {% endif %}
+{% endblock %}
+
+
+{% block menu %}
+ {{ super() }}
+
+ {% if menu_links %}
+
+
+ {% if menu_links_name %}
+ {{ menu_links_name }}
+ {% else %}
+ External links
+ {% endif %}
+
+
+
+ {% for text, link in menu_links %}
+ {{ text }}
+ {% endif %}
+{% endblock %}
\ No newline at end of file
diff --git a/docs/_templates/versioning.html b/docs/_templates/versioning.html
new file mode 100644
index 000000000..e69de29bb
diff --git a/docs/api/index.rst b/docs/api/index.rst
new file mode 100644
index 000000000..6c7be682c
--- /dev/null
+++ b/docs/api/index.rst
@@ -0,0 +1,209 @@
+.. _api:
+
+API Reference
+=============
+
+.. automodule:: hyppo.independence
+
+.. currentmodule:: hyppo.independence
+
+Independence
+-------------
+
+.. autosummary::
+ :toctree: generated/
+
+ MaxMargin
+ KMERF
+ MGC
+ Dcorr
+ Hsic
+ HHG
+ CCA
+ RV
+ FriedmanRafsky
+
+
+
+.. automodule:: hyppo.d_variate
+
+.. currentmodule:: hyppo.d_variate
+
+*D*-Variate
+-------------
+
+.. autosummary::
+ :toctree: generated/
+
+ dHsic
+
+
+
+.. automodule:: hyppo.ksample
+
+.. currentmodule:: hyppo.ksample
+
+*K*-Sample
+-------------
+
+.. autosummary::
+ :toctree: generated/
+
+ KSample
+ Energy
+ MMD
+ DISCO
+ MANOVA
+ Hotelling
+ SmoothCFTest
+ MeanEmbeddingTest
+ KSampleHHG
+
+
+.. automodule:: hyppo.time_series
+
+.. currentmodule:: hyppo.time_series
+
+Time-Series
+-------------
+
+.. autosummary::
+ :toctree: generated/
+
+ MGCX
+ DcorrX
+
+
+
+.. automodule:: hyppo.discrim
+
+.. currentmodule:: hyppo.discrim
+
+Discriminability
+-----------------
+
+.. autosummary::
+ :toctree: generated/
+
+ DiscrimOneSample
+ DiscrimTwoSample
+
+.. automodule:: hyppo.conditional
+
+.. currentmodule:: hyppo.conditional
+
+Conditional Independence
+-------------------------
+
+.. autosummary::
+ :toctree: generated/
+
+ FCIT
+ KCI
+
+
+.. automodule:: hyppo.kgof
+
+.. currentmodule:: hyppo.kgof
+
+Kernel Goodness-of-Fit
+-----------------------
+
+.. autosummary::
+ :toctree: generated/
+
+ FSSD
+
+
+
+.. automodule:: hyppo.tools
+
+.. currentmodule:: hyppo.tools
+
+Simulations
+-------------
+
+Independence Simulations
+""""""""""""""""""""""""
+
+.. autosummary::
+ :toctree: generated/
+
+ linear
+ exponential
+ cubic
+ joint_normal
+ step
+ quadratic
+ w_shaped
+ spiral
+ uncorrelated_bernoulli
+ logarithmic
+ fourth_root
+ sin_four_pi
+ sin_sixteen_pi
+ square
+ two_parabolas
+ circle
+ ellipse
+ diamond
+ multiplicative_noise
+ multimodal_independence
+ indep_sim
+
+*K*-Sample Simulations
+""""""""""""""""""""""""
+
+.. autosummary::
+ :toctree: generated/
+
+ rot_ksamp
+ gaussian_3samp
+
+Time-Series Simulations
+""""""""""""""""""""""""
+
+.. autosummary::
+ :toctree: generated/
+
+ indep_ar
+ cross_corr_ar
+ nonlinear_process
+ ts_sim
+
+
+.. automodule:: hyppo
+
+.. currentmodule:: hyppo
+
+Miscellaneous
+-----------------
+
+.. autosummary::
+ :toctree: generated/
+
+ independence.sim_matrix
+ ksample.k_sample_transform
+ tools.compute_kern
+ tools.multi_compute_kern
+ tools.compute_dist
+ tools.perm_test
+ tools.multi_perm_test
+ tools.chi2_approx
+ tools.power
+ ksample.smoothCF.smooth_cf_distance
+ ksample.mean_embedding.mean_embed_distance
+
+
+Base Classes
+-------------
+
+.. autosummary::
+ :toctree: generated/
+
+ independence.base.IndependenceTest
+ d_variate.base.DVariateTest
+ ksample.base.KSampleTest
+ time_series.base.TimeSeriesTest
+ discrim.base.DiscriminabilityTest
+ kgof.base.GofTest
diff --git a/docs/changelog/index.rst b/docs/changelog/index.rst
new file mode 100644
index 000000000..e5b7051ab
--- /dev/null
+++ b/docs/changelog/index.rst
@@ -0,0 +1,18 @@
+Release Notes
+================
+
+This is the list of changes to hyppo between each release. For full details, see the `commit logs `_.
+
+.. toctree::
+ :maxdepth: 1
+
+ v0.3.2.md
+ v0.3.1.md
+ v0.3.0.md
+ v0.2.2.md
+ v0.2.1.md
+ v0.2.0.md
+ v0.1.3.md
+ v0.1.2.md
+ v0.1.1.md
+ v0.1.0.md
\ No newline at end of file
diff --git a/docs/changelog/v0.1.0.md b/docs/changelog/v0.1.0.md
new file mode 100644
index 000000000..0b5e4e381
--- /dev/null
+++ b/docs/changelog/v0.1.0.md
@@ -0,0 +1,49 @@
+# hyppo v0.1.0
+
+hyppo 0.1.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. All users are encouraged to use this release instead of the mgcpy, which this package is replacing, are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on this branch, and on adding new features on the development master branch.
+
+Release highlights (as compared to mgcpy):
+
+* New time series and discriminability modules
+* More user-friendly implementation of *k*-Sample Testing
+
+This release requires Python 3.5+.
+
+## New features
+
+### Independence testing
+
+Tests have been given a unique class as compared to mgcpy. Parallelization in this module makes tests faster and ``Hsic`` has been added as a standalone test within this package (uses exact equivalence implementation).
+
+### *k*-sample testing
+
+*k*-Sample testing is now organized within a class that is modeled similarly to how independence tests are run. More information about the specifics of how this works can be found in the docs.
+
+### Time series
+
+Time series based independence tests have been included as a separate module from independence tests.
+
+### Simulations
+
+Simulations have been included as a separate module and ``hyppo.sims`` now includes k-sample simulations and time series simulations.
+
+### Discriminability
+
+Discriminability has been included from the r-mgc package and has been changed so it conforms to the ``hyppo`` API.
+
+### Benchmarks
+
+A benchmarks folder as been added that contains notebooks comparing statistical power, algorithm wall times, and test statistics comparisons between the algorithms and sometimes between the respective R implementations. Many of those notebooks have been condensed into tutorials in the documentation.
+
+## Other changes
+
+API has been changed as compared to mgcpy and is modeled after scikit-learn with the inclusion of the ``base.py`` containing an abstract class within each module and energy with a ``.test`` method calculating test statistic in p-value.
+
+## Authors
+
+This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order by last name):
+
+* Jayanta Dey +
+* Sambit Panda +
+
+A total of 2 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
diff --git a/docs/changelog/v0.1.1.md b/docs/changelog/v0.1.1.md
new file mode 100644
index 000000000..5a3d48085
--- /dev/null
+++ b/docs/changelog/v0.1.1.md
@@ -0,0 +1,46 @@
+# hyppo v0.1.1
+
+hyppo 0.1.1 does not include any new features, just removal of extraneous features.
+
+Release highlights:
+
+* Kernel matrices calculated once before calculating p-values
+* Pearson, Kendall, and Spearman are no longer tests
+
+This release requires Python 3.6+.
+
+## Improvements
+
+### Independence testing
+
+Null distribution added as a class atribute. Calculate kernel once before calculating p-value.
+
+### *k*-sample testing
+
+Null distribution added as a class atribute. Calculate kernel once before calculating p-value. Upper and lower-case inputs are available for ``indep_test``.
+
+### Time series
+
+Reference docs and tutorials added to Time Series module.
+
+### Other changes
+
+OS/Software requirements and license changes updated in ReadME. ``pairwise_distances``
+from ``sklearn`` used instead of ``cdist`` from ``scipy``.
+
+### Removed features
+
+Pearson, Spearman, and Kendall are no longer tests within the package. arXiv
+badge added to docs. Python 3.5 no longer supported.
+
+## Authors
+
+This release contains work by the following people (contributed at least
+one patch to this release, names in alphabetical order by last name):
+
+* Ronak Mehta +
+* Sambit Panda
+* Bijan Varjavand +
+
+A total of 3 people contributed to this release.
+People with a "+" by their names contributed a patch for the first time.
diff --git a/docs/changelog/v0.1.2.md b/docs/changelog/v0.1.2.md
new file mode 100644
index 000000000..f3f472b78
--- /dev/null
+++ b/docs/changelog/v0.1.2.md
@@ -0,0 +1,17 @@
+# hyppo v0.1.2
+
+hyppo 0.1.2 does not include any new features, just fixes. This release requires Python 3.6+.
+
+## Fixes
+
+### *k*-sample testing
+
+Fixed MMD/k-samp;e Hsic not running bug.
+
+## Authors
+
+This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order by last name):
+
+* Sambit Panda
+
+A total of 1 person contributed to this release. People with a "+" by their names contributed a patch for the first time.
\ No newline at end of file
diff --git a/docs/changelog/v0.1.3.md b/docs/changelog/v0.1.3.md
new file mode 100644
index 000000000..f02d1f2d3
--- /dev/null
+++ b/docs/changelog/v0.1.3.md
@@ -0,0 +1,22 @@
+# hyppo v0.1.3
+
+hyppo 0.1.3 does not include any new features, just fixes. This release requires Python 3.6+.
+
+## Fixes
+
+### *k*-sample testing
+
+Prevent division by zero when calculating using default gaussian median kernel.
+
+### Other
+
+Used `chi2.sf` instead of `1 - chi2.cdf`
+
+## Authors
+
+This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order by last name):
+
+* Benjamin Pedigo +
+* Anton Alayakin +
+
+People with a "+" by their names contributed a patch for the first time.
diff --git a/docs/changelog/v0.2.0.md b/docs/changelog/v0.2.0.md
new file mode 100644
index 000000000..922c29aee
--- /dev/null
+++ b/docs/changelog/v0.2.0.md
@@ -0,0 +1,67 @@
+# hyppo v0.2.0
+
+**Note: People with a "+" by their names contributed a patch for the first time.**
+
+**Release date:** 08 February 2021
+**Supports:** Python 3.6+.
+
+## New features
+
+* Added restricted permutation functionality for Dcorr
+* Kernel functions now wrap scikit-learn and support keyword arguments
+* `hyppo.ksample.Energy`
+* `hyppo.ksample.DISCO`
+* `hyppo.ksample.MMD`
+* Fast 1D exact Dcorr `O(n log n)`
+* `hyppo.ksample.Hotelling`
+* `hyppo.ksample.MANOVA`
+* `hyppo.independence.MaxMargin`
+
+## Bug Fixes
+
+* Fixed error check for k-sample tests to be between samples instead of within
+* Time series doesn't clip negative values
+* Fix docs not building on netlify
+* Fix p-value calculations for permutation tests to be more in line with the literature
+* Fix `hyppo.independence.Dcorr` and `hyppo.independence.Hsic` incorrect stats
+
+## Documentation
+
+* Update badges and README to FIRM guidelines
+* Incorrect equation in :meth:`hyppo.tools.circle` docstring
+* Update README to be in line with scikit-learn
+* Remove literature reference section in docstrings, add links to papers
+* Add docstrings for :mod:`hyppo.tools` functions
+* Add ``overview.py`` file for an overview of the package
+* Add tutorials folder, rewrite so it is more user-friendly (port independence, k-sample, time series)
+* Add examples folder with more information about unique features
+* Move to ``sphinx-gallery`` instead of nbconvert
+* Use ``automodule`` instead of ``autoclass``
+* Make clear about the package requirements and docs requirements
+* Make ``changelog`` into a single file
+* Add external links to neurodata and code of conduct
+* Add citing page to cite the package papers
+* Make index page a clone of README
+* Update MakeFile for more options
+* Add intersphinx mapping with links externally (``numpy``, ``scipy``, etc.) and internally
+* Add docs for statistic function
+* Add discriminability tutorial
+
+## Maintenance
+
+* Fix typos in warning commits
+* Updated tests to precalculate distance matrix
+* Moved from Travis CI to Circle CI
+* Raise base ``requirements.txt`` to fix failing tests on CircleCI
+* Add code coverage config files
+* Add documentation folders and files to ``.gitignore``
+* Remove ``reps`` warning test
+* Cache numba after first call to speed up runs
+* Fix netlify config to new doc build structure
+
+## Authors
+
+* Sambit Panda
+* Vivek Gopalakrishnan +
+* Ronak Mehta
+* Ronan Perry +
diff --git a/docs/changelog/v0.2.1.md b/docs/changelog/v0.2.1.md
new file mode 100644
index 000000000..c305c1e35
--- /dev/null
+++ b/docs/changelog/v0.2.1.md
@@ -0,0 +1,29 @@
+# hyppo v0.2.1
+
+**Note: People with a "+" by their names contributed a patch for the first time.**
+
+**Release date:** 25 February 2021
+**Supports:** Python 3.6+.
+
+## Bug Fixes
+
+* `hyppo.independence.Dcorr` and `hyppo.independence.Hsic` when `auto=True` had low finite power, fixed.
+* Fast exact statistic for `hyppo.independence.Dcorr` was incorrect, fixed
+
+## Documentation
+
+* Fix PyPi description rendering issues
+* Add more descriptive contributing guidelines
+* Add ROLES.md for specification about maintainers
+* Added power computation for independence increasing sample size and dimension
+* Add benchmark section and move relevant examples there
+* Add base classes
+
+## Maintenance
+
+* Fix gaussian kernel to prevent division by 0
+* Add checks for Type I error
+
+## Authors
+
+* Sambit Panda
diff --git a/docs/changelog/v0.2.2.md b/docs/changelog/v0.2.2.md
new file mode 100644
index 000000000..e589b41ee
--- /dev/null
+++ b/docs/changelog/v0.2.2.md
@@ -0,0 +1,39 @@
+# hyppo v0.2.2
+
+**Note: People with a "+" by their names contributed a patch for the first time.**
+
+**Release date:** 7 December 2021
+**Supports:** Python 3.6+.
+
+## New features
+
+* Functions now can be reproducible using a `random_state` parameter
+* Add warning for redundant rows when running :class:`hyppo.independence.MGC`
+* Tests now also retun a ``namedtuple``
+
+## Bug Fixes
+
+* Change from L1 to L2 distance for the median heuristic
+* Make median heuristic default for RBF and Gaussian kernels
+
+## Documentation
+
+* Update license in ``setup.py``
+* Change reference style and store reference in a ``refs.bib`` file
+* Updated papers in documentation from preprint to published versions
+* Rename badges in README
+
+## Maintenance
+
+* Added support for Python 3.9
+* Update ``pytest`` orbs version
+* Make repository citeable with the new GitHub feature
+
+## Authors
+
+* @sampan501
+* @kareef928 +
+* @rflperry
+* @Verathagnus +
+* @PSSF23 +
+* @hadasarik +
diff --git a/docs/changelog/v0.3.0.md b/docs/changelog/v0.3.0.md
new file mode 100644
index 000000000..bc5cb1c3e
--- /dev/null
+++ b/docs/changelog/v0.3.0.md
@@ -0,0 +1,87 @@
+# hyppo v0.3.0
+
+hyppo v0.3.0 is the culmination of 12 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better
+documentation. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations.
+
+This release requires Python 3.6+.
+
+## Highlights of this release
+
+## New features
+
+* Added goodness of fit module with FSSD
+* Added d-variate independence testing module with dHsic
+* Added fast HHG
+* Added Friedman Rafsky test
+* Added Smooth CF Test
+* Added Mean Embedding Test
+
+## Bug fixes
+
+* Copy `_contains_nan` function from SciPy since it is gone
+
+## Documentation
+
+* Made documentation more accessible with a new theme
+* Added example for block permutation
+* Mentioned types of inputs of `ndarrays`
+
+## Maintenance
+
+* Drafted PR to include Dcorr in SciPy
+* Bumped iPython for security reasons
+
+## Authors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Issues Closed
+
+* [#103](https://github.com/neurodata/hyppo/issues/103): Create a goodness of fit module in hyppo
+* [#104](https://github.com/neurodata/hyppo/issues/104): Add dHsic to hyppo
+* [#106](https://github.com/neurodata/hyppo/issues/106): Add fast HHG
+* [#140](https://github.com/neurodata/hyppo/issues/140): Discriminability `y` should allow arbitrary type or make docs more clear
+* [#187](https://github.com/neurodata/hyppo/issues/187): potential k-sample tests to add
+* [#211](https://github.com/neurodata/hyppo/issues/211): Fast Two-Sample Testing with Analytic Representations of Probability Measures
+* [#235](https://github.com/neurodata/hyppo/issues/235): Add existing permutation tree simulation notebook to docs
+* [#245](https://github.com/neurodata/hyppo/issues/245): Add more description about types of ndarray in all methods in documentation
+* [#249](https://github.com/neurodata/hyppo/issues/249): Add contributors to README
+* [#303](https://github.com/neurodata/hyppo/issues/303): [BUG] scipy 1.8 breaks hyppo import
+
+## PRs Merged
+
+* [#232](https://github.com/neurodata/hyppo/pull/232): Creating a goodness-of-fit module in hyppo
+* [#233](https://github.com/neurodata/hyppo/pull/233): Adding dHSIC
+* [#234](https://github.com/neurodata/hyppo/pull/234): Fast tstest
+* [#238](https://github.com/neurodata/hyppo/pull/238): Fast HHG Test
+* [#239](https://github.com/neurodata/hyppo/pull/239): Friedman Rafsky PR
+* [#242](https://github.com/neurodata/hyppo/pull/242): add permutation test example to docs
+* [#244](https://github.com/neurodata/hyppo/pull/244): Edited the types in the documentation section.
+* [#299](https://github.com/neurodata/hyppo/pull/299): Bump ipython from 7.19.0 to 7.31.1 in /docs
+* [#304](https://github.com/neurodata/hyppo/pull/304): Copy SciPy private _contains_nan function
diff --git a/docs/changelog/v0.3.1.md b/docs/changelog/v0.3.1.md
new file mode 100644
index 000000000..09b299c8e
--- /dev/null
+++ b/docs/changelog/v0.3.1.md
@@ -0,0 +1,17 @@
+# hyppo v0.3.1
+
+This release just fixes the bug of not adding autograd as a dependency.
+
+## Authors
+
+
+
+
+## Issues Closed
+
+N/A
+
+## PRs Merged
+
+N/A
diff --git a/docs/changelog/v0.3.2.md b/docs/changelog/v0.3.2.md
new file mode 100644
index 000000000..ecb6e370e
--- /dev/null
+++ b/docs/changelog/v0.3.2.md
@@ -0,0 +1,17 @@
+# hyppo v0.3.2
+
+This release just fixes the bug of using emojis in the long description so the package can build on Windows.
+
+## Authors
+
+
+
+
+## Issues Closed
+
+N/A
+
+## PRs Merged
+
+N/A
diff --git a/docs/changelog/v0.3.3.md b/docs/changelog/v0.3.3.md
new file mode 100644
index 000000000..1eec255cb
--- /dev/null
+++ b/docs/changelog/v0.3.3.md
@@ -0,0 +1,17 @@
+# hyppo v0.3.3
+
+> **Note:** hyppo v0.3.3 has not been released yet!
+
+This is a minor release for general bug fixes, documentation improvements, and general package maintenance.
+
+## Authors
+
+
+
+
+## Issues Closed
+
+## PRs Merged
+
+* [#232](https://github.com/neurodata/hyppo/pull/310): run pytest in parallel
diff --git a/docs/changelog/v0.4.0.md b/docs/changelog/v0.4.0.md
new file mode 100644
index 000000000..fe7bba667
--- /dev/null
+++ b/docs/changelog/v0.4.0.md
@@ -0,0 +1,27 @@
+# hyppo v0.3.0
+
+> **Note:** hyppo v0.4.0 has not been released yet!
+
+## Highlights of this release
+
+hyppo v0.3.0 is the culmination of 12 months of hard work. It contains
+many new features, numerous bug-fixes, improved test coverage and better
+documentation. All users are encouraged to
+upgrade to this release, as there are a large number of bug-fixes and
+optimizations.
+
+This release requires Python 3.6+.
+
+## New features
+
+## Bug fixes
+
+## Documentation
+
+## Maintenance
+
+## Authors
+
+## Issues Closed
+
+## PRs Merged
diff --git a/docs/conf.py b/docs/conf.py
index dac21b0ab..9d3066d41 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -8,23 +8,24 @@
# -- Path setup --------------------------------------------------------------
+import datetime
+
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
-import shutil
sys.path.insert(0, os.path.abspath(".."))
-sys.path.insert(0, os.path.abspath("sphinxext"))
-from github_link import make_linkcode_resolve
# -- Project information -----------------------------------------------------
+# General information about the project
+year = datetime.date.today().year
project = "hyppo"
-copyright = "2018"
-authors = u"Sambit Panda"
+copyright = "2018-{}, NeuroData".format(year)
+authors = "Sambit Panda"
# The short X.Y version
# Find hyppo version.
@@ -42,35 +43,104 @@
"sphinx.ext.autosummary",
"sphinx.ext.todo",
"sphinx.ext.mathjax",
- "numpydoc",
+ # "numpydoc",
"sphinx.ext.ifconfig",
"sphinx.ext.githubpages",
"nbsphinx",
"sphinx.ext.intersphinx",
- "sphinx.ext.linkcode",
+ "sphinx.ext.napoleon",
+ "sphinx_gallery.gen_gallery",
+ "sphinxcontrib.bibtex",
+ "myst_parser",
]
+bibtex_bibfiles = ["refs.bib"]
+bibtex_reference_style = "super"
+bibtex_default_style = "unsrt"
+
+autodoc_typehints = "none"
+
+napoleon_google_docstring = False
+napoleon_numpy_docstring = True
+
+napoleon_use_param = False
+napoleon_use_rtype = False
+napoleon_preprocess_types = True
+napoleon_type_aliases = {
+ # general terms
+ "sequence": ":term:`sequence`",
+ "iterable": ":term:`iterable`",
+ "callable": ":py:func:`callable`",
+ "dict_like": ":term:`dict-like `",
+ # objects without namespace
+ "ndarray": "~numpy.ndarray",
+}
+
# -- numpydoc
# Below is needed to prevent errors
+numpydoc_class_members_toctree = True
numpydoc_show_class_members = False
# -- sphinx.ext.autosummary
-autosummary_generate = True
+autosummary_generate = []
+
+# Otherwise, the Return parameter list looks different from the Parameters list
+napoleon_use_rtype = False
+# Otherwise, the Attributes parameter list looks different from the Parameters
+# list
+napoleon_use_ivar = True
+
+
+# uncomment line 55
+from sphinx_gallery.sorting import FileNameSortKey
+
+sphinx_gallery_conf = {
+ # path to your examples scripts
+ "examples_dirs": ["../examples", "../tutorials", "../sample_data", "../benchmarks"],
+ # path where to save gallery generated examples
+ "gallery_dirs": ["gallery", "tutorials", "sample_data", "benchmarks"],
+ "filename_pattern": r"\.py",
+ # Remove the "Download all examples" button from the top level gallery
+ "download_all_examples": False,
+ # Sort gallery example by file name instead of number of lines (default)
+ "within_subsection_order": FileNameSortKey,
+ # directory where function granular galleries are stored
+ "backreferences_dir": "api/generated/backreferences",
+ # Modules for which function level galleries are created. In
+ # this case sphinx_gallery and numpy in a tuple of strings.
+ "doc_module": "hyppo",
+ # Insert links to documentation of objects in the examples
+ "reference_url": {"hyppo": None},
+}
+
+html_css_files = [
+ "css/custom.css",
+]
# -- sphinx.ext.autodoc
-autoclass_content = "both"
-autodoc_default_flags = ["members", "inherited-members"]
-autodoc_member_order = "bysource" # default is alphabetical
+# autoclass_content = "both"
+# autodoc_default_flags = ["members", "inherited-members"]
+# autodoc_member_order = "bysource" # default is alphabetical
# -- sphinx.ext.intersphinx
intersphinx_mapping = {
- "numpy": ("https://docs.scipy.org/doc/numpy", None),
- "python": ("https://docs.python.org/3", None),
+ "python": ("https://docs.python.org/3/", None),
+ "numpy": ("https://numpy.org/doc/stable", None),
"scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
+ "matplotlib": ("https://matplotlib.org", None),
+ "sklearn": ("http://scikit-learn.org/stable", None),
}
+# Always show the source code that generates a plot
+plot_include_source = True
+plot_formats = ["png"]
+
# -- sphinx options ----------------------------------------------------------
-source_suffix = ".rst"
+source_suffix = {
+ ".rst": "restructuredtext",
+ ".txt": "markdown",
+ ".md": "markdown",
+}
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"]
master_doc = "index"
source_encoding = "utf-8"
@@ -79,45 +149,58 @@
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
html_static_path = ["_static"]
+html_extra_path = []
modindex_common_prefix = ["hyppo."]
+html_last_updated_fmt = "%b %d, %Y"
+add_function_parentheses = False
+html_show_sourcelink = False
+html_show_sphinx = True
+html_show_copyright = True
+
+import warnings
+
+warnings.filterwarnings("ignore", category=UserWarning)
pygments_style = "sphinx"
smartquotes = False
-# Use RTD Theme
-import sphinx_rtd_theme
-
-html_theme = "sphinx_rtd_theme"
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-html_theme_options = {"collapse_navigation": False, "navigation_depth": 4}
+html_theme = "pydata_sphinx_theme"
+html_theme_options = {
+ "icon_links_label": "Quick Links",
+ "icon_links": [
+ {
+ "name": "GitHub",
+ "url": "https://github.com/neurodata/hyppo",
+ "icon": "fab fa-github-square",
+ },
+ {
+ "name": "Paper",
+ "url": "https://arxiv.org/abs/1907.02088",
+ "icon": "fas fa-book",
+ },
+ {
+ "name": "NeuroData",
+ "url": "https://neurodata.io/",
+ "icon": "fas fa-brain",
+ },
+ ],
+ "use_edit_page_button": True,
+}
html_context = {
- # Enable the "Edit in GitHub link within the header of each page.
- "display_github": True,
- # Set the following variables to generate the resulting github URL for each page.
- # Format Template: https://{{ github_host|default("github.com") }}/{{ github_user }}/{{ github_repo }}/blob/{{ github_version }}{{ conf_py_path }}{{ pagename }}{{ suffix }}
"github_user": "neurodata",
"github_repo": "hyppo",
- "github_version": "master/docs/",
+ "github_version": "main",
+ "doc_path": "docs",
+ "galleries": sphinx_gallery_conf["gallery_dirs"],
+ "gallery_dir": dict(
+ zip(
+ sphinx_gallery_conf["gallery_dirs"],
+ sphinx_gallery_conf["examples_dirs"],
+ )
+ ),
}
-linkcode_resolve = make_linkcode_resolve(
- "hyppo",
- u"https://github.com/neurodata/"
- "hyppo/blob/{revision}/"
- "{package}/{path}#L{lineno}",
-)
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself. Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
@@ -186,27 +269,19 @@
epub_exclude_files = ["search.html"]
-def setup(app):
- # to hide/show the prompt in code examples:
- app.add_javascript("js/copybutton.js")
+# -- Other -------------------------------------------------------------------
-# -- Jupyter Notebook --------------------------------------------------------
+def setup(app):
+ # to hide/show the prompt in code examples:
+ app.add_js_file("js/copybutton.js")
-# def all_but_ipynb(dir, contents):
-# result = []
-# for c in contents:
-# if os.path.isfile(os.path.join(dir, c)) and (not c.endswith(".ipynb")):
-# result += [c]
-# return result
+# -- Accessibility -----------------------------------------------------------
+language = "en"
+extensions += ["sphinx_sitemap"]
-# shutil.rmtree(
-# os.path.join(PROJECT_PATH, "..", "docs/tutorials/benchmarks"), ignore_errors=True
-# )
-# shutil.copytree(
-# os.path.join(PROJECT_PATH, "..", "benchmarks"),
-# os.path.join(PROJECT_PATH, "..", "docs/tutorials/benchmarks"),
-# ignore=all_but_ipynb,
-# )
+html_baseurl = os.environ.get("SPHINX_HTML_BASE_URL", "https://127.0.0.1:8000/")
+sitemap_locales = [None]
+sitemap_url_scheme = "{link}"
diff --git a/docs/contributing.rst b/docs/contributing.rst
deleted file mode 100644
index f10bcead9..000000000
--- a/docs/contributing.rst
+++ /dev/null
@@ -1,167 +0,0 @@
-Contributing to hyppo
-*********************
-
-(adopted from scikit-learn)
-
-Submitting a bug report or a feature request
---------------------------------------------
-
-We use GitHub issues to track all bugs and feature requests; feel free to open
-an issue if you have found a bug or wish to see a feature implemented.
-
-In case you experience issues using this package, do not hesitate to submit a
-ticket to the
-`Issue Tracker `_. You are
-also welcome to post feature requests or pull requests.
-
-It is recommended to check that your issue complies with the
-following rules before submitting:
-
-- Verify that your issue is not being currently addressed by other
- `issues `_
- or `pull requests `_.
-
-- If you are submitting a bug report, we strongly encourage you to follow the
- guidelines in :ref:`filing_bugs`.
-
-.. _filing_bugs:
-
-How to make a good bug report
------------------------------
-
-When you submit an issue to `Github
-`__, please do your best to
-follow these guidelines! This will make it a lot easier to provide you with
-good feedback:
-
-- The ideal bug report contains a **short reproducible code snippet**, this way
- anyone can try to reproduce the bug easily (see `this
- `_ for more details). If your snippet is
- longer than around 50 lines, please link to a `gist
- `_ or a github repo.
-
-- If not feasible to include a reproducible snippet, please be specific about
- what **classes and/or functions are involved and the shape of the data**.
-
-- If an exception is raised, please **provide the full traceback**.
-
-- Please include your **operating system type and version number**, as well as
- your **Python and hyppo versions**. This information
- can be found by running the following code snippet::
-
- import platform; print(platform.platform())
- import sys; print("Python", sys.version)
- import hyppo; print("hyppo", hyppo.__version__)
-
-- Please ensure all **code snippets and error messages are formatted in
- appropriate code blocks**. See `Creating and highlighting code blocks
- `_
- for more details.
-
-Contributing Code
------------------
-
-The preferred workflow for contributing to `hyppo` is to fork the main
-repository on GitHub, clone, and develop on a branch. Steps:
-
-1. Fork the `project repository `__ by clicking
- on the βForkβ button near the top right of the page. This creates a copy
- of the code under your GitHub user account. For more details on how to
- fork a repository see `this
- guide `` is enough.
- ``Fix #`` is not enough.
-- All public methods should have informative docstrings with sample
- usage presented as doctests when appropriate.
-- At least one paragraph of narrative documentation with links to
- references in the literature (with PDF links when possible) and
- the example.
-- All functions and classes must have unit tests. These should include,
- at the very least, type checking and ensuring correct computation/outputs.
-- Ensure all tests are passing locally using ``pytest``. Install the necessary
- packages by:
-
- .. code:: bash
-
- $ pip install pytest pytest-cov
-
- then run
-
- .. code:: bash
-
- $ pytest
-
- or you can run pytest on a single test file by
-
- .. code:: bash
-
- $ pytest path/to/test.py
-
-- Run an autoformatter. We use ``black`` and would like for you to
- format all files using ``black``. You can run the following lines to
- format your files.
-
- .. code:: bash
-
- $ pip install black
- $ black path/to/module.py
-
-Coding Guidelines
------------------
-
-Uniformly formatted code makes it easier to share code ownership. ``hyppo``
-package closely follows the official Python guidelines detailed in
-`PEP8 `__
-guidelines. Refer to the
-`example.py `__
-provided by numpydoc.
diff --git a/docs/development/contributing.rst b/docs/development/contributing.rst
new file mode 100644
index 000000000..e779ad338
--- /dev/null
+++ b/docs/development/contributing.rst
@@ -0,0 +1,2 @@
+.. include:: ../../CONTRIBUTING.md
+ :parser: myst_parser.sphinx_
\ No newline at end of file
diff --git a/docs/development/index.rst b/docs/development/index.rst
new file mode 100644
index 000000000..5343de764
--- /dev/null
+++ b/docs/development/index.rst
@@ -0,0 +1,7 @@
+Development
+================
+
+.. toctree::
+
+ contributing.rst
+ license.rst
\ No newline at end of file
diff --git a/docs/development/license.rst b/docs/development/license.rst
new file mode 100644
index 000000000..150554d81
--- /dev/null
+++ b/docs/development/license.rst
@@ -0,0 +1,2 @@
+.. include:: ../../LICENSE.md
+ :parser: myst_parser.sphinx_
\ No newline at end of file
diff --git a/docs/get_start/citing.rst b/docs/get_start/citing.rst
new file mode 100644
index 000000000..d06774e3d
--- /dev/null
+++ b/docs/get_start/citing.rst
@@ -0,0 +1,24 @@
+Citing hyppo
+*************
+
+|arXivshield|_
+
+.. |arXivshield| image:: https://img.shields.io/badge/arXiv-1907.02088-red.svg?style=flat
+.. _arXivshield: https://arxiv.org/abs/1907.02088
+
+If you use hyppo in a scientific publication, we would appreciate
+citations to the following paper (APA below):
+
+ Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, & Joshua T. Vogelstein. (2020). hyppo: A Comprehensive Multivariate Hypothesis Testing Python Package.
+
+ Bibtex entry::
+
+ @misc{panda2020hyppo,
+ title={hyppo: A Comprehensive Multivariate Hypothesis Testing Python Package},
+ author={Sambit Panda and Satish Palaniappan and Junhao Xiong and Eric W. Bridgeford
+ and Ronak Mehta and Cencheng Shen and Joshua T. Vogelstein},
+ year={2020},
+ eprint={1907.02088},
+ archivePrefix={arXiv},
+ primaryClass={stat.CO}
+ }
\ No newline at end of file
diff --git a/docs/get_start/index.rst b/docs/get_start/index.rst
new file mode 100644
index 000000000..c622295a2
--- /dev/null
+++ b/docs/get_start/index.rst
@@ -0,0 +1,8 @@
+Getting Started
+================
+
+.. toctree::
+
+ install.rst
+ overview.rst
+ citing.rst
\ No newline at end of file
diff --git a/docs/get_start/install.rst b/docs/get_start/install.rst
new file mode 100644
index 000000000..d7a0a8106
--- /dev/null
+++ b/docs/get_start/install.rst
@@ -0,0 +1,73 @@
+Installing
+============
+
+Which Python?
+-------------
+
+Youβll need **Python 3.6 or greater**.
+
+If you want to
+create and work with Python virtual environments, please follow instructions
+on `venv `_ and `virtual
+environments `_.
-
-History
--------
-
-``hyppo`` is a rebranding of ``mgcpy``, which was founded in September 2018.
-The original version was designed and written by Satish Palaniappan, Sambit
-Panda, Junhao Xiong, Sandhya Ramachandran, and Ronak Mehtra. This new version
-was written by Sambit Panda.
-
-Documentation
-=============
-
-.. toctree::
- :maxdepth: 1
-
- install
- tutorials
- reference/index
- contributing
- news
- license
-
+.. include:: ../README.md
+ :parser: myst_parser.sphinx_
+
.. toctree::
- :maxdepth: 1
- :caption: Useful Links
-
- hyppo @ GitHub
- hyppo @ PyPi
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`search`
+ :hidden:
+ :caption: Getting Started
+
+ get_start/index.rst
+ user_guide/index.rst
+ api/index.rst
+ changelog/index.rst
+ development/index.rst
diff --git a/docs/install.rst b/docs/install.rst
deleted file mode 100644
index f1fb12179..000000000
--- a/docs/install.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-Install
-=======
-
-Below we assume you have the default Python environment already configured on
-your computer and you intend to install ``hyppo`` inside of it. If you want to
-create and work with Python virtual environments, please follow instructions
-on `venv `_ and `virtual
-environments "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_wall_times()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/tutorials/independence/indep_alg_walltimes.R b/docs/tutorials/independence/indep_alg_walltimes.R
deleted file mode 100644
index bae98be9f..000000000
--- a/docs/tutorials/independence/indep_alg_walltimes.R
+++ /dev/null
@@ -1,33 +0,0 @@
-require("energy")
-require("kernlab")
-require("mgc")
-require("HHG")
-require("microbenchmark")
-
-num_samples_range = c(50, 100, 200, 500, 1000, 2000, 5000, 10000)
-linear_data <- list()
-i <- 1
-for (num_samples in num_samples_range){
- data <- mgc.sims.linear(num_samples, 1)
- x <- data$X
- y <- data$Y
- times = seq(1, 3, by=1)
- executions <- list()
- for (t in times){
- #x <- as.matrix(dist((x), diag = TRUE, upper = TRUE))
- #y <- as.matrix(dist((y), diag = TRUE, upper = TRUE))
- #time_taken <- microbenchmark(kmmd(x, y, ntimes=1000), times=1, unit="secs") # best of 5 executions
- #time_taken <- microbenchmark(dcor.test(x, y, R=1000), times=1, unit="secs") # best of 5 executions
- #time_taken <- microbenchmark(dcor.test(x, y, R=1000), times=1, unit="secs") # best of 5 executions
- time_taken <- microbenchmark(dcor2d(x, y), times=1, unit="secs") # best of 5 executions
- #time_taken <- microbenchmark(hhg.test(x, y, nr.perm = 1000), times=1, unit="secs") # best of 5 executions
- executions <- c(executions, list(time_taken[1, 2]/(10^9)))
- }
- linear_data <- c(linear_data, list(sapply(executions, mean)))
-
- print("Finished")
- i <- i + 1
-}
-
-df <- data.frame(matrix(unlist(linear_data), nrow=length(linear_data), byrow=T), stringsAsFactors=FALSE)
-write.csv(df, row.names=FALSE)
diff --git a/docs/tutorials/independence/indep_power.ipynb b/docs/tutorials/independence/indep_power.ipynb
deleted file mode 100644
index a226ee7a8..000000000
--- a/docs/tutorials/independence/indep_power.ipynb
+++ /dev/null
@@ -1,502 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys, os\n",
- "from joblib import Parallel, delayed\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "\n",
- "# custom seaborn plot options to make the figures pretty\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=2)\n",
- "PALETTE = sns.color_palette(\"Set1\")\n",
- "sns.set_palette(PALETTE[3:])\n",
- "\n",
- "sys.path.append(os.path.realpath('..'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Independence Power"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## vs. Sample Size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we are evaluating the statistical power of each of the independence tests implemented in `hyppo` when increasing the sample size of the 20 independence simulations included in the package. The testing power for a given level of $\\alpha$ (Type 1 error level) test is equal to the probability of correctly rejecting the null hypothesis when the alternative is true. For a test to be consistent, statistical power must converge to 1 as the sample size increases to $\\infty$.\n",
- "\n",
- "The following imports from `hyppo` are for all the independence tests, simulations, and the `power_sample` method located within this directory."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from benchmarks import power_sample\n",
- "from hyppo.independence import *\n",
- "from hyppo.sims import *"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are some constants that are used in this section of the notebook. If running these notebook, please only manipulate these constants if you are not running more tests."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# sample sizes and number of replications to average over for power curves\n",
- "MAX_SAMPLE_SIZE = 100\n",
- "STEP_SIZE = 5\n",
- "SAMP_SIZES = range(5, MAX_SAMPLE_SIZE + STEP_SIZE, STEP_SIZE)\n",
- "POWER_REPS = 5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "simulations = [\n",
- " linear,\n",
- " exponential,\n",
- " cubic,\n",
- " joint_normal,\n",
- " step,\n",
- " quadratic,\n",
- " w_shaped,\n",
- " spiral,\n",
- " uncorrelated_bernoulli,\n",
- " logarithmic,\n",
- " fourth_root,\n",
- " sin_four_pi,\n",
- " sin_sixteen_pi,\n",
- " square,\n",
- " two_parabolas,\n",
- " circle,\n",
- " ellipse,\n",
- " diamond,\n",
- " multiplicative_noise,\n",
- " multimodal_independence\n",
- "]\n",
- "\n",
- "# add/comment out tests here to run/not run them\n",
- "tests = [\n",
- " MGC,\n",
- " CCA,\n",
- " Dcorr,\n",
- " HHG,\n",
- " Hsic,\n",
- " RV,\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following function calculates the estimated power ``POWER_REPS`` number off times and averages them. It does this iterating over the number of sample sizes. Notebooks containing the script used to reproduce these curves can be found under the `benchmarks` folder in the repo."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def estimate_power(sim, test):\n",
- " est_power = np.array([np.mean([power_sample(test, sim, n=i) for _ in range(POWER_REPS)])\n",
- " for i in SAMP_SIZES])\n",
- " np.savetxt('../independence/vs_samplesize/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " est_power, delimiter=',')\n",
- " \n",
- " return est_power"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "outputs = Parallel(n_jobs=-1, verbose=100)(\n",
- " [delayed(estimate_power)(sim, test) for sim in simulations for test in tests]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we plot the results from the power computed from the CSVs in the `vs_sampsize` directory over each of the 20 simulations and for each test run. Using the saved CSVs ensures that the power curves do not have to be run every time the script is run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_power():\n",
- " fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28,24))\n",
- " \n",
- " sim_title = [\n",
- " \"Linear\",\n",
- " \"Exponential\",\n",
- " \"Cubic\",\n",
- " \"Joint Normal\",\n",
- " \"Step\",\n",
- " \"Quadratic\",\n",
- " \"W-Shaped\",\n",
- " \"Spiral\",\n",
- " \"Bernoulli\",\n",
- " \"Logarithmic\",\n",
- " \"Fourth Root\",\n",
- " \"Sine 4\\u03C0\",\n",
- " \"Sine 16\\u03C0\",\n",
- " \"Square\",\n",
- " \"Two Parabolas\",\n",
- " \"Circle\",\n",
- " \"Ellipse\",\n",
- " \"Diamond\",\n",
- " \"Multiplicative\",\n",
- " \"Independence\"\n",
- " ]\n",
- " \n",
- " for i, row in enumerate(ax):\n",
- " for j, col in enumerate(row):\n",
- " count = 5*i + j\n",
- " sim = simulations[count]\n",
- " \n",
- " for test in tests:\n",
- " mgc_power = np.genfromtxt('../independence/vs_samplesize/{}_MGC.csv'.format(sim.__name__),\n",
- " delimiter=',')\n",
- " power = np.genfromtxt('../independence/vs_samplesize/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " delimiter=',')\n",
- " \n",
- " custom_color = {\n",
- " \"Dcorr\" : \"#377eb8\",\n",
- " \"Hsic\" : \"#4daf4a\",\n",
- " \"MGC\" : \"#e41a1c\",\n",
- " }\n",
- " if test.__name__ in custom_color.keys():\n",
- " if test.__name__ == \"MGC\":\n",
- " col.plot(SAMP_SIZES, power - mgc_power, custom_color[test.__name__], label=test.__name__, lw=5)\n",
- " else:\n",
- " col.plot(SAMP_SIZES, power - mgc_power, custom_color[test.__name__], label=test.__name__, lw=3)\n",
- " else:\n",
- " col.plot(SAMP_SIZES, power - mgc_power, label=test.__name__, lw=1)\n",
- " col.set_xticks([])\n",
- " if i == 3:\n",
- " col.set_xticks([SAMP_SIZES[0], SAMP_SIZES[-1]])\n",
- " col.set_ylim(-1.05, 1.05)\n",
- " col.set_yticks([])\n",
- " if j == 0:\n",
- " col.set_yticks([-1, 1])\n",
- " col.set_title(sim_title[count])\n",
- " \n",
- " fig.text(0.5, 0.08, 'Sample Size', ha='center')\n",
- " fig.text(0.08, 0.5, 'Absolute Statistical Power', va='center', rotation='vertical')\n",
- " leg = plt.legend(bbox_to_anchor=(0.5, 0.08), bbox_transform=plt.gcf().transFigure,\n",
- " ncol=5, loc='upper center')\n",
- " leg.get_frame().set_linewidth(0.0)\n",
- " for legobj in leg.legendHandles:\n",
- " legobj.set_linewidth(5.0)\n",
- " plt.subplots_adjust(hspace=.50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": "",
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- },
- "metadata": {}
- }
- ],
- "source": [
- "plot_power()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this figure, thoses tests that have higher statistical power MGC at a given sample size are above the red line, and those that have a lower statistical power than MGC are below it. Here, we see that in most simulation settings, MGC performs as good as or better than many simulations as sample size increases. For some of the more monotonically increasing simulations, some of classical independence tests. HHG outperforms MGC ocassionally with some of the more sinusodially based algorithms, but is much slower to calculate p-values and independence tests."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## vs. Dimension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we are evaluating the statistical power of each of the independence tests implemented in `hyppo` when increasing the dimension of the 20 independence simulations included in the package.\n",
- "\n",
- "These are some constants that are used in this section of the notebook. If running these notebook, please only manipulate these constants if you are not running more tests."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "POWER_REPS = 5\n",
- "\n",
- "tests = [\n",
- " MGC,\n",
- " CCA,\n",
- " Dcorr,\n",
- " HHG,\n",
- " Hsic,\n",
- " RV,\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These helper functions return the number of dimensions to test over and the range for each simulation. Limits were chosen based on when it is clear to determine that the tests were tending to 0 statistical power."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "def find_dim(sim):\n",
- " if sim not in simulations:\n",
- " raise ValueError(\"Invalid simulation\")\n",
- " \n",
- " if sim in [joint_normal, sin_four_pi, sin_sixteen_pi, multiplicative_noise]:\n",
- " dim = 10\n",
- " elif sim in [multimodal_independence, uncorrelated_bernoulli, logarithmic]:\n",
- " dim = 100\n",
- " elif sim in [linear, exponential, cubic]:\n",
- " dim = 1000\n",
- " elif sim in [square, diamond]:\n",
- " dim = 40\n",
- " else:\n",
- " dim = 20\n",
- " \n",
- " return dim\n",
- "\n",
- "def find_dim_range(dim):\n",
- " if dim < 20:\n",
- " lim = 10\n",
- " else:\n",
- " lim = 20\n",
- " \n",
- " dim_range = list(range(int(dim/lim), dim+1, int(dim/lim)))\n",
- " if int(dim/lim) != 1:\n",
- " dim_range.insert(0, 1)\n",
- " return dim_range"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following function calculates the estimated power ``POWER_REPS`` number off times and averages them. It does this iterating over the number of dimensions.\n",
- "\n",
- "**Note: We only recommend running this code if running the next 2 cells ONCE to generate the csv files used to visualize the plots. This code takes a very long time to run and if running, we recommend using a machine with many cores.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def estimate_power(sim, test):\n",
- " dim = find_dim(sim)\n",
- " dim_range = find_dim_range(dim)\n",
- " est_power = np.array([np.mean([power_dim(test, sim, p=dim) for _ in range(POWER_REPS)])\n",
- " for dim in dim_range])\n",
- " np.savetxt('../independence/vs_dimension/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " est_power, delimiter=',')\n",
- " \n",
- " return est_power"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "outputs = Parallel(n_jobs=-1, verbose=100)(\n",
- " [delayed(estimate_power)(sim, test) for sim in simulations for test in tests]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we plot the results from the power computed from the CSVs in the `vs_dimension` directory over each of the 20 simulations and for each test run. Using the saved CSVs ensures that the power curves do not have to be run every time the script is run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_power():\n",
- " fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28,24))\n",
- " \n",
- " sim_title = [\n",
- " \"Linear\",\n",
- " \"Exponential\",\n",
- " \"Cubic\",\n",
- " \"Joint Normal\",\n",
- " \"Step\",\n",
- " \"Quadratic\",\n",
- " \"W-Shaped\",\n",
- " \"Spiral\",\n",
- " \"Bernoulli\",\n",
- " \"Logarithmic\",\n",
- " \"Fourth Root\",\n",
- " \"Sine 4\\u03C0\",\n",
- " \"Sine 16\\u03C0\",\n",
- " \"Square\",\n",
- " \"Two Parabolas\",\n",
- " \"Circle\",\n",
- " \"Ellipse\",\n",
- " \"Diamond\",\n",
- " \"Multiplicative\",\n",
- " \"Independence\"\n",
- " ]\n",
- " \n",
- " for i, row in enumerate(ax):\n",
- " for j, col in enumerate(row):\n",
- " count = 5*i + j\n",
- " sim = simulations[count]\n",
- " \n",
- " for test in tests:\n",
- " mgc_power = np.genfromtxt('../independence/vs_dimension/{}_MGC.csv'.format(sim.__name__),\n",
- " delimiter=',')\n",
- " power = np.genfromtxt('../independence/vs_dimension/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " delimiter=',')\n",
- " dim_range = find_dim_range(find_dim(sim))\n",
- " \n",
- " custom_color = {\n",
- " \"Dcorr\" : \"#377eb8\",\n",
- " \"Hsic\" : \"#4daf4a\",\n",
- " \"MGC\" : \"#e41a1c\",\n",
- " }\n",
- " if test.__name__ in custom_color.keys():\n",
- " if test.__name__ == \"MGC\":\n",
- " col.plot(dim_range, power - mgc_power, custom_color[test.__name__], label=test.__name__, lw=5)\n",
- " else:\n",
- " col.plot(dim_range, power - mgc_power, custom_color[test.__name__], label=test.__name__, lw=3)\n",
- " else:\n",
- " col.plot(dim_range, power - mgc_power, label=test.__name__, lw=1)\n",
- " col.set_xticks([])\n",
- " if i == 3:\n",
- " col.set_xticks([dim_range[0], dim_range[-1]])\n",
- " col.set_ylim(-1.05, 1.05)\n",
- " col.set_yticks([])\n",
- " if j == 0:\n",
- " col.set_yticks([-1, 1])\n",
- " col.set_title(sim_title[count])\n",
- " \n",
- " fig.text(0.5, 0.08, 'Dimensions', ha='center')\n",
- " fig.text(0.08, 0.5, 'Absolute Statistical Power', va='center', rotation='vertical')\n",
- " leg = plt.legend(bbox_to_anchor=(0.5, 0.08), bbox_transform=plt.gcf().transFigure,\n",
- " ncol=5, loc='upper center')\n",
- " leg.get_frame().set_linewidth(0.0)\n",
- " for legobj in leg.legendHandles:\n",
- " legobj.set_linewidth(5.0)\n",
- " plt.subplots_adjust(hspace=.50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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uWIAP7Oq65qLR/QcLL6XgIt73sVpbxgKW9jn80U+u62ob1WZs1HH9U0e1aXJ6tszJmRInZ8qcmimtSrUU8EmEVLklEkGfvLwdVC/viwYUkmGVRMi3LEGfdM9qnbiue9c+23Fczi1UqWomiZCPZMhHLOhrk25rYCPajY04prVwfqHCH//jeV65kFvX8bGgskwqDaVCdMb8HJ8s8sLZRQpXRMjs6pvmix/6LvFggx+c+hTpyFM8tq2LZNhHqWEyW2wwm6/QrL1GVH2FrYMnCQUbHD3vkUqvjm+moV/9YvqezSk+88gwh0dSt/X7L9R0Xjqf49vHZlelhwMIKPBbH5/kqV3fpFns4siXH8Coj3Lg07vp399zjc8t4tjP4TrPIErH0es7qGYeoDC5H9uM0bcvTLR7HkFYIozOAxN4i8RbgK0tvQ3vBX3pMxwcW+P0d09x6tun2PPxEbZ/cBBRsgCjJXVc91s47quMZz7Ed44/xg/HYSJb41bfMiXR5v07TvGZR44SDTT4u9cP8+alTXSF9/D5J7eztSd644usggP8I/Dl1ph/Gdh33TPMpknmbI75U4ssnF5k4UKeSG+E1PY0yS1pxJrB2LfP4o/4eOQXCnRu+xqCsAP4l7zTtUdh49qNjTquewsL05mibpxBs8/iupNI4ixBOYsqNck3/GQbEiEFEn6LiKpj2wqaHsXSErhaGkdLYzYTmM04RjOB2YhhajFcx8t64TouruviOt58yXVdWGqv3O+C67gYdeMq0sixXQIxP/6oij+i4o+qqC0diKqIEYGSv0hOXiTjzDNnzDFdn2axkbkj31LACRIxYoSqHtHkKwRICkk6g110RTqJdwWJdvkId/gIdSiEkjKKagMml+1jATiLZZwCzmKbLjNTES7WfJyyTE5WdTINibXejx1bwawOoFeGMCrDOOadJ6YFqYkczKIEsgTCC2ztmWFHd3GZXAorDueKl6OWzhUVaubVZJpPdEkHbHrCEt1Bga4QdAQckn6LhN8krmpIgkvNDNEwI2h2FN2OYzkpbCeNSyfQjU9KLBNTfjmAX1LxywFkcf1RnRvVZmzUcd3PMDWL2mINvW6SGo6jBJR7fUv3KTQ8x6ZLV8gUHnE03JI+PEJ6YYVk8NYae7hMLK0kmnqB5E3djV4zUMP3/zpkm0ja4Gg/FN6d0KozVBe/RjD+fSzdYvbEYRB+lP59+2ioEq+O55FEgUe2pEmGVcDBdd/GNv8RQXgO2xIoTh1i7sQOZt9OUc3UaZY0RFlElERERSDWUyS1KUNyMENicIHEwDy2KVOe76My30ct248r7CTasYnUpiSxvuiaKeZ00+Y7x+f4zvG5ljeny1KqfGdpku66XvyM64ILjgsuLv2JIJ97bBMPb07fYFHEAN4EzuB5C8lXiHKN7SCwg5UT5EprYfnUTIlTM14B51Jjda55RRJ4YkcXHzvQxwMjqfYC6gpsVJuxUcf1Twm243IxW+PktPfbPjFTWneh+FuBqojLREsitJpkSoZ9pMMqqYhKOqwS9ss3vfBr2Q5zpSZTuTpT+QbTeU9P5evkqzojnWEOj6Z5cCTJgaEkft+tFyyuNE1eHc/x8rkcL1/IUayvXhgXBYgFvXF5Y1ZJhr2xLxFsEb+MabuYtoNhOcvasBxMy8GwV2vHdRnpDLNvMEFvInDPSDnHcXl+bJG/eHGCmUKD3/74Lp7ctb60sRvRbmzEMa3EXLHJnz17ge++PbeKaJAEB3uNiKCbwUAqx7946mkODM8yU/gcw+n/jpB/fZ6KeuMixbnv4EovkeyZYHq6j5endvL0uWEuZLpXRSuNdoX5zMPDfGhPD751pP51XZfxxRpHzmY5cnaRU7Plq0gWWYLf+OgCH933TfSyytE/O4zZ3MOBn9pN766um/gW6sBLwLPAy0AAqAGjrCaNNgOhdV+1PF/lhT9+BbNp8sSvPExqOHHFETPAfwKeBj5IQ/8Mp2YjnJgucXK6xImpIlX9+onkgqrGTz7wJp966CVK9QTPnXmKi7lDpMNBfuxAH9t7b5ZAuhIW8C3gK3jj/wIeeXZj2KZN9kKBhdOLzJ9eRFZlDv6US3Loqy3HqV8DDt7m/d06Nqrd2KjjultwXZeG1SDfzFI2JtCt89j2RSRmCSk54oEqqaBOUROYrcnM1SVP1yTmajLZhohzFanhEvG5pPw2Sb9DKuDQoUKXKtHhF0gGLKIBg6BqoGtBjGYcu5HC0hKYWtzTzQSmnsDSkzhmGEEUEUQBQQAEAV9IwR/1E2gRRf6oityat5m2yUxthqnqJFOVSaYqU0xVJ8k15lEkB5/kokrgk1x8oosqt7Tk4lvql1yCskAqEEUVXXSnBhgoIiii64m0oi2CIq1oi25r22vLovcubdkiti1hmyK2IeGYCoLtR0RFEFW0RpCz4wHeLrmckXRmxEVc4dp20Gom0SvDGJUhzHrvNaOOOiIqfckg/SskHVEpNwxyVd2Tmk5+RbtYN9ZF7guigRzIIQcXSSdm2TUwzc6uAtuTBpvjFgVNZLyk4JcdOgIO6YCNX3bJNyWyTZG8JpFriOSaEtmmRK7ptWvmUjr4m8ehrgf4nYd+d12E0ka1GRt1XO9WuK6LVtWpLdapZuvUsvWr2pZuEe4IoQRkijMV4n1RurZ30L29g+4dHYQ71j/HujEsvPrnMW43AscybPSajlEz0OsGeu2yGHUDo2l62YZ0C2sp25BhY63U5uVt27CRFAk17LssERU15EON+FDDaqtfIpjMEoxfwhe6iOSbRBCmEIQ8Hkk0vEKGWhK+wWgcPML+SnJpqT3T+s4OtuQQHum0GrZpM350kpPfOkv+YpHUpgSj7x1i9NGhO/x3vPx59XwDQRC8Z6EoIEgC4tKzcXl7xf6bfE9vE0kbHO2HwrsH1Uye4szf4488Tax3isXz+3Hsj9K59X1kdNvLuX8msyp/vSjAoU0pntrdzft2dLaiaFxgDHimJRrwfrwX9wutfefwjNo2YHtLtuGlD1gf6prFN16f5q9fvrSufPXXw6FNSb74wa3s6Ivd1nWuhGU7XMhUOTlT5vRMmVOzZSZzN7ew3Bn189H9vXz0QB/9yVvPz2xaDhezNeq6xY7e2G0t+t5LbFSbsVHHda/hOK5HIMPyBGRpGrI0H7lyYmLZDrrloJv2Vdqwrt43X9I4NVPi9GyZhnHjihMhVcbFpXGDRcU7CVUWSUVUUmGV9ArttX0oksh0iyRaIo1mi801C9ivBUUS2DMQXyaWtvfGrkuAu67LhUyVl87leOl8lhPTJdb5UXcFHRGVvYMJ9g3G2T+UYLQrctcJfNd1efFslq88e4FzC9Xl/ke3dvAHn13fguxGtBsbcUwApbrB//PiBH/7wynMFXnChlJZfvWxZzm85wSVxSQz491cmOzlXGaQsUIveUmlKHg+3tdCf7jG//Kxlzi0+S0E4WeRpZ/hdl60HbtGZeEZmuVnUZMnUaI6r10c4bWJEV6/OMp0PgUIpMI+PnV4kE8+OHBVFLVhObx1qcCLZ7McObfIQklb87MkEb74wQKffODvsRs6L//5YSz9MAc+vYfOLeufE64NAy/6uQfPCej24LouZ78/zqt/+RY7P7yFgz+1B0m58roFLOOvEIRvUJrbyqlvvYcLL/gJd4WRNyepdAYR4wE6OkIkYn4ifpl4qEAq/Heo8rcRhIeAzwK7b/t+rw0D+AbwVbyFhf8Rb+ECwG7t11eIgTeXX9n/PeBtvOimH+H2an7ePjaq3dio47oV2K5NSSuS1/Lkm3kWK4sUatO4ziV8ygKRQIFUqE53xKA3ZGM4MFtdTRbN1mQW6hKmc+ef76LgklCdZbJpIOKnN+yjMyCSDFhEfDpBuY4omEAagU4EwUv5DQKO26RhFmlaRQy7gunUsN0GAvoyGeSRRaBK3jNEtwUMW0B3wGi1DVsA/MhiGJ8UwS/FCClJgkoSUfADKuBDt1wqRpOS3qSo1ShoVfLNCtlmicVmCc1yMB0wbQHTEby2I7S2WU75thZcR8HRoyCaSGrlmsddjjryyKOlqCNBgK6ofxVR1J/ydF8iQMB383XXLNuhWDdWkUzZaotsqnmEU76qU6gbV899BQvZn8cXyjDae4mt3fNotkNOE8hpUDadVqTq3cO/e/wP2J7cfsPjNqrNuN/HZVgOs0Xv/SpfncKv6ESDYWKBKMlQlGQkil95Z6JAbNOmUahRL1XQylW0ShWtUkOv1tDrdep5jXqxiSiKBBNBgokgoUSIYCpIKBkkmAoRToZQo34EQQRUbDNM9kKBzFiWhTNZFsYWkRXJI5Z2dNC1o4PUcOK66TEd26GWa1BZqFLN1Kjl8/gCb5Dof5PObWexTQk1rOE6Ino1gF4LodeDGPUQRjOE2QhhNMNYeqQlUcymj2bFQSs7NEs2etXCcVyP5An78K0kf8Ie+aMEZGSfhNQS2Scj+UQk5XLb05J3nCJimw56VUevG2hVA7NeB3ECWR1HjUwQTEwSSs9TqUSYmO3m9FwPEwudzCymyZbigIwoCEiiR56IoteWJHFZy7LI9sE4Tz44wK7+GPK6Uo06eBH4b64QFY9QOkijtJPT36lx5nsXSA4l2P1j2+jf38P8qUXGX7zExVenSfTHGH1smJFHBgkmbi0TlOu4FCZLzL49z8zxBRZOZ1EjPmhF5bqOg+Ncjth17BXRuy1BgMe+8BA7P7xlXZ/ZJpI2OO73h8L9BkuvUc9N06zMotfnsPRFcLNISpbunedpFgewjB8h1v8TTBQcnmuRR+vxqpdEgYdGPVLp8e2dhP0KHql0EY9QmsQjk5ZIo1sjbfI1na+/MsnfvjZNTbuzk7YP7OrmC09tuS3CxnZcXh3P8a03Zzl6PotuOjc8J+KX2dUfY7QzwhuXCozNrT3pPjCc4GMH+nj/zq7rTqI102Y8U+XsfJWz8xXOzlcYz1SXF6/8isSjW9O8b2cXj27pIKjeXiHkhm7x2kSeI+eyvHWpgCyJDKdDDHeEGUqH2NQRYjAVuu3PgY1rMzbquG4GjuO2iBob3XTQWlpfoTXTpqHb1DSTum5R0yxqukVjRbuuW9Rb7eY6iJ27CUkU2NIdYXd/zCu63h+jP+kVXHccl6Zpe/fbGkNdv3LbG0e5YVKsGxTr3ktusWagWze2LfcKEb/MoU1JHhxJcXg0RX8ySMOweX0iz0vnPfIoW9GveX4i5GMoHVoe88p0gO8EgqrE3oE4+wYT7BtKsLMvhv+qBeNbg+u6HDmX5SvPjnN2frWt9ysS/8fPHOCBkevVPbmMjWg3NtqYNMPmr1+Z5C+PXKSuX/4/Hk4v8iuPP8fB0fM0ch8nOfgLCOIicArHOQnOCQRxDrM5RKM8SmZ+kHMXexibiTLfMCkYNqO9Mv/DTxyjr+NbCMKP4tWlid/R+zcaJhdffZ252e/j3z7J6NYJXFfg9YsjvD4xyusXR6g0E/zY/j4+drBvOfLo1Qu5NYl1VTZJhOo8OCLzyFabh0e/i6sv8ur/+xCW/jgHPrV3jWifdxfq+QZHvvwapdkKT3zxPQQTfm/h5EyWzFiWaqZG984wuz56mr69P0AUNyHK/z0r6xN5OI0XxfQK8GPAZ1jLU/TuoQF8rXUPFh5BZOMtMiyJb422D9iFR3jdrfTSN4eNZjeWsFHHdSVs16aoFcnWF5kvzLNQXGCxtkjZWMAUsuArEgpW6YlY9IUtekM2fWEbVXZbBJEXUbREFs3VJeprpCBbhisguWFEJ4JoR8CO4JphHCuErYexrQCi3ETyVRGVKigVkCs4UgVHLOMKtz4n8UkuSdWmMyDRFw7SHZQxHI2iUUWz3RYZ5BFDeksMW8BwvP6lbccVicgdJJRe4nIvcbmHmNxDROpEQMZ1vUwcTiszx1KWDt1ammfay/PolfNOb+5sISo1JF8FSa0g+cqILS35Kki+W4+2X4o6smrDpOURBpIR+hKryaLeeAD1Ds25bha241KqG6sjmq4gnwo1ffl94/JqoguihSCaq0Uyr9FnIYjGZS2tca7oHWdUB/nTH/19RjpuHJW6UW3G/TAu23HJlJte9oZcnelCg/liCUU+R2/iPLv7p9kzMIkAVJoBVMXCrxioiolfMbEdEcPyYTk+bEfFdVVwVQT8iK6K6wrYjoXjOjiujevaOK6Diw20tOuC4IDgIOAgSxaKZCGLNopseduyDbjYtoxlyzi2jOPKuK6Ci+JFgUhLDpdLNbHhco1sF6GlEVwUUcN240jCART5ILAf1x2kMl9bNTeq5Rp0bEnRvb2DWF+Eeq5BZaFGJVOjmqlRzzeI97tsfmyagQNnSQxcQK9txjIeQ/F/ADUy0CqHUcW1izh2AdctgFvCq8ldArGEKJYRpTKiXEaUGq3SExYIJp7TizdOx/HGb9gyhiWhmSKaIWHYPnBDSGIIRQ7jV6IEfVEi/jiSFMKLZg/hZR4K482DMnh1Nsdw3TFcLlHTupjOD/D2VDcvnU9xeraL+hppo28FAQH2dkV438F+Ht3eSWdsvdd1gUtUMi/QLB4h0nUOZB/5+m7Gslv5wak+zsxF2TeY4CP7ejk0GGfhRIbxFyeZfG2W9GiS0fcOsunhXgJRX+t/wmpJHS8DQB2o0yznKM3OU8st0izl8EctYj0i4Q4XKWTiYCJLQRQphCAE8Bzggq3v09/S3rbr+nEdP4K4DUFYX5rTNpG0wXE/PBTuD9i4bhZLn8VszmDps9hGBsfJIUp5ZLWEGq4iKRZaJYzRjOGYcSCNKHeiBHpQY09wcjbIc2cyPH9mkYXytbxHBR7YlES3HI5NFtc8xieLPLw5zVO7u3nvto5b8hy6EjOFBv/56CW+fWwW44oF1I6I6uXtH00hCl6qAC9bwOq252Duad1y+OuXJ/nmmzOrPI8kUeATDwzw80+MtNL2rQ/T+TrfPjbHt4/NXneBdGlheVdfjF2txeWBZBBxhff7+YUq335rlu++PXdV2jvwFjmf2tXNxw72M9IZ5vzCZcLo7HyFS9n6uiMJVFnkoc1p3r+zi8e2dbQIwBtjptDg6LksL53L8ualwioP62uhK+ZnOB1iqEUyDXd47WTIt+5w1Y1qMzbquJZQ16xWtMvqNGm5qr4c3XPl7/p+RDqisrs/xu7+OLsH4mzvid6V6D/XdWkYdoto8aTQSt1RaLVzVZ18TSdXNdDMWyPUOqN+BlNBBlIhBlNBhtJejZZ40MeJ6RI/HM/z2kSe8cXada/TEVUp1g2sa9gJQYAdvTEe3ZrmkS0dbOuJrrKJpuVQbFwe5xKZVlgx9ppuoUgiPlm8hhZQJBFVllBkEdN2vBSE06VVC/xrQZYEdvTG2DsQZ+9gnD0D8Zt6PoD3N3v5fI4/e/bCqshe8NIUfurBQT776PBNXXcj2o2NMibLdvjWW7N85blxctXLc4LhjkW++PizHNx0gUb24ySHfglRutbiUAMvivsUcLKlG3iL+MN4dW8ewEtR1nf3BoP3/zt/MsPr3x7jbX2c6AOz7Bqe4NDwBJVmgNcvjnJiehBfiyiKBRvEgw3iwTrxYJ1EqEkyXEcSHVw7hqWFaJaDnPqHTVjah9j/yb3E++9sVPjdhOu6XHxpiiP/92sIgrDscdu9o4PUpuSKVMwmXvTOX+ClPf45PDLmPwNzeOTRT3Dj9CV3Ezrefap4aZnvv3TKG8VuXImNMC7TMsnkM8zl5pkpTJPXJtGZx5FzKP4ygUCNcFAjrtrEVGdZ4j5vTljSxZZIrRR0ErN1jzQqaCJX/r+6joxthHDMMI4ZxjbDOGYIx1jRNkPcehSdiyBpHsnkqyL5qkjK5bboqyEpdzatsa1HsbQkVjOFpaWwtSSWlgT39t+vbwmCtYpkWiabfBUktYworXh3dXyEnFH6/bvYldzPto5++pNBeuKBdXrUv3vhui5Nw77sxLZStJXba5N2S85iqwmpq+GTRb7xq4+Tjtx4frgRbMZauFfjcl2Xesuhr9I0KTdbumFQKGsUShrzpSYXASl5AAAgAElEQVRzFY2FukEiVGLPwBR7BqbZMzDFaNcCl7IdnJwZ5MS0J/OlOFc/Z10UycavmPgVA79iorbE3xIBF9sVcRzRI5VcAccRcVzBE0fEdoUW4STiuCKmLWFakqdtGcOSMW0J27nadt4qBMFhU0eWfYOXeHjLLHsGLhHwmWjGbgLqIVT5ELANreqQOesRS9WFGuHOENHOMPGBJomBY/gjLyGIY8BhXPd9lOqHmSkqXl3PQoNK00QSRWRRQJYEZFFElgSvTxJa/avbDd1ioayRKWsslBvkqg0KtTpgoEgWimSjyDY+yUKWbHyyhV8xCak6QZ9OyN/SqifJsEUiZBILGIT9BgGfhk/WaBoJpvIDvD3VxZGzKU7PdaCb71ydob6AwsPbOnh8by/7hxJrpp22TZuxFy7xzNPnmdRNGkMxpiyLgDrPweGLHBy+yIGhi/h9BrYjIgoucisSVpI8YhIcBMHFcQRwRUBEEBUEQcZ1ghhNFa0sU8sLGDUfSjBOIJYilO6k4gQ4M+twbNLg3IKDZUv4FZNowKI77tIVg3TYIRm2SYRsIn6LkN9EkXSg2ZJPAR9e13fSJpI2ODbqw+5OwbEdjLqBXstjNGawjRkcZx5ByCAqWWR/HjVSIBCp0qgEKRei1CpxqvUEmpVAt1PopNDFNKbUgSXFsBwwbWe5loRpO1SbJq+O56+qS7EEvyLxns2p5QiWSKuY3mJZ4wenFvj+qQVOzZTXPFdVRN67tYMndnQt5zBOhnzrnjyem6/wl0cu8oNTC1elPhpMBfncezfx4b2968rTvxamcnX+9Afnefb06uKkQZ/EZx8d5jMPD18zkqZpWDx7OsPfv3V1wegldMf97OqLt0ijGNt6ouv2bDcthyPnsnzrrVlePp+97dRP3qRdYDrfWHO/LAkcHknx5K5uHtvWsSpVjWU7HJ8q8dK5LEfOZW86Rd/1sLkrzL/96QPrigTbqDbjfhyX67rYjuft6DheO1vVlj2xpvL15XRpt5t+8nYgiQJLj/uln9D1nv6CAKosoSriCi2iKtKaOhJQ2NkXY3d/jM6o/57V2rkWXNdLo5dbRS5dTueRr+rolkN/MshAKshgizQaSAXX7QSQq+q8NuGRSq+N58lWr02mLyEakHloNM0jWzt4aDR108TMnYLtuIxnqhyfKnJsssTxqeKqhf9roT8ZZO9gvEUuJRhOh1aRX0twXZdXLuT4ynPjVz0nVVnkkw8O8LPv3UTqFsZ/P9qNG+FeLhbYjotlu1iO09LePMmyve0r62wZ16jDpVsO3zk+t+o5OdKZ4Z8//iwHhyZoZD5OeuSXEKVbKR6ew4tkOQc8ileH8Z1FLVfnxHfP862XLnGqQyXSvcCDIxNs752lafgoNYKU6iFwY6TFJMlmGHVWIn/OpjxnEe+LkRyKkxyMM/reISJd95JEuT04trPO3O0OcAT4SzzS5jPAB/CImzZuFxvRFsLNj0szbRYrniPg5TS+whUpfYUV7SVnu6W2sHziWue7joNjOl7dCNOhWq4xUxqnqF1EYxZHXkRRi/gDVcLBBtGgRixgeMSQ6uCXXcq6SNkQPb1SDI8wWtou6SK6vfp35Zj+Fhm0RBJ5BJFjhrDNCI4ZwrVV7jkZKliISt0jmVpRTcvEk+KRTavIlhZsI+wRRc0klrZEGiVwnXe+8HlIlVsiEVJlwn5vO9jqDy/vlwn5L/cFfRKu2KRm5/DJApuTm1HE9Tkp/lPFEiFVv4JcqusWmmGzbzBB3zqzpbRt4Wq4rotuOlQ10/teWxkrqprptVvbNc2ipplUNZNSzaBcN6g0TWqmfdX6iyA4xAJNkuEayVCNka4Me/qn2DMwhV8xPcJoZpATU4OcmetDM30IQFKV6Qn66ImoiKJA0bAp6SZFzaLQNNftBHw/oDNaYu/gFPuHLvHApml64gVKjVFgP4ngYXxKB7b9Arb7LIIwz2zhECen9/PKhVEu5Wzmis11pYu/n9CbCLCjN8qO3hjbe6PEgj4varSVht9xvXfSpQjS5fWVVjRpTTN542KBVy7kl5/za8EnwN6eKI/t7WH/cJLxqRIvvjLF6fkKWZ/E9TO7usSDDQTBxXbEFiHpkZT9qTAf3t3Ph/b2klIVJl+bZfzIJeZOZAilgtRyDXp2dtC3r4e+fd1E+6McnyrxwtgiL57NMl9q3tL3Fg0o9CYC9CUCHBhO8hOH+te1jtwmkjY4buYP3Mxk+MI3/jX5ZIHLy4HrResX4wqXt91r9Qu4RgDXCIIRwDVC3rYZwjWCuEYAz4tJWD7VBSTRRVIcFMVGlh1k2UZZ1rbXLzkoikVQMYioTcKqRsSvEVY1Qn79sg54OuTXCKo6muFjsRQlW46xUI4xX4kzX0mSaW1nq1Es+86+jIb0Og9MHeehS2+yf+Y0qn39heBMOM1LIw9wdORBLqaHrnus4DrEmhWSjTKJRolkvUSyUfLaLV1TQ3xzz4d5a2DPVedvXrzIJ97+Dg9OvoV0h37G5zpG+IvDn+JMz9ZV/fFGmZ9685t84OwRZNfGBc53jPCDbY9ydOQwTd/VaT2izQrvO/8y7z93lMHS3B25v0IwxvObH+aZre9lLn7jQuy9pQU25ScZyU0xkp9kU36aiO4tak3He3h50yFeGT7EZGpgzfNFx2bP3Bh7584wnh7iWN8uGuq1J7FD+WkOTb/NoekTyLbJTLyH2ZbMxHtYiHZgX6dI6M/n3+DzX/olpPT1ayK0J8hg53L8m6/8G94eyuGKNznJcsXVsuRR4opeMXVn9X7HCOM049jNJKaWwsGHI4g4goQj3nlPQp9l4LNMfPa1ddDQCJpNgkaDgKERNJqEzCYBo0lwSUxP+00dcR3Pi2WCCQHhupnf27gRXGA23sPxvp283buDUz3blu3kptwUB2ZOcGj6BFsWJ5Dcd18UmgsshtOc6d7Cme7NnOneymz8xummQnqdrYsTbM9cYFvmAluyFznbtZmvHfw4Z7s2rzpWsUw+NPYcnzj+XRLNFeSSqqI++giJP/z3N7SFsDHt4c2O6esvf4+/uvBXWLUUzqX9YN94wc1FwBZFbFHCEmVPS3dnQX9z1wK/9Nj3OTAwSe6rPpQ/PY9Qf/f9398KbEFiPrGbF7d8kNf6B8gFJZKNJoPlJt11ia5aiWhjgUhzgWhzgUgzQ0jPIb4Lf/dtvAtxE/ZwI9pCuLlxnTw1yW/+1zdIdZbxKTqqYuCTPa0oJqqi45O9NEqKbOKTLXyy2RILRbZQZc9D29fy0FYkB59se1qyUSQXRXTwSQ6K5KJKLg1LWBcpVNZF6ua1Z1iOGfBIIiPcIoXC2EYYUQsQqEGoCj7TQbEtFNtCdiwU20R2bBTb9PpsC+WKfrl1vOJc3i+3jl/Zv3Q9xbaQHBtHELEkGav1nFjdbulW21zRXn6mrHHcUtuQRAwV9LCNETQRTQVfJYhkSt4c1PVExEVwHa99jT64vE/ARWztWz6ndbzYel9WbHPVXDmwPKduEjQ0Aub6585tvENo28KbGpexmOX3fv9rvOzrpu4LXLH+4CKJDrJkIy9rb41OFm2Cqk4yVPdIohZRdKWOBRvUND/FeohCPcxUPs2JKS/aqDoj0VPO0lvJ0FvO0NOS7uoiPvva2Q8cBKr+EMVgnGIwRiEYX9UuBWJIro3f1D2xdPymdlU7sNynoVoGtiihyz50WcWQfeiSD0NWWn0r+lfs82yHZzM87bRszOW26DiIeFpTVCbSQ0wm+6651hP2N9ndP82+wUn2DV6iO1Li6IVtPDe2i2OTw9jOO5vKMqg3SNcLdNTydNTypGsFT+oFbFFiMZxiMZK+LOE0hVAcV1jf2keyXmA0O8nm3CVGs5cYzU0S1a+fvWO9cIHpeC/HBnbzVv9uTndvwZJunaz3GxpbshfZujjOtsVxolqNlzY9wIujD1EMXZ0uW3Adds6f44kLL/PwxTdQHJeav4NYYx5dlnmrfzevDR3gzf491PyhNT9Tcix6yhkKwcR11xfXwhezr/KZ//2X76ktbBNJ7wLczB/4v/zGF3j5R8b51w+X7voinyC0At4Ft6VZreFymjRAlhwcV8C0JAxb8UJILS/nptfXCiW1ZAxbomn4qOt+apqfmq5S15baLb2iXdfVd8y4xhslHrr0Fg9NvsWuubPI7q15AsxFuzg68iBHRx5kOnnn0q3smznFJ45/h93zY3flf8AF3hjYy3968Cevuu/e0gKPXHyNV4YPMpO4ekyiY3Nw+gRPnjvCoakTt/zdrecez3aO8sy293J004MYso/+0hwjuall4mi4ME3QvLanwkrMRbt4ZdNBXhk+xHjH8Lrvw2fp7Jkb49DU2xyafpt0fe2IrCVYgsRCtIPZePcyuTQb62E23k2qXuR3vvdHDBzeR/ov/+K612lPkGHm536OX/7xIv/hg4uElZt7jFmtYrnWisK51+ozbIGqIVLURUqap/OVKNlSB/V6Ryu1xvq8JWXbpLuySG/58sS6t5yho5bHb+n4LAPFttovrhsQliAxk+ghqlVJNtaOXH23o+wPM9a1mbNdmxnr2sx4euiGk3bBda564ZBtkw+NvcAnjv/Ddb8L9cn339AWwsa0hzc7pk9/9ef5xfef41CXgesKuLbfI8XvMHTTR7kRpNwMeLoRWtEOUmqEqDSDlBsBys0gQ+ksv/j4M+zrm2Lxq378f3IWsbGxvCtXohzoYSGxi4BRItLMEGlmkJ17F4naxsbBeuzhRrSFcHPj+oPf+DfYnzjOp3ctYNhgOV5dHdMB0xYwW21jqW3T6rt8jLFiHuids+L4K65jOgKa5aVXuhGWSCJXDyE3ffjqMsGaSKTiECtZJIs68UaDqFYlrNWJajUiepWoVkO1jLZzTxtt0LaFsL5xHf2FX+Nv93Xz+//sb9DkALLoIC0TRw6WLWI5IrYtYTkili1hORKWLdI0fBTqYQq18LKullW0ooSRFzBzLuRtgo0mYb1ORKuRaJS9d9tKZt3rLxsRhiQzlejnQscw4+khxtPDTCd6ccRbW8cMGg26Klm6qlm6K1lizQqO4DmBeWT9ar0swuU+xbFIryCKOup5UrUiIfPmo2NMUSYbTi4TS0skUzacImQ0GM1e8oij3KV39H1Xk32c6tnGsf7dvNW/i/nY9R3Oe8oLbMtMLBNHA8XZNR3zbUHgRO8OXtj8Hl4dPoimXF2HyWcZPDh5jC3Zixzv28mJ3u3XfD8OGg0OTJ/g8OQxDkyfXP4b1HxBMpEOFiNpMivIO68vddX1fv2ZL/PkcPie2sI2kfQuwM38gc8ePMhv/tpW4ukSdztzkOu2Ssm5rVJyS9ur9rVKzbne4qyzFKHkCjhGBEtPYGsJbD2OpcextQSOGeaO5SN1HVTTQLUN1JY3gmrp+E1j2Xtfdixkx172tFrZVq7oGyrMsHVx4o4v5E7Fezk68iDjHcOUAjEKoTjlwI2LRa4c58MX3+Anjn+X0fzkHb23a8EWBJ7f/Ah/fejHyYeT1z22t7TAB84d4YnzL6/2LH8H4LS8qhXnzixOZcJpXt10kJeHD3Gua/Sq/elq3os6mnqb3fNjqPbVaRluB0IsRu/pk9c9pj1Bhrmdu/lXP38Ac0cORVr/71UAZNFFFkFZoRXR61ckF1mg5XHq7Yv6HBJ+LyVJwu+QUG3ifgfLEShqnpdpURMpNPwUqmGqtRBGRcIpgZyzCcw3SMwW6JzOEm5aBJo2gaaNqjuI7SdwG/cpTFFmPD3E2a5RxlrkUuU6zzXZNnnq7It88th3SDWuT7rD+mwhbEx7eLNj+t9+/ac59f4ywRap7joijYXDaIWdN/3ZguMiu543uug4yI6N5NhIrk1I1kgE6sQCDaIhnWhQIxzWiYQ8HQ7png5rhEJNGjU/c1+NEPrj04jNWy/M3kYb/9TRnhuub1x//Cvv53sfuDnP3jsCQ11FDoVb5FC6oNORb9KVq5Gs1YhoNfyW3iaF2mjjFtG2hesb1+Tuffz2E1/g54e+TtIt8SXjfwYN/E0dX1MnaGqE9MaKSLwGoVaUXkhvENFrhPX6stypdZZ/itAlH5dS/Vzo2LRMLs3Gu3EFEcF1SNZLdFezdFUW6arm6F6hw3q9/by4BSxEOjjWv4u3+nczmeynq5pl6+IE2zLjbF2cuKXIqKas8sPhAzy/+T2c6N257qw0qVqBByePcXjqGDvnz970b8lBoBiMLZNMHbUCOxfOId5jW9hOSn2fIbVrN//nbz3LVHeyFRbvosZ0Ir1Vor01BJ9NbS5MZS5CMxdcTlfnrshi57a2vQx2wnJGO1dYEgFbhlpEpBKVqUYlKlGJSkzy2jGZeuT6rLoguF7RSbUC0dXEh2I4dC7qdCwadGRN0lmTVM4mnbWQDRFb9FJGWaKXNsoWPDYdQF1BFKmWjs827wvjOliaY/DN/29VnylKlAPR5bDdQjBOIbTUjlEMxtFklX2zp/n4ie/RW1l8R+9Zcl2ePH+URyd+yD/s+gDf2Pejq8Iu/abGIxOv8YFzR9mWuXDP/g5eSPGdm9x01XJ8/MTTfPzE0+SDCV7ZdJCJ1CB9pQUOTb/NYHH2ro7Vd+jgXbz6xoHv0EF+7w+fZT4ZRfMp+BNN0jtyqFGD/FiK0lTUS1F3BVwBbBEcScCWBBwJbNHTliS0+lv7RTB9IpmUxMlOmcVuP7m0giN5tjcoux6x5HdIqA4Jv00iUqQvlSficwkpDhGfS1hxCPtcFDFE3fQinOqmSMYQaGoi9aqIeNYk/UqFriM1mHFuPnNpG228w1Aci+2L42xfHOfHTzyNCyxEOznbeZlYmk72IdsWT547wk8e+/YNozZXom0L149/kfXxzFcX+Juf7kL3teZnydfYNfs8n/ibPIHm2gZFch2kZecajzC6E440NaAKIAhE2v5qbbRx22jbw/XhcauXY9kFMh1+ZNNBMV0U00ExHXymi8/w2orheinilvYbV+w3W/uN1W3f8vFL5zv4NQfFatu5Ntp4J9C2hetD6MA+/t1/+z1q/iD9j8zydd/nyT+d5gbFYe4qDEUgn/SRT/koJBVkyyVRNEkUTZJFA5+5Me2oahtsW5xg2+LEcl9TUSn7oyQbxeum+2vj1tBdzfKRM8/xkTPP3bFrBiydJy68whMXXqEQjHFk9CGe3/weLqUGrzp2OD/lkUeTx9iUn7qttUMRl1SjRKpRYkfmwnL/vbaF7YikdwFuti5I8dd+Hf3oS6BfXQhbjpn4h5sENjWRIjbapB/tUgBt1g/2nXtwWJJAJSpTiimUYzLFhEK2QyXTqbLYqVJIKrhrFNy+EaJlk66MTtei7umMTndGJ5U32p779xhVNcQ393yI6XgvhyeP8fDF1wlYNy7G3sY60c79fEdsoa9TJ3KoghyzqL4VpXHuMqF+J2BJAplOHws9fua7/cz3qMz3+Ml0qi2C6dqQBZewzyGkeOTSEsmU8Dv0hm16wxa9QZuI4qDNOYhnTdwLFtaEjXXRwrpo42TadTXauH0IcQH/UyrqYQXjNZPmd3Xc6p1/yDYVtVWP4SbI/naNpJse05I9nD77Gn/++S6mui7n407mDT7/H6cYudS4K/faRhtt3EW054Y3PTes/PZnCQSfQxIc6qdDNM6HcI07n+qzjTbaeAfRtoW3/p5saCSfLIDkUvh+6rpk0vG9USYHA/g1B79m49ccAk0bv2YTWG57+6QrXklNWaCQVMinPLIon/SRS/nIpxQKKR+V6PXTYYdq1jKxlCgaJK9ox0vmVZ/ZRhv3GpOJPl7Y/B5yoSRbsxM8OHmMzlr+7n3gu8QWtiOS7jNI6fS6agYAUJokNPbfCJ38GqgR+Nz37u7NrYBu6yzU55mtzTJXm2W2NsdsbYa52iwVo3LN8yoxhUpM4fzW8Kp+WZTpDfWxKbaJkdgII/FRNsVGiPrWnx6ujdvH9nt9A2200cINbeHUURLPfYlEYRwe/19h3+fgNoowrsTQGn2mYzJfm2e6OkVRL9Iw69TNBg2rTsNsUDfrNKxWn1mnoDVoWmvnJlYll56QRV/MYc+TMXb9TJSeiIVPWgA0YAAYBTavkE7uVMrQNt4NqAHTeNO0TdyZ6VoGeK4lp4EHgQOEPvsmCV4HDgIfAh4H1i4MevdhASXgxgRSG5exZA/TwB/aJl997jf4VvU8AIWUj3//m9v4uZ3/jB/f/AnEdRbJbaONNtq43yCl0yT+7Hte7vWpo8Rf+xPiF74DOz8ND/4y9Oy/17fYRhtttHHXcdV7sm3C1z9N34dF+PTX1nwnfjt7nP/r6L9a92eokkpQDhJUgjStJgWtcFv3XA/L1MMyMwOBNfcLCCT8STqDHXQEOukMdtIR7KRzRTsgr31uG+8uWI61vCYsCxKyqCCLMrIo33fvKX3AI/f6Ju4B2kTSRkZ8CN7zP8EDX4A/GobMSeja/Y58tCqpDEWHGYoOX7WvalSXCaaZ2gyz1Rlvuz6L5awd2mk5FlPVSaaqkzw/89xyfzrQ4RFLsRFGYh651BnsRLjbBaTaaKONdzcGH4Wf+0eYfBGe+xK8+Hvw+O/C3p8F6c4/+hRRYTA6yGD06vDma8F2bRpmg4nyOD+cf5VX519hsbmIbgtcqihcqsDReQ3GvKKlXcIgjw0c4L2jQ/QE6/jlGQThVWAcMLiaXBoFInd6qLcM0zD5x1d/wHemv82cMk1EiDLasZktXZsZig4zHNtEV7DrHZ9Amk2N2RPP0SxdQhAfIjU0SHI4jqTcWmHU9aOORxZNA1Mr9Ayu20Cz0whYqFIJQdgM7GjJTmCY9U3hLgLPAs/hurMYzQc5N7mLIxe2MVa7SJHv0scAH+n+LR7YVyOY+C6C8G+Bw3ik0nuBd+KlzMFxn6ap/RHTuV6Ge/4Ev3x1QdM2bgxFUvilD/wRu5/7Hf5D/g3qkoTt2nz11H/kRO5tfvXgrxNVY/f6Nttoo4027h4EAYbe60l1Ad76c/gvH4fYgEco7fwUyOq9vss22mijjXcGkuIRSF/7Sfjbz8JP/tVV78MnczeuS7oSuq2j2zpFfX1pq0VBpCPQQWewi85gF5Zjkm1myTVz5Js5bPf62QtcXApanoKWZ4yxNY+JKBGPXAp2tsimDlKBNEl/iqQ/SdKfxCf5bmqcbdwYjutQN+uU9RIlvUxZL11uGyXKepmS7umyXqJmXrtGkSiIHrEkSC1yySOZFFFGEX2MxjfzSO8j7O84gHKHnITbuHm0U9u9C/COhN8++yWoZ+DH/vTufcZtwnZtso1FZqozzNY8cmlJ34yHQ0gJMRIbYVNshI5AB3F/gqSaJOFPkPAnCcrBdRNNtmuTb+bJ1BfINDJkGgtk6hkyjQzZ5iIhJcQDXQ/ynp6H2ZLYet8x6G3cn2iH7N8CLj3vEUpaCX7pdRDvNklw83Bdl8nKJD9ceIUfLrzKueK56x4vuAIhN0xYjNAbDLA5KTCcNOmNNUmFS4SVRVzCuO4osjiEIHQDS9KFF/Vxd/1JHNth4sQl/v7Y3/Oa9Aq1yLUjUgH8kp/B6CDD0U0tZ4QhhqPDd3zh23WzVBa+j159jmjPacxmDNfpJJg4S+5iHxMvbqI4s49wehPp0STp0RSp4Tiyeivfl4XrnsE23kCQLiGKMyBMIwh1oB8YQLc6makJjBUavL6wwP/P3n2Ht1mefR//ag8veW/HiXcG2cNOAknYEAijZZYOoC0UOukASlugez1v+5SW1V1G24e998okIRCynGU7seO9l/Z4/7jkRZan5HF+juM+dEuypEuOc1u+f9d5Xh81VuIPfjyz6P3MTbSyMNlKfqyfZGsrZn0HGvLQaHqCpUJUuKTF592Fu/t1dIb3CGCnrCyTzQfNbG520BTXgl93/D/UElqSmbGvgIWxBRSccZTUmTswRx9Coy1GhUrFwGiHOwGcnjfpdv6GNncn/9hnYUejkTOzzuHrC74xqGeYjMfD0XpP9W/dxa+btnDA2HfCNN4cz7cXfZdZCaGZWCSECI3JeCyEUXxfPi8ceAG2/QnqP4b518Oim9TESyHEpCHHwpPwOOHfl4A1Hi7954C/h3//4f/jzco3AJiftIBESyIOrwO7V3XWcHjt2D3BzWsn8Ik1NbVoibfEk2xNIcmaRJI1meSIZJKDwVG8OR7dCf7+9gf8tLnaaLQ30ORooikYMKmgSe23OluPec3hiDJEEWeJGxAuHW//RGMdTb6AD6fXidPrwOF14PA6cXjtOL1Odd3nwOFR9zl9Dix6K2dkrCItMm3Mx9af0+ukydFIo6ORRnsjjY6G4GVj77/RiQoCxopVb2VxyhKK00pYmLQQ0ySdgOj1e3H5XLiDwW2sOQ6TbnATYcbyWChB0jgQkl92nbXwx5nw9Qqw2MbudcaI3WPnSMdhKtrLKQ9uRzoO4/F7hvxcRp2JWJNNBUv9AqYYUwwdrg4a+gVGjY7GU86O6BFvjmdpajHFacXMip+NXisFf2JsyAfkEbh/Hpz3O5i+auxeY5S0OFvYVreVbXXvs6NhB26/e0iP1xAgyepjWrSXZEuANKuWVKuGhAg/cVY3FoMLty8arz8eSMGgzcCoy0KrSQPyUWHT0AUCARoONPP+pm283fomR7IO4dV7MOkCLE1xsjLDybxEN+1uLfV2HfXdOurtOur6Xba5tPRv1RdnjiPWFItJb8akM2HWmTHrzZh0Zsx6dd2kM2HSm3vvs5limR4znQhDBKpl2k48zvV4ne+hM9ZTVzqdQGAZiTkXYo3NDr6SA9iC3/cWsAFnZyL1pbM4+G4WVdtNRKdGk5ATR2JOHLFZNnxuH84uF65ON67gpdvZidV2iOjUg8RlVhA3rYb2uhga9qfTUplEa3U0da0GaiwOmpObaE5soDOqfUjfY4veT0EkzI4wkh/jIyuhm6gIJ163gS67jp2VcWxs0bPd3jXkJcJidbHMdywhc/8M3NVNzE4TaWYAACAASURBVCg5RN4Zh4hOrcFtX4Ip4lx0huWAdWhP/AlNnW/gdP+GgL6Nf+2LYEutiZ5/83Ozz+OWeV8d1PNMxuPhqL2nQADPq9/mkZaPeNraV1mmRcs1Rddyef6n0WnGX6guhBi6yXgshDF6X00H4IP74eN/QuZyVaWUcw5oZUKgEBOdHAtPweOAxy6C6AxY99fe495dG+5kZ9PHAPyo+B4WJi864VMEAoFg8GGn22PHqDOQYEkc0/NfHr+HZkcTDfYGGu0NNDgae/dVoNEwamGGFi2x5ljiLQkkWBJItCT27idYEkm0JGAzxx73M3QgEKDT00mLo4UWZzOtzhaanS20OltoCW6tzhba3O24fUNfb1yDhmWpxVyadzmFcaO78ESHu4NNNRup6qgMhkbqe3uypUmGQ4uWKGMUWo0Wr9+LN+DF6/cO69wuqG5YC5MXUZxWwuLkJVgNI/sbday4fW4qO49Q3lZORXs5TY4mFRL53bi8zuClC5ff3Rsc+QMDFwaL0Edw7/Kfkhebd8rXkyBpkgvZL7snroH0xVD8zbF9nRDx+r0c7TwaDJfKggFTGd2e7nAPjUhDJItTllKcWsz8pPmTNiEfba3OVvY078ait1AQW0ikMfLUD5qC5APyCKz/ObRXjuvqzONxeZ3saNzB1rr3KW3eS6urdcTHOr0mQLzFT4LFR6LFR6LVT6LFT2qEhlybC4c3klZnERrtcuLNq7GZTt42tPlwK4fWV7B+/wYOZO+hLuEoek2ABckuTs9wsiDZzaFWM+2uZeTaPo/b30aLcyd2z0H8gSoM2kbiLU6SI3xYdAEVMvVs3Tq6PFrcPg0uH8HLvs3de6nu86MhweJjYZKL4jQtRXHdtLVFUb4jDdpLmF50EakzU09RneoFPqJnXaFAwISjbSkNB2ZTuT2O1qpODGY9EQkeEnOOEptVQUzqQczRR3Has+hy5NLuyqHVl0m3RkOnt5MDrfvZ27yHJkfTSf9tNGhINaRhxESN9yjuwKlDRKveT6QxQIP95MFAakQaM+NnMSt+FonWRN448jobqzfgDQz848uoM7E6YzVnRJ+J6aiF1qPlGK3rSZyxi6SCOuyt+cAZRCaei06fesrx9ThQ+zx+7iM2uo3H9kXwbpUZfzBAijBEcF72+VxVeM24mG0VLqP6ngIBeO6LbOuq5He2ODo9nb13LUlZwh1L75IwSYhJYDIeC2GM35e7G3b/G7b+EVwdsPhmmPcFsMaN/msJIUJCjoWD4LbDoxdAfB6sfRC0Wr70+o3UddcCcN+a+4fUrn086Klq6guaVNVMTzu8nhDnkyfmh0ur0RJnjifBkkC0MZoOd7sKj1wtIavOKYqbyaW5l7E4dcmwP8t7/B62133AW1Vv8kHdtmP+Hhwsi95CjMmGLbjFmGKIMdqwmWKICV5Xt9uINEaeMITzB/x4/R48/QImr9+D1++j3dXG1rqtbK7ZSJ297rjj0Gv1zE9cQElaCUtSlxFlPLbNv9vnpsPdQYernU5PJx2uDjrcHXS61aXD68RmshFviSfBnEC8JZ44czw2s23Q3+cOdwcV7RWUt5VR0V5ORUc5VZ1Vo/Lz97mZn+fy/E+f8uskSJrkQvbLrnITPP1Z+OqBSTvjKhAI0OhopLytjMrOSlqdLbS6Wml1tqpZAK7WISf/MSYbyVZVlpsckdK7n2RNoqqzii21m9la+/6AkzP9mXQm5ictoDi1hMUpi4k8zsFsNAQCAdpcbXj9XrQaLVqNFp1Wh5Z++8HbtWjHzTpSDfYGNtdsYnPtJkqb9/aWLGvQkBWdRWFcEUVxMymMKyI14lQnfk/M5XNR111LTVcNHe4OIgwRRBmjiTZGEWWMJsoYNegTl+EmH5BHoKUc/rwMbqsZk7WSQsntcwf7D6ut1dka7D/cRqurlTZnG22u1mGFTloC5MV6WJDsZmGyi7RIH3vqzew7HM+RA5loG9OJcyUQ70kkUhtJa1srBxL3cqToIO26ZmYneFiZ4aQ4zUlVh55dTWkkWq6iJH3tSRdCbXO1sb96Pzs+3kJL924iMluIjGsjweomwhDApAtg1AUw6cCo7X+9b9+oA58f7F4NHzWY+LDeyEcNJtrdfb/3Yk2xzLDl9K6vN8OWQ4o1BY/f09vGweGxB2fbObB7uzFoy4gz7yTZug+jzkF1VyIxplZijN0c7oiktMXEzkYtO5v8uH1DO07pNXpyY/OYGT+TWfGzKYor6v1d4Qv4qO2qoay9jPK2vokTnYOYHaZFS3bMdGbFz2Jm/Cxmxs8k1nzsCbIWZwsvV7zIKxUv0+4+tjpqbuJcLpqxjkUpi/E6fNTvr8DR9jamqPdJLjiAqzMeR8cSTJFnY0tbhOYTnzF8fh+bDz6O2fIPpie2838HInjtiAWvX32f0iLSuDhnHWuyzhry2kiT8Xg46u/J74MnrqbJ5+I36fnsbdnbe9dX5t7KedPPH53XEUKEzWQ8FkKI3lcgAEffV23vDjwPhZfA4lsg/cQz8oUQ45McCwfJ1QWPnAfJp+G/4A986oXLegOQ/6x94qR/r01U/oCfdld7MFTqC5danM29FUTNzmbaXG0hGY8GDWa9GYvegkVvwawzY9FbB9zWs5n0ZnY37WJ7/bH//mkRaazLvZQ1WWcO6nxWIBDgUNtB3qp8k/eq3zvl35Q6jS5YmZVEojWRREsiCcHLREsSCZaEkFYBBQIBKtrL2VSzic21G6nqrDrhuGfGz0SvNdDhag+GRZ04fc5hva4KD+N6A8R4czzxwUu9Vs/hjopgkUMFTY7GkbzFAa9p0pkw6kyYtEZm2HL48mk3E2+JP+VjJUia5EL2yy4QgIcWweofQ/4FY/ta41RPGa4Kl1podbbS4myhzaVOwEYaIgcERknW5EGd1PL5fexu3s2Wmk28X7flhLPNNWhIsiaRGZVFZlQWWVGZZEZnkRGZOaSDb5urjcqOI2rrrKSys5IjHYeHdLK4J1SymWzk2vLIteWRF5tPni13zMKuHkc7j7KpZiNbajdxqO3QoB8XY7JRFFdEUVwRhXFF5NryBiyy5/P7qLfXU9tdQ3VXNTVd1dR01VDTVU2jo/GUfXWNOhNRhsjeYCnKGEV0cH/ATAqjDZvZRpQxKiwzueUD8gg9tATW/ARyzxnb1xlH3D53byl976WrlRZHM62u1uCH6dYTfpCMMfqZn+xiYZKb+UkuWpw6Pmwwsr3eRFVrPA6/i2m2bk7PcLI8zUmbS8v6oxY63Es5I/MqTkuYO+QQ2Of1U7G5kl0vllLfVY8+WYtH48GjcePRePBqPHi0avMGN4/Wg1fnQWNw02rqoCOmA79mcDN/tGjxM7ivTY3wMjPeQ2WHnvJ2Pb4h9o+z6C0UxhX1VgblxeYPKcgOBAI0OZr6qnHbyihvL6PT3ckMW05vcFQYVxRs7Tc4bp+b9Uff5bny56hoLz/m/pSIVBYkLQi2FFTtBHUeDRGOMpJN+8hILUen9VFbmY/DuRhLwnJ2t7xLXPyTLEjv5JlDEbxQbsUVDNrmJJzGupxLWJSyeNhrDE7G4+GYvCevG/69Dp81gQenL+WVIy8DEG2M5oGzHpYKYCEmuMl4LIQwvK/uRvjor/DBA2BNVG3vZl8Jhsl3UlWIyUiOhUPg7IBHzqUldT6f9xwB1BpCj17479F7jQnI4/PQ7Gzut15TI02O5t71mpodTcedeNfDqreqtuwD1l2KJc4c33ubzWTDrDcP+e+fIx2HeebQM7xb9fYx1UPRxmgunHERF0y/kJjjrC/c7Gjinaq3eavqzROGLwWxhSxNXUpKRGqwjV/ikCpxwqGqs5JNNZvYVLPxuH+/jiepEWnMiJnB9JgZZERmYNabVUikM2HSGYP7xt7bRtIuUoKkSS6kv+w++jvs+Q985uWxf60pqifh31y7mS01mzjadXRQj0uwJJIZlUlWMGTKjMok0ZpEfXddMCg6QlXnESo7Kk/6i2s0pEWkqVApNp88Wx4zbDkjqtYJBAKUt5f3Vh5VdVYe9+u0aCmML8Ltc1HeXn7K0k+9Vk+uLY8oQxQ13TXUd9cNuxx3ODRoiDZGf6JcV13GmuOCfXQTiDeP7iwN+YA8Qpt+C417Yd1fxvZ1JiCPz0OLs4Wa7hoqOw73O/ZU4vA6gIHVSouC1UpdHg0en4b11Wa21cUyO2EtF0y/kJSIwbc8O5nWqnacHS40Wg0arQatVgNa0Gq1vbdpNKDR9V232sz4tX4qO49Q1tbT/vQQFe0VuIbRk3qwNGiIMEQQaYgk0hhJpCEqeBlJemQGsxJmMT16RkgWcR2uQCDAnuY9PF/2LO/Xbhl0wAYBMiJ9LElxsSTVRVa0F38AXqmw8vQhK90eLXqNnpUZZ7AuZx0zbDkjHutkPB6O2Xty2+GRc3ElzeErBjuNwdly63Iu5YY5N47uawkhQmoyHgshjO/L74NDr6i2dzXbYN7nYdFNEDfy31tCiLEjx8IhcrSx//Hz+U6c6lgwPWYGv1/9h9F9jUnI7XP3Bk1d7i6iTTHB0ChuyN0VhqPZ0cwL5c/zSsVLdHsHTiY3ao2syTqLdbmXEG+OZ0vtZt6qfJOPG3ccd2J1oiWR1ZlrWJ11JumR6WM+9rFU213L5mCodKB1/3G/Rq/RqwnjpujgpHF12bNv0ptoc7bR7Gyi2aGq1JodTUNaK8qgNTAtOrs3NJoeM4Ps6OyQVm5JkDTJhfSXnccJ/y8LbtioeqKKMXe0syoYKm2mrP3QqPVlPR6L3oJVb8Uf8OMP+PEFfMfdP1VlzidpNVqmRWeTZ8tjWnQ2Wo2WQMBPAILvJ4A/EIDgMweCr+EPBGh3tfN+3RYa7PXHfW69Rs/cpHkUp5awJHUpNpMNAKfXyYHW/exrKaW0pZR9LaUjWhNGi5YkaxJpkenEmuPo9nTR6e6k091Jl6eTDnfHmPazteqt/RZpVOFSQr/rKRGpGHXGQT2XfEAeofYqeGAe3FYL+sF9z6e6nrahlR1HOBIMtKs6j1DZWYVF5yDG5AdmsDbnYlZlrA7JB+jhOlmrOL1Gj8VgHdBKwKrvf13tW4Nf88mwKMoQicVgHdeztoaqvruelype4LUjrw75GBxt9BMIQKdHS5QxmvOzL+CCGRcSd5z2esM1GY+HY/qenO3w99Wszy7m187DgGr9cN+Z90/4Px6FmMom47EQxsn7aimDDx6EHX+DtEWqSinvAhjHE0KEmKrGxTFjDIzl+9pQ/jK/2nkfAEtTlvH9ZT8Y9dcQY8PusfP6kdd4ruyZ3gliPTRoMOlMx23lZtaZKUlbzpqsM5mdMGfYnSHGs0Z7Iwda92PSmXpDo2hjDBa9ZVjLZagOL80DwqXm4HWXz0lmVBbTY2YwI2YG6ZEZYZ80OpbHjIm9QIQYOoMZFtygZled/7twj2ZKyIjK5NNRmXw6/wo8Pg813dVUdVYFT8RWUtVZRU1X9ZAqaUw6E1lR08iKziIrKous6GlkRU0jwZIwqINi/3CprruWA60HONh6kENtB6hor8AX8B3z9RXt5aNWKmrUmViYtJDitBIWpyw5busls97MaYlzOS1xbu8YjnZWUdpSSmnzXva1lFLTXXPM4+LMcaRFpJMWmUZ6ZDppkWo/xZo6oA3eJwUCAZw+ZzBc6qAjeNlzvd3VHlx/pp12dxttrvZBrVHSw+61Y++sPGE1Vpw5nruL7yU7JnvQzymGKSYTEoqg7DUoWBvu0UwIGo1qy5lkTWJRyuLe230BHw32Bnx+L+mRGeNm7bWT0Wl0ZERlkhGVyRkZqwD1/98X8I2ofHyySo5I5guzb+Dqwmv5oH4brc5WXD4nLp8Lp9eFy9ezqdtcXhfO3n0nNrON66adw+rMNRNmHbpJzRwD173KiocW8+Ls89jbXYUv4OOvu/7MD4p/FO7RCSHE+BOXA+f8ClbfA3v+C+/9BF76qqpQWnAjRCSEe4RCCDFsjT57735i/W7obpLj2gRhNVhZl3sJa2dcxMaaDTx98EnK2ssANcW7f4ikQcNpiaexJvMslqUVT8p1sPpLtCaSaE0ctecz6oykRKSOWseViUzOmExFi26GB+erNUJM0hM/lAw6VeI4LTob+k389fq91HXXUhkMlqo6K6nqqKTR0UiiNYlpwdBoWnQ2mVFZJFmTRjRroGd9JD363vGcPU2tF+P2ualoL+dg20EOth7kYOt+qruqh1zF9EkR+ggWpyyhOK1ErbExxIoFrUarArPoaZybfR6g1ora37IPj99DakQaqRGpwy4X1Wg0vVUHSdakQT3G6/fS6e6gzaWCpXZXG+2uNtpcbcGZCk29/XQ9fs9Jn6vF2cxHDR9KkBQqs69UbT4lSBoRnUZH6iT4MKXRaNBr5CPRyZj1Zlakrwz3MMRoiEhEM/ezfNHdwrfQECDAtvqtfNTwIfOTFoR7dEJMOnaPnfL2crQaLQatAaPOgEFrwKA1YtAZMGqNGLQG9Fr9hJiQMWUZLDDvc2qr/gC2/Qn+WAQrvw9LboGTTFgTQojxqn8lS6LGCPcVQsm3YdnXZX24CUKn1XF6xhmsTD+dXU07eebQU3xQrypR0iMzWJN1JqsyVo9qsCKmLjlrMhXZsiB7Fez8Fyy+OdyjEai1fnpmyIebUWekIK6QgrjC3tvsHjuH2g5ysPUADY4GNGgADVqNZsB+/0uNBjRo0Wt1FMXNZE7iaRi0o/sHls1kY2nqslF9zqHQa/XEBhdSPJlAIECnu4PG4OKMTc6m3oUaey6TI1JYlbk6RCMXzPw0vP1D8DjkA7IQYuopXEfOE1dz5oqv8Ebl6wD8edfD/O/q+8LeikGIycLn9/FixQs8VvoIdq/91A9A9dU3ao1EGCK4NO9yLpwhE17GpfRFkP5XaCyFl78GH/4ZLvgDTJfP8kKIiaXB3tC7n7jwS1DyA3jzTvhDPqz+Mcy9Tlp5ThAajaa3q0+zowmXz0VqRJpMUhGjSoKkqWrJrfDSraokXw4q4hSsBuuANnNiaDQaDdGmGKJNMeSMwuLyYhREpUDKfDj4Msy8LNyjEUKI0EpdAF4H1yUXs7FmAw6vg6rOSl4+/BJrZ1wU7tEJMeHtadrNAzvv50jH4SE9zuP34PF76PZ288+9f+e86edPqnX3Jp3EIrjuNSh9Gp75AmQshXN+o9ooCyHEBDCgIsmSCHH5cOUTULUZXvsObP4fOPtXkHuunDucQOIt0p5QjI3Jt6KWGJzsVaDRQsVb4R6JEEKER097OyGEmGo0Gii4mNiKd7gi/8remx8rfYSOIaz/J4QYqMXZwv988Bvu2PC9ASFSkiWJwrhCcmJyyIzKIiUilXhzPNHGaCx6yzHtVTVoWJW5WkKkiUCjUZOSbtkLCYXwwDx472fgdYV7ZEIIcUpN9r4gaUCL/8xiuH49rL4XXvkG/PNsqPkwDCMUQownUpE0VWk0qirp/T/AjDPDPRohhAi9osvh9e+CuxuMEeEejRBChFbBxfDOPVxc/C6vHn6FOnsdXZ4uHi99lC/PldbHQgyFz+/jhfLneWzfIzi8jt7bTToTVxZcxbqcSzGcYg0dX8CH1+cJrqupIdIoa9lOKEYrrL4H5n4OXv0W/HEWnP97yL8w3CMTQojjcngddHo6AdW2P8ZkG/gFGg0UXQL5a+Gjv8Bja2H6GrXeemx26AcshAg7qUiayuZcC5XrofVwuEcihBChF5EAGcWw//lwj0QIIUIvexU07sXgaOELs2/ovfnlwy9R2XEkfOMSYoLZ3bSbb7zzVf6y++EBIVJJ2nL+dOaDfCr/ilOGSAA6jQ6T3kykMUpCpIksbgZc/QxccJ8KlB5dC82Hwj0qIYQ4RmO/aqQESyJazQlOEev0sOjL8NUDEJcLDy2EV78N9pYQjVQIMV5IRdJUZopUM6Y+uB/O/mW4RyOEEKHX095uzlXhHokQQoSW3qT63e9/nmULbuC0hLnsbPoYf8DPX3b/mbuL75XFecWE4fF52NH4EVtqN1PeVkZaZDoLkheyIGkhsebYMXnNFmcLf9v9F949+s6A29MjM/jSaTcxP2n+mLyumCDyzoPpu2DL7+DPy9RJ2JV3ShW8EGLcaHQ09O4nWZJO8pVBpkhYfbc6nr1zD9xXAEWXQdIcSJqttghZm0eIyUyCpKluyS3qg+2qu8FgCfdohBAitAovVT2fne1gjgn3aIQQIrQK1sHux9EsvJEb53yJb7z9Vfz4+ajhQz6o38bilCXhHqEQJ2T32PmwYTubazbxQf22AdVAZe1lrK9+D4CcmBwWJC9kYfIiCmIL0WlHtu6Q1+/lxfLneWzfo8e0sbuq4Gouzr0Eg/bUFUhiCtAbYcV34bRrVTvl+4rg3N/CzE/JovVCiLDrX5GUaE0c/AOjUuGiB6D4W1D2GjTsht3/Vpd6c1+o1LMlzgRz9Bi8AyFEqEmQNNXF5UD6Utj1OCy4PtyjEUKI0LLYYNoZsO9ZmPfZcI9GCCFCK+98eOEmcHeTHZPNOdnn8crhlwD4y66HmZc0X06Ii3Glw9XO1rqtbK7ZxI7Gj4LrCZ1cWXsZZe1l/N+B/xJhiGBe4vzeaqV4S/xxH+ML+GhxtNBgr6fB3kC9vY56ewMN9nqqu47S4hzYzmd52gqun33j0E7EiakjOh0ufxQOvwcvfxW23Q8X/AGSZoV7ZEKIKayhX0VS4mAqkj4pIV9tPQIB6KhWgVLDbqjcAB88AE2lYE3oC5bmfR4Si0b+BoQQISdBkoAlt8Kbd8D8L8jMKCHE1DP7Ktj1qARJQoipxxIL6Yuh7HUouoRriz7D+qPv0u3tpqa7hhfLX+CS3EvDPUoxxTU5mthSu5nNNZvY07wbf8B/3K9LiUilOLWE0xJPo6K9nO312ylt2Tvg67s93Wys2cDGmg0ATI+ezoLkhZh05t7QqMFeT6OjEV/Ad8qxZQTb2M2TNnZiMLJPhy9tVydW/75aVSqtuluq4oUQYdFo7xckjcZECI0GYjLUlnde3+1+H7RWqHCp5gP460o4/S5Y+jXQnmBdJiHEuCRBkoCcc+Dlr0HlRpi2ItyjEUKI0Cq4CF68WS0Wao0L92iEECK0CtbB/meh6BJiTDFcVXg1f9n9ZwD+s+9xVmeuIcYkJzlFaPj8Pio7j7C/dT8HWvZzoPUAlZ1HTvj12dHTKU4rYVlqMdnR2b3rei1MXsSn8q+g29PNx407+LB+O9vrP6DZ2Tzg8RUdFVR0VAx5nBa9hSsKruLinHVStSeGRqeHpbeqdTrfvBPuK4Qzfw5zPysnVIUQIdXo6Gttl2QdRkXSYGl1EJ+rtqJLVEXSM5+D/c/BJX8HW9bYvbYQYlRJkCTUB9Ylt8LW+yRIEkJMPaYomHE2lD4FC28M92iEECK0CtfBez9Ws0W1Oi6YsZZXDr9MdVc13d5uHi39F1+Zd2u4RykmoUAgQJOjkQOtBzjQup/9rfspazuEy+c66eMK4wopTi1hWVoJqRGpJ/3aCEMEJWnLKUlbTiAQoLLzCNvrt/Nh/Xb2Nu/BG/Ce8LE2k40kazLJ1mSSrEkkBS+TrckkW1Mw6CRAEiMQkQgXPwzV2+ClW2H7g3DBfZC2MNwjE0JMEQMqkiwhbM0anwtfeA82/hoeWgjn/EaF6dIhSYhxT4Ikocz7HLxzN3TUQHRauEcjhBChNfsq9Qe8BElCiKnGNg2i0qFqE0xbiUFr4PrZN/LjLfcA8NrhVzl/+oVMj5ke5oGKiczr99LoaKSmq5ry9vJgtdF+Wl2tp3ysTqNjdsIcitNKWJqy7ITrGp2KRqNhWnQ206KzuSzvcuweO7uadrK7aTc6rXZgaGRJwqQ3D+t1hBiS9MVww2b4+B/w2FoouBjW/BQiEsI9MiHEJObz+wZU6SaEMkgCVaW08na1XudT18G+Z2DtgxA5hpVRQogRkyBJKOYYmHO16te85t5wj0YIIUIr7wJ47kboapAPr0KIqadwnWovMm0lAIuSF7MgaSEfNmzHj58/73qInyz/WW/bMCGOx+PzUG+vo7a7lpquGmq7a6ntVpcN9voTrm30SQmWBPJjC8iLzacgtoAcWy4WvWXUx2s1WFmauoylqctG/bmFGBKtVq1XXHgpvPMj+ONMtXbSoi+rk61CCDHKWpzNvb+XbSYbRp0xPANJmQtf2gZv/xAemKvCpMKLwzMWIcQpSZAk+iy5VS36efr3QW8K92iEECJ0jFYVJu19ApZ8JdyjEUKI0Cq4GJ64Gs7+FWg0aDQabphzIzve+gh/wM+upp1srt1ESdrycI9UjDGH14HdY8fpc+L0OnB4nbh8ThxeB05v8DafE5fXicPrxO6102hvoLa7hiZHEwECQ3o9i95Cri2P/NgC8mPzyY8tGHbF0YTktkPLIWg5CM0HofmA2nd3w5e3S5ufqcZig/N/DwtuhJe+CtsfUu3upP28EGKU9V8fKdES5omUehOc/UvIXwvPfF6t3Xnu/wNzdHjHJYQ4hgRJok9iESTNVidST7s23KMRQojQmn0VbP6tBElCiKkndQF4HdC0T30eBDKjsrhw+lqeL38OgL/t/guLkheHb8aqGHWBQIDa7lr2Nu9hT/Me9jbvoba7ZsxeL94cT0pEKhlRGb3BUUZUJjrNJK+4CARUQNS0PxgYHVChUctB6G6E2BkQnwdxeZCxFOZeBwlFEiJNZclz4PNvw57/wpNXw7QzVNAvLejFVOG2Q9VGqNwAC74IMRnhHtGk09B/fSRriNvanci0lXDTDnj1NlWddMnfIfuMcI9KCNGPBElioKVfhfU/lyBJCDH15J6rZkB1VEN0erhHI4QQoaPRqKqkfc/2BkkAVxVewztVb9Pp6aTeXs9P3/8xdyz5PmZZO2ZC8gf8HOk4wp7m3ewNBkctzpZRe34tWhKsiaRGpPbb0kiNTCXFmjI11xyqeAfe+j60HYHk01RglDQHii5X+zFZ0rpMHJ9GiRygbgAAIABJREFUA7OvhLwLYf3P4L5CSF8CBRdB/kUQNyPcIxRi9Pg8UL0Vyt+EiregdjukzIPpZ4IpKtyjm5TGVUVSf6YouPgh2P+Cqpafcw2s+QkYpuBnCCHGIQmSxED5a+Hlr0P1NrXwpxBCTBV6kzqRuuf/oPgb4R6NEEKEVsE6eOdutfBxUJQxiutmfY4/7bgPgI8aPuSHm77PD5bdTZRRTuyMN4FAAG/Ai8fnwev34PF7aXQ0sKd5D3uadlPaspduT/dJn0Ov1RNtjMakM2PWm7HoLZiD++rSgkWvLntujzPHkRqRRrI1GYPOEKJ3O84d3aoCpNZytdbNnGskMBLDY4qEs34GK++E8jfgwPNq4qclri9UyiyWny8xsfj9UP9xX3BUtRHicmH6GlhxO2StUD/7Ysw02vsFSeOlIqm/grWQsRNeuAkeWqh+NrQG0BlOcqnv29dbIGs5RKWG+50IMalIkCQG0upg8Vdg631w6T/CPRohhAit2VfBu/dIkCSEmHqyV0FTKXTVQ2Ry783nTjuPNmcrj+17FIB9Lfu4c8P3uKfkJ8SZ48I02Kmnor2c/+7/D9Vd1Xj8Hjx+D16/N3jZd32orHorRfEzmRU/i5nxs8mz5UkYNBJ1O+HtH0Dth3D6XTD/enVCS4iRMkVC0SVq8/uh5gMVKr10K3QchbzzVaiUey6YY8I9WiEG6mnx2RMcHX4HIhJVOLDgRrjsX2CdQuvjjQONjr7WdknjqSKpv4gEuOL/4OBL0FIGfo+qXvN71b67q99tn7h0d8LzX1StYosuhcJLIT433O9IiAlPgiRxrAU3wP/mQXsVxGSGezRCCBE6M86Ep6+D1sMQmx3u0QghROjojZBzDux/Hhbe2HuzRqPhqsJriDRG8dDOBwA40nGE29/7Dvcu/wkpETLTcyy1Olt5tPRfvH7kNQIERvx8MSYbs+JnMSt+NrPiZzEtJnvyr1EUCk0H4J0fQcXbsOJ78Kn/SBseMXa0WshYorY1P4a2Sjj4Iuz4Ozx3o+oskn+RqliKywn3aMVU1VapQqOKt6DiTdDo1N9ahZfA+b+XVuJhNu4rknpoNJB/4fAe63XD4beh9Gn420qwJqifv8JLIXW+rEUoxDBIkCSOZY2HhV9UJfNr/xTu0QghROjoDOqD5Z7/worvhns0QggRWgXrYPfjA4KkHmtnXESkIZLfffg/+AN+6ux13L7+u9xd/GOyY7JDP9ZJzuPz8Fz5s/x3/79xeB2DfpxOo8OgNaDX6jFoDVgNEeTHFqjwKGE2aRFpaOTEyehpq4R374V9z0DxN+Gih6Udkwg9WxYsvllt7m7VAm//87DhF6oFXv5aFSplFINOTgGJMdLdqML0imDVkbNNVRxNXwNn/FCFmvL7Z1wIBAIDKpISreO0Immk9EZVpZl7Llz4J6h+X4VKT1wJPndfqJS1Qo6N41EgoI4j7ZXQWQux0yEuT02mEGEj/1PE8ZV8Wy3oufIOqUoSQkwts6+C178jQZIQYurJu0D1ond3gzHimLtXZa7Gqrfyq22/wO130+Js4Y4N3+VHxfdQGFcUhgFPPoFAgC21m/nb7r9QZ68bcN/C5EV8Ku/TRBmjMegMAwKjnn2tRv64DonOOlj/M9j1KCy6Cb52ECyx4R6VEOrYXbhObb0t8F6Al7+mOo7kna+CpdzzpAXeZGJvUZc6A+iMap2YsT7Z6uyAI+/1VRy1HYZpp6vgaPEtkDRbTviOU92ert5JKiadiSjDFFj3UqtV68llFsPZv4SGPbDvaXjtNhVU5F+kKp8ikkBvVmss6c1gsAy8Lj/To8fjhI4q9bupvVJtHT37wUutXp2TjkhW607aGyFlHqTMh9QFaksskjbCISRBkji+iERYIFVJQogpKPsM6KiG5oMQnxfu0QghROhYbJC+BMpeV+twHMeS1KXcXXIvP95yDw6vg25PNz/Y+H3uXHoX85MWhHjAk0t5Wxl/2f0wu5p2Dbg9IzKDG+Z8kYXJi8I0MoHfD037oHI9HFkPh16GuZ+FW0ohcpLO5BYT34AWePeqE3MHXoSP/wnPfTHYAm+tOoEqa4dMLIEA1O+EvU9C6VPBE646VWXRs06MRqsCJZ1RnWQd9H6/MOp4+wE/HN0C9bsgY6kKjtY+CGkL5WTuBNHo6Gtrl2RNmnqVyhoNJM9W2xk/gLYjqrL4o7+Bqx08DvA6wRu87L3uVD/j/YOlyGRY/j21DtNU+z5+UiAAznYV9nQ3Bi8b+u33u+w4qr7X0RkQnQkxWWpLWwRFl6n96EwwRw98DUcr1H4EdR9B+euw8Zfq+Jc4S7Ur7AmXkuaMzxbDfp8KMbtqITYHbNkTrhpuYo1WhFbJbVKVJISYerQ6mPVp2P0fOOOucI9GCCFCq+Bi2P/sCYMkgNkJc/jZil9w96Yf0u5ux+Vz8ePN93Dbou+wPH1FCAc7OZxoHaQoQxRXF13Lednno9fKn22nVL8LPngQSp+EyFQ1G77/FpM5+JM8XjfUbofKDcFto6reyFoJ2avUbGZZ30NMNDGZsPgmtfW0wDvwAmz8FZhtwRn5ayGzZMKd2JoS/H6o3qqCo9InVaBTdDlc9BBkLBtYKREIgN/bFyr5PCpkGsq+3zMwmOrZD/hhzU/Uz4nBEr7vhxi2BntfW7sEyzheHylUbNNg2dfVdjKBAHhdA0Omhj3w1l2w6ddw9q9g2srRHZvXDbv/DTXbIC4X4vPVFjNt5MdpZwc0lUJjqZos01SqgpohCYCrIxgSNalwLSJRVXZZE9W+NVEFRinz+11PV1VGQ63wssTCjDVq6+HqgvqPVcB09H3Ydj80H1Ct8Mw2FfoZrOp4ZbAGr/ffD96nt6jPenG5atObhvi9OA5Xl2qpWLkRqjaq8UUmq+9HSxl01amfv7g8NYk5Pr9vPzpzXFbAyacDcWK9VUk/g7X3h3s0QggROrOuhBdvliBJCDH1FK6D936sZsxpdSf8shxbLj9f+St+uOkumhyNeANefr3tl3R5ujg3+7wQDnhwGuwNtLlaiTbGEGOKwawzh30G7onWQdJqtFw4fS1XFV5DlHEKtJsZCY8T9j4BHzwAbRWw4Eb4wnvqREjDbrWVv6EuPd1qxuonA6bIJHUy5ehmFRodWa/agcXnqeBozrVw4f0QnRbudyvE6PlkC7za7SpUeuUbanZ34TqYdYWqNpEqk/DxedVxqfRJtbaLKVrN1r/iCdXe6US/xzSaYAWR/NuJY/WvSEqUIGnwNBpV5WIwAzZ1W+x01Rp616Pw1HWQfBqc9QtImjmy17K3wPYHYet9kDgTcs5VHVMOvKhCku56sE3vC5b6b5HJfceGQECFFY2lx4ZGznZIKICEItUebu5nVcgzVKaovtBoNMKXIb9+JGQtV1sPj1N9n1wd4LGr4M/j6LdvV9fdnep72XOfq119n9sOq7ArvkB9j/pfRqWe+NjbXtUXGlVuhOb9KkDLLIHFX4HLHlHfp/7jbC1XY205qMKwPf9VY3A0q6ql+DwVLp32GUg5bUy/lYMhQZI4uZJvw30FsOIOtYinEEJMBZklamHHhj2QNCvcoxFCiNCxTYOodKjadMpZlRlRGfxy5a/54aa7qO46ih8/f9zxB7o8XVye96kQDfjkytoO8fi+x9ha9/6A241aIzEmFSr1hEvRxhhsphiiTTHEGGNIsCSQHTN91Ncd6nC18+7Rd3m+7Nlj1kFalLyIL8y+gcwo+dx9Us0HYftDsOMfqpVJyW2qkqL/SdOMJQMfY2+Bxj19AVPpk+oS1AzjtEVqwe0Vt6s1FGT9GDFVaLWqzV36Ylh9D7RVqv8f79wNT16rFqSfdQVMXy3BRCh43WrNob1PqgrhmCwVHn32dXWyV4gRauxXkZRklfasI6bVwtzrYOanYduf4O+roOAiWHUPxGQM7bmaD8GW38Gux1SXgGtfgpS5x36dxwEth1QA0XxABRc7/qb2fW4VKGn1KjjSGfrCooQi9XkpsWjcVryMCoN5ZKGLz6MCnqb9Kgyq+UCFhU37VSVafH5fsGSKUpVGVRvV58nMEhVqXXC1avl5snDNYFah4/GCR3d38N/4oAqZ3F3Dfz+jSIIkcXIRCbDwS7Dh51KVJISYOrRa9Qfz7v+onvJCCDGVFK6Dfc8Oqj1HojWRX6z8FXdv+gFl7WUA/GPP3+h0d3BN4Wcw6oxjPdrjKm8r4/F9j/F+3Zbj3u/2u2l0NA6YlXs8CZYElqetYHn6SgpiC4ZdxeTz+/io4UPeqHydrbXv4w14B9yfGZXJ9bNvlHWQTsbngf3Pwwf3Q93HMO/zcMOmwa/tYo1TP9P9f64DAdW/32wLzyxaIcYjWxYUf1NtbZWq6u/tH8JTZX2hUvZqaX83mtx2OPSKalt38EVVgVB0OZz+fVXxIMQoGliRJEHSqDGYoeRbMP96tXbPA3PV+dTl31PrkJ5IIKAqDzf/j7pc+CW4ZY+qfDnha1kgeY7aPsneogIlv0cFRxEJI39vU43OEKzWKjj2PkdrX8DUtF9VfOWeB2t+rFrijVbHA2OEChGPFySGkfzmF6dWfJtUJQkhpp5ZV8LT16mZmVN94UwhxNRSsA6euBLO+fWgjn8xphh+uuIX/GTLvexu3gXAUwef5LmyZ8m15VEUN5OZ8TMpiisi2jS2VR4V7eU8vu8xttRuPua+adHT6PbY6XC14/a7B/V8TY4mni17hmfLniHRksjy9BUsT1tB/iBDpZquat6ofIO3Kt+kxdl8zP2yDtIgtFfB9ofhwz9DXA4suhlmXj46wY9Go1rACCGOz5alToyWfEstSL/3CbUeSGs5FF4aDJVWSag0HM521aaq9EnVgjNtsao8OvtX0kpTjKn+ayQlSkXS6LPY4Kyfw+JbVGXnH/JVtfOSWwZ+dvF5VOXh5t+qbijLvqlanxkjRvb61jiwLhvZc4gTs8RC5jK1TUHy216cWk9V0vqfwUUPhHs0QggRGumL1SK1VZshqyTcoxFCiNBJna/aNjTtG3QbHavByo9K7uHX237B1rqtAHj9Xva1lLKvpZSnDz0JQEZkBkXxM5kZN5Oi+JmkRqSNylpFh9sP8/i+R9lcu+mY+0rSlnN14TVMi84GIBAI4PQ5aXe10e5qp8PdQburnXZ3e/C2DtpdbRxsPUCnp7P3eRodjTxz6GmeOfQ0SZYklqevYEX6SnJteQPeg8PrYGP1Bt6ofJ29zXuOO9782ALOmnY2p6efgdVgHfH7nxQCAeisUdVG9TvVwsl1H6uZnqddC9e9Bsmzwz1KIaYu2zTVRrLkNmg9rEKlN+9Ua0kUBUOlaWdIqHQy3U2qXV3pU2o9tmmnq2B87YNSNSBCpknWSAqNmAxY92fVLv/NO+H9/1VVK/lr4cO/qOux0+H0u9RtJ1mbVIjxQn7Di8HpqUpaeYf6ACmEEJOdRqNmDr11F3zuTalKEkJMHRqN6su+79khrcdg0pm4fcn3+e/+/7C++l2qu6qP+ZqjXUc52nWU14+8BkCMyUZRXBFFcUWkRqQRb0kgwRJPjMk2qLWJjnQc5vF9j7GpZuMx9xWnlnBV4TVMjxnYFkij0WDRW7DoLaREnLhtiNfvZVfTTjZUr2dzzSa6PH29yRscDTx96CmePvQUSdZkVqSvoDCuiPdr32dj9XqcPucxzxdjsrEmcw1nZp1NVvQUr/L3uqBxbzA0CgZHdR+DRqtaeCTPVYtXr7hdtWXRh6dFohDiBGKzYfm31dZaoUKlN25XVUtFlwVDpdMlVALoqIZ9z6jKg9rtkHOOWjT98sfBHB3u0YkpxuPz0OJsAUCLlnhLfJhHNAUkzYKrn1Xh8evfg+e+qI6TVz4F6dLSWEws8ltdDE5EAiz8Mqz/uVQlCSGmjnlfgI2/Vu0mcs4O92iEmNxKn1G9pld8L9wjEaDa273zI1h5+5AeptfquaboWq4pupY2Vxv7mkspbdnL3ua9lLUdOmZtoHZXG1tqNx/Tik6n0RFvjifOEk+CJYF4swqY4i0JxJvj0Wq0PFv2DBurNxAgMOCxy1KLuargambYcob33vu9l/lJC5iftICb597Cx4072FC9gS21m+j2dPd+XYO9nqcOPnnc59BqtCxOXsJZ085mYfKiqdm+zu+HplKo2gSVG9WCxa1lqo98crD3e+55aj8qJdyjFUIMVex0WP4dtbWUq1Dp9e9CR9XAUCkcs+3d3dBZqyobu+qg37EbgpPEeieL9Zs09snbhvM1bYdV5VHTPlVtsPRrkHuuWttEiDBpcjb17sdZ4qbm55JwmbYSbtgIrk4JkcWEJUcMMXjF35KqJCHE1KLTw5qfqFL0GWdJVZIQY6H9KLz8VWgshXV/DfdoRI/sVerkV2fdsE/u20w2lqUVsyytGACXz8XB1oOUtuyltHkPpS2lAwKZ/nwBHw2OBhocDce9/3iWpizjqsJryBlhgHQ8eq2ehcmLWJi8iK/4b+Hjhh1sqF7P+7Vb6PYe+x4yozI5K+tsVmWuJtYcN+rjGRM+r5otb4qG6AwwRQ3vedzdUL21Lzg6uhmsCZBZApnLYdnX1ULyo7HGkRBifImbASu+q7aWctj7f/Dad4Kh0uXBUGnlyEIlnxfsjSoY6h8S9W79bvN7ITIVIlPUZowMPklwAkKg/0SEUb4tIhFW/QiyV0tVpRg3Gvutj5Qgbe1CT6OREElMaBIkicHrrUr6GVz0YLhHI4QQoTHzU7DhF2pG4czLwz0aISYPvw+2/QnevVctRvupf8uJ5fFEb4Scc+HAC7DwxlF5SpPOxOyE2cxOUOvc+AN+qjorKW0upaz9EM2OJpoczTQ7mgasTXQqS1KWcnXhNeTYckdlnKdi0BpYlLKYRSmL8fg87Gj8iA3VG6jqrCTHlsNZWWeTH1swKms/hUT9LtjxD9j1KEQkqbZzHVWg1atAqf8WlT7wuiVWtW2q2tgXHDXtU1VGmSVqndVL/gaRyeF+l0KIUIuboaqMV3wPWspgz//Ba7epY8bMYKiUtUKFSoGAmqXf9Ylg6JigqBYcLWCJ7wuHera4HMhaPvA2U7RMBBOin/5BUpI1KYwjEUJMRBIkiaEpuQ3+kA8r75SqJCHE1KDVwpk/g1e/qVo9Sa93IUaubic8/0XQmeAL7w1pHR4RQgUXw+7HRy1I+iStRsu06GymRWcfc5/L66TZ2Uyzo5kmZ9OAkKnZ2UyHq50cWy6fLriCXFvemIxvMAw6A4tTlrA4ZUnYxjAs3U3q33bH36G7AeZ+Fj7/DiQUqPsDAXC2Q8fRvq2zGmq2wb6n+27z2MEUo0KjrOVw/u8hdSEYzOF8d0KI8SYuR7VKXXk7NB9SlUqvfFMdVwwRKiTS6gZWD/VsCQUDr1sT5fO4EMPU6Gjs3U+UiiQhxBDJb18xNNZ4WHSTVCUJIaaW3HNhQxJ8/E9YcH24RyPExOW2qwqkj/6qAtr516uwVoxPeRfACzepVmXGiJC+tElvJi0ynbTI9JC+7qTm88DBl+Hjf0D5m5B/IZz1C5i+5tg2UxoNWGxqS5594ud029V6HzLjXwgxWPG5ql3+yjug9TAEfBCRDKbIUz5UCDEyA4IkqUgSQgyRBEli6Iq/paqSVtwBsdnhHo0QQow9jQbO/Dk8eTXMuUZmWouJy94CR7eoNlT1OyE6E5JmQeIsdRkxhjMTD70GL94M6Uvg5p3DXndHhJDFpv69yl6DokvDPRoxXHUf97Wui8uDeZ9X65GZY0b+3EbryJ9DCDF1yfkEIUKqoV9rO6lIEkIMlQRJYuj6VyVd/FC4RyOEEKGRVQLJc+GDB6D4G+EejRCn5vdD8wG1bknP1nFUBQOZJepkcke1CpR2PQ6Ne0BrgMSZA8OlxFlqncTh6mqAV7+lwqsL/wR554/aWxQhULgO9j8nQdJEEgio/9cHXlTto+zNqnXd9RsgPnxtAIUQQggRXk0DWttJRZIQYmgkSBLD01OVtPJOmUUkhJg6zvwp/PNsWHADmKLCPRohBnJ3Q/W2vtDo6Oa+tUsyS2DJrZA0+8TrCgQCalHrxj3QsAfqPoKdj6jrerMKlKJS1cLVx2wxx799z3/gjdvVSeyv7A55ezQxCgouVu0I/b5j25+J8cPjgIq34MALatMaoOAiOOe3kL1KWkgKIYQQU1wgEKDR3hckJUlrOyHEEEmQJIbHGg+LbpaqJCHE1JI8B2acBZv/B1b9KNyjEZOVsx223Q/VW8HnPsHmOs5tHkiZp0Kj+dfDxQ+r4GewNBqITlNbztl9twcCqnKpcS/YG8HVoTZnuwqeeq67OsDVPvB60hz4zCuQOn/0v08iNGzTICpdhZPTVoZ7NKK/9qNw8EUVHB1+V/0/y18L170GCYWybpEQQggherW723H73QBE6COwGqQ9rRBiaCRIEsNX/M1gVdIdEDs93KMRQojQWH0vPLwEFt8ysnZfQnxSZx1s+R18+DDkXaDW49KbQWccuOlNx96mM6kF73WG0R+XRgMxGWoTU9Pcz6qqpM+8KpUt4eT3Q822vqqj9krIPU8dKy75B1jjwj1CIYQQQoxTjf3XR5JqJCHEMEiQJIZvQFXSw+EejRBChEbcDJh9JWz4OZz723CPRkwGrRWw8dew+9/qhPCXtkvbWDG+LP2aWmtn6x9g2dfDPZqpqWoLvHgzeJ2q3eD5/wsZxSduVSmEEEII0U//tnaJlsQwjkQIMVHJlEIxMsXfgtKn1EkwIYSYKk6/C3b8Hdqrwj0SMZHV74InPwMPLQazDW4thQvvkxBJjD86PVz2CLz3E7V+lggdRys8fxP85zIo+Q7cshfO/qVqMyghkhBCCCEGqdHRvyJJgiQhxNBJkCRGxhoHi7+iqpKEEGKqiEqFhV+Gd+4J90jERFS5CR67CP51jlp36+tlcNbPIDI53CMT4sTicuDMn8NTnwGvO9yjmfwCAfj4X/DHmaDVqQDptGtk3SMhhBBCDEtD/4okaW0nhBgGCZLEyC37pqpKqvkw3CMRQojQWf4d2P8sNO0P90jERBAIwMFX4G9nqBPxeRfA18thxffAHBPu0QkxOAtugJgsePuH4R7J5Na4D/6xBjb/P7jqWbjwj2CxhXtUQgghhJjABlQkSWs7IcQwSD8EMXLWOLjoYXj0AvjMy5A6P9wjEkKIsWeJheLb4K0fwBX/DfdoxHgQCIC9SbV7basYeNl8AExRsOJ2mHWltKQSE5NGoz7zPTBPhaHZp4d7RJOLxwHv/RQ+eADO+AEsvkWOFUIIIYQYFY32viApSSqShBDDIH+ZiNEx8zIgAI+cL2GSmLo6quHQK1D2Otgb1UnlgB8InGTfr64v/y7MviLMb0AM2dKvwf/mQs12SFsY7tGIUOmsVf/mrWUqJGotV4FR22HQGiB2Otimq8vkuVB4iboenw9aKQYXE1xkElz8MDzzObhph1TUjZaDL8NLt0LqQrj5Y4hOD/eIhBBCCDGJNDr6tbaTiiQhxDBIkCRGz8zL1aWESWKq8LqhahMcelkFSB1HYcbZkHseRGeomdsaLRC81GgG7vfep4GEojC/GTEsRquaNf7mnXDdq+EejRgLbjvUfgjV78PR9+HoFvB0Q9piiM9TAVH2GX3BkZxUF1NB/oWw/3l4+Wtw6T/CPZqJraMaXvmGOs5c8EfIOy/cIxJCCCHEJOP0OulwdwCg1+iJNceFeURCiIlIgiQxugaESa9A6rzwjkeIT3K0qpM1ljiISAJrIuiNg398W6UKjQ69DBVvqxPJuefDhfdD+hJpQTMVzb8BNv0GKt6B6avCPRoxEn6/akHXExpVvw9N+yBxFmQshYKL4cyfQlyuLHgvxLm/hQfmw54nYNanwj2aicfRCtsfgo2/hsU3w6X/BIMl3KMSQgghxCTU1K8aKd4Sj1YjXRKEEEMnZzzF6OsNk86TMEmMD36faje34+8qAEo+DVyd0N2gWtAZIlSoFJEYvAwGTD37BgscWa8e21UPuedC0eWw9kHV4kdMbXojrL4X3rwDbtgkAcNEY2+G3f+G/c9B9VYwx6rQKH0pzL0OUuaDwRzuUQox/hgj4LJH4PGLILMEotPCPaKJofYj2PpH2PuEWmfq+g2QWBjuUQkhhBBiEhvY1k7OYQghhkeCJDE2JEwS40H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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_power()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Again, thoses tests that have higher statistical power MGC at a given dimension are above the red line, and those that have a lower statistical power than MGC are below it. Here, we see that in most simulation settings, MGC performs as good as or better than many simulations as dimension increases. HHG outperforms MGC ocassionally with some of the more sinusodially based algorithms, but is much slower to calculate p-values and independence tests."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.1-final"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/docs/tutorials/independence/indep_power_dimension.ipynb b/docs/tutorials/independence/indep_power_dimension.ipynb
deleted file mode 100644
index 2b0867fa4..000000000
--- a/docs/tutorials/independence/indep_power_dimension.ipynb
+++ /dev/null
@@ -1,382 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Independence Tests Power over Increasing Dimension"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are same useful functions to import. Since we are calculating the statistical power over all the tests for all the simulations, we can just use a wild card import from the respective modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys, os\n",
- "import multiprocessing as mp\n",
- "from joblib import Parallel, delayed\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from benchmarks import power_dim\n",
- "from hyppo.independence import *\n",
- "from hyppo.sims import *\n",
- "\n",
- "sys.path.append(os.path.realpath('..'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import seaborn as sns\n",
- "sns.color_palette('Set1')\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=2)\n",
- "sns.set_palette(sns.color_palette(['c', 'm', 'y', 'k']))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are some constants that are used in this notebook. If running these notebook, please only manipulate these constants if you are not running more tests. They define the number of replications. The simulations tested over and the independence tests tested over are defined also."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "POWER_REPS = 5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "simulations = [\n",
- " linear,\n",
- " exponential,\n",
- " cubic,\n",
- " joint_normal,\n",
- " step,\n",
- " quadratic,\n",
- " w_shaped,\n",
- " spiral,\n",
- " uncorrelated_bernoulli,\n",
- " logarithmic,\n",
- " fourth_root,\n",
- " sin_four_pi,\n",
- " sin_sixteen_pi,\n",
- " square,\n",
- " two_parabolas,\n",
- " circle,\n",
- " ellipse,\n",
- " diamond,\n",
- " multiplicative_noise,\n",
- " multimodal_independence\n",
- "]\n",
- "\n",
- "tests = [\n",
- " MGC,\n",
- " CCA,\n",
- " Dcorr,\n",
- " HHG,\n",
- " Hsic,\n",
- " RV,\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These helper functions return the number of dimensions to test over and the range for each simulation. Limits were chosen based on when it is clear to determine that the tests were tending to 0 statistical power."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def find_dim(sim):\n",
- " if sim not in simulations:\n",
- " raise ValueError(\"Invalid simulation\")\n",
- " \n",
- " if sim in [joint_normal, sin_four_pi, sin_sixteen_pi, multiplicative_noise]:\n",
- " dim = 10\n",
- " elif sim in [multimodal_independence, uncorrelated_bernoulli, logarithmic]:\n",
- " dim = 100\n",
- " elif sim in [linear, exponential, cubic]:\n",
- " dim = 1000\n",
- " elif sim in [square, diamond]:\n",
- " dim = 40\n",
- " else:\n",
- " dim = 20\n",
- " \n",
- " return dim\n",
- "\n",
- "def find_dim_range(dim):\n",
- " if dim < 20:\n",
- " lim = 10\n",
- " else:\n",
- " lim = 20\n",
- " \n",
- " dim_range = list(range(int(dim/lim), dim+1, int(dim/lim)))\n",
- " if int(dim/lim) != 1:\n",
- " dim_range.insert(0, 1)\n",
- " return dim_range"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following function calculates the estimated power ``POWER_REPS`` number off times and averages them. It does this iterating over the number of sample sizes.\n",
- "\n",
- "**Note: We only recommend running this code if running the next 2 cells ONCE to generate the csv files used to visualize the plots. This code takes a very long time to run and if running, we recommend using a machine with many cores.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "def estimate_power(sim, test):\n",
- " dim = find_dim(sim)\n",
- " dim_range = find_dim_range(dim)\n",
- " est_power = np.array([np.mean([power_dim(test, sim, p=dim) for _ in range(POWER_REPS)])\n",
- " for dim in dim_range])\n",
- " np.savetxt('../benchmarks/vs_dimension/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " est_power, delimiter=',')\n",
- " \n",
- " return est_power"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 48 concurrent workers.\n",
- "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 1.3min\n",
- "[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 1.7min\n",
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- ]
- }
- ],
- "source": [
- "outputs = Parallel(n_jobs=-1, verbose=100)(\n",
- " [delayed(estimate_power)(sim, test) for sim in simulations for test in tests]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following code loops over each saved independence test file and generates absolute power curves for each test and for each simulation modality."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_power():\n",
- " fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28,24))\n",
- " \n",
- " sim_title = [\n",
- " \"Linear\",\n",
- " \"Exponential\",\n",
- " \"Cubic\",\n",
- " \"Joint Normal\",\n",
- " \"Step\",\n",
- " \"Quadratic\",\n",
- " \"W-Shaped\",\n",
- " \"Spiral\",\n",
- " \"Bernoulli\",\n",
- " \"Logarithmic\",\n",
- " \"Fourth Root\",\n",
- " \"Sine 4\\u03C0\",\n",
- " \"Sine 16\\u03C0\",\n",
- " \"Square\",\n",
- " \"Two Parabolas\",\n",
- " \"Circle\",\n",
- " \"Ellipse\",\n",
- " \"Diamond\",\n",
- " \"Multiplicative\",\n",
- " \"Independence\"\n",
- " ]\n",
- " \n",
- " for i, row in enumerate(ax):\n",
- " for j, col in enumerate(row):\n",
- " count = 5*i + j\n",
- " sim = simulations[count]\n",
- " \n",
- " for test in tests:\n",
- " power = np.genfromtxt('../benchmarks/vs_dimension/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " delimiter=',')\n",
- " dim_range = find_dim_range(find_dim(sim))\n",
- " \n",
- " custom_color = {\n",
- " \"Dcorr\" : \"b\",\n",
- " \"Hsic\" : \"g\"\n",
- " }\n",
- " if test.__name__ in custom_color.keys():\n",
- " if test.__name__ not in [\"MGC\", \"MGCRF\"]:\n",
- " col.plot(dim_range, power, custom_color[test.__name__], label=test.__name__, lw=5)\n",
- " else:\n",
- " col.plot(dim_range, power, custom_color[test.__name__], label=test.__name__, lw=2)\n",
- " else:\n",
- " col.plot(dim_range, power, label=test.__name__, lw=1)\n",
- " col.set_xticks([dim_range[0], dim_range[-1]])\n",
- " col.set_ylim(0, 1.05)\n",
- " col.set_yticks([])\n",
- " if j == 0:\n",
- " col.set_yticks([0, 1])\n",
- " col.set_title(sim_title[count])\n",
- " \n",
- " fig.text(0.5, 0.08, 'Dimensions', ha='center')\n",
- " fig.text(0.08, 0.5, 'Absolute Statistical Power', va='center', rotation='vertical')\n",
- " leg = plt.legend(bbox_to_anchor=(0.5, 0.08), bbox_transform=plt.gcf().transFigure,\n",
- " ncol=5, loc='upper center')\n",
- " leg.get_frame().set_linewidth(0.0)\n",
- " for legobj in leg.legendHandles:\n",
- " legobj.set_linewidth(5.0)\n",
- " plt.subplots_adjust(hspace=.50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_power()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/tutorials/independence/indep_power_sampsize.ipynb b/docs/tutorials/independence/indep_power_sampsize.ipynb
deleted file mode 100644
index 035fd26b0..000000000
--- a/docs/tutorials/independence/indep_power_sampsize.ipynb
+++ /dev/null
@@ -1,421 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Independence Tests Power over Increasing Sample Size"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are same useful functions to import. Since we are calculating the statistical power over all the tests for all the simulations, we can just use a wild card import from the respective modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys, os\n",
- "import multiprocessing as mp\n",
- "from joblib import Parallel, delayed\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from benchmarks import power_sample\n",
- "from hyppo.independence import *\n",
- "from hyppo.sims import *\n",
- "\n",
- "sys.path.append(os.path.realpath('..'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import seaborn as sns\n",
- "sns.color_palette('Set1')\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=2)\n",
- "sns.set_palette(sns.color_palette(['c', 'm', 'y', 'k']))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are some constants that are used in this notebook. If running these notebook, please only manipulate these constants if you are not running more tests. They define the sample sizes tested upon and the number of replications. The simulations tested over and the independence tests tested over are defined also."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "MAX_SAMPLE_SIZE = 100\n",
- "STEP_SIZE = 5\n",
- "SAMP_SIZES = range(5, MAX_SAMPLE_SIZE + STEP_SIZE, STEP_SIZE)\n",
- "POWER_REPS = 5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "simulations = [\n",
- " linear,\n",
- " exponential,\n",
- " cubic,\n",
- " joint_normal,\n",
- " step,\n",
- " quadratic,\n",
- " w_shaped,\n",
- " spiral,\n",
- " uncorrelated_bernoulli,\n",
- " logarithmic,\n",
- " fourth_root,\n",
- " sin_four_pi,\n",
- " sin_sixteen_pi,\n",
- " square,\n",
- " two_parabolas,\n",
- " circle,\n",
- " ellipse,\n",
- " diamond,\n",
- " multiplicative_noise,\n",
- " multimodal_independence\n",
- "]\n",
- "\n",
- "tests = [\n",
- " MGC,\n",
- " CCA,\n",
- " Dcorr,\n",
- " HHG,\n",
- " Hsic,\n",
- " Kendall,\n",
- " Pearson,\n",
- " RV,\n",
- " Spearman,\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following function calculates the estimated power ``POWER_REPS`` number off times and averages them. It does this iterating over the number of sample sizes.\n",
- "\n",
- "**Note: We only recommend running this code if running the next 2 cells ONCE to generate the csv files used to visualize the plots. This code takes a very long time to run and if running, we recommend using a machine with many cores.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def estimate_power(sim, test):\n",
- " est_power = np.array([np.mean([power_sample(test, sim, n=i) for _ in range(POWER_REPS)])\n",
- " for i in SAMP_SIZES])\n",
- " np.savetxt('../benchmarks/vs_samplesize/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " est_power, delimiter=',')\n",
- " \n",
- " return est_power"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
- "source": [
- "outputs = Parallel(n_jobs=-1, verbose=100)(\n",
- " [delayed(estimate_power)(sim, test) for sim in simulations for test in tests]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following code loops over each saved independence test file and generates absolute power curves for each test and for each simulation modality."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_power():\n",
- " fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28,24))\n",
- " \n",
- " sim_title = [\n",
- " \"Linear\",\n",
- " \"Exponential\",\n",
- " \"Cubic\",\n",
- " \"Joint Normal\",\n",
- " \"Step\",\n",
- " \"Quadratic\",\n",
- " \"W-Shaped\",\n",
- " \"Spiral\",\n",
- " \"Bernoulli\",\n",
- " \"Logarithmic\",\n",
- " \"Fourth Root\",\n",
- " \"Sine 4\\u03C0\",\n",
- " \"Sine 16\\u03C0\",\n",
- " \"Square\",\n",
- " \"Two Parabolas\",\n",
- " \"Circle\",\n",
- " \"Ellipse\",\n",
- " \"Diamond\",\n",
- " \"Multiplicative\",\n",
- " \"Independence\"\n",
- " ]\n",
- " \n",
- " for i, row in enumerate(ax):\n",
- " for j, col in enumerate(row):\n",
- " count = 5*i + j\n",
- " sim = simulations[count]\n",
- " \n",
- " for test in tests:\n",
- " power = np.genfromtxt('../benchmarks/vs_samplesize/{}_{}.csv'.format(sim.__name__, test.__name__),\n",
- " delimiter=',')\n",
- " \n",
- " custom_color = {\n",
- " \"Dcorr\" : \"b-\",\n",
- " \"Hsic\" : \"g-\",\n",
- " \"DcorrRF\" : \"b:\",\n",
- " \"HsicRF\" : \"g:\",\n",
- " }\n",
- " if test.__name__ in custom_color.keys():\n",
- " if test.__name__ not in [\"MGC\", \"MGCRF\"]:\n",
- " col.plot(SAMP_SIZES, power, custom_color[test.__name__], label=test.__name__, lw=5)\n",
- " else:\n",
- " col.plot(SAMP_SIZES, power, custom_color[test.__name__], label=test.__name__, lw=2)\n",
- " else:\n",
- " col.plot(SAMP_SIZES, power, label=test.__name__, lw=1)\n",
- " col.set_xticks([])\n",
- " if i == 3:\n",
- " col.set_xticks([SAMP_SIZES[0], SAMP_SIZES[-1]])\n",
- " col.set_ylim(0, 1.05)\n",
- " col.set_yticks([])\n",
- " if j == 0:\n",
- " col.set_yticks([0, 1])\n",
- " col.set_title(sim_title[count])\n",
- " \n",
- " fig.text(0.5, 0.08, 'Sample Size', ha='center')\n",
- " fig.text(0.08, 0.5, 'Absolute Statistical Power', va='center', rotation='vertical')\n",
- " leg = plt.legend(bbox_to_anchor=(0.5, 0.08), bbox_transform=plt.gcf().transFigure,\n",
- " ncol=5, loc='upper center')\n",
- " leg.get_frame().set_linewidth(0.0)\n",
- " for legobj in leg.legendHandles:\n",
- " legobj.set_linewidth(5.0)\n",
- " plt.subplots_adjust(hspace=.50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_power()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/tutorials/independence/indep_stat_same.ipynb b/docs/tutorials/independence/indep_stat_same.ipynb
deleted file mode 100644
index 451b1f2b2..000000000
--- a/docs/tutorials/independence/indep_stat_same.ipynb
+++ /dev/null
@@ -1,188 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import sys, os\n",
- "import matplotlib.pyplot as plt\n",
- "sys.path.append(os.path.realpath('..'))\n",
- "import pandas as pd\n",
- "\n",
- "from hyppo.independence import Dcorr, HHG\n",
- "from hyppo.ksample import KSample\n",
- "from hyppo.sims import spiral, rot_2samp"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "N = 20\n",
- "\n",
- "tests = [\n",
- " HHG,\n",
- " Dcorr,\n",
- " KSample,\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i in range(N):\n",
- " x, y = spiral(1000, 1, noise=True)\n",
- " sim = np.hstack([x, y])\n",
- " np.savetxt('../benchmarks/same_stat/indep/{}.csv'.format(i+1), sim, delimiter=',')\n",
- " sim1, sim2 = rot_2samp(spiral, 200, 1, noise=True)\n",
- " np.savetxt('../benchmarks/same_stat/ksample/sim1_{}.csv'.format(i+1), sim, delimiter=',')\n",
- " np.savetxt('../benchmarks/same_stat/ksample/sim2_{}.csv'.format(i+1), sim, delimiter=',')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "for test in tests:\n",
- " stats = []\n",
- " for i in range(N):\n",
- " if test.__name__ == \"KSample\":\n",
- " sim1 = np.genfromtxt('../benchmarks/same_stat/ksample/sim1_{}.csv'.format(i+1), delimiter=',')\n",
- " sim2 = np.genfromtxt('../benchmarks/same_stat/ksample/sim2_{}.csv'.format(i+1), delimiter=',')\n",
- " stat = test(indep_test=\"Hsic\", bias=True)._statistic(sim1, sim2)\n",
- " else:\n",
- " sim = np.genfromtxt('../benchmarks/same_stat/indep/{}.csv'.format(i+1), delimiter=',')\n",
- " x, y = np.hsplit(sim, 2)\n",
- " stat = test()._statistic(x, y)\n",
- " stats.append(stat)\n",
- " np.savetxt('../benchmarks/same_stat/{}.csv'.format(test.__name__), stats, delimiter=',')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "import seaborn as sns\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=1.5)\n",
- "PALETTE = sns.color_palette(\"Set1\")\n",
- "sns.set_palette(PALETTE[1:])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.ticker as mticker\n",
- "\n",
- "\n",
- "FONTSIZE = 20\n",
- "\n",
- "\n",
- "def plot_stats():\n",
- " fig = plt.figure(figsize=(5,7))\n",
- " ax = plt.subplot(111)\n",
- " \n",
- " test_names = [\n",
- " \"Dcorr\",\n",
- " \"MMD\",\n",
- " \"HHG\"\n",
- " ]\n",
- " \n",
- " data = np.zeros((N, len(test_names)))\n",
- " for i in range(len(test_names)):\n",
- " if test_names[i] == \"MMD\":\n",
- " mgc_stat = np.genfromtxt('../benchmarks/same_stat/KSample.csv', delimiter=',')\n",
- " r_stat = np.genfromtxt('../benchmarks/same_stat/RMMD.csv', delimiter=',')\n",
- " mgc_stat *= 2 * 0.05 / (1000 * 2) # exact equivalence stat correction\n",
- " else:\n",
- " mgc_stat = np.genfromtxt('../benchmarks/same_stat/{}.csv'.format(test_names[i]), delimiter=',')\n",
- " r_stat = np.genfromtxt('../benchmarks/same_stat/R{}.csv'.format(test_names[i]), delimiter=',')\n",
- " if test_names[i] == \"HHG\": # Fix for large HHG stats so difference is comparable\n",
- " mgc_stat *= 1e-9\n",
- " r_stat *= 1e-9\n",
- " data[:, i] = np.abs(mgc_stat) - np.abs(r_stat)\n",
- " \n",
- " data = pd.DataFrame(data=data, columns=test_names)\n",
- " sns.violinplot(data=data, inner=None)\n",
- " sns.swarmplot(data=data, color=\"white\", edgecolor=\"gray\")\n",
- " ax.axhline(y = 0, color='red', linewidth=1)\n",
- " \n",
- " ax.spines['top'].set_visible(False)\n",
- " ax.spines['right'].set_visible(False)\n",
- " ax.spines['bottom'].set_visible(False)\n",
- " #ax.spines['left'].set_visible(False)\n",
- " #ax.text(-1.1, 0, \"Test Statistic Difference\", va='center', rotation='vertical')\n",
- " ax.set_ylabel(\"Test Statistic\\nDifference\")\n",
- " ax.set_ylim(-5e-4, 5e-4)\n",
- " ax.set_yticks([-5e-4, 0, 5e-4])\n",
- " f = mticker.ScalarFormatter(useOffset=False)\n",
- " g = lambda x, pos : \"{}\".format(f._formatSciNotation('%1.1e' % x))\n",
- " ax.yaxis.set_major_formatter(mticker.FuncFormatter(g))\n",
- " \n",
- " plt.savefig('../benchmarks/figs/same_stat.pdf', transparent=True, bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_stats()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/tutorials/independence/independence.ipynb b/docs/tutorials/independence/independence.ipynb
deleted file mode 100644
index feee76379..000000000
--- a/docs/tutorials/independence/independence.ipynb
+++ /dev/null
@@ -1,464 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.ticker as ticker\n",
- "import seaborn as sns\n",
- "\n",
- "# custom seaborn plot options to make the figures pretty\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=1)\n",
- "PALETTE = sns.color_palette(\"Set1\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Independence Tests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Discovering and deciphering relationships in data is often a difficult and opaque process. Identifying causality among variables is particularly important since it allows us to know which relationships to investigate further. This problem, called the independence testing problem, can be defined as follows: Consider random variables $X$ and $Y$ that have joint density $F_{XY} = F_{X|Y} F_Y$. The null hypothesis is thus that $X$ and $Y$ are independently distributed from one another; in other words, $F_{XY} = F_X F_Y$. The alternate hypothesis is that the $X$ and $Y$ are not indepenently distributed from on another. That is,\n",
- "\n",
- "\\begin{align*}\n",
- "H_0: F_{XY} &= F_X F_Y \\\\\n",
- "H_A: F_{XY} &\\neq F_X F_Y\n",
- "\\end{align*}\n",
- "\n",
- "Fortunately, `hyppo` provides an easy-to-use structure to use tests such as these. All tests have a class associated with it and have a `.test` method. The nuances of each test will be examined below; for the sake of this tutorial, the `MGC` class will be used.\n",
- "\n",
- "Importing tests from `hyppo` is similar to importing functions/classes from other packages. `hyppo` also has a `sims` module, which contains several linear and nonlinear dependency structures to test the cases for which each test will perform best."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from hyppo.independence import MGC, Dcorr\n",
- "from hyppo.sims import linear, spiral"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, let's generate some simulated data. The independence simulations included take on required parameters number of samples and number of dimensions with optional paramaters mentioned in the [reference](https://hyppo.neurodata.io/reference/sims.html#independence-simulations) section of the docs. Looking at some linearly distributed data first, let's look at 100 samples of noisey data and generate a unidimensional `x` and `y` (a 1000 sample of no noise simulated data shows the trend in the data)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "x, y = linear(100, 1, noise=True)\n",
- "x_no_noise, y_no_noise = linear(1000, 1, noise=False)\n",
- "\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "ax = sns.scatterplot(x=x[:,0], y=y[:,0])\n",
- "ax = sns.scatterplot(x=x_no_noise[:,0], y=y_no_noise[:,0], alpha=0.5)\n",
- "ax.set_xlabel('Simulated X')\n",
- "ax.set_ylabel('Simulated Y') \n",
- "plt.title(\"Linear\")\n",
- "plt.axis('equal')\n",
- "plt.xlim([-1.5, 1.5])\n",
- "plt.ylim([-1.5, 1.5])\n",
- "plt.xticks([-1, 0, 1])\n",
- "plt.yticks([-1, 0, 1])\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The test statistic the p-value is calculated by running the `.test` method. Some important parameters for the `.test` method:\n",
- "\n",
- " - `reps`: The number of replications to run when running a permutation test\n",
- " - `workers`: The number of cores to parallelize over when running a permutation test\n",
- "\n",
- "Note that when using a permutation test, the lowest p-value is the reciprocal of the number of repetitions. Also, since the p-value is calculated with a random permutation test, there will be a slight variance in p-values with subsequent runs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MGC test statistic: 0.32358732620610425\n",
- "MGC p-value: 0.001\n"
- ]
- }
- ],
- "source": [
- "# test statistic and p-values can be calculated for MGC, but a little slow ...\n",
- "stat, pvalue, mgc_dict = MGC().test(x, y)\n",
- "\n",
- "print(\"MGC test statistic:\", stat)\n",
- "print(\"MGC p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/sampan501/workspace/hyppo/hyppo/_utils.py:67: RuntimeWarning: The number of replications is low (under 1000), and p-value calculations may be unreliable. Use the p-value result, with caution!\n",
- " warnings.warn(msg, RuntimeWarning)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MGC test statistic: 0.32358732620610425\n",
- "MGC p-value: 0.01\n"
- ]
- }
- ],
- "source": [
- "# so fewer reps can be used, while giving a less confident p-value ...\n",
- "stat, pvalue, mgc_dict = MGC().test(x, y, reps=100)\n",
- "\n",
- "print(\"MGC test statistic:\", stat)\n",
- "print(\"MGC p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MGC test statistic: 0.32358732620610425\n",
- "MGC p-value: 0.001\n"
- ]
- }
- ],
- "source": [
- "# or the parallelization can be used (-1 uses all cores)\n",
- "stat, pvalue, mgc_dict = MGC().test(x, y, workers=-1)\n",
- "\n",
- "print(\"MGC test statistic:\", stat)\n",
- "print(\"MGC p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Unique to MGC"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "MGC has a few more outputs stored conviently in `mgc_dict`. The contents of this are:\n",
- "\n",
- " - `mgc_map`: A representation of the latent geometry corresponding to the relationship between the inputs.\n",
- " - `opt_scale`: The estimated optimal scale as a `(x, y)` pair.\n",
- " - `null_dist`: The null distribution generated via permutation\n",
- "\n",
- "The `mgc_map` calculates the test statistic over $k$ and $l$ nearest neighbors and the smoothed maximum of those test statistics corresponds to the optimal scale. This is shown in the plots below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimal Scale: [100, 100]\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# store mgc outputs as variables\n",
- "mgc_map = mgc_dict[\"mgc_map\"]\n",
- "opt_scale = mgc_dict[\"opt_scale\"]\n",
- "\n",
- "print(\"Optimal Scale:\", opt_scale)\n",
- "fig, (ax, cax) = plt.subplots(ncols=2, figsize=(9.45, 7.5), gridspec_kw={\"width_ratios\":[1, 0.05]})\n",
- "\n",
- "# draw heatmap and colorbar\n",
- "ax = sns.heatmap(mgc_map, cmap=\"YlGnBu\", ax=ax, cbar=False)\n",
- "fig.colorbar(ax.get_children()[0], cax=cax, orientation=\"vertical\")\n",
- "ax.invert_yaxis()\n",
- "\n",
- "# optimal scale\n",
- "ax.scatter(opt_scale[0], opt_scale[1], marker='X', s=200, color='red') \n",
- "\n",
- "# make plots look nice\n",
- "fig.suptitle(\"MGC Map\", fontsize=17)\n",
- "ax.xaxis.set_major_locator(ticker.MultipleLocator(10))\n",
- "ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n",
- "ax.yaxis.set_major_locator(ticker.MultipleLocator(10))\n",
- "ax.yaxis.set_major_formatter(ticker.ScalarFormatter())\n",
- "ax.set_xlabel('Neighbors for x')\n",
- "ax.set_ylabel('Neighbors for y') \n",
- "ax.set_xticks([0, 50, 100])\n",
- "ax.set_yticks([0, 50, 100])\n",
- "ax.xaxis.set_tick_params()\n",
- "ax.yaxis.set_tick_params()\n",
- "cax.xaxis.set_tick_params()\n",
- "cax.yaxis.set_tick_params()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The optimal scale is shown on the plot as a red \"x\". For a linear relationship like this one, the optimal scale is equivalent to the global scale, which is $(n, n)$. We can use the same thing for nonlinear data as well (spiral in this case)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "x, y = spiral(100, 1, noise=True)\n",
- "x_no_noise, y_no_noise = spiral(1000, 1, noise=False)\n",
- "\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "ax = sns.scatterplot(x=x[:,0], y=y[:,0])\n",
- "ax = sns.scatterplot(x=x_no_noise[:,0], y=y_no_noise[:,0], alpha=0.5)\n",
- "ax.set_xlabel('Simulated X')\n",
- "ax.set_ylabel('Simulated Y') \n",
- "plt.title(\"Spiral\")\n",
- "plt.axis('equal')\n",
- "plt.xticks([-4, 0, 4])\n",
- "plt.yticks([-4, 0, 4])\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MGC test statistic: 0.07794812078550183\n",
- "MGC p-value: 0.01\n"
- ]
- }
- ],
- "source": [
- "stat, pvalue, mgc_dict = MGC().test(x, y)\n",
- "\n",
- "print(\"MGC test statistic:\", stat)\n",
- "print(\"MGC p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimal Scale: [7, 33]\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# store mgc outputs as variables\n",
- "mgc_map = mgc_dict[\"mgc_map\"]\n",
- "opt_scale = mgc_dict[\"opt_scale\"]\n",
- "\n",
- "print(\"Optimal Scale:\", opt_scale)\n",
- "fig, (ax, cax) = plt.subplots(ncols=2, figsize=(9.45, 7.5), gridspec_kw={\"width_ratios\":[1, 0.05]})\n",
- "\n",
- "# draw heatmap and colorbar\n",
- "ax = sns.heatmap(mgc_map, cmap=\"YlGnBu\", ax=ax, cbar=False)\n",
- "fig.colorbar(ax.get_children()[0], cax=cax, orientation=\"vertical\")\n",
- "ax.invert_yaxis()\n",
- "\n",
- "# optimal scale\n",
- "ax.scatter(opt_scale[0], opt_scale[1], marker='X', s=200, color='red') \n",
- "\n",
- "# make plots look nice\n",
- "fig.suptitle(\"MGC Map\", fontsize=17)\n",
- "ax.xaxis.set_major_locator(ticker.MultipleLocator(10))\n",
- "ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n",
- "ax.yaxis.set_major_locator(ticker.MultipleLocator(10))\n",
- "ax.yaxis.set_major_formatter(ticker.ScalarFormatter())\n",
- "ax.set_xlabel('Neighbors for x')\n",
- "ax.set_ylabel('Neighbors for y') \n",
- "ax.set_xticks([0, 50, 100])\n",
- "ax.set_yticks([0, 50, 100])\n",
- "ax.xaxis.set_tick_params()\n",
- "ax.yaxis.set_tick_params()\n",
- "cax.xaxis.set_tick_params()\n",
- "cax.yaxis.set_tick_params()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We get a unique MGC-map and the optimal scale has shifted! This is because for nonlinear relationships like this one, smaller $k$ and $l$ nearest neighbors more strongly define the relationship. As such, MGC sets the optimal scale to be local rather than global from before."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Unique to Dcorr/Hsic"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dcorr and Hsic have a fast chi-square approximation that allows for quick calculation of the p-values without significant loss in statistical power. This can be called using setting `auto = True` when running the `.test` method, which runs the fast chi-square approximation whenever the sample size is larger than 20. Due to the implementation, the values of `reps` and `parameters` mentioned before are irrelevant. By default, the `auto` parameter is set to `True`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "4.86 s Β± 13.3 ms per loop (mean Β± std. dev. of 7 runs, 1 loop each)\n",
- "Dcorr test statistic: 0.07794812078550183\n",
- "Slow Dcorr p-value: 0.01\n"
- ]
- }
- ],
- "source": [
- "%timeit stat, pvalue = Dcorr().test(x, y, reps=10000, auto=False)\n",
- "\n",
- "print(\"Dcorr test statistic:\", stat)\n",
- "print(\"Slow Dcorr p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "584 Β΅s Β± 2.81 Β΅s per loop (mean Β± std. dev. of 7 runs, 1000 loops each)\n",
- "Dcorr test statistic: 0.07794812078550183\n",
- "Fast Dcorr p-value: 0.01\n"
- ]
- }
- ],
- "source": [
- "%timeit stat, pvalue = Dcorr().test(x, y, auto=True)\n",
- "\n",
- "print(\"Dcorr test statistic:\", stat)\n",
- "print(\"Fast Dcorr p-value:\", pvalue)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/tutorials/independence/perf/Fast_Dcorr.csv b/docs/tutorials/independence/perf/Fast_Dcorr.csv
deleted file mode 100644
index f2bcddcad..000000000
--- a/docs/tutorials/independence/perf/Fast_Dcorr.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-4.198708338662981987e-03
-9.037879450867573059e-04
-2.461933996528387070e-03
-9.692626345592239914e-03
-6.515823366741339828e-02
-5.110112240460390476e-01
-4.307966047354663708e+00
-1.869900455535389483e+01
diff --git a/docs/tutorials/independence/permutation_tree.ipynb b/docs/tutorials/independence/permutation_tree.ipynb
deleted file mode 100644
index 4482c34b6..000000000
--- a/docs/tutorials/independence/permutation_tree.ipynb
+++ /dev/null
@@ -1,297 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from collections import defaultdict\n",
- "from scipy.spatial.distance import squareform, pdist\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
- "import seaborn as sns\n",
- "\n",
- "# custom seaborn plot options to make the figures pretty\n",
- "sns.set(color_codes=True, style='white', context='notebook', font_scale=1.25)\n",
- "PALETTE = sns.color_palette(\"Set1\")\n",
- "sns.set_palette(PALETTE[3:])\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Independence testing with restricted permutations\n",
- "\n",
- "Here we show how the permutation tests of distance correlation (Dcorr) can be restricted to appropriately match known dependencies under the null distribution. Without such modifications, calculated pvalues are invalid as the permutation generated null distribution is incorrect."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from hyppo.independence import Dcorr"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "def simulate_2sample_null(n1, n2=None, d=100, group_std=0.1, seed=None):\n",
- " \"\"\"\n",
- " Simulates a set of paired observations for a 2-sample test.\n",
- " \n",
- " n1,n2 : size of the two groups. Are both n1 if n2 is None\n",
- " d : dimension of observations\n",
- " group_std : standard deviation of normal distribution around group mean\n",
- " \"\"\"\n",
- " np.random.seed(seed)\n",
- " \n",
- " # Means for each observation\n",
- " mus1 = np.random.normal(0,1,(n1,d))\n",
- " if n2 is not None:\n",
- " mus2 = np.random.normal(0,1,(n2,d))\n",
- " \n",
- " # Paired observations\n",
- " X = np.vstack(\n",
- " [np.random.normal(mu, group_std, (2,d)) for mu in mus1] +\n",
- " [np.random.normal(mu, group_std, (2,d)) for mu in mus2]\n",
- " ) \n",
- " return X"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Simulation parameters\n",
- "n1 = 25\n",
- "n2 = 25\n",
- "d = 100\n",
- "group_std = 0.1\n",
- "\n",
- "# Labels\n",
- "Y_within = np.asarray([0, 1] * (n1 + n2))\n",
- "Y_across = np.hstack(([0] * n1 * 2, [1] * n2 * 2))\n",
- "\n",
- "# Permutation tree blocks\n",
- "blocks_within = -1*np.hstack([[i+1]*2 for i in range(n1+n2)]).T\n",
- "blocks_across = np.c_[\n",
- " np.hstack([[i+1]*2 for i in range(n1+n2)]),\n",
- " -1*Y_within - 1\n",
- "]\n",
- "\n",
- "# Test params\n",
- "test_blocks = [None, None, blocks_within, blocks_across]\n",
- "test_names = ['Unrestricted within', 'Unrestricted across', 'Restricted within', 'Restricted across']\n",
- "test_Ys = [Y_within, Y_across, Y_within, Y_across]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Permutation block examples\n",
- "\n",
- "In order to restrict the permutations, we pass in a list of group labels. Each column is a list of labels which partitions the observations by shared label into blocks and multiple columns repeat this process recursively. At each level, blocks are exchangeable unless the label is a negative number, in which case it is fixed, and all elements of the final blocks are exchangeable. This defines the space of allowable permutations and the $Y$ matrix is permuted accordingly.\n",
- "\n",
- "The block labels used in this notebook are visualized below, corresponding to data where observations are dependent within pairs. Because of the $Y$ values in our 2-sample testing case, block labels of $[1,1,2,2,...]$ would also have been allowable for both cases but would lead to unnecessary permutations being computed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, axes = plt.subplots(1,2, figsize=(6,6))\n",
- "\n",
- "for ax, data in zip(axes, (blocks_within[:,np.newaxis], blocks_across)):\n",
- " ax.matshow(data[:10], cmap='Set3')\n",
- " for (i, j), z in np.ndenumerate(data[:10]):\n",
- " ax.text(j, i, '{:}'.format(int(z)), ha='center', va='center', fontsize=20)\n",
- " ax.set_xticks([])\n",
- " ax.set_yticks([])\n",
- "axes[0].set_title('Within', fontsize=30)\n",
- "axes[1].set_title('Across', fontsize=30)\n",
- "plt.suptitle('Permutation Tree Blocks', y=1.07, fontsize=30)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Independence tests\n",
- "As shown in the following figures, pvalues under unrestricted permutations are heavily skewed and certainly not uniform as one would want to be able to reject at a specified $\\alpha$-level. When the permutations are restricted, the pvalues under the null distribution are empirically approximately uniformly distributed. 95% binomial proportion confidence interval error bars are displayed on the histogram of empirical pvalues for each bin."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "N_DATASETS = 1000\n",
- "REPS = 500\n",
- "test_results = defaultdict(list)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "for i in range(N_DATASETS):\n",
- " X = simulate_2sample_null(n1, n2, d, group_std, seed=i)\n",
- " for test, block, Y in zip(test_names, test_blocks, test_Ys):\n",
- " _, pval = Dcorr().test(\n",
- " X,Y,\n",
- " reps=REPS,\n",
- " workers=-1,\n",
- " perm_blocks=block,\n",
- " )\n",
- " test_results[test].append(pval)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#fig, axes = plt.subplots(2,3, figsize=(4, 4*len(data_dict.keys())))\n",
- "fig = plt.figure(figsize=(16, 8))\n",
- "\n",
- "# Show data example\n",
- "ax = fig.add_subplot(241)\n",
- "\n",
- "X = simulate_2sample_null(n1, n2, d, group_std, seed=0)[:10,:]\n",
- "X = squareform(pdist(X))\n",
- "heatmap = ax.pcolor(X, cmap=plt.cm.Blues)\n",
- "ax.invert_yaxis()\n",
- "ax.xaxis.tick_top()\n",
- "ax.set_xticks([])\n",
- "ax.set_yticks([])\n",
- "ax.set_title('X distance matrix')\n",
- "\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"bottom\", size=\"5%\", pad=0.05)\n",
- "plt.colorbar(heatmap, cax=cax, ticks=[0, 10], orientation='horizontal')\n",
- "\n",
- "# Plot Y matrices\n",
- "ax = fig.add_subplot(242)\n",
- "heatmap = ax.pcolor(squareform(pdist(Y_within[:10, np.newaxis])), cmap=plt.cm.Blues)\n",
- "#ax.colorbar(heatmap)\n",
- "ax.invert_yaxis()\n",
- "ax.xaxis.tick_top()\n",
- "ax.set_xticks([])\n",
- "ax.set_yticks([])\n",
- "ax.set_title('Y within distance matrix')\n",
- "\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"bottom\", size=\"5%\", pad=0.05)\n",
- "plt.colorbar(heatmap, cax=cax, ticks=[0, 1], orientation='horizontal')\n",
- "\n",
- "ax = fig.add_subplot(246)\n",
- "heatmap = ax.pcolor(squareform(pdist(np.hstack((Y_across[:5], Y_across[-5:]))[:, np.newaxis])), cmap=plt.cm.Blues)\n",
- "#ax.colorbar(heatmap)\n",
- "ax.invert_yaxis()\n",
- "ax.xaxis.tick_top()\n",
- "ax.set_xticks([])\n",
- "ax.set_yticks([])\n",
- "ax.set_title('Y across distance matrix')\n",
- "\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"bottom\", size=\"5%\", pad=0.05)\n",
- "plt.colorbar(heatmap, cax=cax, ticks=[0, 1], orientation='horizontal')\n",
- "\n",
- "# Plot pvalue histograms and errorbars using binomial CIs\n",
- "ax = None\n",
- "for i,test_name in zip([3,7,4,8], test_names):\n",
- " ax = fig.add_subplot(int(str(f'24{i}')))#, sharey=ax)\n",
- " n = len(test_results[test_name])\n",
- " entries, edges, _ = ax.hist(test_results[test_name], bins = np.arange(0,1.1,0.1), weights=np.ones(n)/n, color='b')\n",
- " # entries = height of each column = proportion in that bin\n",
- " # calculate bin centers\n",
- " bin_centers = 0.5 * (edges[:-1] + edges[1:])\n",
- " ax.axhline(y=sum(entries) / len(bin_centers), ls='--', c='#333333')\n",
- " # errorbars are binomial proportion confidence intervals\n",
- " ax.errorbar(\n",
- " bin_centers, entries,\n",
- " yerr = 1.96 * np.sqrt(entries * (1 - entries) / n),\n",
- " fmt='.',c='#333333'\n",
- " )\n",
- " ax.set_title(f'{test_name} pvalues')\n",
- " #ax.set_xlim(0,1)\n",
- " if i in [3,4]:\n",
- " ax.set_xticks([])\n",
- " else:\n",
- " ax.set_xticks([0,1])\n",
- " if i in [4,8]:\n",
- " ax.set_yticks([0,0.1])\n",
- " else:\n",
- " ax.set_yticks([0, 0.1, 1])\n",
- "plt.subplots_adjust(hspace=0.3, wspace=0.3)\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.7.7 64-bit ('hyppo': conda)",
- "language": "python",
- "name": "python37764bithyppocondae7ba0f15953e4d058cd7393ca9bad4cf"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/tutorials/independence/power.py b/docs/tutorials/independence/power.py
deleted file mode 100644
index 193e7afcf..000000000
--- a/docs/tutorials/independence/power.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import numpy as np
-from math import ceil
-
-from scipy._lib._util import check_random_state, MapWrapper
-
-
-class _ParallelP(object):
- """
- Helper function to calculate parallel power.
- """
-
- def __init__(self, test, sim, n, p, noise, rngs):
- self.test = test()
- self.test_name = test.__name__
- self.sim = sim
-
- self.n = n
- self.p = p
- self.noise = noise
- self.rngs = rngs
-
- def __call__(self, index):
- if (
- self.sim.__name__ == "multiplicative_noise"
- or self.sim.__name__ == "multimodal_independence"
- ):
- x, y = self.sim(self.n, self.p)
- else:
- x, y = self.sim(self.n, self.p, noise=self.noise)
-
- obs_stat = self.test._statistic(x, y)
-
- permy = self.rngs[index].permutation(y)
-
- # calculate permuted stats, store in null distribution
- perm_stat = self.test._statistic(x, permy)
-
- return obs_stat, perm_stat
-
-
-def _perm_test(
- test, sim, n=100, p=1, noise=False, reps=1000, workers=1, random_state=None
-):
- r"""
- Helper function that calculates the statistical.
-
- Parameters
- ----------
- test : callable()
- The independence test class requested.
- sim : callable()
- The simulation used to generate the input data.
- reps : int, optional (default: 1000)
- The number of replications used to estimate the null distribution
- when using the permutation test used to calculate the p-value.
- workers : int, optional (default: -1)
- The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
-
- Returns
- -------
- null_dist : list
- The approximated null distribution.
- """
- # set seeds
- random_state = check_random_state(random_state)
- rngs = [
- np.random.RandomState(random_state.randint(1 << 32, size=4, dtype=np.uint32))
- for _ in range(reps)
- ]
-
- # use all cores to create function that parallelizes over number of reps
- mapwrapper = MapWrapper(workers)
- parallelp = _ParallelP(test=test, sim=sim, n=n, p=p, noise=noise, rngs=rngs)
- alt_dist, null_dist = map(list, zip(*list(mapwrapper(parallelp, range(reps)))))
- alt_dist = np.array(alt_dist)
- null_dist = np.array(null_dist)
-
- return alt_dist, null_dist
-
-
-def power(
- test,
- sim,
- n=100,
- p=1,
- noise=True,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- [summary]
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- alt_dist, null_dist = _perm_test(
- test,
- sim,
- n=n,
- p=p,
- noise=noise,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
- cutoff = np.sort(null_dist)[ceil(reps * (1 - alpha))]
- empirical_power = (alt_dist >= cutoff).sum() / reps
-
- if empirical_power == 0:
- empirical_power = 1 / reps
-
- return empirical_power
-
-
-def power_sample(
- test,
- sim,
- n=100,
- p=1,
- noise=True,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- [summary]
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- return power(
- test,
- sim,
- n=n,
- p=p,
- noise=noise,
- alpha=alpha,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
-
-
-def power_dim(
- test,
- sim,
- n=100,
- p=1,
- noise=False,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- hello
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- return power(
- test,
- sim,
- n=n,
- p=p,
- noise=noise,
- alpha=alpha,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
diff --git a/docs/tutorials/independence/power_2samp.py b/docs/tutorials/independence/power_2samp.py
deleted file mode 100644
index ee16e30ee..000000000
--- a/docs/tutorials/independence/power_2samp.py
+++ /dev/null
@@ -1,323 +0,0 @@
-import numpy as np
-from math import ceil
-
-from scipy._lib._util import check_random_state, MapWrapper
-
-from hyppo.ksample._utils import k_sample_transform
-
-
-class _ParallelP(object):
- """
- Helper function to calculate parallel power.
- """
-
- def __init__(self, test, ksim, sim, n, p, noise, rngs, angle, trans):
- self.test = test()
- self.ksim = ksim
- self.sim = sim
- self.angle = angle
- self.trans = trans
-
- self.n = n
- self.p = p
- self.noise = noise
- self.rngs = rngs
-
- def __call__(self, index):
- if (
- self.sim.__name__ == "multiplicative_noise"
- or self.sim.__name__ == "multimodal_independence"
- ):
- x, y = self.ksim(
- self.sim, self.n, self.p, degree=self.angle, trans=self.trans
- )
- else:
- x, y = self.ksim(
- self.sim,
- self.n,
- self.p,
- noise=self.noise,
- degree=self.angle,
- trans=self.trans,
- )
-
- u, v = k_sample_transform([x, y])
- obs_stat = self.test._statistic(u, v)
-
- permv = self.rngs[index].permutation(v)
-
- # calculate permuted stats, store in null distribution
- perm_stat = self.test._statistic(u, permv)
-
- return obs_stat, perm_stat
-
-
-def _perm_test(
- test,
- ksim,
- sim,
- n=100,
- p=1,
- noise=False,
- reps=1000,
- workers=1,
- random_state=None,
- angle=90,
- trans=0,
-):
- r"""
- Helper function that calculates the statistical.
-
- Parameters
- ----------
- test : callable()
- The independence test class requested.
- sim : callable()
- The simulation used to generate the input data.
- reps : int, optional (default: 1000)
- The number of replications used to estimate the null distribution
- when using the permutation test used to calculate the p-value.
- workers : int, optional (default: -1)
- The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
-
- Returns
- -------
- null_dist : list
- The approximated null distribution.
- """
- # set seeds
- random_state = check_random_state(random_state)
- rngs = [
- np.random.RandomState(random_state.randint(1 << 32, size=4, dtype=np.uint32))
- for _ in range(reps)
- ]
-
- # use all cores to create function that parallelizes over number of reps
- mapwrapper = MapWrapper(workers)
- parallelp = _ParallelP(
- test=test,
- ksim=ksim,
- sim=sim,
- n=n,
- p=p,
- noise=noise,
- rngs=rngs,
- angle=angle,
- trans=trans,
- )
- alt_dist, null_dist = map(list, zip(*list(mapwrapper(parallelp, range(reps)))))
- alt_dist = np.array(alt_dist)
- null_dist = np.array(null_dist)
-
- return alt_dist, null_dist
-
-
-def power(
- test,
- ksim,
- sim,
- n=100,
- p=1,
- angle=90,
- trans=0,
- noise=True,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- [summary]
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- alt_dist, null_dist = _perm_test(
- test,
- ksim,
- sim,
- n=n,
- p=p,
- angle=angle,
- trans=trans,
- noise=noise,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
- cutoff = np.sort(null_dist)[ceil(reps * (1 - alpha))]
- empirical_power = (alt_dist >= cutoff).sum() / reps
-
- if empirical_power == 0:
- empirical_power = 1 / reps
-
- return empirical_power
-
-
-def power_2samp_sample(
- test,
- ksim,
- sim,
- n=100,
- p=1,
- angle=90,
- trans=0,
- noise=True,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- [summary]
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- return power(
- test,
- ksim,
- sim,
- n=n,
- p=p,
- angle=angle,
- trans=trans,
- noise=noise,
- alpha=alpha,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
-
-
-def power_2samp_dimension(
- test,
- ksim,
- sim,
- n=100,
- p=1,
- angle=90,
- trans=0,
- noise=False,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- hello
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- return power(
- test,
- ksim,
- sim,
- n=n,
- p=p,
- angle=angle,
- trans=trans,
- noise=noise,
- alpha=alpha,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
-
-
-def power_2samp_angle(
- test,
- ksim,
- sim,
- n=100,
- p=1,
- angle=90,
- trans=0,
- noise=True,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- """
- hello
-
- Parameters
- ----------
- test : [type]
- [description]
- sim : [type]
- [description]
- n : int, optional
- [description], by default 100
- p : int, optional
- [description], by default 1
- noise : int, optional
- [description], by default 0
- reps : int, optional
- [description], by default 1000
- alpha : float, optional
- [description], by default 0.05
- """
-
- return power(
- test,
- ksim,
- sim,
- n=n,
- p=p,
- angle=angle,
- trans=trans,
- noise=noise,
- alpha=alpha,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
diff --git a/docs/tutorials/independence/power_3samp.py b/docs/tutorials/independence/power_3samp.py
deleted file mode 100644
index ff4359c04..000000000
--- a/docs/tutorials/independence/power_3samp.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import numpy as np
-from math import ceil
-
-from scipy._lib._util import check_random_state, MapWrapper
-
-from hyppo.ksample._utils import k_sample_transform
-from hyppo.sims import gaussian_3samp
-
-
-class _ParallelP3Samp(object):
- """
- Helper function to calculate parallel power.
- """
-
- def __init__(self, test, n, epsilon=1, weight=0, case=1, rngs=[]):
- self.test = test()
-
- self.n = n
- self.epsilon = epsilon
- self.weight = weight
- self.case = case
- self.rngs = rngs
-
- def __call__(self, index):
- if self.case not in [4, 5]:
- x, y, z = gaussian_3samp(self.n, epsilon=self.epsilon, case=self.case)
- else:
- x, y, z = gaussian_3samp(self.n, weight=self.weight, case=self.case)
- u, v = k_sample_transform([x, y, z])
-
- obs_stat = self.test._statistic(u, v)
-
- permv = self.rngs[index].permutation(v)
-
- # calculate permuted stats, store in null distribution
- perm_stat = self.test._statistic(u, permv)
-
- return obs_stat, perm_stat
-
-
-def _perm_test_3samp(
- test, n=100, epsilon=1, weight=0, case=1, reps=1000, workers=1, random_state=None
-):
- r"""
- Helper function that calculates the statistical.
-
- Parameters
- ----------
- test : callable()
- The independence test class requested.
- sim : callable()
- The simulation used to generate the input data.
- reps : int, optional (default: 1000)
- The number of replications used to estimate the null distribution
- when using the permutation test used to calculate the p-value.
- workers : int, optional (default: -1)
- The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
-
- Returns
- -------
- null_dist : list
- The approximated null distribution.
- """
- # set seeds
- random_state = check_random_state(random_state)
- rngs = [
- np.random.RandomState(random_state.randint(1 << 32, size=4, dtype=np.uint32))
- for _ in range(reps)
- ]
-
- # use all cores to create function that parallelizes over number of reps
- mapwrapper = MapWrapper(workers)
- parallelp = _ParallelP3Samp(test, n, epsilon, weight, case, rngs)
- alt_dist, null_dist = map(list, zip(*list(mapwrapper(parallelp, range(reps)))))
- alt_dist = np.array(alt_dist)
- null_dist = np.array(null_dist)
-
- return alt_dist, null_dist
-
-
-def power_3samp_epsweight(
- test,
- n=100,
- epsilon=0.5,
- weight=0,
- case=1,
- alpha=0.05,
- reps=1000,
- workers=1,
- random_state=None,
-):
- alt_dist, null_dist = _perm_test_3samp(
- test,
- n=n,
- epsilon=epsilon,
- weight=weight,
- case=case,
- reps=reps,
- workers=workers,
- random_state=random_state,
- )
- cutoff = np.sort(null_dist)[ceil(reps * (1 - alpha))]
- empirical_power = (alt_dist >= cutoff).sum() / reps
-
- if empirical_power == 0:
- empirical_power = 1 / reps
-
- return empirical_power
diff --git a/docs/tutorials/independence/same_stat/Dcorr.csv b/docs/tutorials/independence/same_stat/Dcorr.csv
deleted file mode 100644
index 4ed6ab74d..000000000
--- a/docs/tutorials/independence/same_stat/Dcorr.csv
+++ /dev/null
@@ -1,20 +0,0 @@
-1.198666423361074290e-02
-1.712983900065479306e-02
-2.114110166422892428e-02
-1.389518040568413215e-02
-2.086023941050967404e-02
-1.817769044119211391e-02
-1.168036923636290789e-02
-1.550286603050882520e-02
-2.026249299221727015e-02
-1.816970276577377016e-02
-1.359075066787815422e-02
-1.372314725814752050e-02
-2.527639034593677156e-02
-2.339520543750660958e-02
-2.457380651462898191e-02
-1.793838798617524224e-02
-1.878718087962201466e-02
-9.180948118535635707e-03
-1.357135727953913580e-02
-1.339157678223204875e-02
diff --git a/docs/tutorials/independence/same_stat/HHG.csv b/docs/tutorials/independence/same_stat/HHG.csv
deleted file mode 100644
index 60c8afcea..000000000
--- a/docs/tutorials/independence/same_stat/HHG.csv
+++ /dev/null
@@ -1,20 +0,0 @@
-6.963825085723307915e+06
-6.107294782132900320e+06
-7.118616728132924065e+06
-5.878332049385436811e+06
-6.873640625673145987e+06
-7.608596059536607005e+06
-5.370265832924646325e+06
-5.925317833671659231e+06
-7.698512279721004888e+06
-6.538430676813278347e+06
-6.894537688634562306e+06
-6.885050459548111074e+06
-8.436073734102560207e+06
-7.654971609291132540e+06
-6.998206220125582069e+06
-6.791738345173253678e+06
-7.931792032935740426e+06
-6.464737213050819002e+06
-6.637080784117194824e+06
-6.991527681462470442e+06
diff --git a/docs/tutorials/independence/same_stat/KSample.csv b/docs/tutorials/independence/same_stat/KSample.csv
deleted file mode 100644
index 5676b7eb7..000000000
--- a/docs/tutorials/independence/same_stat/KSample.csv
+++ /dev/null
@@ -1,20 +0,0 @@
--9.358787283391685041e-04
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diff --git a/docs/tutorials/independence/same_stat/RDcorr.csv b/docs/tutorials/independence/same_stat/RDcorr.csv
deleted file mode 100644
index 081fcd0fc..000000000
--- a/docs/tutorials/independence/same_stat/RDcorr.csv
+++ /dev/null
@@ -1,20 +0,0 @@
-1.18E-02
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\ No newline at end of file
diff --git a/docs/tutorials/independence/same_stat/RHHG.csv b/docs/tutorials/independence/same_stat/RHHG.csv
deleted file mode 100644
index 536c8d11f..000000000
--- a/docs/tutorials/independence/same_stat/RHHG.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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\ No newline at end of file
diff --git a/docs/tutorials/independence/same_stat/RMMD.csv b/docs/tutorials/independence/same_stat/RMMD.csv
deleted file mode 100644
index b21d0be64..000000000
--- a/docs/tutorials/independence/same_stat/RMMD.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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\ No newline at end of file
diff --git a/docs/tutorials/independence/same_stat/indep/1.csv b/docs/tutorials/independence/same_stat/indep/1.csv
deleted file mode 100644
index 05ab995a2..000000000
--- a/docs/tutorials/independence/same_stat/indep/1.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/10.csv b/docs/tutorials/independence/same_stat/indep/10.csv
deleted file mode 100644
index bdd89525d..000000000
--- a/docs/tutorials/independence/same_stat/indep/10.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/11.csv b/docs/tutorials/independence/same_stat/indep/11.csv
deleted file mode 100644
index 2e5513306..000000000
--- a/docs/tutorials/independence/same_stat/indep/11.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/12.csv b/docs/tutorials/independence/same_stat/indep/12.csv
deleted file mode 100644
index 0f6ca66b2..000000000
--- a/docs/tutorials/independence/same_stat/indep/12.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/13.csv b/docs/tutorials/independence/same_stat/indep/13.csv
deleted file mode 100644
index 974f1aba8..000000000
--- a/docs/tutorials/independence/same_stat/indep/13.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/14.csv b/docs/tutorials/independence/same_stat/indep/14.csv
deleted file mode 100644
index 4f5e1c72f..000000000
--- a/docs/tutorials/independence/same_stat/indep/14.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/15.csv b/docs/tutorials/independence/same_stat/indep/15.csv
deleted file mode 100644
index fac6ec3f6..000000000
--- a/docs/tutorials/independence/same_stat/indep/15.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/16.csv b/docs/tutorials/independence/same_stat/indep/16.csv
deleted file mode 100644
index 097740e61..000000000
--- a/docs/tutorials/independence/same_stat/indep/16.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/17.csv b/docs/tutorials/independence/same_stat/indep/17.csv
deleted file mode 100644
index 2e1f860b7..000000000
--- a/docs/tutorials/independence/same_stat/indep/17.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/18.csv b/docs/tutorials/independence/same_stat/indep/18.csv
deleted file mode 100644
index e403ce159..000000000
--- a/docs/tutorials/independence/same_stat/indep/18.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/19.csv b/docs/tutorials/independence/same_stat/indep/19.csv
deleted file mode 100644
index 03df91008..000000000
--- a/docs/tutorials/independence/same_stat/indep/19.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/2.csv b/docs/tutorials/independence/same_stat/indep/2.csv
deleted file mode 100644
index 43ad4d441..000000000
--- a/docs/tutorials/independence/same_stat/indep/2.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/20.csv b/docs/tutorials/independence/same_stat/indep/20.csv
deleted file mode 100644
index e857d301d..000000000
--- a/docs/tutorials/independence/same_stat/indep/20.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/3.csv b/docs/tutorials/independence/same_stat/indep/3.csv
deleted file mode 100644
index c3efa5d1f..000000000
--- a/docs/tutorials/independence/same_stat/indep/3.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/4.csv b/docs/tutorials/independence/same_stat/indep/4.csv
deleted file mode 100644
index 84941cda6..000000000
--- a/docs/tutorials/independence/same_stat/indep/4.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/5.csv b/docs/tutorials/independence/same_stat/indep/5.csv
deleted file mode 100644
index 2dc6ac2d9..000000000
--- a/docs/tutorials/independence/same_stat/indep/5.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/6.csv b/docs/tutorials/independence/same_stat/indep/6.csv
deleted file mode 100644
index bb0552e49..000000000
--- a/docs/tutorials/independence/same_stat/indep/6.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/7.csv b/docs/tutorials/independence/same_stat/indep/7.csv
deleted file mode 100644
index 7bb76aa51..000000000
--- a/docs/tutorials/independence/same_stat/indep/7.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/indep/8.csv b/docs/tutorials/independence/same_stat/indep/8.csv
deleted file mode 100644
index f6d912a5c..000000000
--- a/docs/tutorials/independence/same_stat/indep/8.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/indep/9.csv b/docs/tutorials/independence/same_stat/indep/9.csv
deleted file mode 100644
index 5a9ec57e7..000000000
--- a/docs/tutorials/independence/same_stat/indep/9.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_1.csv b/docs/tutorials/independence/same_stat/ksample/sim1_1.csv
deleted file mode 100644
index 05ab995a2..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_1.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_10.csv b/docs/tutorials/independence/same_stat/ksample/sim1_10.csv
deleted file mode 100644
index bdd89525d..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_10.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_11.csv b/docs/tutorials/independence/same_stat/ksample/sim1_11.csv
deleted file mode 100644
index 2e5513306..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_11.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_12.csv b/docs/tutorials/independence/same_stat/ksample/sim1_12.csv
deleted file mode 100644
index 0f6ca66b2..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_12.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_13.csv b/docs/tutorials/independence/same_stat/ksample/sim1_13.csv
deleted file mode 100644
index 974f1aba8..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_13.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_14.csv b/docs/tutorials/independence/same_stat/ksample/sim1_14.csv
deleted file mode 100644
index 4f5e1c72f..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_14.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_15.csv b/docs/tutorials/independence/same_stat/ksample/sim1_15.csv
deleted file mode 100644
index fac6ec3f6..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_15.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_16.csv b/docs/tutorials/independence/same_stat/ksample/sim1_16.csv
deleted file mode 100644
index 097740e61..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_16.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_17.csv b/docs/tutorials/independence/same_stat/ksample/sim1_17.csv
deleted file mode 100644
index 2e1f860b7..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_17.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_18.csv b/docs/tutorials/independence/same_stat/ksample/sim1_18.csv
deleted file mode 100644
index e403ce159..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_18.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_19.csv b/docs/tutorials/independence/same_stat/ksample/sim1_19.csv
deleted file mode 100644
index 03df91008..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_19.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_2.csv b/docs/tutorials/independence/same_stat/ksample/sim1_2.csv
deleted file mode 100644
index 43ad4d441..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_2.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_20.csv b/docs/tutorials/independence/same_stat/ksample/sim1_20.csv
deleted file mode 100644
index e857d301d..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_20.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_3.csv b/docs/tutorials/independence/same_stat/ksample/sim1_3.csv
deleted file mode 100644
index c3efa5d1f..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_3.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_4.csv b/docs/tutorials/independence/same_stat/ksample/sim1_4.csv
deleted file mode 100644
index 84941cda6..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_4.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_5.csv b/docs/tutorials/independence/same_stat/ksample/sim1_5.csv
deleted file mode 100644
index 2dc6ac2d9..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_5.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_6.csv b/docs/tutorials/independence/same_stat/ksample/sim1_6.csv
deleted file mode 100644
index bb0552e49..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_6.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_7.csv b/docs/tutorials/independence/same_stat/ksample/sim1_7.csv
deleted file mode 100644
index 7bb76aa51..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_7.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_8.csv b/docs/tutorials/independence/same_stat/ksample/sim1_8.csv
deleted file mode 100644
index f6d912a5c..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_8.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim1_9.csv b/docs/tutorials/independence/same_stat/ksample/sim1_9.csv
deleted file mode 100644
index 5a9ec57e7..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim1_9.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_1.csv b/docs/tutorials/independence/same_stat/ksample/sim2_1.csv
deleted file mode 100644
index 05ab995a2..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_1.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_10.csv b/docs/tutorials/independence/same_stat/ksample/sim2_10.csv
deleted file mode 100644
index bdd89525d..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_10.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_11.csv b/docs/tutorials/independence/same_stat/ksample/sim2_11.csv
deleted file mode 100644
index 2e5513306..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_11.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_12.csv b/docs/tutorials/independence/same_stat/ksample/sim2_12.csv
deleted file mode 100644
index 0f6ca66b2..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_12.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_13.csv b/docs/tutorials/independence/same_stat/ksample/sim2_13.csv
deleted file mode 100644
index 974f1aba8..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_13.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_14.csv b/docs/tutorials/independence/same_stat/ksample/sim2_14.csv
deleted file mode 100644
index 4f5e1c72f..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_14.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_15.csv b/docs/tutorials/independence/same_stat/ksample/sim2_15.csv
deleted file mode 100644
index fac6ec3f6..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_15.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_16.csv b/docs/tutorials/independence/same_stat/ksample/sim2_16.csv
deleted file mode 100644
index 097740e61..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_16.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_17.csv b/docs/tutorials/independence/same_stat/ksample/sim2_17.csv
deleted file mode 100644
index 2e1f860b7..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_17.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_18.csv b/docs/tutorials/independence/same_stat/ksample/sim2_18.csv
deleted file mode 100644
index e403ce159..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_18.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_19.csv b/docs/tutorials/independence/same_stat/ksample/sim2_19.csv
deleted file mode 100644
index 03df91008..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_19.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_2.csv b/docs/tutorials/independence/same_stat/ksample/sim2_2.csv
deleted file mode 100644
index 43ad4d441..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_2.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_20.csv b/docs/tutorials/independence/same_stat/ksample/sim2_20.csv
deleted file mode 100644
index e857d301d..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_20.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_3.csv b/docs/tutorials/independence/same_stat/ksample/sim2_3.csv
deleted file mode 100644
index c3efa5d1f..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_3.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_4.csv b/docs/tutorials/independence/same_stat/ksample/sim2_4.csv
deleted file mode 100644
index 84941cda6..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_4.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_5.csv b/docs/tutorials/independence/same_stat/ksample/sim2_5.csv
deleted file mode 100644
index 2dc6ac2d9..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_5.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_6.csv b/docs/tutorials/independence/same_stat/ksample/sim2_6.csv
deleted file mode 100644
index bb0552e49..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_6.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_7.csv b/docs/tutorials/independence/same_stat/ksample/sim2_7.csv
deleted file mode 100644
index 7bb76aa51..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_7.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_8.csv b/docs/tutorials/independence/same_stat/ksample/sim2_8.csv
deleted file mode 100644
index f6d912a5c..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_8.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/same_stat/ksample/sim2_9.csv b/docs/tutorials/independence/same_stat/ksample/sim2_9.csv
deleted file mode 100644
index 5a9ec57e7..000000000
--- a/docs/tutorials/independence/same_stat/ksample/sim2_9.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/circle_Kendall.csv b/docs/tutorials/independence/vs_samplesize/circle_Kendall.csv
deleted file mode 100644
index 6a93138e7..000000000
--- a/docs/tutorials/independence/vs_samplesize/circle_Kendall.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/circle_Pearson.csv b/docs/tutorials/independence/vs_samplesize/circle_Pearson.csv
deleted file mode 100644
index eab28f19f..000000000
--- a/docs/tutorials/independence/vs_samplesize/circle_Pearson.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/circle_Spearman.csv b/docs/tutorials/independence/vs_samplesize/circle_Spearman.csv
deleted file mode 100644
index 67d847381..000000000
--- a/docs/tutorials/independence/vs_samplesize/circle_Spearman.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/cubic_Kendall.csv b/docs/tutorials/independence/vs_samplesize/cubic_Kendall.csv
deleted file mode 100644
index 860f74ce0..000000000
--- a/docs/tutorials/independence/vs_samplesize/cubic_Kendall.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/cubic_Pearson.csv b/docs/tutorials/independence/vs_samplesize/cubic_Pearson.csv
deleted file mode 100644
index 539f9277e..000000000
--- a/docs/tutorials/independence/vs_samplesize/cubic_Pearson.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/cubic_Spearman.csv b/docs/tutorials/independence/vs_samplesize/cubic_Spearman.csv
deleted file mode 100644
index 5932dfddf..000000000
--- a/docs/tutorials/independence/vs_samplesize/cubic_Spearman.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/diamond_Kendall.csv b/docs/tutorials/independence/vs_samplesize/diamond_Kendall.csv
deleted file mode 100644
index dd2a2b34c..000000000
--- a/docs/tutorials/independence/vs_samplesize/diamond_Kendall.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/diamond_Pearson.csv b/docs/tutorials/independence/vs_samplesize/diamond_Pearson.csv
deleted file mode 100644
index 35017583a..000000000
--- a/docs/tutorials/independence/vs_samplesize/diamond_Pearson.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/diamond_Spearman.csv b/docs/tutorials/independence/vs_samplesize/diamond_Spearman.csv
deleted file mode 100644
index 608a20166..000000000
--- a/docs/tutorials/independence/vs_samplesize/diamond_Spearman.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/ellipse_Kendall.csv b/docs/tutorials/independence/vs_samplesize/ellipse_Kendall.csv
deleted file mode 100644
index 013d4a0e6..000000000
--- a/docs/tutorials/independence/vs_samplesize/ellipse_Kendall.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/ellipse_Pearson.csv b/docs/tutorials/independence/vs_samplesize/ellipse_Pearson.csv
deleted file mode 100644
index 5076901da..000000000
--- a/docs/tutorials/independence/vs_samplesize/ellipse_Pearson.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/ellipse_Spearman.csv b/docs/tutorials/independence/vs_samplesize/ellipse_Spearman.csv
deleted file mode 100644
index c89d3d9bd..000000000
--- a/docs/tutorials/independence/vs_samplesize/ellipse_Spearman.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/exponential_Kendall.csv b/docs/tutorials/independence/vs_samplesize/exponential_Kendall.csv
deleted file mode 100644
index 2f6344633..000000000
--- a/docs/tutorials/independence/vs_samplesize/exponential_Kendall.csv
+++ /dev/null
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index 6568608f6..000000000
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--- a/docs/tutorials/independence/vs_samplesize/fourth_root_Kendall.csv
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index 492f14f67..000000000
--- a/docs/tutorials/independence/vs_samplesize/fourth_root_Spearman.csv
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--- a/docs/tutorials/independence/vs_samplesize/joint_normal_Kendall.csv
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index 16077afbc..000000000
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diff --git a/docs/tutorials/independence/vs_samplesize/linear_Spearman.csv b/docs/tutorials/independence/vs_samplesize/linear_Spearman.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/logarithmic_Kendall.csv b/docs/tutorials/independence/vs_samplesize/logarithmic_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/multimodal_independence_Kendall.csv b/docs/tutorials/independence/vs_samplesize/multimodal_independence_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/multiplicative_noise_Kendall.csv b/docs/tutorials/independence/vs_samplesize/multiplicative_noise_Kendall.csv
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index fbe0b0926..000000000
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deleted file mode 100644
index 21fcc6fc7..000000000
--- a/docs/tutorials/independence/vs_samplesize/quadratic_Spearman.csv
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@@ -1,20 +0,0 @@
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deleted file mode 100644
index d4779749c..000000000
--- a/docs/tutorials/independence/vs_samplesize/sin_four_pi_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/sin_four_pi_Pearson.csv b/docs/tutorials/independence/vs_samplesize/sin_four_pi_Pearson.csv
deleted file mode 100644
index 8b666a32f..000000000
--- a/docs/tutorials/independence/vs_samplesize/sin_four_pi_Pearson.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/sin_four_pi_Spearman.csv b/docs/tutorials/independence/vs_samplesize/sin_four_pi_Spearman.csv
deleted file mode 100644
index 0496aee6b..000000000
--- a/docs/tutorials/independence/vs_samplesize/sin_four_pi_Spearman.csv
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deleted file mode 100644
index b5aa6730a..000000000
--- a/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Kendall.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Pearson.csv b/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Pearson.csv
deleted file mode 100644
index a7098d2dd..000000000
--- a/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Pearson.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Spearman.csv b/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Spearman.csv
deleted file mode 100644
index e99f9063b..000000000
--- a/docs/tutorials/independence/vs_samplesize/sin_sixteen_pi_Spearman.csv
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deleted file mode 100644
index 39ec0cc61..000000000
--- a/docs/tutorials/independence/vs_samplesize/spiral_Kendall.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/vs_samplesize/spiral_Pearson.csv b/docs/tutorials/independence/vs_samplesize/spiral_Pearson.csv
deleted file mode 100644
index 453230825..000000000
--- a/docs/tutorials/independence/vs_samplesize/spiral_Pearson.csv
+++ /dev/null
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diff --git a/docs/tutorials/independence/vs_samplesize/spiral_Spearman.csv b/docs/tutorials/independence/vs_samplesize/spiral_Spearman.csv
deleted file mode 100644
index 1613f6523..000000000
--- a/docs/tutorials/independence/vs_samplesize/spiral_Spearman.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/square_Kendall.csv b/docs/tutorials/independence/vs_samplesize/square_Kendall.csv
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index 4378f56f5..000000000
--- a/docs/tutorials/independence/vs_samplesize/square_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/square_Pearson.csv b/docs/tutorials/independence/vs_samplesize/square_Pearson.csv
deleted file mode 100644
index 384300242..000000000
--- a/docs/tutorials/independence/vs_samplesize/square_Pearson.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/square_Spearman.csv b/docs/tutorials/independence/vs_samplesize/square_Spearman.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/step_Kendall.csv b/docs/tutorials/independence/vs_samplesize/step_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/step_Pearson.csv b/docs/tutorials/independence/vs_samplesize/step_Pearson.csv
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index ddd85b327..000000000
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diff --git a/docs/tutorials/independence/vs_samplesize/step_Spearman.csv b/docs/tutorials/independence/vs_samplesize/step_Spearman.csv
deleted file mode 100644
index e72fe6c01..000000000
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diff --git a/docs/tutorials/independence/vs_samplesize/two_parabolas_Kendall.csv b/docs/tutorials/independence/vs_samplesize/two_parabolas_Kendall.csv
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index 52d6fe73e..000000000
--- a/docs/tutorials/independence/vs_samplesize/two_parabolas_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/two_parabolas_Pearson.csv b/docs/tutorials/independence/vs_samplesize/two_parabolas_Pearson.csv
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index e304f09b3..000000000
--- a/docs/tutorials/independence/vs_samplesize/two_parabolas_Pearson.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/two_parabolas_Spearman.csv b/docs/tutorials/independence/vs_samplesize/two_parabolas_Spearman.csv
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--- a/docs/tutorials/independence/vs_samplesize/two_parabolas_Spearman.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Kendall.csv b/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Kendall.csv
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--- a/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Kendall.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Pearson.csv b/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Pearson.csv
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index e3d4f33f6..000000000
--- a/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Pearson.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Spearman.csv b/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Spearman.csv
deleted file mode 100644
index 41d766ddc..000000000
--- a/docs/tutorials/independence/vs_samplesize/uncorrelated_bernoulli_Spearman.csv
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diff --git a/docs/tutorials/independence/vs_samplesize/w_shaped_Kendall.csv b/docs/tutorials/independence/vs_samplesize/w_shaped_Kendall.csv
deleted file mode 100644
index 2a5e8bb7c..000000000
--- a/docs/tutorials/independence/vs_samplesize/w_shaped_Kendall.csv
+++ /dev/null
@@ -1,20 +0,0 @@
-1.197999999999999898e-01
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diff --git a/docs/tutorials/independence/vs_samplesize/w_shaped_Pearson.csv b/docs/tutorials/independence/vs_samplesize/w_shaped_Pearson.csv
deleted file mode 100644
index 87741473d..000000000
--- a/docs/tutorials/independence/vs_samplesize/w_shaped_Pearson.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/independence/vs_samplesize/w_shaped_Spearman.csv b/docs/tutorials/independence/vs_samplesize/w_shaped_Spearman.csv
deleted file mode 100644
index 3bf62fb93..000000000
--- a/docs/tutorials/independence/vs_samplesize/w_shaped_Spearman.csv
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/docs/tutorials/ksample/ksample.ipynb b/docs/tutorials/ksample/ksample.ipynb
deleted file mode 100644
index eed273eb2..000000000
--- a/docs/tutorials/ksample/ksample.ipynb
+++ /dev/null
@@ -1,279 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.ticker as ticker\n",
- "import seaborn as sns\n",
- "\n",
- "# custom seaborn plot options to make the figures pretty\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=1)\n",
- "PALETTE = sns.color_palette(\"Set1\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# *K*-Sample Tests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Multivariate Nonparametric *K*-Sample Tests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The *k*-sample testing problem can be defined as follows: Consider random variables $X_1, X_2, \\ldots, X_k$ that have densities $F_1, F_2, \\ldots, F_k$. We are testing,\n",
- "\n",
- "\\begin{align*}\n",
- "H_0:\\ &F_1 = F_2 = \\ldots F_k \\\\\n",
- "H_A:\\ &\\exists \\ j \\neq j' \\text{ s.t. } F_j \\neq F_{j'}\n",
- "\\end{align*}\n",
- "\n",
- "Some import things to note:\n",
- "\n",
- " - 2-sample Dcorr is equivalent to Energy\n",
- " - *k*-sample Dcorr is equivalent to DISCO\n",
- " - 2-sample Hsic is equivalent to MMD\n",
- "\n",
- "This formulation of multivariate nonparametric *k*-sample testing reduces the *k*-sample testing problem to the independence testing problem and then uses the tests in the independence module of `hyppo`. `hyppo` provides an easy-to-use structure to use tests such as these.\n",
- "\n",
- "Importing tests from `hyppo` is similar to importing functions/classes from other packages. `hyppo` also has a `sims` module, which contains several linear and nonlinear dependency structures to test the cases for which each test will perform best."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from hyppo.ksample import KSample\n",
- "from hyppo.sims import linear, rot_2samp, gaussian_3samp"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, let's generate some simulated data. The *k*-sample simulations included take on required parameters an independence simulation from the the `sims` module, number of samples, and number of dimensions with optional paramaters mentioned in the [reference](https://hyppo.neurodata.io/reference/sims.html#ksample-simulations) section of the docs. Looking at some linearly distributed data first, let's look at 100 samples of noisey data and generate a 2-dimensional simulation that is rotated by 60 degrees (a 1000 sample of no noise simulated data shows the trend in the data)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "samp1, samp2 = rot_2samp(linear, 100, 1, degree=60, noise=True)\n",
- "samp1_no_noise, samp2_no_noise = rot_2samp(linear, 1000, 1, degree=60, noise=False)\n",
- "\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "ax = sns.scatterplot(samp1[:, 0], samp1[:, 1])\n",
- "ax = sns.scatterplot(samp2[:, 0], samp2[:, 1], alpha=0.5)\n",
- "ax = sns.scatterplot(samp1_no_noise[:, 0], samp1_no_noise[:, 1])\n",
- "ax = sns.scatterplot(samp2_no_noise[:, 0], samp2_no_noise[:, 1], alpha=0.5)\n",
- "ax.set_xlabel('Simulated X')\n",
- "ax.set_ylabel('Simulated Y') \n",
- "plt.title(\"Linear\")\n",
- "plt.axis('equal')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The test statistic the p-value is calculated by running the `.test` method. Some important parameters for the `.test` method:\n",
- "\n",
- " - `indep_test`: This is a required parameter which is a string corresponding to the name of the class for which the non-parametric independence test will be run\n",
- " - `reps`: The number of replications to run when running a permutation test\n",
- " - `workers`: The number of cores to parallelize over when running a permutation test\n",
- "\n",
- "The last two parameters are equivalent to and work the same as those in the `independence` module. Note that when using a permutation test, the lowest p-value is the reciprocal of the number of repetitions. Also, since the p-value is calculated with a random permutation test, there will be a slight variance in p-values with subsequent runs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Energy test statistic: 0.04436470736551818\n",
- "Energy p-value: 0.001\n"
- ]
- }
- ],
- "source": [
- "# test statistic and p-values can be calculated for MGC, but a little slow ...\n",
- "stat, pvalue = KSample(\"Dcorr\").test(samp1, samp2)\n",
- "\n",
- "print(\"Energy test statistic:\", stat)\n",
- "print(\"Energy p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Energy test statistic: 0.04436470736551818\n",
- "Energy p-value: 0.01\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/sampan501/workspace/hyppo/hyppo/_utils.py:67: RuntimeWarning: The number of replications is low (under 1000), and p-value calculations may be unreliable. Use the p-value result, with caution!\n",
- " warnings.warn(msg, RuntimeWarning)\n"
- ]
- }
- ],
- "source": [
- "# so fewer reps can be used, while giving a less confident p-value ...\n",
- "stat, pvalue = KSample(\"Dcorr\").test(samp1, samp2, reps=100)\n",
- "\n",
- "print(\"Energy test statistic:\", stat)\n",
- "print(\"Energy p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Energy test statistic: 0.04436470736551818\n",
- "Energy p-value: 0.001\n"
- ]
- }
- ],
- "source": [
- "# or the parallelization can be used (-1 uses all cores)\n",
- "stat, pvalue = KSample(\"Dcorr\").test(samp1, samp2, workers=-1)\n",
- "\n",
- "print(\"Energy test statistic:\", stat)\n",
- "print(\"Energy p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We see that Energy is able to detect linear relationships very easily (p-value is less than the alpha level of 0.05).\n",
- "\n",
- "It is easy to use our *k*-sample tests for 3 samples as well. Let's look at some random multivariate Gaussian simulations. Case 3 is when all Gaussians are moving away from the origin according to the `epsilon` parameter."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "samp1, samp2, samp3 = gaussian_3samp(100, case=3, epsilon=5)\n",
- "\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "ax = sns.scatterplot(samp1[:, 0], samp1[:, 1])\n",
- "ax = sns.scatterplot(samp2[:, 0], samp2[:, 1])\n",
- "ax = sns.scatterplot(samp3[:, 0], samp3[:, 1])\n",
- "ax.set_xlabel('Simulated X')\n",
- "ax.set_ylabel('Simulated Y') \n",
- "plt.title(\"All Different\")\n",
- "plt.axis('equal')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DISCO test statistic: 0.8485965561961212\n",
- "DISCO p-value: 0.001\n"
- ]
- }
- ],
- "source": [
- "stat, pvalue = KSample(\"Dcorr\").test(samp1, samp2, samp3)\n",
- "\n",
- "print(\"DISCO test statistic:\", stat)\n",
- "print(\"DISCO p-value:\", pvalue)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/tutorials/sims/indep_simulations.ipynb b/docs/tutorials/sims/indep_simulations.ipynb
deleted file mode 100644
index 73c89f7d0..000000000
--- a/docs/tutorials/sims/indep_simulations.ipynb
+++ /dev/null
@@ -1,185 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "\n",
- "# custom seaborn plot options to make the figures pretty\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Independence Simulations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This module contains a benchmark of 20 simulations of various dependency structures to allow benchmark comparisons between included tests. It also can be used to see when to use which test if some information about the shape of the data distribution is already known. \n",
- "\n",
- "Importing simulations from `hyppo` is easy! All simulations are contained withing the `hyppo.sims` module and can be imported like all other Python packages. In this tutorial, we will show you what all of the independence simulations look like and as such import all of them."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from hyppo.sims import (linear, exponential, cubic, joint_normal, step,\n",
- " quadratic, w_shaped, spiral, uncorrelated_bernoulli, logarithmic,\n",
- " fourth_root, sin_four_pi, sin_sixteen_pi, square, two_parabolas,\n",
- " circle, ellipse, diamond, multiplicative_noise, multimodal_independence)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These are some constants that are used in this notebook. If running these notebook, please only manipulate these constants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# number of sample for noisy and not noisy simulations\n",
- "NOISY = 100\n",
- "NO_NOISE = 1000\n",
- "\n",
- "# list containing simulations and plot titles\n",
- "simulations = [\n",
- " (linear, \"Linear\"),\n",
- " (exponential, \"Exponential\"),\n",
- " (cubic, \"Cubic\"),\n",
- " (joint_normal, \"Joint Normal\"),\n",
- " (step, \"Step\"),\n",
- " (quadratic, \"Quadratic\"),\n",
- " (w_shaped, \"W-Shaped\"),\n",
- " (spiral, \"Spiral\"),\n",
- " (uncorrelated_bernoulli, \"Uncorrelated Bernoulli\"),\n",
- " (logarithmic, \"Logarithmic\"),\n",
- " (fourth_root, \"Fourth Root\"),\n",
- " (sin_four_pi, \"Sine 4\\u03C0\"),\n",
- " (sin_sixteen_pi, \"Sine 16\\u03C0\"),\n",
- " (square, \"Square\"),\n",
- " (two_parabolas, \"Two Parabolas\"),\n",
- " (circle, \"Circle\"),\n",
- " (ellipse, \"Ellipse\"),\n",
- " (diamond, \"Diamond\"),\n",
- " (multiplicative_noise, \"Multiplicative\"),\n",
- " (multimodal_independence, \"Independence\")\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following code plots the simulation with noise where applicable in blue overlayed with the simulation with no noise in red. For specific equations for a simulation, please refer to the documentation corresponding to the desired simulation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_sims():\n",
- " # set the figure size\n",
- " fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28,24))\n",
- " \n",
- " count = 0\n",
- " for i, row in enumerate(ax):\n",
- " for j, col in enumerate(row):\n",
- " count = 5*i + j\n",
- " sim, sim_title = simulations[count]\n",
- " \n",
- " # the multiplicative noise and independence simulation don't have a noise parameter\n",
- " if sim_title == \"Multiplicative\" or sim_title == \"Independence\":\n",
- " x, y = sim(NO_NOISE, 1)\n",
- " x_no_noise, y_no_noise = x, y\n",
- " else:\n",
- " x, y = sim(NOISY, 1, noise=True)\n",
- " x_no_noise, y_no_noise = sim(NO_NOISE, 1)\n",
- "\n",
- " col.scatter(x, y, c='b', label=\"noise\", alpha=0.5)\n",
- " col.scatter(x_no_noise, y_no_noise, c='r', label=\"no noise\")\n",
- " \n",
- " # make the plot look pretty\n",
- " col.set_title('{}'.format(sim_title))\n",
- " col.set_xticks([])\n",
- " col.set_yticks([])\n",
- " if count == 16:\n",
- " col.set_ylim([-1, 1])\n",
- " sns.despine(left=True, bottom=True, right=True)\n",
- " \n",
- " leg = plt.legend(bbox_to_anchor=(0.5, 0.1), bbox_transform=plt.gcf().transFigure,\n",
- " ncol=5, loc='upper center')\n",
- " leg.get_frame().set_linewidth(0.0)\n",
- " for legobj in leg.legendHandles:\n",
- " legobj.set_linewidth(5.0)\n",
- " plt.subplots_adjust(hspace=.75)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_sims()"
- ]
- }
- ],
- "metadata": {
- "file_extension": ".py",
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- },
- "mimetype": "text/x-python",
- "name": "python",
- "npconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": 3
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- "nbformat": 4,
- "nbformat_minor": 4
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diff --git a/docs/tutorials/time_series/fmri_analysis.ipynb b/docs/tutorials/time_series/fmri_analysis.ipynb
deleted file mode 100644
index d083a84e9..000000000
--- a/docs/tutorials/time_series/fmri_analysis.ipynb
+++ /dev/null
@@ -1,331 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/ronak/miniconda3/envs/mgc/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
- " import pandas.util.testing as tm\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import nibabel.cifti2 as ci\n",
- "from itertools import product\n",
- "from joblib import Parallel, delayed\n",
- "import pickle\n",
- "\n",
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "from graspy.plot import heatmap\n",
- "%matplotlib inline\n",
- "\n",
- "from hyppo.time_series import MGCX"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Look At It"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Load image - individual 100307.\n",
- "img = ci.load(\"fmri_data/rfMRI_REST1_LR_Atlas_hp2000_clean_filt_sm6.HCPMMP.ptseries.nii\")\n",
- "fmri_data = np.array(img.get_fdata())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Visualize data, i.e. inspect the first 60 timesteps of each parcel.\n",
- "\n",
- "# Generate heatmap.\n",
- "timesteps = 60\n",
- "displayed_data = np.transpose(fmri_data[range(timesteps),:])\n",
- "plt.subplots(figsize=(15,10))\n",
- "ax = sns.heatmap(displayed_data, yticklabels=False)\n",
- "\n",
- "# Plot parameters.\n",
- "plt.title('Resting fMRI Signal by Parcel - Individual 100307 LR', fontsize = 20)\n",
- "plt.ylabel('Parcel', fontsize = 15)\n",
- "plt.xlabel('Timestep', fontsize = 15)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set Up Hyperparameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# From Ting: Regions-of-Interest (ROIs)\n",
- "roi_keys = np.array([1, 23, 18, 53, 24, 96, 117, 50, 143, 109, 148, 60, 38, 135, 93, 83, 149, 150, 65, 161, 132, 71]) - 1\n",
- "roi_labels = np.array([\n",
- " \"Visual\",\n",
- " \"Visual\",\n",
- " \"Visual\",\n",
- " \"SM\",\n",
- " \"SM\",\n",
- " \"dAtt\",\n",
- " \"dAtt\",\n",
- " \"dAtt\",\n",
- " \"dAtt\",\n",
- " \"vAtt\",\n",
- " \"vAtt\",\n",
- " \"vAtt\",\n",
- " \"vAtt\",\n",
- " \"Limbic\",\n",
- " \"Limbic\",\n",
- " \"FP\",\n",
- " \"FP\",\n",
- " \"DMN\",\n",
- " \"DMN\",\n",
- " \"DMN\",\n",
- " \"DMN\",\n",
- " \"DMN\",\n",
- "])\n",
- "\n",
- "roi_data = fmri_data[0:300, roi_keys]\n",
- "num_roi = len(roi_keys)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Hyperparameters\n",
- "max_lag = 1 # number of lags to check in the past\n",
- "reps = 1000 # number of bootstrap replicates\n",
- "workers = 1 # number of workers in internal MGC parallelization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Subsample to test experiment.\n",
- "# pairs = list(product(range(num_roi), repeat = 2)) # Fake param for testing.\n",
- "pairs = list(product(range(num_roi), repeat = 2))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Run Experiment"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mgcx = MGCX(max_lag = max_lag)\n",
- "\n",
- "def worker(i, j):\n",
- " X = roi_data[:, i]\n",
- " Y = roi_data[:, j]\n",
- " \n",
- " stat, pval, mgcx_dict = mgcx.test(X, Y, reps = reps, workers = workers)\n",
- " opt_lag = mgcx_dict['opt_lag']\n",
- " opt_scale_x, opt_scale_y = mgcx_dict['opt_scale']\n",
- " \n",
- " return stat, pval, opt_lag, opt_scale_x, opt_scale_y\n",
- "\n",
- "output = np.array(Parallel(n_jobs=-2)(delayed(worker)(i, j) for i, j in pairs))\n",
- "pickle.dump(output, open('fmri_data/mgcx_fmri_output.pkl', 'wb'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Load results into num_roi-by-num_roi matrices.\n",
- "results = pickle.load(open('fmri_data/mgcx_fmri_output.pkl', 'rb'))\n",
- "test_outputs = ['stat', 'pval', 'opt_lag', 'opt_scale_x', 'opt_scale_y']\n",
- "\n",
- "matrices = np.zeros((len(test_outputs), num_roi, num_roi))\n",
- "for p, pair in enumerate(pairs):\n",
- " i, j = pair\n",
- " for t in range(len(test_outputs)):\n",
- " matrices[t, i, j] = results[p, t]\n",
- " \n",
- "for t, test_output in enumerate(test_outputs):\n",
- " pickle.dump(matrices[t], open('fmri_data/%s_matrix.pkl' % test_output, 'wb'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize Matrices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_heatmap(matrix, labels, title, filename):\n",
- "# sns.set()\n",
- " cmap = mpl.cm.get_cmap('Purples')\n",
- " cc = np.linspace(0, 1, 256)\n",
- " cmap = mpl.colors.ListedColormap(cmap(cc))\n",
- "\n",
- " heatmap_kws = dict(\n",
- " cbar=False,\n",
- " font_scale=1.4,\n",
- " inner_hier_labels=labels,\n",
- " hier_label_fontsize=20,\n",
- " cmap=cmap,\n",
- " center=None,\n",
- " )\n",
- " side_label_kws = dict(labelpad=45, fontsize=24)\n",
- "\n",
- " fig, ax = plt.subplots(1, 1, figsize=(20, 16))\n",
- "\n",
- " # Plot heatmap via graspy.\n",
- " heatmap(matrix, ax=ax, **heatmap_kws)\n",
- " ax.set_title(title, pad = 100, fontdict = {'fontsize' : 23})\n",
- " \n",
- " # Create ticks.\n",
- " num_ticks = 8\n",
- " top_val = np.max(matrix)\n",
- " ticks = [i * np.max(matrix) / num_ticks for i in range(num_ticks+1)]\n",
- " yticks = [('%.2f' % np.round(10 ** -p, 2)) for p in ticks]\n",
- "\n",
- " # Add colorbar.\n",
- " sm = plt.cm.ScalarMappable(cmap=cmap)\n",
- " sm.set_array(matrix)\n",
- " cbar = fig.colorbar(sm, ax=ax, fraction=0.0475, pad=-0.1, ticks=ticks)\n",
- " cbar.ax.set_yticklabels(yticks)\n",
- " cbar.ax.tick_params(labelsize=25)\n",
- "\n",
- " plt.savefig(\n",
- " \"%s.pdf\" % filename,\n",
- " facecolor=\"w\",\n",
- " format=\"pdf\",\n",
- " bbox_inches=\"tight\",\n",
- " )\n",
- "\n",
- " plt.tight_layout()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### p-value Matrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Apply negative log10 transform.\n",
- "# matrix = pickle.load(open('fmri_data/pval_matrix.pkl', 'rb'))\n",
- "# matrix = -np.log10(matrix)\n",
- "# pickle.dump(matrix, open('fmri_data/nl10_pval_matrix.pkl', 'wb'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "matrix = pickle.load(open('fmri_data/nl10_pval_matrix.pkl', 'rb'))\n",
- "plot_heatmap(matrix, roi_labels, 'p-Value', 'pval')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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diff --git a/docs/tutorials/time_series/time_series.ipynb b/docs/tutorials/time_series/time_series.ipynb
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--- a/docs/tutorials/time_series/time_series.ipynb
+++ /dev/null
@@ -1,295 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from scipy.stats import pearsonr\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.ticker as ticker\n",
- "import seaborn as sns\n",
- "\n",
- "# custom seaborn plot options to make the figures pretty\n",
- "sns.set(color_codes=True, style='white', context='talk', font_scale=1)\n",
- "PALETTE = sns.color_palette(\"Set1\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Time-Series Independence Tests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Especially in the realm of data such as fMRI images, identifying causality within time series data is difficult and often difficult and sometimes impossible to do among multivarate and nonlinear dependence modalities. We can formulate our approach to mitigate this problem as follow: Let $\\mathbb{N}$ be the non-negative integers $\\{0, 1, 2, ...\\}$, and $\\mathbb{R}$ be the real line $(-\\infty, \\infty)$. Let $F_X$, $F_Y$, and $F_{X,Y}$ represent the marginal and joint distributions of random variables $X$ and $Y$, whose realizations exist in $\\mathcal{X}$ and $\\mathcal{Y}$, respectively. Similarly, Let $F_{X_t}$, $F_{Y_s}$, and $F_{(X_t, Y_s)}$ represent the marginal and joint distributions of the time-indexed random variables $X_t$ and $Y_s$ at timesteps $t$ and $s$. For this work, assume $\\mathcal{X} = \\mathbb{R}^p$ and $\\mathcal{Y} = \\mathbb{R}^q$ for $p, q > 0$. Finally, let $\\{(X_t, Y_t)\\}_{t = -\\infty}^{\\infty}$ represent the full, jointly-sampled time series, structured as a countably long list of observations $(X_t, Y_t)$. Consider a strictly stationary time series $\\{(X_t, Y_t)\\}_{t = -\\infty}^{\\infty}$, with the observed sample $\\{(X_1, Y_1), \\ldots,(X_n, Y_n)\\}$. Choose some $M \\in \\mathbb{N}$, the maximum_lag hyperparameter. We test the independence of two series via the following hypothesis.\n",
- "\n",
- "\\begin{align*}\n",
- " H_0: F_{(X_t,Y_{t-j})} &= F_{X_t} F_{Y_{t-j}} \\text{ for each } j \\in \\{0, 1, ..., M\\}\\\\\n",
- " H_A: F_{(X_t,Y_{t-j})} &\\neq F_{X_t} F_{Y_{t-j}} \\text{ for some } j \\in \\{0, 1, ..., M\\}\n",
- "\\end{align*}\n",
- "\n",
- "The null hypothesis implies that for any $(M+1)$-length stretch in the time series, $X_t$ is pairwise independent of present and past values $Y_{t-j}$ spaced $j$ timesteps away (including $j=0$). A corresponding test for whether $Y_t$ is dependent on past values of $X_t$ is available by swapping the labels of each time series. Finally, the hyperparameter $M$ governs the maximum number of timesteps in the past for which we check the influence of $Y_{t-j}$ on $X_t$. This $M$ can be chosen for computation considerations, as well as for specific subject matter purposes, e.g. a signal from one region of the brain might only influence be able to influence another within 20 time steps implies $M = 20$.\n",
- "\n",
- "Importing tests from `hyppo` is similar to importing any other test."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from hyppo.time_series import MGCX, DcorrX\n",
- "from hyppo.sims import indep_ar, cross_corr_ar, nonlinear_process"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, let's generate some simulated data. The independence simulations included take on required parameters number of samples with optional paramaters mentioned in the [reference](https://mgc.neurodata.io/reference/sims.html#time-series-simulations) section of the docs. There is also a `max_lag` parameter corresponding to the maximum difference between time points to test independence on."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "x, y = indep_ar(40)\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "n = x.shape[0]\n",
- "t = range(1, n + 1)\n",
- "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,7.5))\n",
- "sns.lineplot(t, x, ax=ax1)\n",
- "sns.lineplot(t, y, ax=ax1)\n",
- "sns.scatterplot(x, y, ax=ax2)\n",
- "ax1.legend(['X_t', 'Y_t'], loc = 'upper left', prop={'size': 12})\n",
- "ax1.set_xlabel(r'$t$')\n",
- "ax2.set_xlabel(r'$X_t$') \n",
- "ax2.set_ylabel(r'$Y_t$')\n",
- "fig.suptitle(\"Independent AR (lag=1)\")\n",
- "plt.axis('equal')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The test statistic the p-value is calculated by running the `.test` method. Some important parameters for the `.test` method:\n",
- "\n",
- " - `reps`: The number of replications to run when running a permutation test\n",
- " - `workers`: The number of cores to parallelize over when running a permutation test\n",
- "\n",
- "Note that when using a permutation test, the lowest p-value is the reciprocal of the number of repetitions. Also, since the p-value is calculated with a random permutation test, there will be a slight variance in p-values with subsequent runs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MGCX test statistic: 0.0\n",
- "MGCX p-value: 1.0\n"
- ]
- }
- ],
- "source": [
- "# test statistic and p-values can be calculated for MGC, so ...\n",
- "stat, pvalue = MGCX(max_lag=0).test(x, y)\n",
- "\n",
- "print(\"MGCX test statistic:\", stat)\n",
- "print(\"MGCX p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MGCX test statistic: 0.0\n",
- "MGCX p-value: 1.0\n"
- ]
- }
- ],
- "source": [
- "# or the parallelization can be used (-1 uses all cores)\n",
- "stat, pvalue = MGCX(max_lag=0).test(x, y, workers=-1)\n",
- "\n",
- "print(\"MGCX test statistic:\", stat)\n",
- "print(\"MGCX p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can do the same thing for other kinds of time series processes the same methods."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "x, y = cross_corr_ar(40)\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "n = x.shape[0]\n",
- "t = range(1, n + 1)\n",
- "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,7.5))\n",
- "sns.lineplot(t, x, ax=ax1)\n",
- "sns.lineplot(t, y, ax=ax1)\n",
- "sns.scatterplot(x, y, ax=ax2)\n",
- "ax1.legend(['X_t', 'Y_t'], loc = 'upper left', prop={'size': 12})\n",
- "ax1.set_xlabel(r'$t$')\n",
- "ax2.set_xlabel(r'$X_t$') \n",
- "ax2.set_ylabel(r'$Y_t$')\n",
- "fig.suptitle(\"Independent AR (lag=1)\")\n",
- "plt.axis('equal')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DcorrX test statistic: 0.0\n",
- "DcorrX p-value: 1.0\n"
- ]
- }
- ],
- "source": [
- "stat, pvalue = DcorrX(max_lag=0).test(x, y)\n",
- "\n",
- "print(\"DcorrX test statistic:\", stat)\n",
- "print(\"DcorrX p-value:\", pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# hyppo code used to produce the simulation data\n",
- "x, y = cross_corr_ar(40)\n",
- "\n",
- "# stuff to make the plot and make it look nice\n",
- "n = x.shape[0]\n",
- "t = range(1, n + 1)\n",
- "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,7.5))\n",
- "sns.lineplot(t[1:n], x[1:n], ax=ax1)\n",
- "sns.lineplot(t[0:(n-1)], y[0:(n-1)], ax=ax1)\n",
- "sns.scatterplot(x, y, ax=ax2)\n",
- "ax1.legend(['X_t', 'Y_t'], loc = 'upper left', prop={'size': 12})\n",
- "ax1.set_xlabel(r'$t$')\n",
- "ax2.set_xlabel(r'$X_t$') \n",
- "ax2.set_ylabel(r'$Y_t$')\n",
- "fig.suptitle(\"Independent AR (lag=1)\")\n",
- "plt.axis('equal')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DcorrX test statistic: 0.11933511504916483\n",
- "DcorrX p-value: 0.091\n"
- ]
- }
- ],
- "source": [
- "stat, pvalue = DcorrX(max_lag=1).test(x, y, workers=-1)\n",
- "\n",
- "print(\"DcorrX test statistic:\", stat)\n",
- "print(\"DcorrX p-value:\", pvalue)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/tutorials/time_series/time_series_doc_examples.ipynb b/docs/tutorials/time_series/time_series_doc_examples.ipynb
deleted file mode 100644
index d4e050b03..000000000
--- a/docs/tutorials/time_series/time_series_doc_examples.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Time Series Documentation Examples: DCorr-X and MGC-X"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## DcorrX"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'1.0, 0.01, 0'"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from hyppo.time_series import DcorrX\n",
- "np.random.seed(456)\n",
- "x = np.arange(7)\n",
- "y = x\n",
- "stat, pvalue, dcorrx_dict = DcorrX().test(x, y, reps = 100)\n",
- "'%.1f, %.2f, %d' % (stat, pvalue, dcorrx_dict['opt_lag'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'1.1, 0.01, 1'"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from hyppo.time_series import DcorrX\n",
- "np.random.seed(1234)\n",
- "x = np.random.permutation(10)\n",
- "y = np.roll(x, -1)\n",
- "stat, pvalue, dcorrx_dict = DcorrX(max_lag=1).test(x, y, reps=1000)\n",
- "'%.1f, %.2f, %d' % (stat, pvalue, dcorrx_dict['opt_lag'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'1.0, 0.00'"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from hyppo.time_series import DcorrX\n",
- "np.random.seed(789)\n",
- "x = np.ones((10, 10)) - np.identity(10)\n",
- "y = 2 * x\n",
- "dcorrx = DcorrX(compute_distance=None)\n",
- "stat, pvalue, dcorrx_dict = dcorrx.test(x, y)\n",
- "'%.1f, %.2f' % (stat, pvalue)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## MGCX"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'1.0, 0.01, [7, 7]'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from hyppo.time_series import MGCX\n",
- "np.random.seed(456)\n",
- "x = np.arange(7)\n",
- "y = x\n",
- "stat, pvalue, mgcx_dict = MGCX().test(x, y, reps = 100)\n",
- "'%.1f, %.2f, [%d, %d]' % (stat, pvalue, mgcx_dict['opt_scale'][0], mgcx_dict['opt_scale'][1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'1.1, 0.01, 1'"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from hyppo.time_series import MGCX\n",
- "np.random.seed(1234)\n",
- "x = np.random.permutation(10)\n",
- "y = np.roll(x, -1)\n",
- "stat, pvalue, mgcx_dict = MGCX(max_lag=1).test(x, y, reps=1000)\n",
- "'%.1f, %.2f, %d' % (stat, pvalue, mgcx_dict['opt_lag'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'1.0, 0.00'"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from hyppo.time_series import MGCX\n",
- "np.random.seed(789)\n",
- "x = np.ones((10, 10)) - np.identity(10)\n",
- "y = 2 * x\n",
- "mgcx = MGCX(compute_distance=None)\n",
- "stat, pvalue, mgcx_dict = mgcx.test(x, y)\n",
- "'%.1f, %.2f' % (stat, pvalue)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/user_guide/benchmarks.rst b/docs/user_guide/benchmarks.rst
new file mode 100644
index 000000000..a30d35735
--- /dev/null
+++ b/docs/user_guide/benchmarks.rst
@@ -0,0 +1 @@
+.. include:: ../benchmarks/index.rst
\ No newline at end of file
diff --git a/docs/user_guide/conditional.rst b/docs/user_guide/conditional.rst
new file mode 100644
index 000000000..7a86bd58a
--- /dev/null
+++ b/docs/user_guide/conditional.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/conditional.rst
\ No newline at end of file
diff --git a/docs/user_guide/d_variate.rst b/docs/user_guide/d_variate.rst
new file mode 100644
index 000000000..87f58f7b8
--- /dev/null
+++ b/docs/user_guide/d_variate.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/dvariate.rst
\ No newline at end of file
diff --git a/docs/user_guide/discrim.rst b/docs/user_guide/discrim.rst
new file mode 100644
index 000000000..6d084efb8
--- /dev/null
+++ b/docs/user_guide/discrim.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/discrim.rst
\ No newline at end of file
diff --git a/docs/user_guide/examples.rst b/docs/user_guide/examples.rst
new file mode 100644
index 000000000..9828bf777
--- /dev/null
+++ b/docs/user_guide/examples.rst
@@ -0,0 +1 @@
+.. include:: ../gallery/index.rst
\ No newline at end of file
diff --git a/docs/user_guide/independence.rst b/docs/user_guide/independence.rst
new file mode 100644
index 000000000..6a30e3f0d
--- /dev/null
+++ b/docs/user_guide/independence.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/independence.rst
\ No newline at end of file
diff --git a/docs/user_guide/index.rst b/docs/user_guide/index.rst
new file mode 100644
index 000000000..a1a99a319
--- /dev/null
+++ b/docs/user_guide/index.rst
@@ -0,0 +1,15 @@
+User Guide
+================
+
+.. toctree::
+
+ sims.rst
+ independence.rst
+ d_variate.rst
+ ksample.rst
+ time_series.rst
+ discrim.rst
+ conditional.rst
+ kgof.rst
+ benchmarks.rst
+ examples.rst
\ No newline at end of file
diff --git a/docs/user_guide/kgof.rst b/docs/user_guide/kgof.rst
new file mode 100644
index 000000000..2167c5dc4
--- /dev/null
+++ b/docs/user_guide/kgof.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/kgof.rst
\ No newline at end of file
diff --git a/docs/user_guide/ksample.rst b/docs/user_guide/ksample.rst
new file mode 100644
index 000000000..b791c2c3f
--- /dev/null
+++ b/docs/user_guide/ksample.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/ksample.rst
\ No newline at end of file
diff --git a/docs/user_guide/sims.rst b/docs/user_guide/sims.rst
new file mode 100644
index 000000000..efde5e455
--- /dev/null
+++ b/docs/user_guide/sims.rst
@@ -0,0 +1 @@
+.. include:: ../sample_data/index.rst
\ No newline at end of file
diff --git a/docs/user_guide/time_series.rst b/docs/user_guide/time_series.rst
new file mode 100644
index 000000000..a366f5843
--- /dev/null
+++ b/docs/user_guide/time_series.rst
@@ -0,0 +1 @@
+.. include:: ../tutorials/time_series.rst
\ No newline at end of file
diff --git a/examples/README.txt b/examples/README.txt
new file mode 100644
index 000000000..97f7a135e
--- /dev/null
+++ b/examples/README.txt
@@ -0,0 +1,6 @@
+.. _gallery:
+
+Gallery
+=======
+
+This gallery contains a selection examples of what ``hyppo`` can do.
diff --git a/examples/data/circle_0.csv b/examples/data/circle_0.csv
new file mode 100644
index 000000000..102a478e5
--- /dev/null
+++ b/examples/data/circle_0.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+3.912957002824530295e-01
+1.621577836105778797e-02
+1.764736558250808440e-01
+0.000000000000000000e+00
diff --git a/examples/data/circle_1.csv b/examples/data/circle_1.csv
new file mode 100644
index 000000000..feae62f2d
--- /dev/null
+++ b/examples/data/circle_1.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+0.000000000000000000e+00
+8.646265890358428852e-02
+1.656611607515931506e-01
+9.723569914924758972e-01
diff --git a/examples/data/circle_10.csv b/examples/data/circle_10.csv
new file mode 100644
index 000000000..11a425feb
--- /dev/null
+++ b/examples/data/circle_10.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+2.667975302838993534e-01
+9.008139524701758161e-02
+1.848461398241799947e-01
+0.000000000000000000e+00
diff --git a/examples/data/circle_11.csv b/examples/data/circle_11.csv
new file mode 100644
index 000000000..b3c37ae70
--- /dev/null
+++ b/examples/data/circle_11.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+3.860539787942547374e-01
+0.000000000000000000e+00
+3.310328867956732757e-01
+2.974693953824001508e-02
diff --git a/examples/data/circle_12.csv b/examples/data/circle_12.csv
new file mode 100644
index 000000000..e39ce2369
--- /dev/null
+++ b/examples/data/circle_12.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+2.511952498395766953e-01
+3.014978468518936250e-01
+1.513111536961652404e-01
+0.000000000000000000e+00
diff --git a/examples/data/circle_13.csv b/examples/data/circle_13.csv
new file mode 100644
index 000000000..29b061d61
--- /dev/null
+++ b/examples/data/circle_13.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+0.000000000000000000e+00
+3.721609862996452245e-01
+1.933344136992086737e-01
+1.324861878758134548e-01
diff --git a/examples/data/circle_14.csv b/examples/data/circle_14.csv
new file mode 100644
index 000000000..1413fc5cb
--- /dev/null
+++ b/examples/data/circle_14.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+0.000000000000000000e+00
+1.275508999543172872e-01
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diff --git a/examples/data/circle_15.csv b/examples/data/circle_15.csv
new file mode 100644
index 000000000..de84d6ec6
--- /dev/null
+++ b/examples/data/circle_15.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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+1.453173685189278752e-01
+3.972510854913006151e-01
+0.000000000000000000e+00
diff --git a/examples/data/circle_16.csv b/examples/data/circle_16.csv
new file mode 100644
index 000000000..30d0b1014
--- /dev/null
+++ b/examples/data/circle_16.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+9.067649487419748855e-01
+0.000000000000000000e+00
+3.454150827846658167e-01
+3.728375014533908960e-01
diff --git a/examples/data/circle_17.csv b/examples/data/circle_17.csv
new file mode 100644
index 000000000..dc7693efe
--- /dev/null
+++ b/examples/data/circle_17.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+0.000000000000000000e+00
+4.098994075623803995e-02
+3.037319368498632155e-01
+2.957517121855506026e-02
diff --git a/examples/data/circle_18.csv b/examples/data/circle_18.csv
new file mode 100644
index 000000000..0b1b5099f
--- /dev/null
+++ b/examples/data/circle_18.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+0.000000000000000000e+00
+2.267112792657044809e-02
+5.247001330270957553e-02
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diff --git a/examples/data/circle_19.csv b/examples/data/circle_19.csv
new file mode 100644
index 000000000..f8611a2d9
--- /dev/null
+++ b/examples/data/circle_19.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+1.582657025497769682e-02
+3.753244687382380207e-02
+1.072923687210146360e-01
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diff --git a/examples/data/circle_2.csv b/examples/data/circle_2.csv
new file mode 100644
index 000000000..7732eb68f
--- /dev/null
+++ b/examples/data/circle_2.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+3.904673616995624763e-01
+1.124516952156798810e-01
+2.741803825511033299e-01
+0.000000000000000000e+00
diff --git a/examples/data/circle_20.csv b/examples/data/circle_20.csv
new file mode 100644
index 000000000..818f18c03
--- /dev/null
+++ b/examples/data/circle_20.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+1.539102955355080848e-01
+2.169013702991077208e-01
+0.000000000000000000e+00
+1.757024991225881183e-01
diff --git a/examples/data/circle_21.csv b/examples/data/circle_21.csv
new file mode 100644
index 000000000..b934904d7
--- /dev/null
+++ b/examples/data/circle_21.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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+4.594714359344398980e-01
+0.000000000000000000e+00
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diff --git a/examples/data/circle_22.csv b/examples/data/circle_22.csv
new file mode 100644
index 000000000..e849bd575
--- /dev/null
+++ b/examples/data/circle_22.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_23.csv b/examples/data/circle_23.csv
new file mode 100644
index 000000000..1ab3fe4c6
--- /dev/null
+++ b/examples/data/circle_23.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_24.csv b/examples/data/circle_24.csv
new file mode 100644
index 000000000..8d650b54f
--- /dev/null
+++ b/examples/data/circle_24.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_25.csv b/examples/data/circle_25.csv
new file mode 100644
index 000000000..0c33942fe
--- /dev/null
+++ b/examples/data/circle_25.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_26.csv b/examples/data/circle_26.csv
new file mode 100644
index 000000000..9f5c652ba
--- /dev/null
+++ b/examples/data/circle_26.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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+0.000000000000000000e+00
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diff --git a/examples/data/circle_27.csv b/examples/data/circle_27.csv
new file mode 100644
index 000000000..620fbbcad
--- /dev/null
+++ b/examples/data/circle_27.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_28.csv b/examples/data/circle_28.csv
new file mode 100644
index 000000000..856e6fafd
--- /dev/null
+++ b/examples/data/circle_28.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_29.csv b/examples/data/circle_29.csv
new file mode 100644
index 000000000..6c89d261d
--- /dev/null
+++ b/examples/data/circle_29.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_3.csv b/examples/data/circle_3.csv
new file mode 100644
index 000000000..61e3440b4
--- /dev/null
+++ b/examples/data/circle_3.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_30.csv b/examples/data/circle_30.csv
new file mode 100644
index 000000000..88219c1ab
--- /dev/null
+++ b/examples/data/circle_30.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_31.csv b/examples/data/circle_31.csv
new file mode 100644
index 000000000..6561ebaa6
--- /dev/null
+++ b/examples/data/circle_31.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_32.csv b/examples/data/circle_32.csv
new file mode 100644
index 000000000..d9c52fb26
--- /dev/null
+++ b/examples/data/circle_32.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_33.csv b/examples/data/circle_33.csv
new file mode 100644
index 000000000..ce281e288
--- /dev/null
+++ b/examples/data/circle_33.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_34.csv b/examples/data/circle_34.csv
new file mode 100644
index 000000000..4ad4573b9
--- /dev/null
+++ b/examples/data/circle_34.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_35.csv b/examples/data/circle_35.csv
new file mode 100644
index 000000000..292dfb942
--- /dev/null
+++ b/examples/data/circle_35.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_36.csv b/examples/data/circle_36.csv
new file mode 100644
index 000000000..127c5d862
--- /dev/null
+++ b/examples/data/circle_36.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_37.csv b/examples/data/circle_37.csv
new file mode 100644
index 000000000..ab5ef6c24
--- /dev/null
+++ b/examples/data/circle_37.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_38.csv b/examples/data/circle_38.csv
new file mode 100644
index 000000000..42b4f33c2
--- /dev/null
+++ b/examples/data/circle_38.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
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diff --git a/examples/data/circle_39.csv b/examples/data/circle_39.csv
new file mode 100644
index 000000000..acab03637
--- /dev/null
+++ b/examples/data/circle_39.csv
@@ -0,0 +1,5 @@
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diff --git a/examples/data/circle_4.csv b/examples/data/circle_4.csv
new file mode 100644
index 000000000..8303a4e44
--- /dev/null
+++ b/examples/data/circle_4.csv
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diff --git a/examples/data/circle_40.csv b/examples/data/circle_40.csv
new file mode 100644
index 000000000..49360230d
--- /dev/null
+++ b/examples/data/circle_40.csv
@@ -0,0 +1,5 @@
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+++ b/examples/data/w_shaped_88.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+5.797549518207438801e-01
+1.319576554752536213e-01
+2.047244490150200991e-01
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_89.csv b/examples/data/w_shaped_89.csv
new file mode 100644
index 000000000..84f811665
--- /dev/null
+++ b/examples/data/w_shaped_89.csv
@@ -0,0 +1,5 @@
+7.446898870905260814e-01
+1.000000000000000000e+00
+4.722531617502867540e-01
+0.000000000000000000e+00
+8.623846556403323982e-01
diff --git a/examples/data/w_shaped_9.csv b/examples/data/w_shaped_9.csv
new file mode 100644
index 000000000..728f74aa5
--- /dev/null
+++ b/examples/data/w_shaped_9.csv
@@ -0,0 +1,5 @@
+9.640790009592736798e-01
+6.563797754340020552e-01
+8.737433266485329364e-01
+0.000000000000000000e+00
+1.000000000000000000e+00
diff --git a/examples/data/w_shaped_90.csv b/examples/data/w_shaped_90.csv
new file mode 100644
index 000000000..79541e27a
--- /dev/null
+++ b/examples/data/w_shaped_90.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+4.275662221044552735e-01
+4.485729453348013540e-01
+8.821518326111665864e-01
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_91.csv b/examples/data/w_shaped_91.csv
new file mode 100644
index 000000000..c1f96c04d
--- /dev/null
+++ b/examples/data/w_shaped_91.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+4.024921055871008568e-01
+2.271703504994213862e-01
+0.000000000000000000e+00
+2.100642665009024579e-01
diff --git a/examples/data/w_shaped_92.csv b/examples/data/w_shaped_92.csv
new file mode 100644
index 000000000..ee1c9d52f
--- /dev/null
+++ b/examples/data/w_shaped_92.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+1.981052806876657801e-01
+2.007524629298130248e-01
+0.000000000000000000e+00
+4.102097279297590299e-02
diff --git a/examples/data/w_shaped_93.csv b/examples/data/w_shaped_93.csv
new file mode 100644
index 000000000..5ca272082
--- /dev/null
+++ b/examples/data/w_shaped_93.csv
@@ -0,0 +1,5 @@
+6.600956296682249924e-01
+1.000000000000000000e+00
+1.848517452210151346e-01
+1.176229853662557646e-02
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_94.csv b/examples/data/w_shaped_94.csv
new file mode 100644
index 000000000..02073d5fd
--- /dev/null
+++ b/examples/data/w_shaped_94.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+7.092365071710410307e-01
+1.605826097718824896e-01
+3.081994130652286334e-01
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_95.csv b/examples/data/w_shaped_95.csv
new file mode 100644
index 000000000..3148752d9
--- /dev/null
+++ b/examples/data/w_shaped_95.csv
@@ -0,0 +1,5 @@
+4.780437112717214609e-01
+1.000000000000000000e+00
+1.017738428290845543e-01
+9.208093632541783369e-02
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_96.csv b/examples/data/w_shaped_96.csv
new file mode 100644
index 000000000..cbf4311e2
--- /dev/null
+++ b/examples/data/w_shaped_96.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+6.087762600350873354e-01
+4.437776803767892186e-01
+6.952526053452591503e-02
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_97.csv b/examples/data/w_shaped_97.csv
new file mode 100644
index 000000000..236c8433b
--- /dev/null
+++ b/examples/data/w_shaped_97.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+1.521700676992134293e-01
+3.617245437666088276e-02
+1.721123738649507628e-01
+0.000000000000000000e+00
diff --git a/examples/data/w_shaped_98.csv b/examples/data/w_shaped_98.csv
new file mode 100644
index 000000000..d1667c6cd
--- /dev/null
+++ b/examples/data/w_shaped_98.csv
@@ -0,0 +1,5 @@
+8.963216266071449834e-01
+4.222405552041530452e-01
+1.000000000000000000e+00
+0.000000000000000000e+00
+8.573224316244922039e-02
diff --git a/examples/data/w_shaped_99.csv b/examples/data/w_shaped_99.csv
new file mode 100644
index 000000000..489835c0f
--- /dev/null
+++ b/examples/data/w_shaped_99.csv
@@ -0,0 +1,5 @@
+1.000000000000000000e+00
+3.167163071265653995e-01
+3.845965029181086514e-01
+0.000000000000000000e+00
+2.061251496584347656e-02
diff --git a/examples/feat_import.py b/examples/feat_import.py
new file mode 100644
index 000000000..b2de4acd1
--- /dev/null
+++ b/examples/feat_import.py
@@ -0,0 +1,169 @@
+"""
+Feature Importances
+=================================
+
+:class:`hyppo.independence.KMERF` class is a powerful
+independence test built upon random forest and kernel embedding methods.
+All random forests return feature importances, and since KMERF uses random forest as a
+kernel, we can use feature importances to tell use which of our dimensions (or features)
+contributes most to the decision of accepting or rejecting the null hypothesis. This
+idea tremendously improves the interpretability of the tests.
+Here, we will show you, given our simulations, which we modify such that `y` is one
+dimensional and dimensions become less important as dimensionality increases. We do
+this to verify that feature importance is being correctly measured. To see how we
+modified the simulations, checkout this
+`notebook `_.
+It is worth noting the last simulation does not follow this general trend. That is
+because we each dimension in that one is equally important.
+"""
+
+import os
+import sys
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from hyppo.independence import KMERF
+from hyppo.tools import SIMULATIONS
+from joblib import Parallel, delayed
+from scipy.stats import norm
+
+# add file path for precomputed data
+sys.path.append(os.path.realpath(".."))
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=2)
+
+# constants
+REPS = 100 # number of replications to average
+DIM = 5 # 5D simulation
+FOREST_SIZE = 5000 # size of forest
+SIM_SIZE = 200 # size of simulation
+
+# simulation titles
+SIM_TITLES = [
+ "Linear",
+ "Exponential",
+ "Cubic",
+ "Joint Normal",
+ "Step",
+ "Quadratic",
+ "W-Shaped",
+ "Spiral",
+ "Bernoulli",
+ "Logarithmic",
+ "Fourth Root",
+ "Sine 4\u03C0",
+ "Sine 16\u03C0",
+ "Square",
+ "Two Parabolas",
+ "Circle",
+ "Ellipse",
+ "Diamond",
+ "Noise",
+ "Independence",
+]
+
+
+def tree_import(sim_name):
+ """Train a random forest for a given simulation, calculate feature importance."""
+ # simulate data
+ x, y = SIMULATIONS[sim_name](SIM_SIZE, DIM)
+ if y.shape[1] == 1:
+ y = y.ravel()
+
+ with warnings.catch_warnings():
+ # get feature importances
+ _, _, importances = KMERF(forest="regressor", ntrees=FOREST_SIZE).test(
+ x, y, reps=0
+ )
+
+ return importances
+
+
+def estimate_featimport(sim_name, rep):
+ """Run this function to calculate the feature importances"""
+ est_featimpt = tree_import(sim_name)
+ np.savetxt(
+ "../examples/data/{}_{}.csv".format(sim_name, rep), est_featimpt, delimiter=","
+ )
+
+ return est_featimpt
+
+
+# At this point, we would run this bit of code to generate the data for the figure and
+# store it under the "data" directory. Since this code takes a very long time, we have
+# commented out these lines of codes. If you would like to generate the data, uncomment
+# these lines and run the file.
+#
+# outputs = Parallel(n_jobs=-1, verbose=100)(
+# [
+# delayed(estimate_featimport)(sim_name, rep)
+# for sim_name in SIMULATIONS.keys()
+# for rep in range(REPS)
+# ]
+# )
+
+
+def plot_featimport_confint():
+ """Plot feature importances and 95% confidence intervals"""
+ fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(25, 20))
+
+ plt.suptitle("Feature Importances", y=0.93, va="baseline")
+
+ for i, row in enumerate(ax):
+ for j, col in enumerate(row):
+
+ # get the panel location and simulation name
+ count = 5 * i + j
+ sim_name = list(SIMULATIONS.keys())[count]
+
+ # extract data from the CSV file and store the data in an array
+ all_impt = np.zeros((REPS, DIM))
+ for rep in range(REPS):
+ forest_impt = np.genfromtxt(
+ "../examples/data/{}_{}.csv".format(sim_name, rep), delimiter=","
+ )
+ all_impt[rep, :] = forest_impt
+
+ # get the mean importances for the simulation, also calculate 95% CI
+ # interval
+ mean_impt = np.mean(all_impt, axis=0)
+ mean_impt -= np.min(mean_impt)
+ mean_impt /= np.max(mean_impt)
+ low, high = norm.interval(
+ 0.95, loc=mean_impt, scale=np.std(mean_impt, axis=0) / np.sqrt(REPS)
+ )
+
+ # plot the figure lines, and the 95% CI
+ col.plot(
+ range(1, DIM + 1),
+ mean_impt,
+ color="#525252",
+ lw=5,
+ label="Averaged Data",
+ )
+ col.fill_between(range(1, DIM + 1), low, high, color="#d9d9d9")
+
+ # make the data look pretty for each pannel
+ col.set_xticks([])
+ if i == 3:
+ col.set_xticks([1, 5])
+ col.set_xlim([1, 5])
+ col.set_ylim([0, 1])
+ col.set_yticks([])
+ if j == 0:
+ col.set_yticks([0, 1])
+ col.set_ylabel("")
+ col.set_xlabel("")
+ col.set_title(SIM_TITLES[count])
+
+ # misc entire plot
+ fig.text(0.5, 0.04, "Dimension", ha="center")
+ fig.text(0.08, 0.5, "Feature Importance", va="center", rotation="vertical")
+ plt.subplots_adjust(hspace=0.50, wspace=0.40)
+
+
+# plot the feature importances
+plot_featimport_confint()
diff --git a/examples/mgc_map.py b/examples/mgc_map.py
new file mode 100644
index 000000000..39e62c369
--- /dev/null
+++ b/examples/mgc_map.py
@@ -0,0 +1,85 @@
+"""
+MGC Map
+========
+
+hyppo offers the :class:`hyppo.independence.MGC` class, which is a powerful independence
+test to fit a host of different types of data.
+The MGC Map is essentially an array of local correlations (i.e. the test statistic that
+at each nearest neighbor pair). The optimal scale is the nearest neighbor pair that
+maximizes the test statistic, and this test statistic is the one we return as part of
+MGC.
+Let's look at how we can use hyppo
+to find a trend in linear data sets (:meth:`hyppo.tools.linear`) and nonlinear data sets
+(for example :meth:`hyppo.tools.spiral`) and visualize this map and optimal scale.
+"""
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import seaborn as sns
+from hyppo.independence import MGC
+from hyppo.tools import linear, spiral
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=1)
+
+# make a dictionary that maps the simulation function to a string
+sims = {
+ "Linear": linear,
+ "Spiral": spiral,
+}
+
+
+# make a function that runs the code depending on the simulation
+def run_test(sim_type="Linear"):
+ """Runs all the tests for both simulations."""
+
+ # simulate our data (100 samples, 3 dimensions)
+ x, y = sims[sim_type](n=100, p=3, noise=True)
+
+ # calculate the MGC test statistic, p-value, and get a dictionary with the
+ # mgc_map (shows the geometric nature of the relationship) and the optimal
+ # scale, which is the k,l nearest neighbors that maximize the test statistic
+ _, _, mgc_dict = MGC().test(x, y, reps=0)
+
+ # plot the MGC map with a map of the local correlations (as mentioned before,
+ # shows the geometric nature of the relationship), and the optimal scale,
+ # which is the maximimum smoothed test statistic
+ mgc_map = mgc_dict["mgc_map"]
+ opt_scale = mgc_dict["opt_scale"] # i.e. maximum smoothed test statistic
+ print("Optimal Scale:", opt_scale)
+
+ # create figure
+ fig, (ax, cax) = plt.subplots(
+ ncols=2, figsize=(9.45, 7.5), gridspec_kw={"width_ratios": [1, 0.05]}
+ )
+
+ # draw heatmap and colorbar
+ ax = sns.heatmap(mgc_map, cmap="YlGnBu", ax=ax, cbar=False)
+ fig.colorbar(ax.get_children()[0], cax=cax, orientation="vertical")
+ ax.invert_yaxis()
+
+ # optimal scale
+ ax.scatter(opt_scale[1], opt_scale[0], marker="X", s=200, color="red")
+
+ # make plots look nice
+ ax.set_title("MGC Map - {}".format(sim_type))
+ ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
+ ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
+ ax.yaxis.set_major_locator(ticker.MultipleLocator(10))
+ ax.yaxis.set_major_formatter(ticker.ScalarFormatter())
+ ax.set_xlabel("Neighbors for x")
+ ax.set_ylabel("Neighbors for y")
+ ax.set_xticks([0, 50, 100])
+ ax.set_yticks([0, 50, 100])
+ ax.xaxis.set_tick_params()
+ ax.yaxis.set_tick_params()
+ cax.xaxis.set_tick_params()
+ cax.yaxis.set_tick_params()
+ plt.show()
+
+
+# run the created function for linear and spiral simulations. Notice how the
+# MGC map looks different for a spiral simulation and linear simulation.
+# Optimal scale informs this, linearly related data has a scale at the max
+# scale, while nonlinearly related data does not.
+run_test(sim_type="Linear")
+run_test(sim_type="Spiral")
diff --git a/examples/permutation_tree.py b/examples/permutation_tree.py
new file mode 100644
index 000000000..1a0b37b7a
--- /dev/null
+++ b/examples/permutation_tree.py
@@ -0,0 +1,214 @@
+"""
+Block Permutations
+=============================
+
+The permutations of :class:`hyppo.independence.Dcorr` can be restricted to appropriately match known
+dependencies of samples under the null distribution (i.e. multilevel and longitudinal data). Without such modifications, calculated
+pvalues are invalid as the default space of permutations are misspecified.
+
+In order to restrict the permutations, we pass in a list of group labels. Each column
+is a list of labels which partitions the observations by shared label into blocks and
+multiple columns repeat this process recursively. At each level, blocks are exchangeable
+unless the label is a negative number, in which case it is fixed, and all elements of
+the final blocks are exchangeable. This defines the space of allowable permutations
+and the :math:`Y` matrix is permuted accordingly.
+
+The block labels used in this notebook are visualized below, corresponding to data
+where observations are dependent within pairs. Because of the :math:`Y` values in our
+2-sample testing case, block labels of :math:`[1, 1, 2, 2, \ldots]` would also have been
+allowable for both cases but would lead to unnecessary permutations being computed.
+
+As shown in the following figures, pvalues under the default permutations are heavily
+skewed and certainly not uniform, thus presenting either an inflated false positive rate or potentially incredibly low power. When the permutations are restricted, the pvalues under
+the null distribution are empirically approximately uniformly distributed, as we would hope for. 95% binomial
+proportion confidence interval error bars are displayed on the histogram of empirical
+p-values for each bin.
+"""
+
+from collections import defaultdict
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from scipy.spatial.distance import pdist, squareform
+
+# custom seaborn plot options to make the figures pretty
+sns.set(color_codes=True, style="white", context="notebook", font_scale=1.25)
+PALETTE = sns.color_palette("Set1")
+sns.set_palette(PALETTE[3:])
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+from hyppo.independence import Dcorr
+
+
+def simulate_2sample_null(n1, n2=None, d=100, group_std=0.1, seed=None):
+ """
+ Simulates a set of paired observations for a 2-sample test.
+
+ n1,n2 : size of the two groups. Are both n1 if n2 is None
+ d : dimension of observations
+ group_std : standard deviation of normal distribution around group mean
+ """
+ np.random.seed(seed)
+
+ # Means for each observation
+ mus1 = np.random.normal(0, 1, (n1, d))
+ if n2 is not None:
+ mus2 = np.random.normal(0, 1, (n2, d))
+
+ # Paired observations
+ X = np.vstack(
+ [np.random.normal(mu, group_std, (2, d)) for mu in mus1]
+ + [np.random.normal(mu, group_std, (2, d)) for mu in mus2]
+ )
+ return X
+
+
+# Simulation parameters
+n1 = 25
+n2 = 25
+d = 100
+group_std = 0.1
+
+# Labels
+Y_within = np.asarray([0, 1] * (n1 + n2))
+Y_across = np.hstack(([0] * n1 * 2, [1] * n2 * 2))
+
+# Permutation tree blocks
+blocks_within = -1 * np.hstack([[i + 1] * 2 for i in range(n1 + n2)]).T
+blocks_across = np.c_[
+ np.hstack([[i + 1] * 2 for i in range(n1 + n2)]), -1 * Y_within - 1
+]
+
+# Test params
+test_blocks = [None, None, blocks_within, blocks_across]
+test_names = [
+ "Unrestricted within",
+ "Unrestricted across",
+ "Restricted within",
+ "Restricted across",
+]
+test_Ys = [Y_within, Y_across, Y_within, Y_across]
+
+# Plot permutation tree blocks figure
+fig, axes = plt.subplots(1, 2, figsize=(6, 6))
+
+for ax, data in zip(axes, (blocks_within[:, np.newaxis], blocks_across)):
+ ax.matshow(data[:10], cmap="Set3")
+ for (i, j), z in np.ndenumerate(data[:10]):
+ ax.text(j, i, "{:}".format(int(z)), ha="center", va="center", fontsize=20)
+ ax.set_xticks([])
+ ax.set_yticks([])
+axes[0].set_title("Within", fontsize=30)
+axes[1].set_title("Across", fontsize=30)
+plt.suptitle("Permutation Tree Blocks", y=1.07, fontsize=30)
+plt.show()
+
+
+# Independence tests figures
+N_DATASETS = 100
+REPS = 100
+test_results = defaultdict(list)
+
+for i in range(N_DATASETS):
+ X = simulate_2sample_null(n1, n2, d, group_std, seed=i)
+ for test, block, Y in zip(test_names, test_blocks, test_Ys):
+ _, pval = Dcorr().test(
+ X,
+ Y,
+ reps=REPS,
+ workers=-1,
+ perm_blocks=block,
+ )
+ test_results[test].append(pval)
+
+# fig, axes = plt.subplots(2,3, figsize=(4, 4*len(data_dict.keys())))
+fig = plt.figure(figsize=(16, 8))
+
+# Show data example
+ax = fig.add_subplot(241)
+
+X = simulate_2sample_null(n1, n2, d, group_std, seed=0)[:10, :]
+X = squareform(pdist(X))
+heatmap = ax.pcolor(X, cmap=plt.cm.Blues)
+ax.invert_yaxis()
+ax.xaxis.tick_top()
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title("X distance matrix")
+
+divider = make_axes_locatable(ax)
+cax = divider.append_axes("bottom", size="5%", pad=0.05)
+plt.colorbar(heatmap, cax=cax, ticks=[0, 10], orientation="horizontal")
+
+
+# Plot Y matrices
+ax = fig.add_subplot(242)
+heatmap = ax.pcolor(squareform(pdist(Y_within[:10, np.newaxis])), cmap=plt.cm.Blues)
+# ax.colorbar(heatmap)
+ax.invert_yaxis()
+ax.xaxis.tick_top()
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title("Y within distance matrix")
+
+divider = make_axes_locatable(ax)
+cax = divider.append_axes("bottom", size="5%", pad=0.05)
+plt.colorbar(heatmap, cax=cax, ticks=[0, 1], orientation="horizontal")
+
+ax = fig.add_subplot(246)
+heatmap = ax.pcolor(
+ squareform(pdist(np.hstack((Y_across[:5], Y_across[-5:]))[:, np.newaxis])),
+ cmap=plt.cm.Blues,
+)
+# ax.colorbar(heatmap)
+ax.invert_yaxis()
+ax.xaxis.tick_top()
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title("Y across distance matrix")
+
+divider = make_axes_locatable(ax)
+cax = divider.append_axes("bottom", size="5%", pad=0.05)
+plt.colorbar(heatmap, cax=cax, ticks=[0, 1], orientation="horizontal")
+
+
+# Plot pvalue histograms and errorbars using binomial CIs
+ax = None
+for i, test_name in zip([3, 7, 4, 8], test_names):
+ ax = fig.add_subplot(int(str(f"24{i}"))) # , sharey=ax)
+ n = len(test_results[test_name])
+ entries, edges, _ = ax.hist(
+ test_results[test_name],
+ bins=np.arange(0, 1.1, 0.1),
+ weights=np.ones(n) / n,
+ color="b",
+ )
+ # entries = height of each column = proportion in that bin
+ # calculate bin centers
+ bin_centers = 0.5 * (edges[:-1] + edges[1:])
+ ax.axhline(y=sum(entries) / len(bin_centers), ls="--", c="#333333")
+ # errorbars are binomial proportion confidence intervals
+ ax.errorbar(
+ bin_centers,
+ entries,
+ yerr=1.96 * np.sqrt(entries * (1 - entries) / n),
+ fmt=".",
+ c="#333333",
+ )
+ ax.set_title(f"{test_name} pvalues")
+ # ax.set_xlim(0,1)
+ if i in [3, 4]:
+ ax.set_xticks([])
+ else:
+ ax.set_xticks([0, 1])
+ if i in [4, 8]:
+ ax.set_yticks([0, 0.1])
+ else:
+ ax.set_yticks([0, 0.1, 1])
+plt.subplots_adjust(hspace=0.3, wspace=0.3)
+plt.show()
diff --git a/hyppo/__init__.py b/hyppo/__init__.py
index 9ee8ae010..ac86c50ae 100644
--- a/hyppo/__init__.py
+++ b/hyppo/__init__.py
@@ -1,7 +1,10 @@
+import hyppo.discrim
import hyppo.independence
import hyppo.ksample
import hyppo.time_series
+import hyppo.kgof
import hyppo.tools
-import hyppo.discrim
+import hyppo.d_variate
+import hyppo.conditional
-__version__ = "0.1.3"
+__version__ = "0.3.2"
diff --git a/hyppo/conditional/FCIT.py b/hyppo/conditional/FCIT.py
new file mode 100644
index 000000000..333fa9dc1
--- /dev/null
+++ b/hyppo/conditional/FCIT.py
@@ -0,0 +1,240 @@
+import time
+import joblib
+
+import numpy as np
+from scipy.stats import ttest_1samp
+from sklearn.metrics import mean_squared_error as mse
+from sklearn.preprocessing import StandardScaler
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import ShuffleSplit
+from sklearn.tree import DecisionTreeRegressor
+
+from .base import ConditionalIndependenceTest, ConditionalIndependenceTestOutput
+
+
+class FCIT(ConditionalIndependenceTest):
+ r"""
+ Fast Conditional Independence test statistic and p-value
+
+ The Fast Conditional Independence Test is a non-parametric
+ conditional independence test :footcite:p:`chalupka2018FastConditionalIndependence`.
+
+ Parameters
+ ----------
+ model: Sklearn regressor
+ Regressor used to predict input data :math:`Y`
+ cv_grid: dict
+ Dictionary of parameters to cross-validate over when training regressor.
+ num_perm: int
+ Number of data permutations to estimate the p-value from marginal stats.
+ prop_test: float
+ Proportion of data to evaluate test stat on.
+ discrete: tuple of string
+ Whether :math:`X` or :math:`Y` are discrete
+ Notes
+ -----
+ The motivation for the test rests on the assumption that if :math:`X \not\!\perp\!\!\!\perp Y \mid Z`,
+ then :math:`Y` should be more accurately predicted by using both
+ :math:`X` and :math:`Z` as covariates as opposed to only using
+ :math:`Z` as a covariate. Likewise, if :math:`X \perp \!\!\! \perp Y \mid Z`,
+ then :math:`Y` should be predicted just as accurately by solely
+ using :math:`X` or soley using :math:`Z` :footcite:p:`chalupka2018FastConditionalIndependence`.
+ Thus, the test works by using a regressor (the default is decision tree) to
+ to predict input :math:`Y` using both :math:`X` and :math:`Z` and using
+ only :math:`Z` :footcite:p:`chalupka2018FastConditionalIndependence`. Then,
+ accuracy of both predictions are measured via mean-squared error (MSE).
+ :math:`X \perp \!\!\! \perp Y \mid Z` if and only if MSE of the algorithm
+ using both :math:`X` and :math:`Z` is not smaller than the MSE of the
+ algorithm trained using only :math:`Z` :footcite:p:`chalupka2018FastConditionalIndependence`.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(
+ self,
+ model=DecisionTreeRegressor(),
+ cv_grid={"min_samples_split": [2, 8, 64, 512, 1e-2, 0.2, 0.4]},
+ num_perm=8,
+ prop_test=0.1,
+ discrete=(False, False),
+ ):
+
+ self.model = model
+ self.cv_grid = cv_grid
+ self.num_perm = num_perm
+ self.prop_test = prop_test
+ self.discrete = discrete
+ ConditionalIndependenceTest.__init__(self)
+
+ def statistic(self, x, y, z=None):
+ r"""
+ Calculates the FCIT test statistic.
+
+ Parameters
+ ----------
+ x,y,z : ndarray of float
+ Input data matrices.
+
+ Returns
+ -------
+ stat : float
+ The computed FCIT test statistic.
+ two_sided: float
+ Two-sided p-value associated with test statistic
+ """
+
+ n_samples = x.shape[0]
+ n_test = int(n_samples * self.prop_test)
+
+ data_permutations = [
+ np.random.permutation(x.shape[0]) for i in range(self.num_perm)
+ ]
+
+ clf = _cross_val(x, y, z, self.cv_grid, self.model, prop_test=self.prop_test)
+ datadict = {
+ "x": x,
+ "y": y,
+ "z": z,
+ "data_permutation": data_permutations,
+ "n_test": n_test,
+ "reshuffle": False,
+ "clf": clf,
+ }
+ d1_stats = np.array(
+ joblib.Parallel(n_jobs=-1, max_nbytes=100e6)(
+ joblib.delayed(_obtain_error)((datadict, i))
+ for i in range(self.num_perm)
+ )
+ )
+
+ if z.shape[1] == 0:
+ x_indep_y = x[np.random.permutation(n_samples)]
+ else:
+ x_indep_y = np.empty([x.shape[0], 0])
+
+ clf = _cross_val(
+ x_indep_y, y, z, self.cv_grid, self.model, prop_test=self.prop_test
+ )
+
+ datadict["reshuffle"] = True
+ datadict["x"] = x_indep_y
+ d0_stats = np.array(
+ joblib.Parallel(n_jobs=-1, max_nbytes=100e6)(
+ joblib.delayed(_obtain_error)((datadict, i))
+ for i in range(self.num_perm)
+ )
+ )
+
+ stat, two_sided = ttest_1samp(d0_stats / d1_stats, 1)
+
+ return stat, two_sided
+
+ def test(self, x, y, z=None):
+ r"""
+ Calculates the FCIT test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y,z : ndarray of float
+ Input data matrices.
+
+ Returns
+ -------
+ stat : float
+ The computed FCIT statistic.
+ pvalue : float
+ The computed FCIT p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.conditional import FCIT
+ >>> from sklearn.tree import DecisionTreeRegressor
+ >>> np.random.seed(1234)
+ >>> dim = 2
+ >>> n = 100000
+ >>> z1 = np.random.multivariate_normal(mean=np.zeros(dim), cov=np.eye(dim), size=(n))
+ >>> A1 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+ >>> B1 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+ >>> x1 = (A1 @ z1.T + np.random.multivariate_normal(mean=np.zeros(dim), cov=np.eye(dim), size=(n)).T)
+ >>> y1 = (B1 @ z1.T + np.random.multivariate_normal(mean=np.zeros(dim), cov=np.eye(dim), size=(n)).T)
+ >>> model = DecisionTreeRegressor()
+ >>> cv_grid = {"min_samples_split": [2, 8, 64, 512, 1e-2, 0.2, 0.4]}
+ >>> stat, pvalue = FCIT(model=model, cv_grid=cv_grid).test(x1.T, y1.T, z1)
+ >>> '%.2f, %.3f' % (stat, pvalue)
+ '-3.59, 0.995'
+ """
+
+ n_samples = x.shape[0]
+
+ if z is None:
+ z = np.empty([n_samples, 0])
+
+ if self.discrete[0] and not self.discrete[1]:
+ x, y = y, x
+ elif x.shape[1] < y.shape[1]:
+ x, y = y, x
+
+ y = StandardScaler().fit_transform(y)
+
+ stat, two_sided = self.statistic(x, y, z)
+
+ if stat < 0:
+ pvalue = 1 - two_sided / 2
+ else:
+ pvalue = two_sided / 2
+
+ return ConditionalIndependenceTestOutput(stat, pvalue)
+
+
+def _cross_val(x, y, z, cv_grid, model, prop_test):
+ """
+ Choose the regression hyperparameters by
+ cross-validation.
+ """
+
+ splitter = ShuffleSplit(n_splits=3, test_size=prop_test)
+ cv = GridSearchCV(estimator=model, cv=splitter, param_grid=cv_grid, n_jobs=-1)
+ cv.fit(_interleave(x, z), y)
+
+ return type(model)(**cv.best_params_)
+
+
+def _interleave(x, z, seed=None):
+ """Interleave x and z dimension-wise."""
+ state = np.random.get_state()
+ np.random.seed(seed or int(time.time()))
+ total_ids = np.random.permutation(x.shape[1] + z.shape[1])
+ np.random.set_state(state)
+ out = np.zeros([x.shape[0], x.shape[1] + z.shape[1]])
+ out[:, total_ids[: x.shape[1]]] = x
+ out[:, total_ids[x.shape[1] :]] = z
+ return out
+
+
+def _obtain_error(data_and_i):
+ """
+ A function used for multithreaded computation of the fcit test statistic.
+ Calculates MSE error for both trained regressors.
+ """
+ data, i = data_and_i
+ x = data["x"]
+ y = data["y"]
+ z = data["z"]
+ if data["reshuffle"]:
+ perm_ids = np.random.permutation(x.shape[0])
+ else:
+ perm_ids = np.arange(x.shape[0])
+ data_permutation = data["data_permutation"][i]
+ n_test = data["n_test"]
+ clf = data["clf"]
+
+ x_z = _interleave(x[perm_ids], z, seed=i)
+
+ clf.fit(x_z[data_permutation][n_test:], y[data_permutation][n_test:])
+ return mse(
+ y[data_permutation][:n_test], clf.predict(x_z[data_permutation][:n_test])
+ )
diff --git a/hyppo/conditional/__init__.py b/hyppo/conditional/__init__.py
new file mode 100644
index 000000000..ff876ea3a
--- /dev/null
+++ b/hyppo/conditional/__init__.py
@@ -0,0 +1,7 @@
+from .FCIT import FCIT
+from .kci import KCI
+
+
+__all__ = [s for s in dir()] # add imported tests to __all__
+
+COND_INDEP_TESTS = {"fcit": FCIT, "kci": KCI}
diff --git a/hyppo/conditional/base.py b/hyppo/conditional/base.py
new file mode 100644
index 000000000..102e24aff
--- /dev/null
+++ b/hyppo/conditional/base.py
@@ -0,0 +1,51 @@
+from abc import ABC, abstractmethod
+from typing import NamedTuple
+
+
+class ConditionalIndependenceTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+
+
+class ConditionalIndependenceTest(ABC):
+ """
+ A base class for a conditional independence test.
+
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ @abstractmethod
+ def statistic(self, x, y, z):
+ r"""
+ Calculates the conditional independence test statistic.
+
+ Parameters
+ ----------
+ x,y,z : ndarray of float
+ Input data matrices.
+
+ Returns
+ -------
+ stat : float
+ The computed conditional independence test statistic.
+ """
+
+ @abstractmethod
+ def test(self, x, y, z):
+ r"""
+ Calculates the conditional independence test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y,z : ndarray of float
+ Input data matrices.
+
+ Returns
+ -------
+ stat : float
+ The computed conditional independence test statistic.
+ pvalue : float
+ The computed conditional independence test p-value.
+ """
diff --git a/hyppo/conditional/kci.py b/hyppo/conditional/kci.py
new file mode 100644
index 000000000..97f874829
--- /dev/null
+++ b/hyppo/conditional/kci.py
@@ -0,0 +1,122 @@
+import numpy as np
+from scipy.stats import gamma
+from sklearn.gaussian_process import GaussianProcessClassifier
+from sklearn.gaussian_process.kernels import RBF
+
+from .base import ConditionalIndependenceTest, ConditionalIndependenceTestOutput
+
+
+class KCI(ConditionalIndependenceTest):
+ r"""
+ Kernel Conditional Independence Test Statistic and P-Value.
+
+ This is a conditional indpendence test utilizing a radial basis
+ function to calculate the kernels of two datasets. The trace
+ of the normalized matrix product is then calculated to extract the test
+ statistic. A Gaussian distribution is then utilized to calculate
+ the p-value given the statistic and approximate mean and variance
+ of the trace values of the independent kernel matrices.
+ This test is consistent against similar tests.
+
+ Notes
+ -----
+ Let :math:`x` be a combined sample of :math:`(n, p)` sample
+ of random variables :math:`X` and let :math:`y` be a :math:`(n, 1)`
+ labels of sample classes :math:`Y`. We can then generate
+ :math:`Kx` and :math:`Ky` kernel matrices for each of the respective
+ samples. Normalizing, multiplying, and taking the trace of these
+ kernel matrices gives the resulting test statistic.
+ The p-value and null distribution for the corrected statistic are calculated a
+ gamma distribution approximation.
+ """
+
+ def __init__(self, **kwargs):
+
+ ConditionalIndependenceTest.__init__(self, **kwargs)
+
+ def compute_kern(self, x, y):
+
+ T = len(y)
+
+ x = np.array(x)
+ y = np.array(y)
+ x = x - np.mean(x)
+ x = x / np.std(x)
+ y = y - np.mean(y)
+ y = y / np.std(y)
+
+ if T < 200:
+ width = 0.8
+ elif T < 1200:
+ width = 0.5
+ else:
+ width = 0.3
+
+ theta = 1 / (width**2)
+
+ Kx = 1.0 * RBF(theta).__call__(x, x)
+ Ky = 1.0 * RBF(theta).__call__(y, y)
+
+ return Kx, Ky
+
+ def statistic(self, x, y):
+
+ T = len(y)
+
+ H = np.eye(T) - np.ones((T, T)) / T
+
+ Kx, Ky = self.compute_kern(x, y)
+
+ Kx = (H @ Kx) @ H
+ Ky = (H @ Ky) @ H
+
+ stat = np.trace(Kx @ Ky)
+
+ return stat
+
+ def test(self, x, y):
+ r"""
+ Calculates the Kernel Conditional Independence test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ columns. That is, the shapes must be ``(n, p)`` and ``(n, 1)`` where
+ `n` is the dimension of samples and `p` is the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed Kernel Conditional Independence statistic.
+ pvalue : float
+ The computed Kernel Conditional Independence p-value.
+
+ Example
+ --------
+ >>> from hyppo.conditional import KCI
+ >>> from hyppo.tools.indep_sim import linear
+ >>> np.random.seed(123456789)
+ >>> x, y = linear(n, 1)
+ >>> stat, pvalue = KCI().test(x, y)
+ >>> print("Statistic: ", stat)
+ >>> print("p-value: ", pvalue)
+ """
+
+ T = len(y)
+
+ Kx, Ky = self.compute_kern(x, y)
+ stat = self.statistic(x, y)
+
+ mean_appr = (np.trace(Kx) * np.trace(Ky)) / T
+ var_appr = (
+ 2 * np.trace(Kx @ Kx) * np.trace(Ky @ Ky) / T**2
+ )
+ k_appr = mean_appr**2 / var_appr
+ theta_appr = var_appr / mean_appr
+ pvalue = 1 - np.mean(gamma.cdf(stat, k_appr, theta_appr))
+
+ self.stat = stat
+
+ return ConditionalIndependenceTestOutput(stat, pvalue)
diff --git a/hyppo/conditional/tests/__init__.py b/hyppo/conditional/tests/__init__.py
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/hyppo/conditional/tests/__init__.py
@@ -0,0 +1 @@
+
diff --git a/hyppo/conditional/tests/test_FCIT.py b/hyppo/conditional/tests/test_FCIT.py
new file mode 100644
index 000000000..e05eebccf
--- /dev/null
+++ b/hyppo/conditional/tests/test_FCIT.py
@@ -0,0 +1,89 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from hyppo.tools import rot_ksamp
+from hyppo.conditional import FCIT
+
+
+class TestFCIT:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [
+ (2000, 11.677197, 3.8168e-06),
+ (1000, 7.733, 5.6549e-05),
+ ],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ np.random.seed(123456789)
+ stat, pvalue = FCIT().test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=-1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=4)
+
+ @pytest.mark.parametrize(
+ "dim, n, obs_stat, obs_pvalue",
+ [(1, 100000, -0.16024, 0.56139), (2, 100000, -4.59882, 0.99876)],
+ )
+ def test_null(self, dim, n, obs_stat, obs_pvalue):
+ np.random.seed(12)
+ z1 = np.random.multivariate_normal(
+ mean=np.zeros(dim), cov=np.eye(dim), size=(n)
+ )
+ A1 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+ B1 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+ x1 = (
+ A1 @ z1.T
+ + np.random.multivariate_normal(
+ mean=np.zeros(dim), cov=np.eye(dim), size=(n)
+ ).T
+ )
+ y1 = (
+ B1 @ z1.T
+ + np.random.multivariate_normal(
+ mean=np.zeros(dim), cov=np.eye(dim), size=(n)
+ ).T
+ )
+
+ np.random.seed(122)
+ stat, pvalue = FCIT().test(x1.T, y1.T, z1)
+
+ assert_almost_equal(pvalue, obs_pvalue, decimal=4)
+ assert_almost_equal(stat, obs_stat, decimal=4)
+
+ @pytest.mark.parametrize(
+ "dim, n, obs_stat, obs_pvalue",
+ [
+ (1, 100000, 89.271754, 2.91447597e-12),
+ (2, 100000, 161.35165, 4.63412957e-14),
+ ],
+ )
+ def test_alternative(self, dim, n, obs_stat, obs_pvalue):
+ np.random.seed(12)
+ z2 = np.random.multivariate_normal(
+ mean=np.zeros(dim), cov=np.eye(dim), size=(n)
+ )
+
+ A2 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+ B2 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+
+ x2 = (
+ A2 @ z2.T
+ + np.random.multivariate_normal(
+ mean=np.zeros(dim), cov=np.eye(dim), size=(n)
+ ).T
+ )
+ y2 = (
+ B2 @ x2
+ + np.random.multivariate_normal(
+ mean=np.zeros(dim), cov=np.eye(dim), size=(n)
+ ).T
+ )
+
+ np.random.seed(122)
+ stat, pvalue = FCIT().test(x2.T, y2.T, z2)
+
+ assert_almost_equal(pvalue, obs_pvalue, decimal=12)
+ assert_almost_equal(stat, obs_stat, decimal=4)
diff --git a/hyppo/conditional/tests/test_kci.py b/hyppo/conditional/tests/test_kci.py
new file mode 100644
index 000000000..60cb21015
--- /dev/null
+++ b/hyppo/conditional/tests/test_kci.py
@@ -0,0 +1,39 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises, assert_warns
+
+from ...tools import linear, logarithmic, power
+from .. import KCI
+
+
+class TestKCI:
+ @pytest.mark.parametrize("n", [100])
+ @pytest.mark.parametrize("obs_stat", [544.691])
+ @pytest.mark.parametrize("obs_pvalue", [0])
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = linear(n, 1)
+ stat1, pvalue1 = KCI().test(x, y)
+
+ assert_almost_equal(stat1, obs_stat, decimal=2)
+ assert_almost_equal(pvalue1, obs_pvalue, decimal=2)
+
+ @pytest.mark.parametrize("n", [100])
+ @pytest.mark.parametrize("obs_stat", [88.526])
+ @pytest.mark.parametrize("obs_pvalue", [.00027])
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = logarithmic(n, 1)
+ stat1, pvalue1 = KCI().test(x, y)
+
+ assert_almost_equal(stat1, obs_stat, decimal=2)
+ assert_almost_equal(pvalue1, obs_pvalue, decimal=2)
+
+ @pytest.mark.parametrize("n", [100, 200])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ stat1, pvalue1 = KCI().test(x, y)
+ stat2, pvalue2 = KCI().test(x, y)
+
+ assert stat1 == stat2
+ assert pvalue1 == pvalue2
diff --git a/hyppo/d_variate/__init__.py b/hyppo/d_variate/__init__.py
new file mode 100644
index 000000000..c4da0383d
--- /dev/null
+++ b/hyppo/d_variate/__init__.py
@@ -0,0 +1,7 @@
+from .dhsic import dHsic
+
+__all__ = [s for s in dir()] # add imported tests to __all__
+
+MULTI_TESTS = {
+ "dhsic": dHsic,
+}
diff --git a/hyppo/d_variate/_utils.py b/hyppo/d_variate/_utils.py
new file mode 100644
index 000000000..bbb8b2810
--- /dev/null
+++ b/hyppo/d_variate/_utils.py
@@ -0,0 +1,68 @@
+import numpy as np
+
+from sklearn.utils import check_array
+from ..tools import check_reps, contains_nan
+
+
+class _CheckInputs:
+ """Checks inputs for d_variate independence tests"""
+
+ def __init__(self, *args, reps=None):
+ self.args = args
+ self.reps = reps
+
+ def __call__(self):
+ for mat in self.args:
+ check_array(mat)
+ contains_nan(mat)
+ self._check_dim(mat)
+ self._convert_float64(mat)
+ self._check_min_samples(mat)
+ self._check_variance(mat)
+
+ if self.reps:
+ check_reps(self.reps)
+
+ return self.args
+
+ def _check_dim(self, mat):
+ """Converts input data matrices to proper dimensions"""
+ if mat.ndim == 1:
+ mat = mat[:, np.newaxis]
+ elif mat.ndim != 2:
+ raise ValueError(
+ "Expected a 2-D array, found shape " "{}".format(mat.shape)
+ )
+
+ self._check_nd_indeptest(mat)
+
+ return mat
+
+ def _convert_float64(self, mat):
+ """Converts input data matrices to np.float 64 (if not already done)"""
+ mat = np.asarray(mat).astype(np.float64)
+
+ return mat
+
+ def _check_nd_indeptest(self, mat):
+ """Check if number of samples is the same"""
+ samples = []
+ n, _ = mat.shape
+ samples.append(n)
+ if any(sample != samples[0] for sample in samples):
+ raise ValueError(
+ "Shape mismatch, all input data matrices must have shape "
+ "[n, p] and [n, q]."
+ )
+
+ def _check_min_samples(self, mat):
+ """Check if the number of samples is at least 3"""
+ samples = []
+ n = mat.shape[0]
+ samples.append(n)
+ if any(sample <= 2 for sample in samples):
+ raise ValueError("Number of samples is too low. Must be at least 3.")
+
+ def _check_variance(self, mat):
+ if np.var(mat) == 0:
+ raise ValueError("Test cannot be run, one of the inputs has 0 variance")
diff --git a/hyppo/d_variate/base.py b/hyppo/d_variate/base.py
new file mode 100644
index 000000000..33b0fa414
--- /dev/null
+++ b/hyppo/d_variate/base.py
@@ -0,0 +1,99 @@
+from abc import ABC, abstractmethod
+from typing import NamedTuple
+
+from ..tools import multi_perm_test
+
+
+class DVariateTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+
+
+class DVariateTest(ABC):
+ r"""
+ A base class for a :math:`d`-variate independence test.
+
+ Parameters
+ ----------
+ compute_kernel : str, callable, or None, default: "gaussian"
+ A function that computes the kernel similarity among the samples within each
+ data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ Set to ``None`` or ``"precomputed"`` if ``args`` are already similarity
+ matrices. To call a custom function, either create the similarity matrix
+ before-hand or create a function of the form :func:`metric(x, **kwargs)`
+ where ``x`` is the data matrix for which pairwise kernel similarity matrices are
+ calculated and kwargs are extra arguments to send to your custom function.
+ **kwargs
+ Arbitrary keyword arguments for ``multi_compute_kern``.
+ """
+
+ def __init__(self, compute_kernel=None, **kwargs):
+ # set statistic and p-value
+ self.stat = None
+ self.pvalue = None
+ self.compute_kernel = compute_kernel
+ self.kwargs = kwargs
+
+ super().__init__()
+
+ @abstractmethod
+ def statistic(self, *args):
+ r"""
+ Calculates the :math:`d`-variate independence test statistic.
+
+ Parameters
+ ----------
+ *args: ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples. That is, the shapes must be ``(n, p)``, ``(n, q)``,
+ etc., where `n` is the number of samples and `p` and `q` are the
+ number of dimensions.
+ """
+
+ @abstractmethod
+ def test(self, *args, reps=1000, workers=1):
+ r"""
+ Calculates the d_variate independence test statistic and p-value.
+
+ Parameters
+ ----------
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples. That is, the shapes must be ``(n, p)``, ``(n, q)``,
+ etc., where `n` is the number of samples and `p` and `q` are the
+ number of dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+
+ Returns
+ -------
+ stat : float
+ The computed :math:`d`-variate independence test statistic.
+ pvalue : float
+ The computed :math:`d`-variate independence p-value.
+ """
+ self.args = args
+
+ stat, pvalue, null_dist = multi_perm_test(
+ self.statistic,
+ *args,
+ reps=reps,
+ workers=workers,
+ )
+ self.stat = stat
+ self.pvalue = pvalue
+ self.null_dist = null_dist
+
+ return DVariateTestOutput(stat, pvalue)
diff --git a/hyppo/d_variate/dhsic.py b/hyppo/d_variate/dhsic.py
new file mode 100644
index 000000000..01a63a24c
--- /dev/null
+++ b/hyppo/d_variate/dhsic.py
@@ -0,0 +1,147 @@
+from .base import DVariateTest, DVariateTestOutput
+from ..tools import multi_compute_kern
+from ._utils import _CheckInputs
+
+import numpy as np
+
+
+class dHsic(DVariateTest):
+ r"""
+ :math:`d`-variate Hilbert Schmidt Independence Criterion (dHsic) test
+ statistic and p-value.
+
+ dHsic is a non-parametric kernel-based independence test between an
+ arbitrary number of variables. The dHsic statistic is 0 if the variables
+ are jointly independent and positive if the variables are dependent
+ :footcite:p:`grettonKernelJointIndependence2016`.
+ The default choice is the Gaussian kernel, which uses the median distance
+ as the bandwidth, which is a characteristic kernel that guarantees that
+ dHsic is a consistent test
+ :footcite:p:`grettonKernelJointIndependence2016`
+ :footcite:p:`grettonKernelStatisticalTest2007`
+ :footcite:p:`grettonConsistentNonparametricTests2010`.
+
+ Parameters
+ ----------
+ compute_kernel : str, callable, or None, default: "gaussian"
+ A function that computes the kernel similarity among the samples within each
+ data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ Set to ``None`` or ``"precomputed"`` if ``args`` are already similarity
+ matrices. To call a custom function, either create the similarity matrix
+ before-hand or create a function of the form :func:`metric(x, **kwargs)`
+ where ``x`` is the data matrix for which pairwise kernel similarity matrices are
+ calculated and kwargs are extra arguments to send to your custom function.
+ bias : bool, default: False
+ Whether or not to use the biased or unbiased test statistics.
+ **kwargs
+ Arbitrary keyword arguments for ``multi_compute_kern``.
+
+ Notes
+ -----
+ The statistic can be derived as follows
+ :footcite:p:`grettonKernelJointIndependence2016`:
+
+ dHsic builds on the two-variable Hilbert Schmidt Independence Criterion (Hsic),
+ implemented in :class:`hyppo.independence.Hsic`, but allows for an arbitrary
+ number of variables. For a given kernel, the joint distribution and the product
+ of the marginals is mapped to the reproducing kernel Hilbert space and the
+ squared distance between the embeddings is calculated. The dHsic statistic can
+ be calculated by,
+
+ .. math::
+
+ \mathrm{dHsic} (\mathbb{P}^{(X^1, ..., X^d)}) = \Big\Vert \Pi(\mathbb{P}^{X^1}
+ \otimes \cdot\cdot\cdot \otimes \mathbb{P}^{X^d}) - \Pi(\mathbb{P}^
+ {(X^1, ..., X^d)}) \Big\Vert^2_{\mathscr{H}}
+
+ Similar to Hsic, dHsic uses a gaussian median kernel by default, and the p-value
+ is calculated using a permutation test using :meth:`hyppo.tools.multi_perm_test`.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, compute_kernel="gaussian", bias=True, **kwargs):
+ self.compute_kernel = compute_kernel
+ self.bias = bias
+
+ DVariateTest.__init__(self, compute_kernel=self.compute_kernel, **kwargs)
+
+ def statistic(self, *args):
+ """
+ Helper function that calculates the dHsic test statistic.
+
+ Parameters
+ ----------
+ *args: ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples. That is, the shapes must be ``(n, p)``, ``(n, q)``,
+ etc., where `n` is the number of samples and `p` and `q` are the
+ number of dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed dHsic statistic.
+ """
+ kerns = multi_compute_kern(*args, metric=self.compute_kernel, **self.kwargs)
+
+ n = kerns[0].shape[0]
+ term1 = np.ones((n, n))
+ term2 = 1
+ term3 = (2 / n) * np.ones((n,))
+ for kern in kerns:
+ term1 = np.multiply(term1, kern)
+ term2 = (1 / n**2) * term2 * np.sum(kern)
+ term3 = (1 / n) * np.multiply(term3, np.sum(kern, axis=1))
+
+ stat = (1 / n**2) * np.sum(term1) + term2 - np.sum(term3)
+ self.stat = stat
+
+ return stat
+
+ def test(self, *args, reps=1000, workers=1):
+ """
+ Calculates the dHsic test statistic and p-value.
+
+ Parameters
+ ----------
+ *args: ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples. That is, the shapes must be ``(n, p)``, ``(n, q)``,
+ etc., where `n` is the number of samples and `p` and `q` are the
+ number of dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+
+ Returns
+ -------
+ stat : float
+ The computed dHsic statistic.
+ pvalue : float
+ The computed dHsic p-value.
+ """
+ check_input = _CheckInputs(
+ *args,
+ reps=reps,
+ )
+ args = check_input()
+
+ stat, pvalue = super(dHsic, self).test(*args, reps=reps, workers=workers)
+ self.stat = stat
+ self.pvalue = pvalue
+
+ return DVariateTestOutput(stat, pvalue)
diff --git a/hyppo/d_variate/tests/__init__.py b/hyppo/d_variate/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/hyppo/d_variate/tests/test_dhsic.py b/hyppo/d_variate/tests/test_dhsic.py
new file mode 100644
index 000000000..a734a04a2
--- /dev/null
+++ b/hyppo/d_variate/tests/test_dhsic.py
@@ -0,0 +1,33 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from ...tools import linear, power
+from .. import dHsic # type: ignore
+
+
+class TestdHsicStat:
+ @pytest.mark.parametrize("n, obs_stat", [(100, 0.04561), (200, 0.03911)])
+ @pytest.mark.parametrize("obs_pvalue", [1 / 1000])
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = linear(n, 1)
+ stat, pvalue = dHsic(gamma=0.5).test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=2)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+
+
+class TestdHsicTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "dhsic",
+ sim_type="multi",
+ sim="multimodal_independence",
+ n=100,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/discrim/_utils.py b/hyppo/discrim/_utils.py
index b9b0fb29f..15f0f8d78 100644
--- a/hyppo/discrim/_utils.py
+++ b/hyppo/discrim/_utils.py
@@ -1,13 +1,7 @@
import numpy as np
-
from sklearn.metrics import pairwise_distances
-from ..tools import (
- contains_nan,
- check_ndarray_xy,
- convert_xy_float64,
- check_reps,
-)
+from ..tools import check_ndarray_xy, check_reps, contains_nan, convert_xy_float64
class _CheckInputs:
diff --git a/hyppo/discrim/base.py b/hyppo/discrim/base.py
index 8ac4655c6..efe3fefef 100644
--- a/hyppo/discrim/base.py
+++ b/hyppo/discrim/base.py
@@ -1,10 +1,11 @@
from abc import ABC, abstractmethod
+
import numpy as np
class DiscriminabilityTest(ABC):
r"""
- A base class for a Discriminability test.
+ A base class for a discriminability test.
"""
def __init__(self):
@@ -12,47 +13,21 @@ def __init__(self):
super().__init__()
@abstractmethod
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Calulates the independence test statistic.
Parameters
----------
- x, y : ndarray
+ x, y : ndarray of float
Input data matrices.
"""
- rdfs = self._discr_rdf(x, y)
+ rdfs = _discr_rdf(x, y)
stat = np.nanmean(rdfs)
return stat
- def _discr_rdf(self, dissimilarities, labels):
- # calculates test statistics distribution
- rdfs = []
-
- for i, label in enumerate(labels):
- di = dissimilarities[i]
-
- # All other samples except its own label
- idx = labels == label
- Dij = di[~idx]
-
- # All samples except itself
- idx[i] = False
- Dii = di[idx]
-
- rdf = [
- 1 - ((Dij < d).sum() + 0.5 * (Dij == d).sum()) / Dij.size for d in Dii
- ]
- rdfs.append(rdf)
-
- out = np.full((len(rdfs), max(map(len, rdfs))), np.nan)
- for i, rdf in enumerate(rdfs):
- out[i, : len(rdf)] = rdf
-
- return out
-
def _perm_stat(self, index):
r"""
Helper function that is used to calculate parallel permuted test
@@ -70,8 +45,47 @@ def _perm_stat(self, index):
"""
@abstractmethod
- def test(self):
+ def test(self, *args, **kwargs):
r"""
- Calculates the test statistic and p-value for Discriminability one sample
- and two sample test.
+ Calculates the Discriminability test statistic and p-value.
"""
+
+
+def _discr_rdf(dissimilarities, labels):
+ """
+ Calculates the distributions of the test statistics.
+
+ Parameters
+ ----------
+ dissimilarities : ndarray of float
+ Input matrix containing the disimilarities.
+ labels : ndarray of float
+ The label matrix corresponding to each dissimilarity.
+
+ Returns
+ -------
+ [type]
+ [description]
+ """
+ # calculates test statistics distribution
+ rdfs = []
+
+ for i, label in enumerate(labels):
+ di = dissimilarities[i]
+
+ # All other samples except its own label
+ idx = labels == label
+ Dij = di[~idx]
+
+ # All samples except itself
+ idx[i] = False
+ Dii = di[idx]
+
+ rdf = [1 - ((Dij < d).sum() + 0.5 * (Dij == d).sum()) / Dij.size for d in Dii]
+ rdfs.append(rdf)
+
+ out = np.full((len(rdfs), max(map(len, rdfs))), np.nan)
+ for i, rdf in enumerate(rdfs):
+ out[i, : len(rdf)] = rdf
+
+ return out
diff --git a/hyppo/discrim/discrim_one_samp.py b/hyppo/discrim/discrim_one_samp.py
index 590b1d3a2..407c82341 100644
--- a/hyppo/discrim/discrim_one_samp.py
+++ b/hyppo/discrim/discrim_one_samp.py
@@ -1,34 +1,30 @@
-from ._utils import _CheckInputs
+from typing import NamedTuple
+
import numpy as np
-from .base import DiscriminabilityTest
from scipy._lib._util import MapWrapper
+from ._utils import _CheckInputs
+from .base import DiscriminabilityTest
+from sklearn.utils import check_random_state
+
+
+class DiscrimOneSampleTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+ null_dist: float
class DiscrimOneSample(DiscriminabilityTest):
r"""
- A class that performs a one sample test of discriminability.
+
+ One Sample Discriminability test statistic and p-value.
Discriminability index is a measure of whether a data acquisition and
preprocessing pipeline is more discriminable among different subjects.
The key insight is that each repeated mesurements of the same item should
be the more similar to one another than measurements between different
items. The one sample test measures whether the discriminability
- for a dataset differs from random chance. More details are in [#1Dscr]_.
+ for a dataset differs from random chance. More details are in `[1]`_.
- Parameters
- ----------
- is_dist : bool, optional (default: False)
- Whether `x1` and `x2` are distance matrices or not.
- remove_isolates : bool, optional (default: True)
- Whether to remove the measurements with a single instance or not.
-
- See Also
- --------
- DiscrimTwoSample : Two sample test for discriminability of a two different
- measurements.
-
- Notes
- -----
With :math:`D_x` as the sample discriminability of :math:`x`,
one sample test performs the following test,
@@ -39,10 +35,14 @@ class DiscrimOneSample(DiscriminabilityTest):
where :math:`D_0` is the discriminability that would be observed by random chance.
- References
+ .. _[1]: https://www.biorxiv.org/content/10.1101/802629v1
+
+ Parameters
----------
- .. [#1Dscr] Eric W. Bridgeford, et al. "Optimal Decisions for Reference
- Pipelines and Datasets: Applications in Connectomics." Bioarxiv (2019).
+ is_dist : bool, default: False
+ Whether inputs are distance matrices.
+ remove_isolates : bool, default: True
+ Whether to remove the measurements with a single instance or not.
"""
def __init__(self, is_dist=False, remove_isolates=True):
@@ -51,47 +51,48 @@ def __init__(self, is_dist=False, remove_isolates=True):
self.remove_isolates = remove_isolates
DiscriminabilityTest.__init__(self)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
"""
Helper function that calculates the discriminability test statistics.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
stat : float
The computed two sample discriminability statistic.
"""
- stat = super(DiscrimOneSample, self)._statistic(x, y)
+ stat = super(DiscrimOneSample, self).statistic(x, y)
return stat
- def test(self, x, y, reps=1000, workers=-1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the test statistic and p-value for Discriminability one sample test.
Parameters
----------
- x : ndarray
- Input data matrices. `x` must have shape `(n, p)` `n` is the number of
- samples and `p` are the number of dimensions. Alternatively, `x` can be
- distance matrices, where the shape must be `(n, n)`, and ``is_dist`` must
+ x : ndarray of float
+ Input data matrices. ``x`` must have shape ``(n, p)`` where `n` is the
+ number of
+ samples and `p` are the number of dimensions. Alternatively, ``x`` can be
+ distance matrices, where the shape must be ``(n, n)``, and ``is_dist`` must
set to ``True`` in this case.
- y : ndarray
- A vector containing the sample ids for our :math:`n` samples.
- reps : int, optional (default: 1000)
+ y : ndarray of float
+ A vector containing the sample ids for our `n` samples.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: -1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
@@ -122,12 +123,13 @@ def test(self, x, y, reps=1000, workers=-1):
self.x = np.asarray(x[0])
self.y = y
- stat = self._statistic(self.x, self.y)
+ stat = self.statistic(self.x, self.y)
self.stat = stat
+ random_state = np.random.randint(np.iinfo(np.int32).max, size=reps)
# use all cores to create function that parallelizes over number of reps
mapwrapper = MapWrapper(workers)
- null_dist = np.array(list(mapwrapper(self._perm_stat, range(reps))))
+ null_dist = np.array(list(mapwrapper(self._perm_stat, random_state)))
self.null_dist = null_dist
# calculate p-value and significant permutation map through list
@@ -135,9 +137,9 @@ def test(self, x, y, reps=1000, workers=-1):
self.pvalue_ = pvalue
- return stat, pvalue
+ return DiscrimOneSampleTestOutput(stat, pvalue, null_dist)
- def _perm_stat(self, index): # pragma: no cover
+ def _perm_stat(self, index, random_state=None): # pragma: no cover
r"""
Helper function that is used to calculate parallel permuted test
statistics.
@@ -152,8 +154,9 @@ def _perm_stat(self, index): # pragma: no cover
perm_stat : float
Test statistic for each value in the null distribution.
"""
- permy = np.random.permutation(self.y)
+ rng = check_random_state(random_state)
+ permy = rng.permutation(self.y)
- perm_stat = self._statistic(self.x, permy)
+ perm_stat = self.statistic(self.x, permy)
return perm_stat
diff --git a/hyppo/discrim/discrim_two_samp.py b/hyppo/discrim/discrim_two_samp.py
index 405d880c8..73f9993de 100644
--- a/hyppo/discrim/discrim_two_samp.py
+++ b/hyppo/discrim/discrim_two_samp.py
@@ -1,30 +1,27 @@
-from ._utils import _CheckInputs
+from typing import NamedTuple
+
import numpy as np
-from numba import njit
-from .base import DiscriminabilityTest
+from numba import jit
from scipy._lib._util import MapWrapper
+from ._utils import _CheckInputs
+from .base import DiscriminabilityTest
+from sklearn.utils import check_random_state
+
+
+class DiscrimTwoSampleTestOutput(NamedTuple):
+ d1: float
+ d2: float
+ pvalue: float
+
class DiscrimTwoSample(DiscriminabilityTest):
r"""
- A class that compares the discriminability of two datasets.
+ Two Sample Discriminability test statistic and p-value.
Two sample test measures whether the discriminability is different for
- one dataset compared to another. More details can be described in [#1Dscr]_.
-
- Parameters
- ----------
- is_dist : bool, optional (default: False)
- Whether `x1` and `x2` are distance matrices or not.
- remove_isolates : bool, optional (default: True)
- Whether to remove the measurements with a single instance or not.
+ one dataset compared to another. More details can be described in `[1]`_.
- See Also
- --------
- DiscrimOneSample : One sample test for discriminability of a single measurement
-
- Notes
- -----
Let :math:`\hat D_{x_1}` denote the sample discriminability of one approach,
and :math:`\hat D_{x_2}` denote the sample discriminability of another approach.
Then,
@@ -36,6 +33,15 @@ class DiscrimTwoSample(DiscriminabilityTest):
Alternatively, tests can be done for :math:`D_{x_1} < D_{x_2}` and
:math:`D_{x_1} \neq D_{x_2}`.
+
+ .. _[1]: https://www.biorxiv.org/content/10.1101/802629v1
+
+ Parameters
+ ----------
+ is_dist : bool, default: False
+ Whether `x1` and `x2` are distance matrices or not.
+ remove_isolates : bool, default: True
+ Whether to remove the measurements with a single instance or not.
"""
def __init__(self, is_dist=False, remove_isolates=True):
@@ -43,13 +49,13 @@ def __init__(self, is_dist=False, remove_isolates=True):
self.remove_isolates = remove_isolates
DiscriminabilityTest.__init__(self)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
"""
Helper function that calculates the discriminability test statistic.
Parameters
----------
- x, y : ndarray
+ x, y : ndarray of float
Input data matrices. `x` and `y` must have the same number of
samples. That is, the shapes must be `(n, p)` and `(n, q)` where
`n` is the number of samples and `p` and `q` are the number of
@@ -61,25 +67,25 @@ def _statistic(self, x, y):
stat : float
The computed two sample discriminability statistic.
"""
- stat = super(DiscrimTwoSample, self)._statistic(x, y)
+ stat = super(DiscrimTwoSample, self).statistic(x, y)
return stat
- def test(self, x1, x2, y, reps=1000, alt="neq", workers=-1):
+ def test(self, x1, x2, y, reps=1000, alt="neq", workers=-1, random_state=None):
r"""
Calculates the test statistic and p-value for a two sample test for
discriminability.
Parameters
----------
- x1, x2 : ndarray
+ x1, x2 : ndarray of float
Input data matrices. `x1` and `x2` must have the same number of
samples. That is, the shapes must be `(n, p)` and `(n, q)` where
`n` is the number of samples and `p` and `q` are the number of
dimensions. Alternatively, `x1` and `x2` can be distance matrices,
where the shapes must both be `(n, n)`, and ``is_dist`` must set
to ``True`` in this case.
- y : ndarray
+ y : ndarray of float
A vector containing the sample ids for our `n` samples. Should be matched
to the inputs such that ``y[i]`` is the corresponding label for
``x_1[i, :]`` and ``x_2[i, :]``.
@@ -110,7 +116,8 @@ def test(self, x1, x2, y, reps=1000, alt="neq", workers=-1):
>>> x1 = np.ones((100,2), dtype=float)
>>> x2 = np.concatenate([np.zeros((50, 2)), np.ones((50, 2))], axis=0)
>>> y = np.concatenate([np.zeros(50), np.ones(50)], axis=0)
- >>> '%.1f, %.1f, %.2f' % DiscrimTwoSample().test(x1, x2, y) # doctest: +SKIP
+ >>> discrim1, discrim2, pvalue = DiscrimTwoSample().test(x1, x2, y)
+ >>> '%.1f, %.1f, %.2f' % (discrim1, discrim2, pvalue)
'0.5, 1.0, 0.00'
"""
@@ -126,13 +133,14 @@ def test(self, x1, x2, y, reps=1000, alt="neq", workers=-1):
self.x2 = np.asarray(x[1])
self.y = y
- self.d1 = self._statistic(self.x1, y)
- self.d2 = self._statistic(self.x2, y)
+ self.d1 = self.statistic(self.x1, y)
+ self.d2 = self.statistic(self.x2, y)
self.da = self.d1 - self.d2
# use all cores to create function that parallelizes over number of reps
+ random_state = np.random.randint(np.iinfo(np.int32).max, size=reps)
mapwrapper = MapWrapper(workers)
- null_dist = np.array(list(mapwrapper(self._perm_stat, range(reps))))
+ null_dist = np.array(list(mapwrapper(self._perm_stat, random_state)))
self.diff_null = np.asarray(calculate_diff_null(null_dist, reps))
@@ -146,25 +154,25 @@ def test(self, x1, x2, y, reps=1000, alt="neq", workers=-1):
msg = "You have not entered a valid alternative."
raise ValueError(msg)
- # check if discrim is correct
if pvalue == 0:
pvalue = 1 / reps
self.pvalue = pvalue
- return self.d1, self.d2, self.pvalue
+ return DiscrimTwoSampleTestOutput(self.d1, self.d2, self.pvalue)
- def _get_convex_comb(self, x):
+ def _get_convex_comb(self, x, random_state=None): # pragma: no cover
"""Get random convex combination of input x."""
n, _ = x.shape
- q1 = np.random.choice(n, n)
- q2 = np.random.choice(n, n)
- lamda = np.random.uniform(size=n)
+ rng = check_random_state(random_state)
+ q1 = rng.choice(n, n)
+ q2 = rng.choice(n, n)
+ lamda = rng.uniform(size=n)
return (lamda * (x[q1]).T + (1 - lamda) * (x[q2]).T).T
- def _perm_stat(self, index): # pragma: no cover
+ def _perm_stat(self, index, random_state=None): # pragma: no cover
r"""
Helper function that is used to calculate parallel permuted test
statistics.
@@ -179,17 +187,17 @@ def _perm_stat(self, index): # pragma: no cover
perm_stat1, perm_stat2 : float
Test statistic for each value in the null distribution.
"""
- permx1 = self._get_convex_comb(self.x1)
- permx2 = self._get_convex_comb(self.x2)
+ permx1 = self._get_convex_comb(self.x1, random_state)
+ permx2 = self._get_convex_comb(self.x2, random_state)
- perm_stat1 = self._statistic(permx1, self.y)
- perm_stat2 = self._statistic(permx2, self.y)
+ perm_stat1 = self.statistic(permx1, self.y)
+ perm_stat2 = self.statistic(permx2, self.y)
return perm_stat1, perm_stat2
-@njit
-def calculate_diff_null(null_dist, reps):
+@jit(nopython=True, cache=True)
+def calculate_diff_null(null_dist, reps): # pragma: no cover
"""
Helper function to calculate the distribution of thedifference under
null.
diff --git a/hyppo/discrim/tests/test_discrim_one_samp.py b/hyppo/discrim/tests/test_discrim_one_samp.py
index ca657089e..680bffd62 100644
--- a/hyppo/discrim/tests/test_discrim_one_samp.py
+++ b/hyppo/discrim/tests/test_discrim_one_samp.py
@@ -1,10 +1,10 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_warns, assert_raises
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises, assert_warns
+
from .. import DiscrimOneSample
-@pytest.mark.skip(reason="reformat code to speed up test")
class TestOneSample:
def test_same_one(self):
# matches test calculated statistics and p-value for indiscriminable subjects
@@ -13,8 +13,8 @@ def test_same_one(self):
np.random.seed(123456789)
obs_stat = 0.5
- obs_p = 1
- stat, p = DiscrimOneSample().test(x, y)
+ obs_p = 1.0
+ stat, p, _ = DiscrimOneSample().test(x, y, reps=10)
assert_almost_equal(stat, obs_stat, decimal=2)
assert_almost_equal(p, obs_p, decimal=2)
@@ -26,8 +26,8 @@ def test_diff_one(self):
np.random.seed(123456789)
obs_stat = 1.0
- obs_p = 0.001
- stat, p = DiscrimOneSample().test(x, y)
+ obs_p = 0.0909090909090909
+ stat, p, _ = DiscrimOneSample().test(x, y, reps=10)
assert_almost_equal(stat, obs_stat, decimal=3)
assert_almost_equal(p, obs_p, decimal=3)
@@ -62,14 +62,6 @@ def test_error_reps(self, reps):
assert_raises(ValueError, DiscrimOneSample().test, x, y, reps=reps)
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x = np.ones((100, 2), dtype=float)
- y = np.concatenate((np.zeros(50), np.ones(50)), axis=0)
-
- reps = 100
- assert_warns(RuntimeWarning, DiscrimOneSample().test, x, y, reps=reps)
-
def test_min_sample(self):
# raises warning when sample number is less than 10
x = np.ones((6, 2), dtype=float)
diff --git a/hyppo/discrim/tests/test_discrim_two_samp.py b/hyppo/discrim/tests/test_discrim_two_samp.py
index ad5bb4dba..36cfdc2e1 100644
--- a/hyppo/discrim/tests/test_discrim_two_samp.py
+++ b/hyppo/discrim/tests/test_discrim_two_samp.py
@@ -1,10 +1,12 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_warns, assert_raises
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises, assert_warns
+
from .. import DiscrimTwoSample
-@pytest.mark.skip(reason="reformat code to speed up test")
+# TODO: timeout CircleCI, make discrim faster
+@pytest.mark.skip(reason="no way of currently testing this")
class TestTwoSample:
def test_greater(self):
# test whether discriminability for x1 is greater than it is for x2
@@ -15,12 +17,10 @@ def test_greater(self):
np.random.seed(123456789)
obs_d1 = 0.5
obs_d2 = 1.0
- obs_p = 1.0
- d1, d2, p = DiscrimTwoSample().test(x1, x2, y, alt="greater")
+ d1, d2, _ = DiscrimTwoSample().test(x1, x2, y, alt="greater", reps=3)
assert_almost_equal(d1, obs_d1, decimal=2)
assert_almost_equal(d2, obs_d2, decimal=2)
- assert_almost_equal(p, obs_p, decimal=2)
def test_less(self):
# test whether discriminability for x1 is less than it is for x2
@@ -31,12 +31,10 @@ def test_less(self):
np.random.seed(123456789)
obs_d1 = 0.5
obs_d2 = 1.0
- obs_p = 0.000999000999000999
- d1, d2, p = DiscrimTwoSample().test(x1, x2, y, alt="less")
+ d1, d2, _ = DiscrimTwoSample().test(x1, x2, y, alt="less", reps=3)
assert_almost_equal(d1, obs_d1, decimal=2)
assert_almost_equal(d2, obs_d2, decimal=2)
- assert_almost_equal(p, obs_p, decimal=4)
def test_neq(self):
# test whether discriminability for x1 is not equal compared to
@@ -48,77 +46,18 @@ def test_neq(self):
np.random.seed(123456789)
obs_d1 = 0.5
obs_d2 = 1.0
- obs_p = 0.000999000999000999
- d1, d2, p = DiscrimTwoSample().test(x1, x2, y, alt="neq")
+ d1, d2, _ = DiscrimTwoSample().test(x1, x2, y, alt="neq", reps=3)
assert_almost_equal(d1, obs_d1, decimal=2)
assert_almost_equal(d2, obs_d2, decimal=2)
- assert_almost_equal(p, obs_p, decimal=4)
-
-
-class TestTwoSampleWarn:
- """
- Tests errors and warnings derived from one sample test.
- """
-
- def test_error_one_id(self):
- # checks whether y has only one id
- x1 = np.ones((100, 2), dtype=float)
- x2 = np.concatenate((np.zeros((50, 2)), np.ones((50, 2))), axis=0)
- y = np.ones((100, 1))
-
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y)
-
- def test_error_unequal_row(self):
- # checks x1 & x2 for unequal number of instances
- x1 = np.ones((99, 2), dtype=float)
- x2 = np.concatenate((np.zeros((50, 2)), np.ones((50, 2))), axis=0)
- y = np.concatenate((np.zeros(50), np.ones(50)), axis=0)
-
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y)
-
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x1 = np.ones((100, 2), dtype=float)
- x2 = np.concatenate((np.zeros((50, 2)), np.ones((50, 2))), axis=0)
- y = np.concatenate((np.zeros(50), np.ones(50)), axis=0)
-
- x1[0] = np.nan
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y)
-
- x1[0] = 1
- x2[0] = np.nan
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y)
-
- x2[0] = 1
- y[0] = np.nan
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y)
-
- def test_error_alt(self):
- # raises error if inputs contain NaNs
- x1 = np.ones((100, 2), dtype=float)
- x2 = np.concatenate((np.zeros((50, 2)), np.ones((50, 2))), axis=0)
- y = np.concatenate((np.zeros(50), np.ones(50)), axis=0)
- x1[0] = np.nan
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y, alt="abc")
- @pytest.mark.parametrize(
- "reps", [-1, "1"] # reps is negative # reps is not integer
- )
- def test_error_reps(self, reps):
- # raises error if reps is negative
- x1 = np.ones((100, 2), dtype=float)
- x2 = np.concatenate((np.zeros((50, 2)), np.ones((50, 2))), axis=0)
- y = np.concatenate((np.zeros(50), np.ones(50)), axis=0)
-
- assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y, reps=reps)
-
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x1 = np.ones((100, 2), dtype=float)
- x2 = np.concatenate((np.zeros((50, 2)), np.ones((50, 2))), axis=0)
- y = np.concatenate((np.zeros(50), np.ones(50)), axis=0)
+class TestDiscrErrorWarn:
+ """Tests errors and warnings."""
- reps = 100
- assert_warns(RuntimeWarning, DiscrimTwoSample().test, x1, x2, y, reps=reps)
+ def test_no_indeptest(self):
+ # raises error if not indep test
+ x1 = np.arange(20)
+ x2 = np.arange(3, 23)
+ y = np.arange(5, 25)
+ assert_raises(ValueError, DiscrimTwoSample().test, x1, x2, y, alt="abcd")
diff --git a/hyppo/discrim/tests/test_utils.py b/hyppo/discrim/tests/test_utils.py
new file mode 100644
index 000000000..84cc50433
--- /dev/null
+++ b/hyppo/discrim/tests/test_utils.py
@@ -0,0 +1,38 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_raises
+
+from .._utils import _CheckInputs
+
+
+class TestErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_error_shape(self):
+ # raises error if number of samples different (n)
+ x = np.arange(100).reshape(25, 4)
+ y = x.reshape(10, 10)
+ assert_raises(ValueError, _CheckInputs(x, y))
+
+ def test_error_lowsamples(self):
+ # raises error if samples are low (< 3)
+ x = np.arange(2).reshape(-1, 1)
+ y = np.arange(2).reshape(-1, 1)
+ assert_raises(ValueError, _CheckInputs(x, y))
+
+ def test_error_nans(self):
+ # raises error if inputs contain NaNs
+ x = np.arange(20, dtype=float).reshape(-1, 1)
+ x[0] = np.nan
+ assert_raises(ValueError, _CheckInputs(x, x))
+
+ y = np.arange(20)
+ assert_raises(ValueError, _CheckInputs(x, y))
+
+ @pytest.mark.parametrize(
+ "reps", [-1, "1"] # reps is negative # reps is not integer
+ )
+ def test_error_reps(self, reps):
+ # raises error if reps is negative
+ x = np.arange(20).reshape(-1, 1)
+ assert_raises(ValueError, _CheckInputs(x, x, reps=reps))
diff --git a/hyppo/independence/__init__.py b/hyppo/independence/__init__.py
index 92912cd1a..6b97a1749 100644
--- a/hyppo/independence/__init__.py
+++ b/hyppo/independence/__init__.py
@@ -1,9 +1,26 @@
-from .rv import RV
+from ._utils import sim_matrix
from .cca import CCA
-from .hhg import HHG
from .dcorr import Dcorr
+from .hhg import HHG
from .hsic import Hsic
-from .mgc import MGC
from .kmerf import KMERF
+from .max_margin import MaxMargin
+from .mgc import MGC
+from .rv import RV
+from .friedman_rafsky import FriedmanRafsky
+
__all__ = [s for s in dir()] # add imported tests to __all__
+
+
+INDEP_TESTS = {
+ "rv": RV,
+ "cca": CCA,
+ "hhg": HHG,
+ "hsic": Hsic,
+ "dcorr": Dcorr,
+ "mgc": MGC,
+ "kmerf": KMERF,
+ "maxmargin": MaxMargin,
+ "friedman_rafsky": FriedmanRafsky,
+}
diff --git a/hyppo/independence/_utils.py b/hyppo/independence/_utils.py
index 397a9b80b..dd6e1bfd9 100644
--- a/hyppo/independence/_utils.py
+++ b/hyppo/independence/_utils.py
@@ -1,11 +1,6 @@
import numpy as np
-from ..tools import (
- contains_nan,
- check_ndarray_xy,
- convert_xy_float64,
- check_reps,
-)
+from ..tools import check_ndarray_xy, check_reps, contains_nan, convert_xy_float64
class _CheckInputs:
@@ -72,12 +67,36 @@ def _check_variance(self):
def sim_matrix(model, x):
+ """
+ Computes the similarity matrix from a random forest.
+
+ The model used must follow the scikit-learn API. That is, the model is a random
+ forest class and is already trained (use :func:`fit`) before running this function.
+ Also, :func:`apply` must be used to push input data down the trained forest. See
+ :class:`sklearn.ensemble.RandomForestClassifier` and
+ :class:`sklearn.ensemble.RandomForestRegressor` for similar classes.
+
+ Parameters
+ ----------
+ model : class
+ A trained random forest object for which the similarity matrix will be
+ calculated.
+ x : ndarray
+ Input data matrice. ``x`` must have shape must be ``(n, p)`` where
+ `n` is the number of samples and `p` are the number of
+ dimensions.
+
+ Returns
+ -------
+ proxMat : ndarray
+ The proximity matrix induced by random kernel.
+ """
terminals = model.apply(x)
ntrees = terminals.shape[1]
- proxMat = 1 * np.equal.outer(terminals[:, 0], terminals[:, 0])
- for i in range(1, ntrees):
- proxMat += 1 * np.equal.outer(terminals[:, i], terminals[:, i])
- proxMat = proxMat / ntrees
+ prox_mat = sum(
+ np.equal.outer(terminals[:, i], terminals[:, i]) for i in range(ntrees)
+ )
+ prox_mat = prox_mat / ntrees
- return proxMat
+ return prox_mat
diff --git a/hyppo/independence/base.py b/hyppo/independence/base.py
index 100f8d1fd..33236f0c1 100644
--- a/hyppo/independence/base.py
+++ b/hyppo/independence/base.py
@@ -1,21 +1,50 @@
from abc import ABC, abstractmethod
+from typing import NamedTuple
from ..tools import perm_test
+class IndependenceTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+
+
class IndependenceTest(ABC):
r"""
A base class for an independence test.
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
- A function that computes the distance or similarity among the samples
- within each data matrix. Set to `None` if `x` and `y` are already
- distance matrices. To call a custom function, either create the
- distance matrix before-hand or create a function of the form
- ``compute_distance(x)`` where `x` is the data matrix for which
- pairwise distances are calculated.
+ compute_distance : str, callable, or None, default: "euclidean" or "gaussian"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Alternatively, this function computes the kernel similarity among the
+ samples within each data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distkern``.
"""
def __init__(self, compute_distance=None, **kwargs):
@@ -28,58 +57,88 @@ def __init__(self, compute_distance=None, **kwargs):
super().__init__()
@abstractmethod
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
- Calulates the independence test statistic.
+ Calculates the independence test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices.
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
"""
@abstractmethod
- def test(self, x, y, reps=1000, workers=1, is_distsim=True, perm_blocks=None):
+ def test(
+ self,
+ x,
+ y,
+ reps=1000,
+ workers=1,
+ is_distsim=True,
+ perm_blocks=None,
+ random_state=None,
+ ):
r"""
- Calulates the independence test p-value.
+ Calculates the independence test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices.
- reps : int, optional
- The number of replications used in permutation, by default 1000.
- workers : int, optional (default: 1)
- Evaluates method using `multiprocessing.Pool `).
- Supply `-1` to use all cores available to the Process.
- perm_blocks : 2d ndarray, optional
- Restricts permutations to account for dependencies in data. Columns
- recursively partition samples based on unique labels. Groups at
- each partition are exchangeable under a permutation but remain
- fixed if label is negative.
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
+ is_distsim : bool, default: True
+ Whether or not ``x`` and ``y`` are input matrices.
+ perm_blocks : None or ndarray, default: None
+ Defines blocks of exchangeable samples during the permutation test.
+ If None, all samples can be permuted with one another. Requires `n`
+ rows. At each column, samples with matching column value are
+ recursively partitioned into blocks of samples. Within each final
+ block, samples are exchangeable. Blocks of samples from the same
+ partition are also exchangeable between one another. If a column
+ value is negative, that block is fixed and cannot be exchanged.
Returns
-------
stat : float
The computed independence test statistic.
pvalue : float
- The pvalue obtained via permutation.
+ The computed independence p-value.
"""
self.x = x
self.y = y
- # calculate p-value
stat, pvalue, null_dist = perm_test(
- self._statistic,
+ self.statistic,
x,
y,
reps=reps,
workers=workers,
is_distsim=is_distsim,
perm_blocks=perm_blocks,
+ random_state=random_state,
)
self.stat = stat
self.pvalue = pvalue
self.null_dist = null_dist
- return stat, pvalue
+ return IndependenceTestOutput(stat, pvalue)
diff --git a/hyppo/independence/cca.py b/hyppo/independence/cca.py
index a463e3d79..f2197d038 100644
--- a/hyppo/independence/cca.py
+++ b/hyppo/independence/cca.py
@@ -1,27 +1,26 @@
import numpy as np
-from .base import IndependenceTest
from ._utils import _CheckInputs
+from .base import IndependenceTest
class CCA(IndependenceTest):
r"""
- Class for calculating the CCA test statistic and p-value.
+ Cannonical Correlation Analysis (CCA) test statistic and p-value.
This test can be thought of inferring information from cross-covariance
- matrices [#1CCA]_. It has been thought that virtually all parametric tests
- of significance can be treated as a special case of CCA [#2CCA]_. The
- method was first introduced by Harold Hotelling in 1936 [#3CCA]_.
-
- See Also
- --------
- RV : RV test statistic and p-value.
+ matrices :footcite:p:`hardleCanonicalCorrelationAnalysis2015`.
+ It has been thought that virtually all parametric tests
+ of significance can be treated as a special case of CCA
+ :footcite:p:`knappCanonicalCorrelationAnalysis1978`. The
+ method was first introduced by :footcite:t:`hotellingRelationsTwoSets1992`.
Notes
-----
- The statistic can be derived as follows [#4CCA]_:
+ The statistic can be derived as follows
+ :footcite:p:`hardoonCanonicalCorrelationAnalysis2004`:
- Let :math:`x` and :math:`y` be `:math:`(n, p)` samples of random variables
+ Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
:math:`X` and :math:`Y`. We can center :math:`x` and :math:`y` and then
calculate the sample covariance matrix :math:`\hat{\Sigma}_{xy} = x^T y`
and the variance matrices for :math:`x` and :math:`y` are defined
@@ -36,37 +35,26 @@ class CCA(IndependenceTest):
{\sqrt{a^T \hat{\Sigma}_{xx} a}
\sqrt{b^T \hat{\Sigma}_{yy} b}}
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`.
References
----------
- .. [#1CCA] HΓ€rdle, W. K., & Simar, L. (2015). Canonical correlation
- analysis. In *Applied multivariate statistical analysis* (pp.
- 443-454). Springer, Berlin, Heidelberg.
- .. [#2CCA] Knapp, T. R. (1978). Canonical correlation analysis: A general
- parametric significance-testing system. *Psychological
- Bulletin*, 85(2), 410.
- .. [#3CCA] Hotelling, H. (1992). Relations between two sets of variates.
- In *Breakthroughs in statistics* (pp. 162-190). Springer, New
- York, NY.
- .. [#4CCA] Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004).
- Canonical correlation analysis: An overview with application to
- learning methods. *Neural computation*, 16(12), 2639-2664.
+ .. footbibliography::
"""
def __init__(self):
IndependenceTest.__init__(self)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the CCA test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples and dimensions. That is, the shapes must be `(n, p)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples and dimensions. That is, the shapes must be ``(n, p)`` where
`n` is the number of samples and `p` is the number of dimensions.
Returns
@@ -85,8 +73,8 @@ def _statistic(self, x, y):
# if 1-d, don't calculate the svd
if varx.size == 1 or vary.size == 1 or covar.size == 1:
- covar = np.sum(covar ** 2)
- stat = covar / np.sqrt(np.sum(varx ** 2) * np.sum(vary ** 2))
+ covar = np.sum(covar**2)
+ stat = covar / np.sqrt(np.sum(varx**2) * np.sum(vary**2))
else:
covar = np.sum(np.linalg.svd(covar, 1)[1] ** 2)
stat = covar / np.sqrt(
@@ -97,22 +85,22 @@ def _statistic(self, x, y):
return stat
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the CCA test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples and dimensions. That is, the shapes must be `(n, p)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples and dimensions. That is, the shapes must be ``(n, p)`` where
`n` is the number of samples and `p` is the number of dimensions.
- reps : int, optional (default: 1000)
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
@@ -130,20 +118,11 @@ def test(self, x, y, reps=1000, workers=1):
>>> stat, pvalue = CCA().test(x, y)
>>> '%.1f, %.2f' % (stat, pvalue)
'1.0, 0.00'
-
- The number of replications can give p-values with higher confidence
- (greater alpha levels).
-
- >>> import numpy as np
- >>> from hyppo.independence import CCA
- >>> x = np.arange(7)
- >>> y = x
- >>> stat, pvalue = CCA().test(x, y, reps=10000)
- >>> '%.1f, %.2f' % (stat, pvalue)
- '1.0, 0.00'
"""
check_input = _CheckInputs(x, y, reps=reps)
x, y = check_input()
# use default permutation test
- return super(CCA, self).test(x, y, reps, workers, is_distsim=False)
+ return super(CCA, self).test(
+ x, y, reps, workers, is_distsim=False, random_state=random_state
+ )
diff --git a/hyppo/independence/dcorr.py b/hyppo/independence/dcorr.py
index f33c5b23b..6033ddae2 100644
--- a/hyppo/independence/dcorr.py
+++ b/hyppo/independence/dcorr.py
@@ -1,14 +1,14 @@
import numpy as np
-from numba import njit
+from numba import jit
-from ..tools import compute_dist, chi2_approx, check_perm_blocks_dim
-from .base import IndependenceTest
+from ..tools import check_perm_blocks_dim, chi2_approx, compute_dist
from ._utils import _CheckInputs
+from .base import IndependenceTest, IndependenceTestOutput
class Dcorr(IndependenceTest):
r"""
- Class for calculating the Dcorr test statistic and p-value.
+ Distance Correlation (Dcorr) test statistic and p-value.
Dcorr is a measure of dependence between two paired random matrices of
not necessarily equal dimensions. The coefficient is 0 if and only if the
@@ -16,20 +16,34 @@ class Dcorr(IndependenceTest):
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
+ compute_distance : str, callable, or None, default: "euclidean"
A function that computes the distance among the samples within each
- data matrix. Set to `None` if `x` and `y` are already distance
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_distance(x)``
- where `x` is the data matrix for which pairwise distances are
- calculated.
- bias : bool (default: False)
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ bias : bool, default: False
Whether or not to use the biased or unbiased test statistics.
-
- See Also
- --------
- Hsic : Hilbert-Schmidt independence criterion test statistic and p-value.
- HHG : Heller Heller Gorfine test statistic and p-value.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
Notes
-----
@@ -42,20 +56,22 @@ class Dcorr(IndependenceTest):
.. math::
- \mathrm{Dcov}_n (x, y) = \frac{1}{n^2} \mathrm{tr} (D^x H D^y H)
+ \mathrm{Dcov}^b_n (x, y) = \frac{1}{n^2} \mathrm{tr} (H D^x H H D^y H)
where :math:`\mathrm{tr} (\cdot)` is the trace operator and :math:`H` is
defined as :math:`H = I - (1/n) J` where :math:`I` is the identity matrix
and :math:`J` is a matrix of ones. The normalized version of this
- covariance is Dcorr [#1Dcor]_ and is
+ covariance is distance correlation
+ :footcite:p:`szekelyMeasuringTestingDependence2007` and is
.. math::
- \mathrm{Dcorr}_n (x, y) = \frac{\mathrm{Dcov}_n (x, y)}
- {\sqrt{\mathrm{Dcov}_n (x, x)
- \mathrm{Dcov}_n (y, y)}}
+ \mathrm{Dcorr}^b_n (x, y) = \frac{\mathrm{Dcov}^b_n (x, y)}
+ {\sqrt{\mathrm{Dcov}^b_n (x, x)
+ \mathrm{Dcov}^b_n (y, y)}}
- This version of distance correlation is defined using the following
+ This is a biased test statistic. An unbiased alternative also exists, and is
+ defined using the following: Consider the
centering process where :math:`\mathbb{1}(\cdot)` is the indicator
function:
@@ -70,29 +86,29 @@ class Dcorr(IndependenceTest):
.. math::
- \mathrm{UDcov}_n (x, y) = \frac{1}{n (n-3)} \mathrm{tr} (C^x C^y)
+ \mathrm{Dcov}_n (x, y) = \frac{1}{n (n-3)} \mathrm{tr} (C^x C^y)
- The normalized version of this covariance [#2Dcor]_ is
+ The normalized version of this covariance
+ :footcite:p:`szekelyPartialDistanceCorrelation2014a` is
.. math::
- \mathrm{UDcorr}_n (x, y) = \frac{\mathrm{UDcov}_n (x, y)}
- {\sqrt{\mathrm{UDcov}_n (x, x)
- \mathrm{UDcov}_n (y, y)}}
+ \mathrm{Dcorr}_n (x, y) = \frac{\mathrm{Dcov}_n (x, y)}
+ {\sqrt{\mathrm{Dcov}_n (x, x)
+ \mathrm{Dcov}_n (y, y)}}
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
- The fast version of the test uses a
- `chi squared approximation `_.
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`. The fast version of the test uses
+ :meth:`hyppo.tools.chi2_approx`.
+
+ When the data is 1 dimension and the distance metric is Euclidean,
+ and even faster version of the algorithm is run (computational
+ complexity is :math:`\mathcal{O}(n \log n)`)
+ :footcite:p:`chaudhuriFastAlgorithmComputing2019`.
References
----------
- .. [#1Dcor] SzΓ©kely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007).
- Measuring and testing dependence by correlation of distances.
- *The annals of statistics*, 35(6), 2769-2794.
- .. [#2Dcor] SzΓ©kely, G. J., & Rizzo, M. L. (2014). Partial distance
- correlation with methods for dissimilarities. *The Annals of
- Statistics*, 42(6), 2382-2412.
+ .. footbibliography::
"""
def __init__(self, compute_distance="euclidean", bias=False, **kwargs):
@@ -101,20 +117,21 @@ def __init__(self, compute_distance="euclidean", bias=False, **kwargs):
if not compute_distance:
self.is_distance = True
self.bias = bias
+ self.is_fast = False
IndependenceTest.__init__(self, compute_distance=compute_distance, **kwargs)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the Dcorr test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
@@ -124,44 +141,53 @@ def _statistic(self, x, y):
distx = x
disty = y
- if not self.is_distance:
+ if not (self.is_distance or self.is_fast):
distx, disty = compute_dist(
x, y, metric=self.compute_distance, **self.kwargs
)
- stat = _dcorr(distx, disty, self.bias)
+ stat = _dcorr(distx, disty, bias=self.bias, is_fast=self.is_fast)
self.stat = stat
return stat
- def test(self, x, y, reps=1000, workers=1, auto=True, bias=False, perm_blocks=None):
+ def test(
+ self, x, y, reps=1000, workers=1, auto=True, perm_blocks=None, random_state=None
+ ):
r"""
Calculates the Dcorr test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- auto : bool (default: True)
- Automatically uses fast approximation when sample size and size of array
- is greater than 20. If True, and sample size is greater than 20, a fast
- chi2 approximation will be run. Parameters ``reps`` and ``workers`` are
- irrelevant in this case.
- perm_blocks : list or ndarray, optional
- Provides hierarchy of dependencies to restrict permutations. Columns
- provide labels for each sample and recursively partition. Groups at
- each partition are exchangeable under a permutation but remain
- fixed if label is negative.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
+ If ``x`` and ``y`` have `p` equal to 1 and ``compute_distance`` set to
+ ``'euclidean'``, then and :math:`\mathcal{O}(n \log n)` version is run.
+ perm_blocks : None or ndarray, default: None
+ Defines blocks of exchangeable samples during the permutation test.
+ If None, all samples can be permuted with one another. Requires `n`
+ rows. At each column, samples with matching column value are
+ recursively partitioned into blocks of samples. Within each final
+ block, samples are exchangeable. Blocks of samples from the same
+ partition are also exchangeable between one another. If a column
+ value is negative, that block is fixed and cannot be exchanged.
Returns
-------
@@ -174,25 +200,14 @@ def test(self, x, y, reps=1000, workers=1, auto=True, bias=False, perm_blocks=No
--------
>>> import numpy as np
>>> from hyppo.independence import Dcorr
- >>> x = np.arange(7)
+ >>> x = np.arange(25)
>>> y = x
>>> stat, pvalue = Dcorr().test(x, y)
>>> '%.1f, %.2f' % (stat, pvalue)
'1.0, 0.00'
- The number of replications can give p-values with higher confidence
- (greater alpha levels).
-
- >>> import numpy as np
- >>> from hyppo.independence import Dcorr
- >>> x = np.arange(7)
- >>> y = x
- >>> stat, pvalue = Dcorr().test(x, y, reps=10000)
- >>> '%.1f, %.2f' % (stat, pvalue)
- '1.0, 0.00'
-
- In addition, the inputs can be distance matrices. Using this is the,
- same as before, except the ``compute_distance`` parameter must be set
+ In addition, the inputs can be distance matrices. In this case,
+ the ``compute_distance`` parameter must be set
to ``None``.
>>> import numpy as np
@@ -213,22 +228,37 @@ def test(self, x, y, reps=1000, workers=1, auto=True, bias=False, perm_blocks=No
if perm_blocks is not None:
check_perm_blocks_dim(perm_blocks, y)
+ if (
+ auto
+ and x.shape[1] == 1
+ and y.shape[1] == 1
+ and self.compute_distance == "euclidean"
+ ):
+ self.is_fast = True
+
if auto and x.shape[0] > 20 and perm_blocks is None:
- stat, pvalue = chi2_approx(self._statistic, x, y)
+ stat, pvalue = chi2_approx(self.statistic, x, y)
self.stat = stat
self.pvalue = pvalue
self.null_dist = None
else:
- x, y = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
- self.is_distance = True
+ if not self.is_fast:
+ x, y = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
+ self.is_distance = True
stat, pvalue = super(Dcorr, self).test(
- x, y, reps, workers, perm_blocks=perm_blocks
+ x,
+ y,
+ reps,
+ workers,
+ perm_blocks=perm_blocks,
+ is_distsim=self.is_distance,
+ random_state=random_state,
)
- return stat, pvalue
+ return IndependenceTestOutput(stat, pvalue)
-@njit
+@jit(nopython=True, cache=True)
def _center_distmat(distx, bias): # pragma: no cover
"""Centers the distance matrices"""
n = distx.shape[0]
@@ -251,17 +281,155 @@ def _center_distmat(distx, bias): # pragma: no cover
return cent_distx
-@njit
-def _dcorr(distx, disty, bias): # pragma: no cover
- """Calculate the Dcorr test statistic"""
- # center distance matrices
- cent_distx = _center_distmat(distx, bias)
- cent_disty = _center_distmat(disty, bias)
+@jit(nopython=True, cache=True)
+def _cpu_cumsum(data): # pragma: no cover
+ """Create cumulative sum since numba doesn't sum over axes."""
+ cumsum = data.copy()
+ for i in range(1, data.shape[0]):
+ cumsum[i, :] = data[i, :] + cumsum[i - 1, :]
+ return cumsum
+
+
+@jit(nopython=True, cache=True)
+def _fast_1d_dcov(x, y, bias=False): # pragma: no cover
+ """
+ Calculate the Dcorr test statistic. Note that though Dcov is calculated
+ and stored in covar, but not called due to a slower implementation.
+
+ See: https://www.sciencedirect.com/science/article/abs/pii/S0167947319300313
+ """
+ n = x.shape[0]
+
+ # sort inputs
+ x_orig = x.ravel()
+ x = np.sort(x_orig)
+ y = y[np.argsort(x_orig)]
+ x = x.reshape(-1, 1) # for numba
+
+ # cumulative sum
+ si = _cpu_cumsum(x)
+ ax = (np.arange(-(n - 2), n + 1, 2) * x.ravel()).reshape(-1, 1) + (si[-1] - 2 * si)
+
+ v = np.hstack((x, y, x * y))
+ nw = v.shape[1]
+
+ idx = np.vstack((np.arange(n), np.zeros(n))).astype(np.int64).T
+ iv1 = np.zeros((n, 1))
+ iv2 = np.zeros((n, 1))
+ iv3 = np.zeros((n, 1))
+ iv4 = np.zeros((n, 1))
+
+ i = 1
+ r = 0
+ s = 1
+ while i < n:
+ gap = 2 * i
+ k = 0
+ idx_r = idx[:, r]
+ csumv = np.vstack((np.zeros((1, nw)), _cpu_cumsum(v[idx_r, :])))
+
+ for j in range(1, n + 1, gap):
+ st1 = j - 1
+ e1 = min(st1 + i - 1, n - 1)
+ st2 = j + i - 1
+ e2 = min(st2 + i - 1, n - 1)
+
+ while (st1 <= e1) and (st2 <= e2):
+ idx1 = idx_r[st1]
+ idx2 = idx_r[st2]
+
+ if y[idx1] >= y[idx2]:
+ idx[k, s] = idx1
+ st1 += 1
+ else:
+ idx[k, s] = idx2
+ st2 += 1
+ iv1[idx2] += e1 - st1 + 1
+ iv2[idx2] += csumv[e1 + 1, 0] - csumv[st1, 0]
+ iv3[idx2] += csumv[e1 + 1, 1] - csumv[st1, 1]
+ iv4[idx2] += csumv[e1 + 1, 2] - csumv[st1, 2]
+ k += 1
+
+ if st1 <= e1:
+ kf = k + e1 - st1 + 1
+ idx[k:kf, s] = idx_r[st1 : e1 + 1]
+ k = kf
+ elif st2 <= e2:
+ kf = k + e2 - st2 + 1
+ idx[k:kf, s] = idx_r[st2 : e2 + 1]
+ k = kf
+
+ i = gap
+ r = 1 - r
+ s = 1 - s
+
+ covterm = np.sum(n * (x - np.mean(x)).T @ (y - np.mean(y)))
+ c1 = np.sum(iv1.T @ v[:, 2].copy())
+ c2 = np.sum(iv4)
+ c3 = np.sum(iv2.T @ y)
+ c4 = np.sum(iv3.T @ x)
+ d = 4 * ((c1 + c2) - (c3 + c4)) - 2 * covterm
+
+ y_sorted = y[idx[n::-1, r], :]
+ si = _cpu_cumsum(y_sorted)
+ by = np.zeros((n, 1))
+ by[idx[::-1, r]] = (np.arange(-(n - 2), n + 1, 2) * y_sorted.ravel()).reshape(
+ -1, 1
+ ) + (si[-1] - 2 * si)
- # calculate covariances and variances
- covar = np.sum(np.multiply(cent_distx, cent_disty.T))
- varx = np.sum(np.multiply(cent_distx, cent_distx.T))
- vary = np.sum(np.multiply(cent_disty, cent_disty.T))
+ if bias:
+ denom = [n**2, n**3, n**4]
+ else:
+ denom = [n * (n - 3), n * (n - 3) * (n - 2), n * (n - 3) * (n - 2) * (n - 1)]
+
+ stat = np.sum(
+ (d / denom[0])
+ + (np.sum(ax) * np.sum(by) / denom[2])
+ - (2 * (ax.T @ by) / denom[1])
+ )
+
+ return stat
+
+
+@jit(nopython=True, cache=True)
+def _dcov(distx, disty, bias=False, only_dcov=True): # pragma: no cover
+ """Calculate the Dcov test statistic"""
+ if only_dcov:
+ # center distance matrices
+ distx = _center_distmat(distx, bias)
+ disty = _center_distmat(disty, bias)
+
+ stat = np.sum(distx * disty)
+
+ if only_dcov:
+ N = distx.shape[0]
+ if bias:
+ stat = 1 / (N**2) * stat
+ else:
+ stat = 1 / (N * (N - 3)) * stat
+
+ return stat
+
+
+@jit(nopython=True, cache=True)
+def _dcorr(distx, disty, bias=False, is_fast=False): # pragma: no cover
+ """
+ Calculate the Dcorr test statistic.
+ """
+ if is_fast:
+ # calculate covariances and variances
+ covar = _fast_1d_dcov(distx, disty, bias=bias)
+ varx = _fast_1d_dcov(distx, distx, bias=bias)
+ vary = _fast_1d_dcov(disty, disty, bias=bias)
+ else:
+ # center distance matrices
+ distx = _center_distmat(distx, bias)
+ disty = _center_distmat(disty, bias)
+
+ # calculate covariances and variances
+ covar = _dcov(distx, disty, bias=bias, only_dcov=False)
+ varx = _dcov(distx, distx, bias=bias, only_dcov=False)
+ vary = _dcov(disty, disty, bias=bias, only_dcov=False)
# stat is 0 with negative variances (would make denominator undefined)
if varx <= 0 or vary <= 0:
diff --git a/hyppo/independence/friedman_rafsky.py b/hyppo/independence/friedman_rafsky.py
new file mode 100644
index 000000000..a3f032d41
--- /dev/null
+++ b/hyppo/independence/friedman_rafsky.py
@@ -0,0 +1,239 @@
+import random
+from typing import NamedTuple
+
+from numba import jit
+import numpy as np
+
+from .base import IndependenceTest
+from ..tools import perm_test
+
+
+class FRTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+ uncor_stat: dict
+
+
+class FriedmanRafsky(IndependenceTest):
+ r"""
+ Friedman-Rafksy (FR) test statistic and p-value.
+ This is a multivariate extension of the Wald-Wolfowitz
+ runs test for randomness. The normal concept of a 'run'
+ is replaced by a minimum spanning tree (MST) calculated between
+ the points in respective data sets with edge weights defined
+ as the Euclidean distance between two such points. After MST
+ has been determined, all edges such that both corresponding
+ nodes do not belong to the same class are severed and the
+ number of independent resulting trees is counted. This test is
+ consistent against similar tests.
+
+ Notes
+ -----
+ The statistic can be derived as follows
+ :footcite:p:`friedmanMultivariateGeneralizationsoftheWaldWolfowitzandSmirnovTwoSampleTests1979`
+
+ Let :math:`x` be a combined sample of :math:`(n, p)` and :math:`(m, p)`
+ samples of random variables :math:`X` and let :math:`y` be a :math:`(n+m, 1)`
+ array of labels :math:`Y`. We can then create a graph such that each point in
+ :math:`X` is connected to each other point in :math:`X` by an edge weighted by
+ the euclidean distance inbetween those points. The minimum spanning tree is then
+ calculated and all edges such that the labels in :math:`Y` are not from the same
+ class are removed. The number of independent graphs is then summed to determine
+ the uncorrected statistic for the test.
+
+ The p-value and null distribution for the corrected statistic are calculated via
+ a permutation test using :meth:`hyppo.tools.perm_test`.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, **kwargs):
+
+ IndependenceTest.__init__(self, **kwargs)
+
+ def statistic(self, x, y):
+ r"""
+ Helper function that calculates the Friedman Rafksy test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ rows. That is, the shapes must be ``(n, p)`` and ``(n, 1)`` where
+ `n` is the number of combined samples and `p` is the number of
+ dimensions. ``y`` is the array of labels corresponding to the two
+ samples, respectively.
+
+ Returns
+ -------
+ stat : float
+ The computed (uncorrected) Friedman Rafsky statistic. A value between
+ ``2`` and ``n``.
+ """
+ x = np.transpose(x)
+ labels = np.transpose(y)
+
+ MST_connections = MST(x, labels)
+ stat = _num_runs(labels, MST_connections)
+
+ return stat
+
+ def test(
+ self,
+ x,
+ y,
+ reps=1000,
+ workers=1,
+ random_state=None,
+ ):
+ r"""
+ Calculates the Friedman Rafsky test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ rows. That is, the shapes must be ``(n, p)`` and ``(n, 1)`` where
+ `n` is the number of combined samples and `p` is the number of
+ dimensions. ``y`` is the array of labels corresponding to the two
+ samples, respectively.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ random_state : int, default: None
+ The random_state for permutation testing to be fixed for
+ reproducibility.
+
+ Returns
+ -------
+ stat : float
+ The computed (corrected) Friedman Rafsky statistic.
+ pvalue : float
+ The computed Friedman Rafsky p-value.
+ uncor_stat : float
+ The computed (uncorrected) Friedman Rafsky statistic.
+ """
+ uncor_stat, pvalue, null_dist = perm_test(
+ self.statistic,
+ x,
+ y,
+ reps,
+ workers,
+ is_distsim=False,
+ random_state=random_state,
+ )
+ self.uncor_stat = uncor_stat
+ stat = (uncor_stat - np.mean(null_dist)) / np.std(null_dist)
+ self.stat = stat
+
+ return FRTestOutput(stat, pvalue, uncor_stat)
+
+
+def _num_runs(labels, MST_connections):
+ r"""
+ Helper function to determine number of independent
+ 'runs' from MST connections.
+
+ Parameters
+ ----------
+ labels : ndarry of float
+ Lables corresponding to respective classes of samples.
+ MST_connections: list of int
+ List containing pairs of points connected in final MST.
+
+ Returns
+ -------
+ run_count : int
+ Number of runs after severing all such edges with nodes of
+ differing class labels.
+ """
+ run_count = 1
+
+ for x in MST_connections:
+ if labels[x[0]] != labels[x[1]]:
+ run_count += 1
+
+ return run_count
+
+
+@jit(nopython=True, cache=True) # pragma: no cover
+def prim(weight_mat, labels):
+ r"""
+ Helper function to read weighted matrix input and compute minimum
+ spanning tree via Prim's algorithm.
+
+ Parameters
+ ----------
+ weight_mat : ndarry of float
+ Weighted connection matrix.
+ labels : ndarry of int
+ Lables corresponding to respective classes of samples.
+
+ Returns
+ -------
+ MST_connections : list of int
+ List of pairs of nodes connected in final MST.
+ """
+ INF = 9999999
+ V = len(labels)
+ selected = np.zeros(len(labels))
+ no_edge = 0
+ selected[0] = True
+ MST_connections = []
+
+ while no_edge < V - 1:
+ minimum = INF
+ x = 0
+ y = 0
+ for i in range(V):
+ if selected[i]:
+ for j in range(V):
+ if (not selected[j]) and weight_mat[i][j]:
+ # not in selected and there is an edge
+ if minimum > weight_mat[i][j]:
+ minimum = weight_mat[i][j]
+ x = i
+ y = j
+ MST_connections.append([x, y])
+ selected[y] = True
+ no_edge += 1
+
+ return MST_connections
+
+
+@jit(nopython=True, cache=True)
+def MST(x, labels): # pragma: no cover
+ r"""
+ Helper function to read input data and calculate Euclidean distance
+ between each possible pair of points before finding MST.
+
+ Parameters
+ ----------
+ x : ndarry of float
+ Dataset such that each column corresponds to a point of data.
+ labels : ndarry of int
+ Lables corresponding to respective classes of samples.
+
+ Returns
+ -------
+ MST_connections : list
+ List of pairs of nodes connected in final MST.
+ """
+
+ G = np.zeros((len(x[0]), len(x[0])))
+
+ for i in range(len(x[0])):
+
+ for j in range(i + 1, len(x[0])):
+ weight = np.linalg.norm(x[:, i] - x[:, j])
+ G[i][j] = weight
+ G[j][i] = weight
+
+ MST_connections = prim(G, labels)
+
+ return MST_connections
diff --git a/hyppo/independence/hhg.py b/hyppo/independence/hhg.py
index 21e6c6b2c..40cb898a1 100644
--- a/hyppo/independence/hhg.py
+++ b/hyppo/independence/hhg.py
@@ -1,39 +1,57 @@
import numpy as np
-from numba import njit
+from numba import jit
+from scipy.sparse.construct import random
from ..tools import compute_dist
-from .base import IndependenceTest
from ._utils import _CheckInputs
+from .base import IndependenceTest, IndependenceTestOutput
+from scipy.stats import rankdata
class HHG(IndependenceTest):
r"""
- Class for calculating the HHG test statistic and p-value.
+ Heller Heller Gorfine (HHG) test statistic and p-value.
This is a powerful test for independence based on calculating pairwise
- euclidean distances and associations between these distance matrices. The
+ Euclidean distances and associations between these distance matrices. The
test statistic is a function of ranks of these distances, and is
- consistent against similar tests [#1HHG]_. It can also operate on multiple
- dimensions [#1HHG]_.
+ consistent against similar tests
+ :footcite:p:`hellerConsistentMultivariateTest2013`. It can also operate on multiple
+ dimensions :footcite:p:`hellerConsistentMultivariateTest2013`.
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
+ compute_distance : str, callable, or None, default: "euclidean"
A function that computes the distance among the samples within each
- data matrix. Set to `None` if `x` and `y` are already distance
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_distance(x)``
- where `x` is the data matrix for which pairwise distances are
- calculated.
-
- See Also
- --------
- Dcorr : Distance correlation test statistic and p-value.
- Hsic : Hilbert-Schmidt independence criterion test statistic and p-value.
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguments to send to your custom
+ function.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
Notes
-----
- The statistic can be derived as follows [#1HHG]_:
+ The statistic can be derived as follows
+ :footcite:p:`hellerConsistentMultivariateTest2013`:
Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
:math:`X` and :math:`Y`. For every sample :math:`j \neq i`, calculate the
@@ -77,34 +95,75 @@ class HHG(IndependenceTest):
\mathrm{HHG}_n (x, y) = \sum_{i=1}^n \sum_{j=1, j \neq i}^n S(i, j)
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
+ The p-value returned is calculated using a permutation test using
+ :math:`hyppo.tools.perm_test`.
+
+ The fast version of this test performs a multivariate independence test
+ based on univariate test statistics :footcite:p:`hellerMultivariateTestsOfAssociation2016`.
+ The univariate test statistic used is Hoeffding's independence test, derived as follows
+ :footcite:p:`sasHoeffdingDependenceCoefficient`:
+
+ Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
+ :math:`X` and :math:`Y`. A center point - the center of mass of points in 'X' and 'Y'
+ - is chosen. For every sample :math:`i`, calculate the distances from the center point
+ in :math:`x` and :math:`y` and denote this as :math:`d_x(x_i)`
+ and :math:`d_y(y_i)`. This will create a 1D collection of distances for each
+ sample group.
+
+ From these distances, we can calculate the Hoeffding's dependence score between
+ the two groups - denoted as :math:`D` - using,
+
+ .. math::
+
+ D &= \frac{(n-2) (n-3) D_{1} + D_{2} - 2(n-2) D_{3}}
+ {n (n-1) (n-2) (n-3) (n-4)}
+
+ D_{1} &= \sum_{i} (Q_{i}-1) (Q_{i}-2)
+
+ D_{2} &= \sum_{i} (R_{i} - 1) (R_{i} - 2) (S_{i} - 1) (S_{i} - 2)
+
+ D_{3} &= \sum_{i} {R_{i} - 2} (S_{i} - 2) (Q_{i}-1)
+
+ where :math:`R_{i}` is the rank of :math:`x_{i}`,
+ :math:`D_{i}` is the rank of :math:`y_{i}`,
+ :math:`Q_{i}` is the bivariate rank = 1 plus the number of points with both x and y
+ values less than the :math:`i`-th point.
+
+ :math:`D` is notably sensitive to ties and gets smaller the more pairs of variables with identical values.
+ If there are no ties in the data,D ranges between -0.5 and 1, with 1 indicating complete dependence.
+ :footcite:p:`sasHoeffdingDependenceCoefficient`
+
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`.
References
----------
- .. [#1HHG] Heller, R., Heller, Y., & Gorfine, M. (2012). A consistent
- multivariate test of association based on ranks of distances.
- *Biometrika*, 100(2), 503-510.
+ .. footbibliography::
"""
def __init__(self, compute_distance="euclidean", **kwargs):
self.is_distance = False
if not compute_distance:
self.is_distance = True
+ self.auto = False
IndependenceTest.__init__(self, compute_distance=compute_distance, **kwargs)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the HHG test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions.
+ Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ For fast version, ``x`` and ``y`` can be 1D collections of distances
+ from a chosen center point, where the shapes must be ``(n,1)`` or ``(n-1,1)``
+ depending on choice of center point.
Returns
-------
@@ -119,29 +178,46 @@ def _statistic(self, x, y):
x, y, metric=self.compute_distance, **self.kwargs
)
- stat = _hhg(distx, disty)
+ if self.auto:
+ if not self.is_distance:
+ distx, disty = _centerpoint_dist(
+ x, y, metric=self.compute_distance, **self.kwargs
+ )
+ stat = hoeffdings(distx, disty)
+ else:
+ S = _pearson_stat(distx, disty)
+ mask = np.ones(S.shape, dtype=bool)
+ np.fill_diagonal(mask, 0)
+ stat = np.sum(S[mask])
self.stat = stat
return stat
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, auto=False, random_state=None):
r"""
Calculates the HHG test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions.
+ Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ For fast version, ``x`` and ``y`` can be 1D collections of distances
+ from a chosen center point, where the shapes must be ``(n,1)`` or ``(n-1,1)``
+ depending on choice of center point.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : boolean, default: False
+ Automatically use fast approximation of HHG test. :class:`hyppo.tools.perm_test`
+ will still be run.
Returns
-------
@@ -160,17 +236,6 @@ def test(self, x, y, reps=1000, workers=1):
>>> '%.1f, %.2f' % (stat, pvalue)
'160.0, 0.00'
- The number of replications can give p-values with higher confidence
- (greater alpha levels).
-
- >>> import numpy as np
- >>> from hyppo.independence import HHG
- >>> x = np.arange(7)
- >>> y = x
- >>> stat, pvalue = HHG().test(x, y, reps=10000)
- >>> '%.1f, %.2f' % (stat, pvalue)
- '160.0, 0.00'
-
In addition, the inputs can be distance matrices. Using this is the,
same as before, except the ``compute_distance`` parameter must be set
to ``None``.
@@ -186,14 +251,27 @@ def test(self, x, y, reps=1000, workers=1):
"""
check_input = _CheckInputs(x, y, reps=reps)
x, y = check_input()
+ self.auto = auto
- x, y = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
- self.is_distance = True
+ # Fast HHG Test
+ if self.auto:
+ distx, disty = _centerpoint_dist(
+ x, y, metric=self.compute_distance, **self.kwargs
+ )
+ self.is_distance = True
+ stat, pvalue = super(HHG, self).test(
+ distx, disty, reps, workers, is_distsim=False
+ )
- return super(HHG, self).test(x, y, reps, workers)
+ else:
+ x, y = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
+ self.is_distance = True
+ stat, pvalue = super(HHG, self).test(x, y, reps, workers)
+
+ return IndependenceTestOutput(stat, pvalue)
-@njit
+@jit(nopython=True, cache=True)
def _pearson_stat(distx, disty): # pragma: no cover
"""Calculate the Pearson chi square stats"""
@@ -219,11 +297,53 @@ def _pearson_stat(distx, disty): # pragma: no cover
return S
-def _hhg(distx, disty):
- """Calculate the HHG test statistic"""
- S = _pearson_stat(distx, disty)
- mask = np.ones(S.shape, dtype=bool)
- np.fill_diagonal(mask, 0)
- stat = np.sum(S[mask])
-
- return stat
+def hoeffdings(x, y):
+ """For fast HHG, calculates the Hoeffding's dependence statistic"""
+ R = rankdata(x)
+ S = rankdata(y)
+
+ # core processing
+ N = x.shape
+ D = _hoeffdings_d_calc(R, S, N)
+ return D
+
+
+@jit(nopython=True, cache=True)
+def _hoeffdings_d_calc(R, S, N): # pragma: no cover
+ Q = np.ones(N[0])
+ for i in range(0, N[0]):
+ Q[i] = Q[i] + np.sum(np.bitwise_and(R < R[i], S < S[i]))
+ Q[i] = Q[i] + 1 / 4 * (np.sum(np.bitwise_and(R == R[i], S == S[i])) - 1)
+ Q[i] = Q[i] + 1 / 2 * (np.sum(np.bitwise_and(R == R[i], S < S[i])))
+ Q[i] = Q[i] + 1 / 2 * (np.sum(np.bitwise_and(R < R[i], S == S[i])))
+
+ D1 = np.sum(np.multiply((Q - 1), (Q - 2)))
+ D2 = np.sum(
+ np.multiply(np.multiply((R - 1), (R - 2)), np.multiply((S - 1), (S - 2)))
+ )
+ D3 = np.sum(np.multiply(np.multiply((R - 2), (S - 2)), (Q - 1)))
+
+ D = (
+ 30
+ * ((N[0] - 2) * (N[0] - 3) * D1 + D2 - 2 * (N[0] - 2) * D3)
+ / (N[0] * (N[0] - 1) * (N[0] - 2) * (N[0] - 3) * (N[0] - 4))
+ )
+ return D
+
+
+def _centerpoint_dist(x, y, metric, **kwargs):
+ """Calculate the distance of x and y from center of mass"""
+ pointer = (np.mean(x, axis=0), np.mean(y, axis=0))
+ zx, zy = pointer
+ zx = np.array(zx).reshape(1, -1)
+ zy = np.array(zy).reshape(1, -1)
+ xin = np.concatenate((zx, x))
+ yin = np.concatenate((zy, y))
+ distx, disty = compute_dist(xin, yin, metric=metric, **kwargs)
+ # take first row of distance matrix = distance of sample points from center point
+ distx = np.delete(distx[0], 0)
+ distx = distx.reshape(-1, 1)
+ disty = np.delete(disty[0], 0)
+ disty = disty.reshape(-1, 1)
+
+ return distx, disty
diff --git a/hyppo/independence/hsic.py b/hyppo/independence/hsic.py
index fef4a6bcc..63bbf11dd 100644
--- a/hyppo/independence/hsic.py
+++ b/hyppo/independence/hsic.py
@@ -1,101 +1,83 @@
import numpy as np
-from .base import IndependenceTest
+from ..tools import chi2_approx, compute_kern
from ._utils import _CheckInputs
-from . import Dcorr
-from ..tools import compute_kern, chi2_approx
+from .base import IndependenceTest, IndependenceTestOutput
+from .dcorr import Dcorr
class Hsic(IndependenceTest):
r"""
- Class for calculating the Hsic test statistic and p-value.
+ Hilbert Schmidt Independence Criterion (Hsic) test statistic and p-value.
Hsic is a kernel based independence test and is a way to measure
- multivariate nonlinear associations given a specified kernel [#1Hsic]_.
+ multivariate nonlinear associations given a specified kernel
+ :footcite:p:`grettonKernelStatisticalTest2007`.
The default choice is the Gaussian kernel, which uses the median distance
as the bandwidth, which is a characteristic kernel that guarantees that
- Hsic is a consistent test [#1Hsic]_ [#2Hsic]_.
+ Hsic is a consistent test
+ :footcite:p:`grettonKernelStatisticalTest2007`
+ :footcite:p:`grettonConsistentNonparametricTests2010`.
Parameters
----------
- compute_kernel : callable(), optional (default: rbf kernel)
- A function that computes the similarity among the samples within each
- data matrix. Set to `None` if `x` and `y` are already similarity
- matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_kernel(x)``
- where `x` is the data matrix for which pairwise similarties are
- calculated.
- bias : bool (default: False)
+ compute_kernel : str, callable, or None, default: "gaussian"
+ A function that computes the kernel similarity among the samples within each
+ data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already similarity
+ matrices. To call a custom function, either create the similarity matrix
+ before-hand or create a function of the form :func:`metric(x, **kwargs)`
+ where ``x`` is the data matrix for which pairwise kernel similarity matrices are
+ calculated and kwargs are extra arguements to send to your custom function.
+ bias : bool, default: False
Whether or not to use the biased or unbiased test statistics.
-
- See Also
- --------
- Dcorr : Distance correlation test statistic and p-value.
- HHG : Heller Heller Gorfine test statistic and p-value.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_kernel``.
Notes
-----
- The statistic can be derived as follows [#1Hsic]_:
-
- Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
- :math:`X` and :math:`Y`. Let :math:`K^x` be the :math:`n \times n` kernel
- similarity matrix of :math:`x` and :math:`D^y` be the :math:`n \times n`
- be the kernel similarity matrix of :math:`y`. The Hsic statistic is,
-
- .. math::
-
- \mathrm{Hsic}_n (x, y) = \frac{1}{n^2} \mathrm{tr} (K^x H K^y H)
-
- where :math:`\mathrm{tr} (\cdot)` is the trace operator and :math:`H` is
- defined as :math:`H = I - (1/n) J` where :math:`I` is the identity matrix
- and :math:`J` is a matrix of ones. The normalized version of Hsic
- [#1Dcor]_ and is
+ The statistic can be derived as follows
+ :footcite:p:`grettonKernelStatisticalTest2007`:
+
+ Hsic is closely related distance correlation (Dcorr), implemented in
+ :class:`hyppo.independence.Dcorr`, and exchanges distance matrices
+ :math:`D^x` and :math:`D^y` for kernel similarity matrices :math:`K^x` and
+ :math:`K^y`. That is, let :math:`x` and :math:`y` be :math:`(n, p)` samples
+ of random variables
+ :math:`X` and :math:`Y`. Let :math:`K^x` be the :math:`n \times n`
+ kernel similarity matrix of :math:`x` and :math:`K^y` be the :math:`n \times n` be
+ the kernel similarity matrix of :math:`y`. The Hsic statistic is,
.. math::
- \mathrm{Hsic}_n (x, y) = \frac{\mathrm{Hsic}_n (x, y)}
- {\sqrt{\mathrm{Hsic}_n (x, x)
- \mathrm{Hsic}_n (y, y)}}
+ \mathrm{Hsic}^b_n (x, y) = \frac{1}{n^2} \mathrm{tr} (D^x H D^y H)
- This version of Hsic is defined using the following centering process
- where :math:`\mathbb{1}(\cdot)` is the indicator
- function:
-
- .. math::
+ Hsic and Dcov are exactly equivalent in the sense that every valid kernel has a
+ corresponding
+ valid semimetric to ensure their equivalence, and vice versa
+ :footcite:p:`shenExactEquivalenceDistance2020`
+ :footcite:p:`sejdinovicEquivalenceDistancebasedRKHSbased2013`.
+ In other words, every Dcorr test is also an Hsic and vice versa. Nonetheless,
+ implementations of Dcorr and Hsic use different metrics by default:
+ Dcorr uses a Euclidean distance while Hsic uses a Gaussian median kernel.
+ We consider the normalized version (see :class:`hyppo.independence`) for the
+ transformation.
- C^x_{ij} = \left[ D^x_{ij} - \frac{1}{n-2} \sum_{t=1}^n D^x_{it}
- - \frac{1}{n-2} \sum_{s=1}^n D^x_{sj}
- + \frac{1}{(n-1) (n-2)} \sum_{s,t=1}^n D^x_{st} \right]
- \mathbb{1}_{i \neq j}
-
- and similarly for :math:`C^y`. Then, this unbiased Dcorr is,
-
- .. math::
-
- \mathrm{UHsic}_n (x, y) = \frac{1}{n (n-3)} \mathrm{tr} (C^x C^y)
-
- The normalized version of this covariance [#2Dcor]_ is
-
- .. math::
-
- \mathrm{UHsic}_n (x, y) = \frac{\mathrm{UHsic}_n (x, y)}
- {\sqrt{\mathrm{UHsic}_n (x, x)
- \mathrm{UHsic}_n (y, y)}}
-
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
- The fast version of the test uses a
- `chi squared approximation `_.
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`. The fast version of the test uses
+ :meth:`hyppo.tools.chi2_approx`.
References
----------
- .. [#1Hsic] Gretton, A., Fukumizu, K., Teo, C. H., Song, L., SchΓΆlkopf,
- B., & Smola, A. J. (2008). A kernel statistical test of
- independence. In *Advances in neural information processing
- systems* (pp. 585-592).
- .. [#2Hsic] Gretton, A., & GyΔΕrfi, L. (2010). Consistent nonparametric
- tests of independence. *Journal of Machine Learning Research*,
- 11(Apr), 1391-1423.
+ .. footbibliography::
"""
def __init__(self, compute_kernel="gaussian", bias=False, **kwargs):
@@ -109,18 +91,19 @@ def __init__(self, compute_kernel="gaussian", bias=False, **kwargs):
IndependenceTest.__init__(self, compute_distance=None, **kwargs)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the Hsic test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be kernel similarity
+ matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
@@ -135,35 +118,38 @@ def _statistic(self, x, y):
distx = 1 - kernx / np.max(kernx)
disty = 1 - kerny / np.max(kerny)
- dcorr = Dcorr(compute_distance=None, bias=self.bias)
- stat = dcorr._statistic(distx, disty)
+ # Hsic and Dcorr are equivalent, cannot use dcov otherwise fast is invalid
+ stat = Dcorr(bias=self.bias, compute_distance=None).statistic(distx, disty)
self.stat = stat
return stat
- def test(self, x, y, reps=1000, workers=1, auto=True):
+ def test(self, x, y, reps=1000, workers=1, auto=True, random_state=None):
r"""
Calculates the Hsic test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions. Alternatively, ``x`` and ``y`` can be kernel similarity
+ matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- auto : bool (default: True)
- Automatically uses fast approximation when sample size and size of array
- is greater than 20. If True, and sample size is greater than 20, a fast
- chi2 approximation will be run. Parameters ``reps`` and ``workers`` are
- irrelevant in this case.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
Returns
-------
@@ -176,35 +162,11 @@ def test(self, x, y, reps=1000, workers=1, auto=True):
--------
>>> import numpy as np
>>> from hyppo.independence import Hsic
- >>> x = np.arange(7)
+ >>> x = np.arange(100)
>>> y = x
>>> stat, pvalue = Hsic().test(x, y)
>>> '%.1f, %.2f' % (stat, pvalue)
'1.0, 0.00'
-
- The number of replications can give p-values with higher confidence
- (greater alpha levels).
-
- >>> import numpy as np
- >>> from hyppo.independence import Hsic
- >>> x = np.arange(7)
- >>> y = x
- >>> stat, pvalue = Hsic().test(x, y, reps=10000)
- >>> '%.1f, %.2f' % (stat, pvalue)
- '1.0, 0.00'
-
- In addition, the inputs can be distance matrices. Using this is the,
- same as before, except the ``compute_kernel`` parameter must be set
- to ``None``.
-
- >>> import numpy as np
- >>> from hyppo.independence import Hsic
- >>> x = np.ones((10, 10)) - np.identity(10)
- >>> y = 2 * x
- >>> hsic = Hsic(compute_kernel=None)
- >>> stat, pvalue = hsic.test(x, y)
- >>> '%.1f, %.2f' % (stat, pvalue)
- '0.0, 1.00'
"""
check_input = _CheckInputs(
x,
@@ -214,13 +176,15 @@ def test(self, x, y, reps=1000, workers=1, auto=True):
x, y = check_input()
if auto and x.shape[0] > 20:
- stat, pvalue = chi2_approx(self._statistic, x, y)
+ stat, pvalue = chi2_approx(self.statistic, x, y)
self.stat = stat
self.pvalue = pvalue
self.null_dist = None
else:
x, y = compute_kern(x, y, metric=self.compute_kernel, **self.kwargs)
self.is_kernel = True
- stat, pvalue = super(Hsic, self).test(x, y, reps, workers)
+ stat, pvalue = super(Hsic, self).test(
+ x, y, reps, workers, random_state=None
+ )
- return stat, pvalue
+ return IndependenceTestOutput(stat, pvalue)
diff --git a/hyppo/independence/kmerf.py b/hyppo/independence/kmerf.py
index d7d0e276e..fdd428506 100644
--- a/hyppo/independence/kmerf.py
+++ b/hyppo/independence/kmerf.py
@@ -1,11 +1,12 @@
+from typing import NamedTuple
+
import numpy as np
-from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.metrics import pairwise_distances
-from .base import IndependenceTest
from ._utils import _CheckInputs, sim_matrix
-from . import Dcorr
-
+from .base import IndependenceTest
+from .dcorr import _dcorr
FOREST_TYPES = {
"classifier": RandomForestClassifier,
@@ -13,33 +14,41 @@
}
+class KMERFTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+ kmerf_dict: dict
+
+
class KMERF(IndependenceTest):
r"""
- Class for calculating the KMERF test statistic and p-value.
+ Kernel Mean Embedding Random Forest (KMERF) test statistic and p-value.
The KMERF test statistic is a kernel method for calculating independence by using
a random forest induced similarity matrix as an input, and has been shown to have
especially high gains in finite sample testing power in high dimensional settings
- [#1KMERF]_.
+ :footcite:p:`shenLearningInterpretableCharacteristic2020`.
Parameters
----------
- forest : {"classifier", "regressor"}
+ forest : "regressor", "classifier", default: "regressor"
Type of forest used when running the independence test. If the `y` input in
``test`` is categorial, use the "classifier" keyword.
- ntrees : int, optional (default: 500)
+ ntrees : int, default: 500
The number of trees used in the random forest.
- **kwargs : optional
+ **kwargs
Additional arguments used for the forest (see
- ``sklearn.ensemble.RandomForestClassifier`` or
- ``sklearn.ensemble.RandomForestRegressor``)
+ :class:`sklearn.ensemble.RandomForestClassifier` or
+ :class:`sklearn.ensemble.RandomForestRegressor`)
Notes
-----
- A description of KMERF in greater detail can be found in [#1KMERF]_. It is computed
+ A description of KMERF in greater detail can be found in
+ :footcite:p:`shenLearningInterpretableCharacteristic2020`. It is computed
using the following steps:
- Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
+ Let :math:`x` and :math:`y` be :math:`(n, p)` and :math:`(n, 1)` samples of random
+ variables
:math:`X` and :math:`Y`.
+ Run random forest with :math:`m` trees. Independent bootstrap samples of size
@@ -55,8 +64,8 @@ class KMERF(IndependenceTest):
\mathbf{K}^{\mathbf{x}}_{ij} = \frac{1}{m} \sum_{w = 1}^{m} I(\phi_w(x_i)
= \phi_w(x_j))
- where :math:`I(\cdot)`$` is the indicator function for how often two observations
- lie in the same partition.
+ where :math:`I(\cdot)`$` is the indicator function for how often two observations
+ lie in the same partition.
+ Compute the induced kernel correlation: Let
@@ -78,17 +87,15 @@ class KMERF(IndependenceTest):
.. math::
- KMERF_n(\mathbf{x}, \mathbf{y}) = \frac{1}{n(n-3)}
+ \mathrm{KMERF}_n(\mathbf{x}, \mathbf{y}) = \frac{1}{n(n-3)}
\mathrm{tr} \left( \mathbf{L}^{\mathbf{x}} \mathbf{L}^{\mathbf{y}} \right)
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`.
References
----------
- .. [#1KMERF] Shen, C., Panda, S., & Vogelstein, J. T. (2018). Learning
- Interpretable Characteristic Kernels via Decision Forests.
- arXiv preprint arXiv:1812.00029.
+ .. footbibliography::
"""
def __init__(self, forest="regressor", ntrees=500, **kwargs):
@@ -98,54 +105,53 @@ def __init__(self, forest="regressor", ntrees=500, **kwargs):
raise ValueError("Forest must be of type classification or regression")
IndependenceTest.__init__(self)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the KMERF test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
- `n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, 1)`` where
+ `n` is the number of samples and `p` is the number of
+ dimensions.
Returns
-------
stat : float
The computed KMERF statistic.
-
- y must be categorical
"""
y = y.reshape(-1)
self.clf.fit(x, y)
distx = np.sqrt(1 - sim_matrix(self.clf, x))
y = y.reshape(-1, 1)
disty = pairwise_distances(y, metric="euclidean")
- stat = Dcorr(compute_distance=None)._statistic(distx, disty)
+ stat = _dcorr(distx, disty, bias=False, is_fast=False)
self.stat = stat
+ # get feature importances from gini-based random forest
+ self.importances = self.clf.feature_importances_
+
return stat
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the KMERF test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
- `n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, 1)`` where
+ `n` is the number of samples and `p` is the number of
+ dimensions.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
@@ -153,6 +159,11 @@ def test(self, x, y, reps=1000, workers=1):
The computed KMERF statistic.
pvalue : float
The computed KMERF p-value.
+ kmerf_dict : dict
+ Contains additional useful returns containing the following keys:
+
+ - feat_importance : ndarray of float
+ An array containing the importance of each dimension
Examples
--------
@@ -160,10 +171,15 @@ def test(self, x, y, reps=1000, workers=1):
>>> from hyppo.independence import KMERF
>>> x = np.arange(100)
>>> y = x
- >>> '%.1f, %.2f' % KMERF().test(x, y) # doctest: +SKIP
+ >>> '%.1f, %.2f' % KMERF().test(x, y)[:1] # doctest: +SKIP
'1.0, 0.001'
"""
check_input = _CheckInputs(x, y, reps=reps)
x, y = check_input()
- return super(KMERF, self).test(x, y, reps, workers, is_distsim=False)
+ stat, pvalue = super(KMERF, self).test(
+ x, y, reps, workers, is_distsim=False, random_state=random_state
+ )
+ kmerf_dict = {"feat_importance": self.importances}
+
+ return KMERFTestOutput(stat, pvalue, kmerf_dict)
diff --git a/hyppo/independence/max_margin.py b/hyppo/independence/max_margin.py
new file mode 100644
index 000000000..503e03c35
--- /dev/null
+++ b/hyppo/independence/max_margin.py
@@ -0,0 +1,207 @@
+import numpy as np
+
+from ..tools import chi2_approx
+from ._utils import _CheckInputs
+from .base import IndependenceTest, IndependenceTestOutput
+from .cca import CCA
+from .dcorr import Dcorr
+from .hhg import HHG
+from .hsic import Hsic
+from .kmerf import KMERF
+from .mgc import MGC
+from .rv import RV
+
+INDEP_NOT_MAXMARGIN = {
+ "rv": RV,
+ "cca": CCA,
+ "hhg": HHG,
+ "hsic": Hsic,
+ "dcorr": Dcorr,
+ "mgc": MGC,
+ "kmerf": KMERF,
+}
+
+
+class MaxMargin(IndependenceTest):
+ r"""
+ Maximal Margin test statistic and p-value.
+
+ This test loops over each of the dimensions of the inputs :math:`x` and :math:`y`
+ and computes the desired independence test statistic. Then, the maximial test
+ statistic is chosen :footcite:p:`shenHighDimensionalIndependenceTesting2020`.
+
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`.
+
+ Parameters
+ ----------
+ indep_test : "CCA", "Dcorr", "HHG", "RV", "Hsic", "MGC", "KMERF"
+ A string corresponding to the desired independence test from
+ :mod:`hyppo.independence`. This is not case sensitive.
+ compute_distkern : str, callable, or None, default: "euclidean" or "gaussian"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Alternatively, this function computes the kernel similarity among the
+ samples within each data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ bias : bool, default: False
+ Whether or not to use the biased or unbiased test statistics (for
+ ``indep_test="Dcorr"`` and ``indep_test="Hsic"``).
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distkern``.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, indep_test, compute_distkern="euclidean", bias=False, **kwargs):
+ indep_test = indep_test.lower()
+ if indep_test not in INDEP_NOT_MAXMARGIN.keys():
+ raise ValueError("Test is not a valid independence test")
+ if indep_test == "hsic" and compute_distkern == "euclidean":
+ compute_distkern = "gaussian"
+ self.indep_test_name = indep_test
+
+ indep_kwargs = {
+ "dcorr": {"bias": bias, "compute_distance": compute_distkern},
+ "hsic": {"bias": bias, "compute_kernel": compute_distkern},
+ "hhg": {"compute_distance": compute_distkern},
+ "mgc": {"compute_distance": compute_distkern},
+ "kmerf": {"forest_type": "classifier"},
+ "rv": {},
+ "cca": {},
+ }
+ self.indep_test = INDEP_NOT_MAXMARGIN[indep_test](
+ **indep_kwargs[indep_test], **kwargs
+ )
+ self.is_fast = False
+ self.bias = bias
+
+ IndependenceTest.__init__(self, compute_distance=compute_distkern, **kwargs)
+
+ def statistic(self, x, y):
+ r"""
+ Helper function that calculates the Maximal Margin test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed Maximal Margin statistic.
+ """
+ stat = np.max(
+ [
+ self.indep_test.statistic(
+ x[:, i].reshape(-1, 1), y[:, j].reshape(-1, 1)
+ )
+ for i in range(x.shape[1])
+ for j in range(y.shape[1])
+ ]
+ )
+ self.stat = stat
+
+ return stat
+
+ def test(self, x, y, reps=1000, workers=1, auto=True, random_state=None):
+ r"""
+ Calculates the Maximal Margin test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Only applies to ``"Dcorr"`` and ``"Hsic"``.
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
+
+ Returns
+ -------
+ stat : float
+ The computed Maximal Margin statistic.
+ pvalue : float
+ The computed Maximal Margin p-value.
+ dict
+ A dictionary containing optional parameters for tests that return them.
+ See the relevant test in :mod:`hyppo.independence`.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.independence import MaxMargin
+ >>> x = np.arange(100)
+ >>> y = x
+ >>> stat, pvalue = MaxMargin("Dcorr").test(x, y)
+ >>> '%.1f, %.3f' % (stat, pvalue)
+ '1.0, 0.000'
+ """
+ check_input = _CheckInputs(
+ x,
+ y,
+ reps=reps,
+ )
+ x, y = check_input()
+
+ if (
+ auto
+ and x.shape[1] == 1
+ and y.shape[1] == 1
+ and self.compute_distance == "euclidean"
+ and self.indep_test_name == "dcorr"
+ ):
+ self.is_fast = True
+
+ if auto and x.shape[0] > 20 and self.indep_test_name in ["dcorr", "hsic"]:
+ stat, pvalue = chi2_approx(self.statistic, x, y)
+ self.stat = stat
+ self.pvalue = pvalue
+ self.null_dist = None
+ else:
+ stat, pvalue = super(MaxMargin, self).test(
+ x, y, reps, workers, is_distsim=False, random_state=None
+ )
+
+ return IndependenceTestOutput(stat, pvalue)
diff --git a/hyppo/independence/mgc.py b/hyppo/independence/mgc.py
index c7281a05f..1a715da52 100644
--- a/hyppo/independence/mgc.py
+++ b/hyppo/independence/mgc.py
@@ -1,18 +1,29 @@
import warnings
+from typing import NamedTuple
+
+import numpy as np
from scipy.stats import multiscale_graphcorr
from ..tools import compute_dist
-from .base import IndependenceTest
from ._utils import _CheckInputs
+from .base import IndependenceTest
+
+
+class MGCTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+ mgc_dict: dict
class MGC(IndependenceTest):
r"""
- Class for calculating the MGC test statistic and p-value.
+ Multiscale Graph Correlation (MGC) test statistic and p-value.
Specifically, for each point, MGC finds the :math:`k`-nearest neighbors for
one property (e.g. cloud density), and the :math:`l`-nearest neighbors for
- the other property (e.g. grass wetness) [#1MGC]_. This pair :math:`(k, l)` is
+ the other property (e.g. grass wetness)
+ :footcite:p:`vogelsteinDiscoveringDecipheringRelationships2019`.
+ This pair :math:`(k, l)` is
called the "scale". A priori, however, it is not know which scales will be
most informative. So, MGC computes all distance pairs, and then efficiently
computes the distance correlations for all scales. The local correlations
@@ -25,27 +36,44 @@ class MGC(IndependenceTest):
determination was made. This is especially important in high-dimensional
data, where simple visualizations do not reveal relationships to the
unaided human eye. Characterizations of this implementation in particular
- have been derived from and benchmarked within in [#2MGC]_.
+ have been derived from and benchmarked within
+ :footcite:t:`pandaHyppoMultivariateHypothesis2021`.
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
+ compute_distance : str, callable, or None, default: "euclidean"
A function that computes the distance among the samples within each
- data matrix. Set to `None` if `x` and `y` are already distance
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_distance(x)``
- where `x` is the data matrix for which pairwise distances are
- calculated.
-
- See Also
- --------
- Hsic : Hilbert-Schmidt independence criterion test statistic and p-value.
- Dcorr : Distance correlation test statistic and p-value.
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
Notes
-----
A description of the process of MGC and applications on neuroscience data
- can be found in [#1MGC]_. It is performed using the following steps:
+ can be found in
+ :footcite:t:`vogelsteinDiscoveringDecipheringRelationships2019`.
+ It is performed using the following steps:
Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
:math:`X` and :math:`Y`. Let :math:`D^x` be the :math:`n \times n`
@@ -53,7 +81,8 @@ class MGC(IndependenceTest):
the distance matrix of :math:`y`. :math:`D^x` and :math:`D^y` are
modified to be mean zero columnwise. This results in two
:math:`n \times n` distance matrices :math:`A` and :math:`B` (the
- centering and unbiased modification) [3]_.
+ centering and unbiased modification)
+ :footcite:p:`shenDistanceCorrelationMultiscale2020`.
+ For all values :math:`k` and :math:`l` from :math:`1, ..., n`,
@@ -63,7 +92,7 @@ class MGC(IndependenceTest):
and :math:`H_l (i, j)` indicates the :math:`l` smallested values of
the :math:`i`-th row of :math:`B`
- * Let :math:`\circ` denotes the entry-wise matrix product, then local
+ * The local
correlations are summed and normalized using the following statistic:
.. math::
@@ -74,31 +103,24 @@ class MGC(IndependenceTest):
+ The MGC test statistic is the smoothed optimal local correlation of
:math:`\{ c^{kl} \}`. Denote the smoothing operation as :math:`R(\cdot)`
(which essentially set all isolated large correlations) as 0 and
- connected large correlations the same as before, see [#3MGC]_.) MGC is,
+ connected large correlations the same as before, see
+ :footcite:t:`shenDistanceCorrelationMultiscale2020`.)
+ MGC is,
.. math::
- MGC_n (x, y) = \max_{(k, l)} R \left(c^{kl} \left( x_n, y_n \right)
+ \mathrm{MGC}_n (x, y) = \max_{(k, l)} R \left(c^{kl} \left( x_n, y_n \right)
\right)
The test statistic returns a value between :math:`(-1, 1)` since it is
normalized.
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`.
References
----------
- .. [#1MGC] Vogelstein, J. T., Bridgeford, E. W., Wang, Q., Priebe, C. E.,
- Maggioni, M., & Shen, C. (2019). Discovering and deciphering
- relationships across disparate data modalities. ELife.
- .. [#2MGC] Panda, S., Palaniappan, S., Xiong, J., Swaminathan, A.,
- Ramachandran, S., Bridgeford, E. W., ... Vogelstein, J. T. (2019).
- mgcpy: A Comprehensive High Dimensional Independence Testing Python
- Package. ArXiv:1907.02088 [Cs, Stat].
- .. [#3MGC] Shen, C., Priebe, C.E., & Vogelstein, J. T. (2019). From distance
- correlation to multiscale graph correlation. Journal of the American
- Statistical Association.
+ .. footbibliography::
"""
def __init__(self, compute_distance="euclidean", **kwargs):
@@ -108,18 +130,18 @@ def __init__(self, compute_distance="euclidean", **kwargs):
self.is_distance = True
IndependenceTest.__init__(self, compute_distance=compute_distance, **kwargs)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the MGC test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
@@ -143,24 +165,24 @@ def _statistic(self, x, y):
return stat
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the MGC test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
@@ -171,10 +193,10 @@ def test(self, x, y, reps=1000, workers=1):
mgc_dict : dict
Contains additional useful returns containing the following keys:
- - mgc_map : ndarray
+ - mgc_map : ndarray of float
A 2D representation of the latent geometry of the relationship.
- opt_scale : (int, int)
- The estimated optimal scale as a `(x, y)` pair.
+ The estimated optimal scale as a ``(x, y)`` pair.
Examples
--------
@@ -206,6 +228,22 @@ def test(self, x, y, reps=1000, workers=1):
)
x, y = check_input()
+ # add test for redundant rows
+ if (
+ np.unique(x, axis=0).shape[0] != x.shape[0]
+ or np.unique(y, axis=0).shape[0] != y.shape[0]
+ ):
+ warnings.warn(
+ "Input x has {} redundant rows, and input y has {} redundant "
+ "rows. MGC Map will be of shape ({}, {}).".format(
+ x.shape[0] - np.unique(x, axis=0).shape[0],
+ y.shape[0] - np.unique(y, axis=0).shape[0],
+ np.unique(x, axis=0).shape[0],
+ np.unique(y, axis=0).shape[0],
+ ),
+ RuntimeWarning,
+ )
+
x, y = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
self.is_distance = True
@@ -216,7 +254,9 @@ def test(self, x, y, reps=1000, workers=1):
_, _, mgc_dict = multiscale_graphcorr(x, y, compute_distance=None, reps=0)
mgc_dict.pop("null_dist")
- stat, pvalue = super(MGC, self).test(x, y, reps, workers)
+ stat, pvalue = super(MGC, self).test(
+ x, y, reps, workers, random_state=random_state
+ )
self.mgc_dict = mgc_dict
- return stat, pvalue, mgc_dict
+ return MGCTestOutput(stat, pvalue, mgc_dict)
diff --git a/hyppo/independence/rv.py b/hyppo/independence/rv.py
index f4199fa7a..423851440 100644
--- a/hyppo/independence/rv.py
+++ b/hyppo/independence/rv.py
@@ -1,26 +1,26 @@
import numpy as np
-from .base import IndependenceTest
from ._utils import _CheckInputs
+from .base import IndependenceTest
class RV(IndependenceTest):
r"""
- Class for calculating the RV test statistic and p-value.
+ Rank Value (RV) test statistic and p-value.
RV is the multivariate generalization of the squared Pearson correlation
- coefficient [#1RV]_. The RV coefficient can be thought to be closely
+ coefficient
+ :footcite:p:`robertUnifyingToolLinear1976`.
+ The RV coefficient can be thought to be closely
related to principal component analysis (PCA), canonical correlation
analysis (CCA), multivariate regression, and statistical classification
- [#1RV]_.
-
- See Also
- --------
- CCA : CCA test statistic and p-value.
+ :footcite:p:`robertUnifyingToolLinear1976`.
Notes
-----
- The statistic can be derived as follows [#1RV]_ [#2RV]_:
+ The statistic can be derived as follows
+ :footcite:p:`robertUnifyingToolLinear1976`
+ :footcite:p:`escoufierTraitementVariablesVectorielles1973`:
Let :math:`x` and :math:`y` be :math:`(n, p)` samples of random variables
:math:`X` and :math:`Y`. We can center :math:`x` and :math:`y` and then
@@ -38,31 +38,26 @@ class RV(IndependenceTest):
where :math:`\mathrm{tr} (\cdot)` is the trace operator.
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
+ The p-value returned is calculated using a permutation test using
+ :meth:`hyppo.tools.perm_test`.
References
----------
- .. [#1RV] Robert, P., & Escoufier, Y. (1976). A unifying tool for linear
- multivariate statistical methods: the RVβcoefficient. *Journal
- of the Royal Statistical Society: Series C (Applied
- Statistics)*, 25(3), 257-265.
- .. [#2RV] Escoufier, Y. (1973). Le traitement des variables vectorielles.
- *Biometrics*, 751-760.
+ .. footbibliography::
"""
def __init__(self):
IndependenceTest.__init__(self)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the RV test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples and dimensions. That is, the shapes must be `(n, p)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples and dimensions. That is, the shapes must be ``(n, p)`` where
`n` is the number of samples and `p` is the number of dimensions.
Returns
@@ -86,22 +81,22 @@ def _statistic(self, x, y):
return stat
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the RV test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples and dimensions. That is, the shapes must be `(n, p)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples and dimensions. That is, the shapes must be ``(n, p)`` where
`n` is the number of samples and `p` is the number of dimensions.
- reps : int, optional (default: 1000)
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
@@ -119,19 +114,10 @@ def test(self, x, y, reps=1000, workers=1):
>>> stat, pvalue = RV().test(x, y)
>>> '%.1f, %.2f' % (stat, pvalue)
'1.0, 0.00'
-
- The number of replications can give p-values with higher confidence
- (greater alpha levels).
-
- >>> import numpy as np
- >>> from hyppo.independence import RV
- >>> x = np.arange(7)
- >>> y = x
- >>> stat, pvalue = RV().test(x, y, reps=10000)
- >>> '%.1f, %.2f' % (stat, pvalue)
- '1.0, 0.00'
"""
check_input = _CheckInputs(x, y, reps=reps)
x, y = check_input()
- return super(RV, self).test(x, y, reps, workers, is_distsim=False)
+ return super(RV, self).test(
+ x, y, reps, workers, is_distsim=False, random_state=random_state
+ )
diff --git a/hyppo/independence/tests/test_cca.py b/hyppo/independence/tests/test_cca.py
index f1d7bf24b..536fa7dac 100644
--- a/hyppo/independence/tests/test_cca.py
+++ b/hyppo/independence/tests/test_cca.py
@@ -1,8 +1,8 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_raises, assert_warns
+import pytest
+from numpy.testing import assert_almost_equal
-from ...tools import linear
+from ...tools import joint_normal, linear, power
from .. import CCA
@@ -19,58 +19,36 @@ def test_linear_oned(self, n, obs_stat, obs_pvalue):
assert_almost_equal(pvalue, obs_pvalue, decimal=2)
@pytest.mark.parametrize("n", [100, 1000, 10000])
- @pytest.mark.parametrize("obs_stat", [0.57])
+ @pytest.mark.parametrize("obs_stat", [0.07])
@pytest.mark.parametrize("obs_pvalue", [1 / 1000])
def test_linear_threed(self, n, obs_stat, obs_pvalue):
np.random.seed(123456789)
- x, y = linear(n, 3)
+ x, y = joint_normal(n, 3)
stat, pvalue = CCA().test(x, y)
assert_almost_equal(stat, obs_stat, decimal=1)
assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+ @pytest.mark.parametrize("n", [1000, 10000])
+ def test_rep(self, n):
+ x, y = joint_normal(n, 3)
+ stat, pvalue = CCA().test(x, y, random_state=2)
+ stat2, pvalue2 = CCA().test(x, y, random_state=2)
-class TestCCAErrorWarn:
- """Tests errors and warnings derived from MGC."""
-
- def test_error_notndarray(self):
- # raises error if x or y is not a ndarray
- x = np.arange(20)
- y = [5] * 20
- assert_raises(TypeError, CCA().test, x, y)
- assert_raises(TypeError, CCA().test, y, x)
-
- def test_error_shape(self):
- # raises error if number of samples different (n)
- x = np.arange(100).reshape(25, 4)
- y = x.reshape(10, 10)
- assert_raises(ValueError, CCA().test, x, y)
+ assert stat == stat2
+ assert pvalue == pvalue2
- def test_error_lowsamples(self):
- # raises error if samples are low (< 3)
- x = np.arange(3)
- y = np.arange(3)
- assert_raises(ValueError, CCA().test, x, y)
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x = np.arange(20, dtype=float)
- x[0] = np.nan
- assert_raises(ValueError, CCA().test, x, x)
-
- y = np.arange(20)
- assert_raises(ValueError, CCA().test, x, y)
-
- @pytest.mark.parametrize(
- "reps", [-1, "1"] # reps is negative # reps is not integer
- )
- def test_error_reps(self, reps):
- # raises error if reps is negative
- x = np.arange(20)
- assert_raises(ValueError, CCA().test, x, x, reps=reps)
-
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x = np.arange(20)
- reps = 100
- assert_warns(RuntimeWarning, CCA().test, x, x, reps=reps)
+class TestCCATypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "CCA",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=1000,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_dcorr.py b/hyppo/independence/tests/test_dcorr.py
index 333ecb390..32277c862 100644
--- a/hyppo/independence/tests/test_dcorr.py
+++ b/hyppo/independence/tests/test_dcorr.py
@@ -1,8 +1,8 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_warns, assert_raises
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises, assert_warns
-from ...tools import linear
+from ...tools import linear, power
from .. import Dcorr
@@ -13,60 +13,61 @@ class TestDcorrStat:
def test_linear_oned(self, n, obs_stat, obs_pvalue):
np.random.seed(123456789)
x, y = linear(n, 1)
- stat, pvalue = Dcorr().test(x, y)
+ stat1, pvalue1 = Dcorr().test(x, y)
+ stat2 = Dcorr().statistic(x, y)
- assert_almost_equal(stat, obs_stat, decimal=2)
- assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+ assert_almost_equal(stat1, obs_stat, decimal=2)
+ assert_almost_equal(stat2, obs_stat, decimal=2)
+ assert_almost_equal(pvalue1, obs_pvalue, decimal=2)
+ @pytest.mark.parametrize("n", [100, 200])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ stat1, pvalue1 = Dcorr().test(x, y, random_state=2)
+ stat2, pvalue2 = Dcorr().test(x, y, random_state=2)
-class TestDcorrErrorWarn:
- """Tests errors and warnings derived from MGC."""
-
- def test_error_notndarray(self):
- # raises error if x or y is not a ndarray
- x = np.arange(20)
- y = [5] * 20
- assert_raises(TypeError, Dcorr().test, x, y)
- assert_raises(TypeError, Dcorr().test, y, x)
+ assert stat1 == stat2
+ assert pvalue1 == pvalue2
- def test_error_shape(self):
- # raises error if number of samples different (n)
- x = np.arange(100).reshape(25, 4)
- y = x.reshape(10, 10)
- assert_raises(ValueError, Dcorr().test, x, y)
- def test_error_lowsamples(self):
- # raises error if samples are low (< 3)
- x = np.arange(3)
- y = np.arange(3)
- assert_raises(ValueError, Dcorr().test, x, y)
+class TestDcorrTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Dcorr",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=100,
+ p=1,
+ alpha=0.05,
+ )
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x = np.arange(20, dtype=float)
- x[0] = np.nan
- assert_raises(ValueError, Dcorr().test, x, x)
+ assert_almost_equal(est_power, 0.05, decimal=2)
- y = np.arange(20)
- assert_raises(ValueError, Dcorr().test, x, y)
+ def test_oned_fast(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Dcorr",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=100,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
- def test_error_wrongdisttype(self):
- # raises error if compute_distance is not a function
- x = np.arange(20)
- compute_distance = 1
- dcorr = Dcorr(compute_distance=compute_distance)
- assert_raises(ValueError, dcorr.test, x, x)
+ assert_almost_equal(est_power, 0.05, decimal=2)
- @pytest.mark.parametrize(
- "reps", [-1, "1"] # reps is negative # reps is not integer
- )
- def test_error_reps(self, reps):
- # raises error if reps is negative
- x = np.arange(20)
- assert_raises(ValueError, Dcorr().test, x, x, reps=reps)
+ def test_threed_fast(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Dcorr",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=100,
+ p=3,
+ alpha=0.05,
+ auto=True,
+ )
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x = np.arange(20)
- reps = 100
- assert_warns(RuntimeWarning, Dcorr().test, x, x, reps=reps)
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_friedman_rafsky.py b/hyppo/independence/tests/test_friedman_rafsky.py
new file mode 100644
index 000000000..f91b3a6f3
--- /dev/null
+++ b/hyppo/independence/tests/test_friedman_rafsky.py
@@ -0,0 +1,37 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises, assert_warns
+
+from ...tools import linear, power
+from .. import FriedmanRafsky
+
+
+class TestFriedmanRafskyStat:
+ @pytest.mark.parametrize("n", [100])
+ @pytest.mark.parametrize("num_runs", [48])
+ @pytest.mark.parametrize("obs_stat", [-0.527])
+ @pytest.mark.parametrize("obs_pvalue", [0.748])
+ def test_linear_oned(self, n, num_runs, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = linear(n, 1)
+ num_rows, num_cols = x.shape
+ y = np.random.choice([0, 1], num_rows, p=[0.5, 0.5])
+ y = np.transpose(y)
+ stat1, pvalue1, _ = FriedmanRafsky().test(x, y)
+ stat2 = FriedmanRafsky().statistic(x, y)
+
+ assert_almost_equal(stat1, obs_stat, decimal=2)
+ assert_almost_equal(stat2, num_runs, decimal=2)
+ assert_almost_equal(pvalue1, obs_pvalue, decimal=2)
+
+ @pytest.mark.parametrize("n", [100, 200])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ num_rows, num_cols = x.shape
+ y = np.random.choice([0, 1], num_rows, p=[0.5, 0.5])
+ y = np.transpose(y)
+ stat1, pvalue1, _ = FriedmanRafsky().test(x, y, random_state=2)
+ stat2, pvalue2, _ = FriedmanRafsky().test(x, y, random_state=2)
+
+ assert stat1 == stat2
+ assert pvalue1 == pvalue2
diff --git a/hyppo/independence/tests/test_hhg.py b/hyppo/independence/tests/test_hhg.py
index a537644c6..39b99655f 100644
--- a/hyppo/independence/tests/test_hhg.py
+++ b/hyppo/independence/tests/test_hhg.py
@@ -1,8 +1,9 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_raises, assert_warns
+import pytest
+from numpy.testing import assert_almost_equal
+from sklearn.metrics import pairwise_distances
-from ...tools import linear
+from ...tools import linear, power
from .. import HHG
@@ -19,55 +20,35 @@ def test_linear_oned(self, n, obs_stat, obs_pvalue):
assert_almost_equal(stat, obs_stat, decimal=2)
assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+ def test_diststat(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ distx = pairwise_distances(x, x)
+ disty = pairwise_distances(y, y)
+ stat = HHG().statistic(distx, disty)
-class TestHHGErrorWarn:
- """Tests errors and warnings derived from MGC."""
-
- def test_error_notndarray(self):
- # raises error if x or y is not a ndarray
- x = np.arange(20)
- y = [5] * 20
- assert_raises(TypeError, HHG().test, x, y)
- assert_raises(TypeError, HHG().test, y, x)
-
- def test_error_shape(self):
- # raises error if number of samples different (n)
- x = np.arange(100).reshape(25, 4)
- y = x.reshape(10, 10)
- assert_raises(ValueError, HHG().test, x, y)
-
- def test_error_lowsamples(self):
- # raises error if samples are low (< 3)
- x = np.arange(3)
- y = np.arange(3)
- assert_raises(ValueError, HHG().test, x, y)
-
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x = np.arange(20, dtype=float)
- x[0] = np.nan
- assert_raises(ValueError, HHG().test, x, x)
+ assert_almost_equal(stat, 950600.0, decimal=2)
- y = np.arange(20)
- assert_raises(ValueError, HHG().test, x, y)
+ @pytest.mark.parametrize("n", [10, 200])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ stat, pvalue = HHG().test(x, y, random_state=2)
+ stat2, pvalue2 = HHG().test(x, y, random_state=2)
- def test_error_wrongdisttype(self):
- # raises error if compute_distance is not a function
- x = np.arange(20)
- compute_distance = 1
- hhg = HHG(compute_distance=compute_distance)
- assert_raises(ValueError, hhg.test, x, x)
+ assert stat == stat2
+ assert pvalue == pvalue2
- @pytest.mark.parametrize(
- "reps", [-1, "1"] # reps is negative # reps is not integer
- )
- def test_error_reps(self, reps):
- # raises error if reps is negative
- x = np.arange(20)
- assert_raises(ValueError, HHG().test, x, x, reps=reps)
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x = np.arange(20)
- reps = 100
- assert_warns(RuntimeWarning, HHG().test, x, x, reps=reps)
+class TestHHGTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "HHG",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=50,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_hhgfast.py b/hyppo/independence/tests/test_hhgfast.py
new file mode 100644
index 000000000..76778dd18
--- /dev/null
+++ b/hyppo/independence/tests/test_hhgfast.py
@@ -0,0 +1,51 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+from sklearn.metrics import pairwise_distances
+
+from ...tools import linear, power
+from .. import HHG
+from ..hhg import hoeffdings
+
+
+class TestFastHHGStat:
+ @pytest.mark.parametrize("n, obs_stat", [(10, 1.0), (50, 1.0), (100, 1.0)])
+ @pytest.mark.parametrize("obs_pvalue", [1 / 1000])
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = linear(n, 1)
+ stat, pvalue = HHG().test(x, y, auto=True)
+
+ assert_almost_equal(stat, obs_stat, decimal=2)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+
+ def test_diststat(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ zx = np.mean(x, axis=0).reshape(1, -1)
+ zy = np.mean(y, axis=0).reshape(1, -1)
+ distx = pairwise_distances(zx, x).reshape(-1, 1)
+ disty = pairwise_distances(zy, y).reshape(-1, 1)
+ test = HHG(compute_distance=None)
+ test.auto = True
+ stat = test.statistic(distx, disty)
+
+ assert_almost_equal(stat, 1.0, decimal=2)
+
+ def test_stat(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ test = HHG()
+ test.auto = True
+ stat = test.statistic(x, y)
+
+ assert_almost_equal(stat, 1.0, decimal=2)
+
+ @pytest.mark.parametrize("n", [10, 200])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ stat, pvalue = HHG().test(x, y, auto=True, random_state=2)
+ stat2, pvalue2 = HHG().test(x, y, auto=True, random_state=2)
+
+ assert stat == stat2
+ assert pvalue == pvalue2
diff --git a/hyppo/independence/tests/test_hsic.py b/hyppo/independence/tests/test_hsic.py
index b1ff19d65..d855f44f8 100644
--- a/hyppo/independence/tests/test_hsic.py
+++ b/hyppo/independence/tests/test_hsic.py
@@ -1,14 +1,14 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_warns, assert_raises
+import pytest
+from numpy.testing import assert_almost_equal
+from sklearn.metrics.pairwise import pairwise_kernels
-from ...tools import linear
+from ...tools import linear, power
from .. import Hsic
class TestHsicStat:
- @pytest.mark.parametrize("n", [100, 200])
- @pytest.mark.parametrize("obs_stat", [1.0])
+ @pytest.mark.parametrize("n, obs_stat", [(100, 1.0), (200, 1.0)])
@pytest.mark.parametrize("obs_pvalue", [1 / 1000])
def test_linear_oned(self, n, obs_stat, obs_pvalue):
np.random.seed(123456789)
@@ -18,55 +18,50 @@ def test_linear_oned(self, n, obs_stat, obs_pvalue):
assert_almost_equal(stat, obs_stat, decimal=2)
assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+ def test_kernstat(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ kernx = pairwise_kernels(x, x)
+ kerny = pairwise_kernels(y, y)
+ stat, pvalue = Hsic(compute_kernel=None).test(kernx, kerny, auto=False)
-class TestHsicErrorWarn:
- """Tests errors and warnings derived from MGC."""
-
- def test_error_notndarray(self):
- # raises error if x or y is not a ndarray
- x = np.arange(20)
- y = [5] * 20
- assert_raises(TypeError, Hsic().test, x, y)
- assert_raises(TypeError, Hsic().test, y, x)
+ assert_almost_equal(stat, 1.0, decimal=2)
+ assert_almost_equal(pvalue, 1 / 1000, decimal=2)
- def test_error_shape(self):
- # raises error if number of samples different (n)
- x = np.arange(100).reshape(25, 4)
- y = x.reshape(10, 10)
- assert_raises(ValueError, Hsic().test, x, y)
+ @pytest.mark.parametrize("n", [100, 200])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ stat, pvalue = Hsic().test(x, y, random_state=2)
+ stat2, pvalue2 = Hsic().test(x, y, random_state=2)
- def test_error_lowsamples(self):
- # raises error if samples are low (< 3)
- x = np.arange(3)
- y = np.arange(3)
- assert_raises(ValueError, Hsic().test, x, y)
+ assert stat == stat2
+ assert pvalue == pvalue2
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x = np.arange(20, dtype=float)
- x[0] = np.nan
- assert_raises(ValueError, Hsic().test, x, x)
- y = np.arange(20)
- assert_raises(ValueError, Hsic().test, x, y)
+class TestHsicTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Hsic",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=200,
+ p=1,
+ alpha=0.05,
+ )
- def test_error_wrongdisttype(self):
- # raises error if compute_distance is not a function
- x = np.arange(20)
- compute_kernel = 1
- hsic = Hsic(compute_kernel=compute_kernel)
- assert_raises(ValueError, hsic.test, x, x)
+ assert_almost_equal(est_power, 0.05, decimal=2)
- @pytest.mark.parametrize(
- "reps", [-1, "1"] # reps is negative # reps is not integer
- )
- def test_error_reps(self, reps):
- # raises error if reps is negative
- x = np.arange(20)
- assert_raises(ValueError, Hsic().test, x, x, reps=reps)
+ def test_oned_fast(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Hsic",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=500,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x = np.arange(20)
- reps = 100
- assert_warns(RuntimeWarning, Hsic().test, x, x, reps=reps)
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_kmerf.py b/hyppo/independence/tests/test_kmerf.py
index fd8ce73c9..5d4b4719b 100644
--- a/hyppo/independence/tests/test_kmerf.py
+++ b/hyppo/independence/tests/test_kmerf.py
@@ -1,8 +1,8 @@
-import pytest
import numpy as np
-from numpy.testing import assert_approx_equal
+import pytest
+from numpy.testing import assert_almost_equal, assert_approx_equal, assert_raises
-from ...tools import linear, spiral, multimodal_independence
+from ...tools import linear, multimodal_independence, power, spiral
from .. import KMERF
@@ -13,9 +13,9 @@ class TestKMERFStat(object):
@pytest.mark.parametrize(
"sim, obs_stat, obs_pvalue",
[
- (linear, 0.253, 1 / 1000), # test linear simulation
- (spiral, 0.037, 0.012), # test spiral simulation
- (multimodal_independence, -0.0363, 0.995), # test independence simulation
+ (linear, 0.253, 1.0), # test linear simulation
+ (spiral, 0.037, 1.0), # test spiral simulation
+ (multimodal_independence, -0.0363, 1.0), # test independence simulation
],
)
def test_oned(self, sim, obs_stat, obs_pvalue):
@@ -25,29 +25,33 @@ def test_oned(self, sim, obs_stat, obs_pvalue):
x, y = sim(n=100, p=1)
# test stat and pvalue
- stat1 = KMERF()._statistic(x, y)
- # stat2, pvalue = KMERF().test(x, y)
+ stat1 = KMERF().statistic(x, y)
+ stat2, pvalue, _ = KMERF().test(x, y, reps=0)
assert_approx_equal(stat1, obs_stat, significant=1)
- # assert_approx_equal(stat2, obs_stat, significant=1)
- # assert_approx_equal(pvalue, obs_pvalue, significant=1)
+ assert_approx_equal(stat2, obs_stat, significant=1)
+ assert_approx_equal(pvalue, obs_pvalue, significant=1)
- # commented out p-value calculation because build stalled
- @pytest.mark.parametrize(
- "sim, obs_stat, obs_pvalue",
- [
- (linear, 0.354, 1 / 1000), # test linear simulation
- (spiral, 0.091, 0.001), # test spiral simulation
- ],
- )
- def test_fived(self, sim, obs_stat, obs_pvalue):
- np.random.seed(12345678)
- # generate x and y
- x, y = sim(n=100, p=5)
+class TestKmerfErrorWarn:
+ """Tests errors and warnings derived from MGC."""
- # test stat and pvalue
- stat1 = KMERF()._statistic(x, y)
- # stat2, pvalue = KMERF().test(x, y)
- assert_approx_equal(stat1, obs_stat, significant=1)
- # assert_approx_equal(stat2, obs_stat, significant=1)
- # assert_approx_equal(pvalue, obs_pvalue, significant=1)
+ def test_no_indeptest(self):
+ # raises error if not indep test
+ assert_raises(ValueError, KMERF, "abcd")
+
+
+# code too slow to run, check benchmarks in docs for verification
+@pytest.mark.skip
+class TestKmerfTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "KMERF",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=5,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_maxmargin.py b/hyppo/independence/tests/test_maxmargin.py
new file mode 100644
index 000000000..c633aaf03
--- /dev/null
+++ b/hyppo/independence/tests/test_maxmargin.py
@@ -0,0 +1,52 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises
+
+from ...tools import linear, power
+from .. import MaxMargin
+
+
+class TestMaxMarginStat:
+ @pytest.mark.parametrize(
+ "n, obs_stat", [(100, 0.6983550238795532), (200, 0.7026459581729386)]
+ )
+ @pytest.mark.parametrize("obs_pvalue", [1 / 1000])
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = linear(n, 5)
+ stat, pvalue = MaxMargin("Dcorr").test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=2)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+
+ def test_kernstat(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ stat, pvalue = MaxMargin("Hsic").test(x, y, auto=False)
+
+ assert_almost_equal(stat, 1.0, decimal=2)
+ assert_almost_equal(pvalue, 1 / 1000, decimal=2)
+
+
+class TestMaxMarginErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_no_indeptest(self):
+ # raises error if not indep test
+ assert_raises(ValueError, MaxMargin, "abcd")
+
+
+class TestMaxMarginTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ ["MaxMargin", "Dcorr"],
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=100,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_mgc.py b/hyppo/independence/tests/test_mgc.py
index caa9601b6..974100479 100644
--- a/hyppo/independence/tests/test_mgc.py
+++ b/hyppo/independence/tests/test_mgc.py
@@ -1,8 +1,13 @@
-import pytest
import numpy as np
-from numpy.testing import assert_approx_equal
+import pytest
+from numpy.testing import (
+ assert_almost_equal,
+ assert_approx_equal,
+ assert_equal,
+ assert_warns,
+)
-from ...tools import linear, spiral, multimodal_independence
+from ...tools import linear, multimodal_independence, power, spiral
from .. import MGC
@@ -14,7 +19,6 @@ class TestMGCStat(object):
[
(linear, 0.97, 1 / 1000), # test linear simulation
(spiral, 0.163, 1 / 1000), # test spiral simulation
- (multimodal_independence, -0.0094, 0.78), # test independence simulation
],
)
def test_oned(self, sim, obs_stat, obs_pvalue):
@@ -24,7 +28,7 @@ def test_oned(self, sim, obs_stat, obs_pvalue):
x, y = sim(n=100, p=1)
# test stat and pvalue
- stat1 = MGC()._statistic(x, y)
+ stat1 = MGC().statistic(x, y)
stat2, pvalue, _ = MGC().test(x, y)
assert_approx_equal(stat1, obs_stat, significant=1)
assert_approx_equal(stat2, obs_stat, significant=1)
@@ -34,7 +38,7 @@ def test_oned(self, sim, obs_stat, obs_pvalue):
"sim, obs_stat, obs_pvalue",
[
(linear, 0.463, 1 / 1000), # test linear simulation
- (spiral, 0.091, 0.003), # test spiral simulation
+ (spiral, 0.091, 0.01), # test spiral simulation
],
)
def test_fived(self, sim, obs_stat, obs_pvalue):
@@ -44,8 +48,44 @@ def test_fived(self, sim, obs_stat, obs_pvalue):
x, y = sim(n=100, p=5)
# test stat and pvalue
- stat1 = MGC()._statistic(x, y)
+ stat1 = MGC().statistic(x, y)
stat2, pvalue, _ = MGC().test(x, y)
assert_approx_equal(stat1, obs_stat, significant=1)
assert_approx_equal(stat2, obs_stat, significant=1)
assert_approx_equal(pvalue, obs_pvalue, significant=1)
+
+ @pytest.mark.parametrize(
+ "sim, obs_stat, obs_pvalue",
+ [
+ (linear, 0.97, 1 / 1000),
+ ], # test linear simulation
+ )
+ def test_rep(self, sim, obs_stat, obs_pvalue):
+ x, y = sim(n=100, p=5)
+ stat1, pvalue1, _ = MGC().test(x, y, random_state=2)
+ stat2, pvalue2, _ = MGC().test(x, y, random_state=2)
+
+ assert stat1 == stat2
+ assert pvalue1 == pvalue2
+
+
+class TestMGCTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "MGC",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=50,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
+
+
+class TestMGCErrorWarnings:
+ def test_redundancy_warning(self):
+ x = np.hstack((np.arange(0, 6), 5, 5, 5, 5, 5))
+ y = np.hstack((np.arange(0, 6), 5, 5, 5, 5, 5))
+ assert_warns(RuntimeWarning, MGC().test, x, y)
diff --git a/hyppo/independence/tests/test_rvcorr.py b/hyppo/independence/tests/test_rvcorr.py
index 1a9d42456..20cb1f751 100644
--- a/hyppo/independence/tests/test_rvcorr.py
+++ b/hyppo/independence/tests/test_rvcorr.py
@@ -1,8 +1,8 @@
-import pytest
import numpy as np
-from numpy.testing import assert_almost_equal, assert_warns, assert_raises
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises, assert_warns
-from ...tools import linear
+from ...tools import linear, power
from .. import RV
@@ -18,48 +18,26 @@ def test_linear_oned(self, n, obs_stat, obs_pvalue):
assert_almost_equal(stat, obs_stat, decimal=2)
assert_almost_equal(pvalue, obs_pvalue, decimal=2)
+ @pytest.mark.parametrize("n", [100, 1000])
+ def test_rep(self, n):
+ x, y = linear(n, 1)
+ stat1, pvalue1 = RV().test(x, y)
+ stat2, pvalue2 = RV().test(x, y)
-class TestRVErrorWarn:
- """Tests errors and warnings derived from MGC."""
-
- def test_error_notndarray(self):
- # raises error if x or y is not a ndarray
- x = np.arange(20)
- y = [5] * 20
- assert_raises(TypeError, RV().test, x, y)
- assert_raises(TypeError, RV().test, y, x)
-
- def test_error_shape(self):
- # raises error if number of samples different (n)
- x = np.arange(100).reshape(25, 4)
- y = x.reshape(10, 10)
- assert_raises(ValueError, RV().test, x, y)
-
- def test_error_lowsamples(self):
- # raises error if samples are low (< 3)
- x = np.arange(3)
- y = np.arange(3)
- assert_raises(ValueError, RV().test, x, y)
-
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x = np.arange(20, dtype=float)
- x[0] = np.nan
- assert_raises(ValueError, RV().test, x, x)
-
- y = np.arange(20)
- assert_raises(ValueError, RV().test, x, y)
+ assert stat1 == stat2
+ assert pvalue1 == pvalue2
- @pytest.mark.parametrize(
- "reps", [-1, "1"] # reps is negative # reps is not integer
- )
- def test_error_reps(self, reps):
- # raises error if reps is negative
- x = np.arange(20)
- assert_raises(ValueError, RV().test, x, x, reps=reps)
- def test_warns_reps(self):
- # raises warning when reps is less than 1000
- x = np.arange(20)
- reps = 100
- assert_warns(RuntimeWarning, RV().test, x, x, reps=reps)
+class TestCCATypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "RV",
+ sim_type="indep",
+ sim="multimodal_independence",
+ n=1000,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/independence/tests/test_utils.py b/hyppo/independence/tests/test_utils.py
new file mode 100644
index 000000000..7947ac891
--- /dev/null
+++ b/hyppo/independence/tests/test_utils.py
@@ -0,0 +1,58 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_raises
+from sklearn.ensemble import RandomForestRegressor
+
+from ...tools.common import _check_kernmat
+from .._utils import _CheckInputs, sim_matrix
+
+
+class TestErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_error_notndarray(self):
+ # raises error if x or y is not a ndarray
+ x = np.arange(20)
+ y = [5] * 20
+ assert_raises(TypeError, _CheckInputs(x, y))
+ assert_raises(TypeError, _CheckInputs(y, x))
+
+ def test_error_shape(self):
+ # raises error if number of samples different (n)
+ x = np.arange(100).reshape(25, 4)
+ y = x.reshape(10, 10)
+ assert_raises(ValueError, _CheckInputs(x, y))
+
+ def test_error_lowsamples(self):
+ # raises error if samples are low (< 3)
+ x = np.arange(3)
+ y = np.arange(3)
+ assert_raises(ValueError, _CheckInputs(x, y))
+
+ def test_error_nans(self):
+ # raises error if inputs contain NaNs
+ x = np.arange(20, dtype=float)
+ x[0] = np.nan
+ assert_raises(ValueError, _CheckInputs(x, x))
+
+ y = np.arange(20)
+ assert_raises(ValueError, _CheckInputs(x, y))
+
+ @pytest.mark.parametrize(
+ "reps", [-1, "1"] # reps is negative # reps is not integer
+ )
+ def test_error_reps(self, reps):
+ # raises error if reps is negative
+ x = np.arange(20)
+ assert_raises(ValueError, _CheckInputs(x, x, reps=reps))
+
+
+class TestHelper:
+ def test_simmat(self):
+ # raises error if x or y is not a ndarray
+ clf = RandomForestRegressor()
+ x = np.arange(20).reshape(-1, 1)
+ y = np.arange(5, 25)
+ clf.fit(x, y)
+ kernx = sim_matrix(clf, x)
+ _check_kernmat(kernx, kernx)
diff --git a/hyppo/kgof/__init__.py b/hyppo/kgof/__init__.py
new file mode 100644
index 000000000..f50b1e803
--- /dev/null
+++ b/hyppo/kgof/__init__.py
@@ -0,0 +1,11 @@
+from ._utils import meddistance, fit_gaussian_draw
+from .fssd import FSSD, FSSDH0SimCovObs
+from .datasource import DataSource
+from .density import Normal, IsotropicNormal
+from .kernel import KGauss
+
+__all__ = [s for s in dir()] # add imported tests to all
+
+GOF_TESTS = {
+ "fssd": FSSD,
+}
diff --git a/hyppo/kgof/_utils.py b/hyppo/kgof/_utils.py
new file mode 100644
index 000000000..04a4ee199
--- /dev/null
+++ b/hyppo/kgof/_utils.py
@@ -0,0 +1,95 @@
+from __future__ import print_function, division, unicode_literals, absolute_import
+
+from builtins import int
+
+import autograd.numpy as np
+from sklearn.metrics.pairwise import euclidean_distances
+from numpy.random import default_rng
+
+
+def constrain(val, min_val, max_val):
+ return min(max_val, max(min_val, val))
+
+
+def meddistance(X, subsample=None, mean_on_fail=True):
+ r"""
+ Compute the median of pairwise distances (not distance squared) of points
+ in the matrix. Useful as a heuristic for setting Gaussian kernel's width.
+
+ Parameters
+ ----------
+ X : n x d numpy array
+ mean_on_fail: True/False. If True, use the mean when the median distance is 0.
+ This can happen especially, when the data are discrete e.g., 0/1, and
+ there are more slightly more 0 than 1. In this case, the m
+
+ Returns
+ -------
+ median distance
+
+ From: https://github.com/wittawatj/fsic-test
+ """
+ if subsample is None:
+ D = euclidean_distances(X, X)
+ Itri = np.tril_indices(D.shape[0], -1)
+ Tri = D[Itri]
+ med = np.median(Tri)
+ if med <= 0:
+ # use the mean
+ return np.mean(Tri)
+ return med
+
+ else:
+ assert subsample > 0
+ rand_state = np.random.get_state()
+ rng = default_rng(9827)
+ n = X.shape[0]
+ ind = rng.choice(n, min(subsample, n), replace=False)
+ np.random.set_state(rand_state)
+ # recursion just one
+ return meddistance(X[ind, :], None, mean_on_fail)
+
+
+def fit_gaussian_draw(X, J, seed=28, reg=1e-7, eig_pow=1.0):
+ r"""
+ Fit a multivariate normal to the data X (n x d) and draw J points
+ from the fit.
+
+ Parameters
+ ----------
+ reg : regularizer to use with the covariance matrix
+ eig_pow : raise eigenvalues of the covariance matrix to this power to construct
+ a new covariance matrix before drawing samples. Useful to shrink the spread
+ of the variance.
+
+ From: https://github.com/wittawatj/fsic-test
+ """
+ rng = default_rng(seed)
+ d = X.shape[1]
+ mean_x = np.mean(X, 0)
+ cov_x = np.cov(X.T)
+ if d == 1:
+ cov_x = np.array([[cov_x]])
+ [evals, evecs] = np.linalg.eig(cov_x)
+ evals = np.maximum(0, np.real(evals))
+ assert np.all(np.isfinite(evals))
+ evecs = np.real(evecs)
+ shrunk_cov = evecs.dot(np.diag(evals**eig_pow)).dot(evecs.T) + reg * np.eye(d)
+ V = rng.multivariate_normal(mean_x, shrunk_cov, J)
+ return V
+
+
+def outer_rows(X, Y):
+ r"""
+ Compute the outer product of each row in X, and Y.
+
+ Parameters
+ ----------
+ X : n x dx numpy array
+ Y : n x dy numpy array
+
+ Returns
+ -------
+ Return an n x dx x dy numpy array.
+ """
+ return np.einsum("ij,ik->ijk", X, Y)
diff --git a/hyppo/kgof/base.py b/hyppo/kgof/base.py
new file mode 100644
index 000000000..b7f3ba044
--- /dev/null
+++ b/hyppo/kgof/base.py
@@ -0,0 +1,78 @@
+from __future__ import division
+
+from abc import ABC, abstractmethod
+
+
+class GofTest(ABC):
+ r"""
+ A base class for a discriminability test.
+
+ Parameters
+ ----------
+ compute_distance : str, callable, or None, default: "euclidean" or "gaussian"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+ Alternatively, this function computes the kernel similarity among the
+ samples within each data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ """
+
+ def __init__(self, p, alpha):
+ """
+ p : an UnnormalizedDensity object
+ alpha : float, significance level of the test
+ """
+ self.p = p
+ self.alpha = alpha
+
+ @abstractmethod
+ def test(self, X):
+ """
+ Perform the goodness-of-fit test and return values
+ computed in a dictionary.
+
+ Parameters
+ ----------
+ dat : an instance of Data (observed data)
+
+ Returns
+ -------
+ {
+ alpha: 0.01,
+ pvalue: 0.0002,
+ test_stat: 2.3,
+ h0_rejected: True,
+ time_secs: ...
+ }
+ """
+ raise NotImplementedError()
+
+ @abstractmethod
+ def statistic(self, X):
+ r"""
+ Calculates the goodness-of-fit test statistic.
+
+ Parameters
+ ----------
+ dat : an instance of Data (observed data)
+ Input data matrices.
+ """
+ raise NotImplementedError()
diff --git a/hyppo/kgof/datasource.py b/hyppo/kgof/datasource.py
new file mode 100644
index 000000000..1d45ed4e0
--- /dev/null
+++ b/hyppo/kgof/datasource.py
@@ -0,0 +1,74 @@
+"""
+Module containing datasources for representing distributions.
+"""
+from __future__ import print_function, division
+
+from builtins import range, object
+from past.utils import old_div
+
+from abc import ABC, abstractmethod
+import autograd.numpy as np
+import scipy.stats as stats
+from numpy.random import default_rng
+
+
+class DataSource(ABC):
+ """
+ A source of data allowing resampling. Subclasses may prefix
+ class names with DS.
+ """
+
+ @abstractmethod
+ def sample(self, n, seed):
+ """Return a Data. Returned result should be deterministic given
+ the input (n, seed)."""
+ raise NotImplementedError()
+
+
+class DSIsotropicNormal(DataSource):
+ """
+ A DataSource providing samples from a mulivariate isotropic normal
+ distribution.
+ """
+
+ def __init__(self, mean, variance):
+ """
+ mean: a numpy array of length d for the mean
+ variance: a positive floating-point number for the variance.
+ """
+ assert len(mean.shape) == 1
+ self.mean = mean
+ self.variance = variance
+
+ def sample(self, n, seed=2):
+ rng = default_rng(seed)
+ d = len(self.mean)
+ mean = self.mean
+ variance = self.variance
+ X = rng.standard_normal(size=(n, d)) * np.sqrt(variance) + mean
+ return X
+
+
+class DSNormal(DataSource):
+ """
+ A DataSource implementing a multivariate Gaussian.
+ """
+
+ def __init__(self, mean, cov):
+ """
+ mean: a numpy array of length d.
+ cov: d x d numpy array for the covariance.
+ """
+ self.mean = mean
+ self.cov = cov
+ assert mean.shape[0] == cov.shape[0]
+ assert cov.shape[0] == cov.shape[1]
+
+ def sample(self, n, seed=3):
+ rng = default_rng(seed)
+ mvn = stats.multivariate_normal(self.mean, self.cov)
+ X = mvn.rvs(size=n)
+ if len(X.shape) == 1:
+ # This can happen if d=1
+ X = X[:, np.newaxis]
+ return X
diff --git a/hyppo/kgof/density.py b/hyppo/kgof/density.py
new file mode 100644
index 000000000..508dbeb8d
--- /dev/null
+++ b/hyppo/kgof/density.py
@@ -0,0 +1,134 @@
+"""
+Module containing implementations of some unnormalized probability density
+functions.
+"""
+from __future__ import division
+
+from builtins import range
+from past.utils import old_div
+
+from abc import ABC, abstractmethod
+import autograd
+import autograd.numpy as np
+from .datasource import DSNormal, DSIsotropicNormal
+import scipy.stats as stats
+
+
+class UnnormalizedDensity(ABC):
+ """
+ An abstract class of an unnormalized probability density function. This is
+ intended to be used to represent a model of the data for goodness-of-fit
+ testing.
+ """
+
+ @abstractmethod
+ def log_den(self, X):
+ """
+ Evaluate this log of the unnormalized density on the n points in X.
+ X: n x d numpy array
+ Return a one-dimensional numpy array of length n.
+ """
+ raise NotImplementedError()
+
+ def log_normalized_den(self, X):
+ """
+ Evaluate the exact normalized log density. The difference to log_den()
+ is that this method adds the normalizer. This method is not
+ compulsory. Subclasses do not need to override.
+ """
+ raise NotImplementedError()
+
+ def get_datasource(self):
+ """
+ Return a DataSource that allows sampling from this density.
+ May return None if no DataSource is implemented.
+ Implementation of this method is not enforced in the subclasses.
+ """
+ return None
+
+ def grad_log(self, X):
+ """
+ Evaluate the gradients (with respect to the input) of the log density at
+ each of the n points in X. This is the score function. Given an
+ implementation of log_den(), this method will automatically work.
+ Subclasses may override this if a more efficient implementation is
+ available.
+ X: n x d numpy array.
+ Return an n x d numpy array of gradients.
+ """
+ g = autograd.elementwise_grad(self.log_den)
+ G = g(X)
+ return G
+
+ @abstractmethod
+ def dim(self):
+ """
+ Return the dimension of the input.
+ """
+ raise NotImplementedError()
+
+
+class IsotropicNormal(UnnormalizedDensity):
+ """
+ Unnormalized density of an isotropic multivariate normal distribution.
+ """
+
+ def __init__(self, mean, variance):
+ """
+ mean: a numpy array of length d for the mean
+ variance: a positive floating-point number for the variance.
+ """
+ self.mean = mean
+ self.variance = variance
+
+ def log_den(self, X):
+ mean = self.mean
+ variance = self.variance
+ unden = old_div(-np.sum((X - mean) ** 2, 1), (2.0 * variance))
+ return unden
+
+ def log_normalized_den(self, X):
+ d = self.dim()
+ return stats.multivariate_normal.logpdf(
+ X, mean=self.mean, cov=self.variance * np.eye(d)
+ )
+
+ def get_datasource(self):
+ return DSIsotropicNormal(self.mean, self.variance)
+
+ def dim(self):
+ return len(self.mean)
+
+
+class Normal(UnnormalizedDensity):
+ """
+ A multivariate normal distribution.
+ """
+
+ def __init__(self, mean, cov):
+ """
+ mean: a numpy array of length d.
+ cov: d x d numpy array for the covariance.
+ """
+ self.mean = mean
+ self.cov = cov
+ assert mean.shape[0] == cov.shape[0]
+ assert cov.shape[0] == cov.shape[1]
+ E, V = np.linalg.eigh(cov)
+ if np.any(np.abs(E) <= 1e-7):
+ raise ValueError("covariance matrix is not full rank.")
+ # The precision matrix
+ self.prec = np.dot(np.dot(V, np.diag(old_div(1.0, E))), V.T)
+
+ def log_den(self, X):
+ mean = self.mean
+ X0 = X - mean
+ X0prec = np.dot(X0, self.prec)
+ unden = old_div(-np.sum(X0prec * X0, 1), 2.0)
+ return unden
+
+ def get_datasource(self):
+ return DSNormal(self.mean, self.cov)
+
+ def dim(self):
+ return len(self.mean)
diff --git a/hyppo/kgof/fssd.py b/hyppo/kgof/fssd.py
new file mode 100644
index 000000000..f55528a43
--- /dev/null
+++ b/hyppo/kgof/fssd.py
@@ -0,0 +1,475 @@
+from __future__ import division
+
+from builtins import str, range, object
+from past.utils import old_div
+
+import autograd.numpy as np
+from ._utils import outer_rows
+from .base import GofTest
+from abc import ABC, abstractmethod
+import logging
+
+import scipy
+import scipy.stats as stats
+from numpy.random import default_rng
+
+
+class H0Simulator(ABC):
+ """
+ An abstract class representing a simulator to draw samples from the
+ null distribution. For some tests, these are needed to conduct the test.
+ """
+
+ def __init__(self, n_simulate, seed):
+ """
+ n_simulate: The number of times to simulate from the null distribution.
+ Must be a positive integer.
+ seed: a random seed
+
+ From: https://github.com/wittawatj/fsic-test
+ """
+ assert n_simulate > 0
+ self.n_simulate = n_simulate
+ self.seed = seed
+
+ @abstractmethod
+ def simulate(self, gof, X):
+ """
+ gof: a GofTest
+ dat: a Data (observed data)
+ Simulate from the null distribution and return a dictionary.
+ One of the item is
+ sim_stats: a numpy array of stats.
+
+ From: https://github.com/wittawatj/fsic-test
+ """
+ raise NotImplementedError()
+
+
+class FSSDH0SimCovObs(H0Simulator):
+ """
+ An asymptotic null distribution simulator for FSSD. Simulate from the
+ asymptotic null distribution given by the weighted sum of chi-squares. The
+ eigenvalues (weights) are computed from the covarince matrix wrt. the
+ observed sample.
+ This is not the correct null distribution; but has the correct asymptotic
+ types-1 error at alpha.
+ """
+
+ def __init__(self, n_simulate=3000, seed=10):
+ super(FSSDH0SimCovObs, self).__init__(n_simulate, seed)
+
+ def simulate(self, gof, X, fea_tensor=None):
+ """
+ fea_tensor: n x d x J feature matrix
+ """
+ assert isinstance(gof, FSSD)
+ n_simulate = self.n_simulate
+ seed = self.seed
+ if fea_tensor is None:
+ _, fea_tensor = gof.statistic(X, return_feature_tensor=True)
+
+ J = fea_tensor.shape[2]
+ n = X.shape[0]
+ # n x d*J
+ Tau = fea_tensor.reshape(n, -1)
+ # Make sure it is a matrix i.e, np.cov returns a scalar when Tau is
+ # 1d.
+ cov = np.cov(Tau.T) + np.zeros((1, 1))
+
+ arr_nfssd, eigs = list_simulate_spectral(cov, J, n_simulate, seed=self.seed)
+ return {"sim_stats": arr_nfssd}
+
+
+class FSSDH0SimCovDraw(H0Simulator):
+ """
+ An asymptotic null distribution simulator for FSSD. Simulate from the
+ asymptotic null distribution given by the weighted sum of chi-squares. The
+ eigenvalues (weights) are computed from the covarince matrix wrt. the
+ sample drawn from p (the density to test against).
+
+ - The UnnormalizedDensity p is required to implement get_datasource() method.
+
+ From: https://github.com/wittawatj/fsic-test
+ """
+
+ def __init__(self, n_draw=2000, n_simulate=3000, seed=10):
+ """
+ n_draw: number of samples to draw from the UnnormalizedDensity p
+ """
+ super(FSSDH0SimCovDraw, self).__init__(n_simulate, seed)
+ self.n_draw = n_draw
+
+ def simulate(self, gof, fea_tensor=None):
+ """
+ fea_tensor: n x d x J feature matrix
+ This method does not use dat.
+
+ From: https://github.com/wittawatj/fsic-test
+ """
+ # p = an UnnormalizedDensity
+ p = gof.p
+ ds = p.get_datasource()
+ if ds is None:
+ raise ValueError("DataSource associated with p must be available.")
+ Xdraw = ds.sample(n=self.n_draw, seed=self.seed)
+ _, fea_tensor = gof.statistic(Xdraw, return_feature_tensor=True)
+
+ X = Xdraw
+ J = fea_tensor.shape[2]
+ n = self.n_draw
+ # n x d*J
+ Tau = fea_tensor.reshape(n, -1)
+ # Make sure it is a matrix i.e, np.cov returns a scalar when Tau is
+ # 1d.
+ cov = old_div(Tau.T.dot(Tau), n) + np.zeros((1, 1))
+ n_simulate = self.n_simulate
+
+ arr_nfssd, eigs = list_simulate_spectral(cov, J, n_simulate, seed=self.seed)
+ return {"sim_stats": arr_nfssd}
+
+
+class FSSD(GofTest):
+ r"""
+ Goodness-of-fit test using The Finite Set Stein Discrepancy statistic.
+ and a set of paired test locations. The statistic is n*FSSD^2.
+ The statistic can be negative because of the unbiased estimator.
+
+ :math:`H0` : the sample follows :math:`p`
+ :math:`H1` : the sample does not follow :math:`p`
+
+ :math:`p` is specified to the constructor in the form of an UnnormalizedDensity.
+
+ Notes
+ -----
+ Given a known probability density :math:`p` (model) and a sample
+ :math:`\{ \mathbf{x}_i \}_{i=1}^n \sim q` where :math:`q` is an unknown
+ density, the GoF test tests whether or not the sample :math:`\{ \mathbf{x}_i \}_{i=1}^n`
+ is distributed according to a known :math:`p`.
+
+ The implemented test relies on a new test statistic called The Finite-Set Stein Discrepancy (FSSD)
+ which is a discrepancy measure between a density and a sample. Unique features of the new goodness-of-fit test are:
+
+ It makes only a few mild assumptions on the distributions :math:`p` and :math:`q`. The model :math:`p`
+ can take almost any form. The normalizer of :math:`p` is not assumed known. The test only assesses the goodness of
+ :math:`p` through :math:`\nabla_{\mathbf{x}} \log p(\mathbf{x})` i.e., the first derivative of the log density.
+
+ The runtime complexity of the full test (both parameter tuning and the actual test) is
+ :math:`\mathcal{O}(n)` i.e., linear in the sample size.
+
+ It returns a set of points (features) which indicate where :math:`p` fails to fit the data.
+
+ The FSSD test requires that the derivative of :math:`\log p` exists.
+ The test requires a technical condition called the "vanishing boundary" condition for it to be consistent.
+ The condition is :math:`\lim_{\|\mathbf{x} \|\to \infty} p(\mathbf{x}) \mathbf{g}(\mathbf{x}) = \mathbf{0}` where
+ :math:`\mathbf{g}` is the so called the Stein witness function which depends on the kernel and
+ :math:`\nabla_{\mathbf{x}} \log p(\mathbf{x})`. For a density :math:`p` which has support everywhere e.g.,
+ Gaussian, there is no problem at all. However, for a density defined on a domain
+ with a boundary, one has to be careful. For example, if :math:`p` is a
+ Gamma density defined on the positive orthant of :math:`\mathbb{R}`, the density itself can actually be evaluated on negative points.
+ Looking at the way the Gamma density is written, there is nothing that tells the test that it cannot be evaluated on negative orthant.
+ Therefore, if :math:`p` is Gamma, and the observed sample also follows :math:`p`
+ (i.e., :math:`H_0` is true), the test will still reject :math:`H_0`!
+ The reason is that the data do not match the left tail (in the negative region!) of the Gamma.
+ It is necessary to include the fact that negative region has 0 density into the density itself.
+ """
+
+ # NULLSIM_* are constants used to choose the way to simulate from the null
+ # distribution to do the test.
+
+ # Same as NULLSIM_COVQ; but assume that sample can be drawn from p.
+ # Use the drawn sample to compute the covariance.
+ NULLSIM_COVP = 1
+
+ def __init__(
+ self, p, k, V, null_sim=FSSDH0SimCovObs(n_simulate=3000, seed=101), alpha=0.01
+ ):
+ r"""
+ Parameters
+ ----------
+ p : an instance of UnnormalizedDensity
+ k : a DifferentiableKernel object
+ V : J x dx numpy array of J locations to test the difference
+ null_sim : an instance of H0Simulator for simulating from the null distribution.
+ alpha : significance level
+ """
+ super(FSSD, self).__init__(p, alpha)
+ self.k = k
+ self.V = V
+ self.null_sim = null_sim
+
+ def test(self, X, return_simulated_stats=False):
+ r"""
+ Perform the goodness-of-fit test using an FSSD test statistic
+ and return values computed in a dictionary.
+
+ Parameters
+ ----------
+ dat : an instance of Data
+
+ Returns
+ -------
+ results : a dictionary containing alpha, p-value, test statistic,
+ and null hypothesis rejection status
+ """
+ alpha = self.alpha
+ null_sim = self.null_sim
+ n_simulate = null_sim.n_simulate
+ n = X.shape[0]
+ J = self.V.shape[0]
+
+ nfssd, fea_tensor = self.statistic(X, return_feature_tensor=True)
+ sim_results = null_sim.simulate(self, X, fea_tensor)
+ arr_nfssd = sim_results["sim_stats"]
+
+ # approximate p-value with the permutations
+ pvalue = np.mean(arr_nfssd > nfssd)
+
+ results = {
+ "alpha": self.alpha,
+ "pvalue": pvalue,
+ "test_stat": nfssd,
+ "h0_rejected": pvalue < alpha,
+ "n_simulate": n_simulate,
+ }
+ if return_simulated_stats:
+ results["sim_stats"] = arr_nfssd
+ return results
+
+ def statistic(self, X, return_feature_tensor=False):
+ r"""
+ Compute the test statistic. The statistic is n*FSSD^2.
+
+ Parameters
+ ----------
+ dat : an instance of Data
+
+ Returns
+ -------
+ stat : the test statistic, n*FSSD^2
+ """
+ n = X.shape[0]
+
+ # n x d x J
+ Xi = self.feature_tensor(X)
+ unscaled_mean = ustat_h1_mean_variance(Xi, return_variance=False)
+ stat = n * unscaled_mean
+
+ if return_feature_tensor:
+ return stat, Xi
+ else:
+ return stat
+
+ def get_H1_mean_variance(self, X):
+ r"""
+ Calculate the mean and variance under H1 of the test statistic (divided by
+ n).
+
+ Parameters
+ ----------
+ dat : an instance of Data
+
+ Returns
+ -------
+ mean : the mean under the alternative hypothesis of the test statistic
+ variance : the variance under the alternative hypothesis of the test statistic
+ """
+ Xi = self.feature_tensor(X)
+ mean, variance = ustat_h1_mean_variance(Xi, return_variance=True)
+ return mean, variance
+
+ def feature_tensor(self, X):
+ r"""
+ Compute the feature tensor which is n x d x J.
+ The feature tensor can be used to compute the statistic, and the
+ covariance matrix for simulating from the null distribution.
+
+ Parameters
+ ----------
+ X : n x d data numpy array
+
+ Returns
+ -------
+ Xi : an n x d x J numpy array
+ """
+ k = self.k
+ J = self.V.shape[0]
+ n, d = X.shape
+ # n x d matrix of gradients
+ grad_logp = self.p.grad_log(X)
+
+ K = k.eval(X, self.V)
+
+ list_grads = np.array([np.reshape(k.gradX_y(X, v), (1, n, d)) for v in self.V])
+ stack0 = np.concatenate(list_grads, axis=0)
+ # a numpy array G of size n x d x J such that G[:, :, J]
+ # is the derivative of k(X, V_j) with respect to X.
+ dKdV = np.transpose(stack0, (1, 2, 0))
+
+ # n x d x J tensor
+ grad_logp_K = outer_rows(grad_logp, K)
+
+ Xi = old_div((grad_logp_K + dKdV), np.sqrt(d * J))
+ return Xi
+
+
+def power_criterion(p, X, k, test_locs, reg=1e-2, use_unbiased=True, use_2terms=False):
+ r"""
+ Compute the mean and standard deviation of the statistic under H1.
+
+ Parameters
+ ----------
+ use_2terms : True if the objective should include the first term in the power
+ expression. This term carries the test threshold and is difficult to
+ compute (depends on the optimized test locations). If True, then
+ the objective will be -1/(n**0.5*sigma_H1) + n**0.5 FSSD^2/sigma_H1,
+ which ignores the test threshold in the first term.
+
+ Returns
+ -------
+ obj : mean/sd
+ """
+ n = X.shape[0]
+ V = test_locs
+ fssd = FSSD(p, k, V, null_sim=None)
+ fea_tensor = fssd.feature_tensor(X)
+ u_mean, u_variance = ustat_h1_mean_variance(
+ fea_tensor, return_variance=True, use_unbiased=use_unbiased
+ )
+
+ # mean/sd criterion
+ sigma_h1 = np.sqrt(u_variance + reg)
+ ratio = old_div(u_mean, sigma_h1)
+ if use_2terms:
+ obj = old_div(-1.0, (np.sqrt(n) * sigma_h1)) + np.sqrt(n) * ratio
+ else:
+ obj = ratio
+ return obj
+
+
+def ustat_h1_mean_variance(fea_tensor, return_variance=True, use_unbiased=True):
+ r"""
+ Compute the mean and variance of the asymptotic normal distribution
+ under H1 of the test statistic.
+
+ Parameters
+ ----------
+ fea_tensor : feature tensor obtained from feature_tensor()
+ return_variance : If false, avoid computing and returning the variance.
+ use_unbiased : If True, use the unbiased version of the mean. Can be
+ negative.
+
+ Returns
+ -------
+ stat : the mean
+ variance: the variance
+ """
+ Xi = fea_tensor
+ n, d, J = Xi.shape
+
+ assert n > 1, "Need n > 1 to compute the mean of the statistic."
+ # n x d*J
+ Tau = np.reshape(Xi, [n, d * J])
+ if use_unbiased:
+ t1 = np.sum(np.mean(Tau, 0) ** 2) * (old_div(n, float(n - 1)))
+ t2 = old_div(np.sum(np.mean(Tau**2, 0)), float(n - 1))
+ # stat is the mean
+ stat = t1 - t2
+ else:
+ stat = np.sum(np.mean(Tau, 0) ** 2)
+
+ if not return_variance:
+ return stat
+
+ # compute the variance
+ # mu: d*J vector
+ mu = np.mean(Tau, 0)
+ variance = 4 * np.mean(np.dot(Tau, mu) ** 2) - 4 * np.sum(mu**2) ** 2
+ return stat, variance
+
+
+def list_simulate_spectral(cov, J, n_simulate=1000, seed=82):
+ r"""
+ Simulate the null distribution using the spectrums of the covariance
+ matrix. This is intended to be used to approximate the null
+ distribution.
+
+ Returns
+ -------
+ sim_fssds : a numpy array of simulated n*FSSD values
+ eigs : eigenvalues of cov
+ """
+ # eigen decompose
+ eigs, _ = np.linalg.eig(cov)
+ eigs = np.real(eigs)
+ # sort in decreasing order
+ eigs = -np.sort(-eigs)
+ sim_fssds = simulate_null_dist(eigs, J, n_simulate=n_simulate, seed=seed)
+ return sim_fssds, eigs
+
+
+def simulate_null_dist(eigs, J, n_simulate=2000, seed=7):
+ r"""
+ Simulate the null distribution using the spectrums of the covariance
+ matrix of the U-statistic. The simulated statistic is n*FSSD^2 where
+ FSSD is an unbiased estimator.
+
+ Parameters
+ ----------
+ eigs : a numpy array of estimated eigenvalues of the covariance
+ matrix. eigs is of length d*J, where d is the input dimension, and
+ J : the number of test locations.
+
+ Returns
+ -------
+ fssds : a numpy array of simulated statistics.
+ """
+ d = old_div(len(eigs), J)
+ assert d > 0
+ # draw at most d x J x block_size values at a time
+ block_size = max(20, int(old_div(1000.0, (d * J))))
+ fssds = np.zeros(n_simulate)
+ from_ind = 0
+ rng = default_rng(seed)
+ while from_ind < n_simulate:
+ to_draw = min(block_size, n_simulate - from_ind)
+ # draw chi^2 random variables.
+ chi2 = rng.standard_normal(size=(d * J, to_draw)) ** 2
+ # an array of length to_draw
+ sim_fssds = eigs.dot(chi2 - 1.0)
+ # store
+ end_ind = from_ind + to_draw
+ fssds[from_ind:end_ind] = sim_fssds
+ from_ind = end_ind
+ return fssds
+
+
+def fssd_grid_search_kernel(p, X, test_locs, list_kernel):
+ r"""
+ Linear search for the best kernel in the list that maximizes
+ the test power criterion, fixing the test locations to V.
+
+ Parameters
+ ----------
+ p : UnnormalizedDensity
+ dat : a Data object
+ list_kernel : list of kernel candidates
+
+ Returns
+ -------
+ besti : best kernel index
+ objs : array of test power criteria
+ """
+ V = test_locs
+ n_cand = len(list_kernel)
+ objs = np.zeros(n_cand)
+ for i in range(n_cand):
+ ki = list_kernel[i]
+ objs[i] = power_criterion(p, X, ki, test_locs)
+ logging.info("(%d), obj: %5.4g, k: %s" % (i, objs[i], str(ki)))
+
+ # Widths that come early in the list
+ # are preferred if test powers are equal.
+
+ besti = objs.argmax()
+ return besti, objs
diff --git a/hyppo/kgof/kernel.py b/hyppo/kgof/kernel.py
new file mode 100644
index 000000000..641af8496
--- /dev/null
+++ b/hyppo/kgof/kernel.py
@@ -0,0 +1,260 @@
+"""
+Module containing kernel related classes
+
+TODO: Replace kernel evaluation with compute_kern found in hyppo.common.tools by reducing
+dependencies on autograd.numpy
+"""
+from __future__ import division
+
+from builtins import str
+from past.utils import old_div
+
+from abc import ABC, abstractmethod
+import autograd
+import autograd.numpy as np
+
+from ..tools import compute_kern
+
+
+class Kernel(ABC):
+ """Abstract class for kernels. Inputs to all methods are numpy arrays."""
+
+ @abstractmethod
+ def eval(self, X, Y):
+ """
+ Evaluate the kernel on data X and Y
+ X: nx x d where each row represents one point
+ Y: ny x d
+ return nx x ny Gram matrix
+ """
+ pass
+
+ @abstractmethod
+ def pair_eval(self, X, Y):
+ """Evaluate k(x1, y1), k(x2, y2), ...
+ X: n x d where each row represents one point
+ Y: n x d
+ return a 1d numpy array of length n.
+ """
+ pass
+
+
+class DifferentiableKernel(ABC):
+ def gradX_y(self, X, y):
+ """
+ Compute the gradient with respect to X (the first argument of the
+ kernel). Base class provides a default autograd implementation for convenience.
+ Subclasses should override if this does not work.
+ X: nx x d numpy array.
+ y: numpy array of length d.
+ Return a numpy array G of size nx x d, the derivative of k(X, y) with
+ respect to X.
+ """
+ yrow = np.reshape(y, (1, -1))
+ f = lambda X: self.eval(X, yrow)
+ g = autograd.elementwise_grad(f)
+ G = g(X)
+ assert G.shape[0] == X.shape[0]
+ assert G.shape[1] == X.shape[1]
+ return G
+
+
+class LinearKSTKernel(ABC):
+ """
+ Interface specifiying methods a kernel has to implement to be used with
+ the linear-time version of Kernelized Stein discrepancy test of
+ Liu et al., 2016 (ICML 2016).
+ """
+
+ @abstractmethod
+ def pair_gradX_Y(self, X, Y):
+ """
+ Compute the gradient with respect to X in k(X, Y), evaluated at the
+ specified X and Y.
+ X: n x d
+ Y: n x d
+ Return a numpy array of size n x d
+ """
+ raise NotImplementedError()
+
+ @abstractmethod
+ def pair_gradY_X(self, X, Y):
+ """
+ Compute the gradient with respect to Y in k(X, Y), evaluated at the
+ specified X and Y.
+ X: n x d
+ Y: n x d
+ Return a numpy array of size n x d
+ """
+ raise NotImplementedError()
+
+ @abstractmethod
+ def pair_gradXY_sum(self, X, Y):
+ """
+ Compute \sum_{i=1}^d \frac{\partial^2 k(X, Y)}{\partial x_i \partial y_i}
+ evaluated at each x_i in X, and y_i in Y.
+ X: n x d numpy array.
+ Y: n x d numpy array.
+ Return a one-dimensional length-n numpy array of the derivatives.
+ """
+ raise NotImplementedError()
+
+
+class KSTKernel(ABC):
+ """
+ Interface specifiying methods a kernel has to implement to be used with
+ the Kernelized Stein discrepancy test of Chwialkowski et al., 2016 and
+ Liu et al., 2016 (ICML 2016 papers) See goftest.KernelSteinTest.
+ """
+
+ @abstractmethod
+ def gradX_Y(self, X, Y, dim):
+ """
+ Compute the gradient with respect to the dimension dim of X in k(X, Y).
+ X: nx x d
+ Y: ny x d
+ Return a numpy array of size nx x ny.
+ """
+ raise NotImplementedError()
+
+ @abstractmethod
+ def gradY_X(self, X, Y, dim):
+ """
+ Compute the gradient with respect to the dimension dim of Y in k(X, Y).
+ X: nx x d
+ Y: ny x d
+ Return a numpy array of size nx x ny.
+ """
+ raise NotImplementedError()
+
+ @abstractmethod
+ def gradXY_sum(self, X, Y):
+ """
+ Compute \sum_{i=1}^d \frac{\partial^2 k(x, Y)}{\partial x_i \partial y_i}
+ evaluated at each x_i in X, and y_i in Y.
+ X: nx x d numpy array.
+ Y: ny x d numpy array.
+ Return a nx x ny numpy array of the derivatives.
+ """
+ raise NotImplementedError()
+
+
+class KGauss(DifferentiableKernel, KSTKernel, LinearKSTKernel):
+ """
+ The standard isotropic Gaussian kernel.
+ Parameterization is the same as in the density of the standard normal
+ distribution. sigma2 is analogous to the variance.
+ """
+
+ def __init__(self, sigma2):
+ self.sigma2 = sigma2
+
+ def eval(self, X, Y):
+ """
+ Evaluate the Gaussian kernel on the two 2d numpy arrays.
+ Parameters
+ ----------
+ X : n1 x d numpy array
+ Y : n2 x d numpy array
+ Return
+ ------
+ K : a n1 x n2 Gram matrix.
+ """
+ sumx2 = np.reshape(np.sum(X**2, 1), (-1, 1))
+ sumy2 = np.reshape(np.sum(Y**2, 1), (1, -1))
+ D2 = sumx2 - 2 * np.dot(X, Y.T) + sumy2
+ K = np.exp(old_div(-D2, (2.0 * self.sigma2)))
+ return K
+
+ def gradX_Y(self, X, Y, dim):
+ """
+ Compute the gradient with respect to the dimension dim of X in k(X, Y).
+ X: nx x d
+ Y: ny x d
+ Return a numpy array of size nx x ny.
+ """
+ sigma2 = self.sigma2
+ K = self.eval(X, Y)
+ Diff = X[:, [dim]] - Y[:, [dim]].T
+ G = -K * Diff / sigma2
+ return G
+
+ def pair_gradX_Y(self, X, Y):
+ """
+ Compute the gradient with respect to X in k(X, Y), evaluated at the
+ specified X and Y.
+ X: n x d
+ Y: n x d
+ Return a numpy array of size n x d
+ """
+ sigma2 = self.sigma2
+ Kvec = self.pair_eval(X, Y)
+ # n x d
+ Diff = X - Y
+ G = -Kvec[:, np.newaxis] * Diff / sigma2
+ return G
+
+ def gradY_X(self, X, Y, dim):
+ """
+ Compute the gradient with respect to the dimension dim of Y in k(X, Y).
+ X: nx x d
+ Y: ny x d
+ Return a numpy array of size nx x ny.
+ """
+ return -self.gradX_Y(X, Y, dim)
+
+ def pair_gradY_X(self, X, Y):
+ """
+ Compute the gradient with respect to Y in k(X, Y), evaluated at the
+ specified X and Y.
+ X: n x d
+ Y: n x d
+ Return a numpy array of size n x d
+ """
+ return -self.pair_gradX_Y(X, Y)
+
+ def gradXY_sum(self, X, Y):
+ r"""
+ Compute \sum_{i=1}^d \frac{\partial^2 k(X, Y)}{\partial x_i \partial y_i}
+ evaluated at each x_i in X, and y_i in Y.
+ X: nx x d numpy array.
+ Y: ny x d numpy array.
+ Return a nx x ny numpy array of the derivatives.
+ """
+ (n1, d1) = X.shape
+ (n2, d2) = Y.shape
+ d = d1
+ sigma2 = self.sigma2
+ D2 = np.sum(X**2, 1)[:, np.newaxis] - 2 * np.dot(X, Y.T) + np.sum(Y**2, 1)
+ K = np.exp(old_div(-D2, (2.0 * sigma2)))
+ G = K / sigma2 * (d - old_div(D2, sigma2))
+ return G
+
+ def pair_gradXY_sum(self, X, Y):
+ """
+ Compute \sum_{i=1}^d \frac{\partial^2 k(X, Y)}{\partial x_i \partial y_i}
+ evaluated at each x_i in X, and y_i in Y.
+ X: n x d numpy array.
+ Y: n x d numpy array.
+ Return a one-dimensional length-n numpy array of the derivatives.
+ """
+ d = X.shape[1]
+ sigma2 = self.sigma2
+ D2 = np.sum((X - Y) ** 2, 1)
+ Kvec = np.exp(old_div(-D2, (2.0 * self.sigma2)))
+ G = Kvec / sigma2 * (d - old_div(D2, sigma2))
+ return G
+
+ def pair_eval(self, X, Y):
+ """
+ Evaluate k(x1, y1), k(x2, y2), ...
+ Parameters
+ ----------
+ X, Y : n x d numpy array
+ Return
+ -------
+ a numpy array with length n
+ """
+ D2 = np.sum((X - Y) ** 2, 1)
+ Kvec = np.exp(old_div(-D2, (2.0 * self.sigma2)))
+ return Kvec
diff --git a/hyppo/kgof/tests/__init__.py b/hyppo/kgof/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/hyppo/kgof/tests/test_density.py b/hyppo/kgof/tests/test_density.py
new file mode 100644
index 000000000..e0e3232c3
--- /dev/null
+++ b/hyppo/kgof/tests/test_density.py
@@ -0,0 +1,75 @@
+import numpy as np
+from numpy import testing
+from past.utils import old_div
+from scipy.linalg.misc import norm
+
+from ..datasource import DSNormal, DSIsotropicNormal
+from ..density import IsotropicNormal, Normal
+import scipy.stats as stats
+from numpy.random import default_rng
+
+import pytest
+
+
+class TestIsotropicNormal:
+ @pytest.mark.parametrize("n", [7])
+ @pytest.mark.parametrize("d", [3, 1])
+ def test_log_den(self, n, d):
+ rng = default_rng(16)
+ variance = 1.1
+ mean = rng.standard_normal(size=d)
+ X = rng.random(size=(n, d)) + 1
+
+ isonorm = IsotropicNormal(mean, variance)
+ ds_isonorm = DSIsotropicNormal(mean, variance)
+ isonorm.log_normalized_den(X)
+ isonorm.dim()
+ log_dens = isonorm.log_den(X)
+ my_log_dens = -np.sum((X - mean) ** 2, 1) / (2.0 * variance)
+
+ # check correctness
+ testing.assert_almost_equal(log_dens, my_log_dens)
+
+ @pytest.mark.parametrize("n", [8])
+ @pytest.mark.parametrize("d", [4, 1])
+ def test_grad_log(self, n, d):
+ rng = default_rng(17)
+ variance = 1.2
+ mean = rng.standard_normal(size=d) + 1
+ X = rng.random(size=(n, d)) - 2
+
+ isonorm = IsotropicNormal(mean, variance)
+ grad_log = isonorm.grad_log(X)
+ my_grad_log = -(X - mean) / variance
+
+ # check correctness
+ testing.assert_almost_equal(grad_log, my_grad_log)
+
+
+class TestNormal:
+ @pytest.mark.parametrize("n", [2])
+ @pytest.mark.parametrize("d", [2])
+ def test_log_den(self, n, d):
+ rng = default_rng(16)
+ cov = np.array([[1.1, 1.2], [1.1, 1.2]])
+ mean = rng.standard_normal(size=(n, d))
+ X = rng.random(size=(n, d)) + 1
+
+ test_mean = np.ones(2)
+ test_cov = np.array([[1.1, 1.2], [1.1, 1.2]])
+
+ norm = Normal(mean, cov)
+ log_dens = norm.log_den(X)
+ E, V = np.linalg.eigh(cov)
+ prec = np.dot(np.dot(V, np.diag(old_div(1.0, E))), V.T)
+ X0 = X - mean
+ X0prec = np.dot(X0, prec)
+ my_log_dens = old_div(-np.sum(X0prec * X0, 1), 2.0)
+
+ ds_norm = DSNormal(test_mean, cov)
+ ds_norm.sample(n=10)
+ norm.get_datasource()
+ norm.dim()
+
+ # check correctness
+ testing.assert_almost_equal(log_dens, my_log_dens)
diff --git a/hyppo/kgof/tests/test_fssd.py b/hyppo/kgof/tests/test_fssd.py
new file mode 100644
index 000000000..b9c325aa9
--- /dev/null
+++ b/hyppo/kgof/tests/test_fssd.py
@@ -0,0 +1,98 @@
+import numpy as np
+import numpy.testing as testing
+
+from ..fssd import (
+ FSSD,
+ ustat_h1_mean_variance,
+ power_criterion,
+ fssd_grid_search_kernel,
+ FSSDH0SimCovObs,
+ FSSDH0SimCovDraw,
+)
+from .._utils import meddistance, fit_gaussian_draw, constrain
+from ..kernel import KGauss
+from ..density import IsotropicNormal
+
+from numpy.random import default_rng
+
+import scipy.stats as stats
+import pytest
+
+
+class TestFSSD:
+ @pytest.mark.parametrize("n", [100])
+ @pytest.mark.parametrize("alpha", [0.01])
+ @pytest.mark.parametrize("d", [1])
+ @pytest.mark.parametrize("J", [1])
+ def test_basic(self, n, alpha, d, J):
+ seed = 12
+ # sample
+ mean = np.zeros(d)
+ variance = 1
+ isonorm = IsotropicNormal(mean, variance)
+
+ # only one dimension of the mean is shifted
+ draw_mean = mean + 0
+ draw_variance = variance + 1
+
+ rng = default_rng(seed)
+ X = rng.standard_normal(size=(n, d)) * np.sqrt(draw_variance) + draw_mean
+
+ # Test
+ sig2 = meddistance(X, subsample=1000) ** 2
+ k = KGauss(sig2)
+ list_k = [k]
+
+ # random test locations
+ V = fit_gaussian_draw(X, J, seed=seed + 1)
+ null_sim = FSSDH0SimCovObs(n_simulate=200, seed=3)
+ extra_sim = FSSDH0SimCovDraw()
+ fssd = FSSD(isonorm, k, V, null_sim=null_sim, alpha=alpha)
+ check_sim = extra_sim.simulate(gof=fssd)
+ power_criterion(p=isonorm, X=X, k=k, test_locs=V)
+ fssd_grid_search_kernel(p=isonorm, X=X, test_locs=V, list_kernel=list_k)
+ fssd.get_H1_mean_variance(X=X)
+
+ tresult = fssd.test(X, return_simulated_stats=True)
+
+ # assertions
+ testing.assert_almost_equal(tresult["pvalue"], 0, decimal=1)
+ testing.assert_almost_equal(tresult["test_stat"], 1.6, decimal=1)
+
+ @pytest.mark.parametrize("n", [200])
+ @pytest.mark.parametrize("alpha", [0.01])
+ @pytest.mark.parametrize("d", [4])
+ @pytest.mark.parametrize("J", [1])
+ def test_ustat_h1_mean_variance(self, n, alpha, d, J):
+ seed = 20
+ constrain(20, 19, 21)
+ # sample
+ mean = np.zeros(d)
+ variance = 1
+ isonorm = IsotropicNormal(mean, variance)
+
+ draw_mean = mean + 2
+ draw_variance = variance + 1
+ rng = default_rng(seed)
+ X = rng.standard_normal(size=(n, d)) * np.sqrt(draw_variance) + draw_mean
+
+ # Test
+ sig_square = meddistance(X, subsample=1000) ** 2
+ k = KGauss(sig_square)
+
+ # random test locations
+ V = fit_gaussian_draw(X, J, seed=seed + 1)
+
+ null_sim = FSSDH0SimCovObs(n_simulate=200, seed=3)
+ fssd = FSSD(isonorm, k, V, null_sim=null_sim, alpha=alpha)
+ fea_tensor = fssd.feature_tensor(X)
+
+ u_mean, u_variance = ustat_h1_mean_variance(fea_tensor)
+
+ random_sim = FSSDH0SimCovObs()
+ random_sim.simulate(gof=fssd, X=X)
+
+ # assertions
+ testing.assert_almost_equal(u_variance, 1, decimal=1)
+ # should reject H0
+ testing.assert_almost_equal(u_mean, 0.8, decimal=1)
diff --git a/hyppo/kgof/tests/test_kernel.py b/hyppo/kgof/tests/test_kernel.py
new file mode 100644
index 000000000..598cdb87a
--- /dev/null
+++ b/hyppo/kgof/tests/test_kernel.py
@@ -0,0 +1,66 @@
+import autograd.numpy as np
+from numpy import testing
+from ..kernel import KGauss
+from ...tools import compute_kern, multi_compute_kern
+
+import pytest
+from numpy.random import default_rng
+
+
+class TestKGauss:
+ @pytest.mark.parametrize("n", [10])
+ @pytest.mark.parametrize("d", [3])
+ def test_basic(self, n, d):
+ # sample
+ rng = default_rng(29)
+ X = rng.standard_normal(size=(n, d)) * 3
+ k = KGauss(sigma2=1)
+ K = k.eval(X, X) # tests usage of compute_kern
+
+ testing.assert_almost_equal(K.shape, (n, n))
+
+ @pytest.mark.parametrize("n", [10])
+ @pytest.mark.parametrize("d", [1, 3])
+ def test_gradX_y(self, n, d):
+ rng = default_rng(10)
+ y = rng.standard_normal(size=d) * 2
+ X = rng.random(size=(n, d)) * 3
+ Y = rng.standard_normal(size=(n, d)) * 2
+
+ sigma2 = 1.3
+ k = KGauss(sigma2=sigma2)
+ # n x d
+ G = k.gradX_y(X, y)
+ k.gradX_Y(X, Y, dim=d - 1)
+ k.gradXY_sum(X, Y)
+ k.pair_gradX_Y(X, Y)
+ k.pair_gradXY_sum(X, Y)
+ k.pair_eval(X, Y)
+ # check correctness
+ K = k.eval(X, y[np.newaxis, :])
+ modG = -K / sigma2 * (X - y)
+
+ testing.assert_equal(G.shape, modG.shape)
+ testing.assert_almost_equal(G, modG)
+
+ @pytest.mark.parametrize("n", [11])
+ @pytest.mark.parametrize("d", [3, 1])
+ def test_gradXY_sum(self, n, d):
+ rng = default_rng(12)
+ X = rng.standard_normal(size=(n, d))
+ sigma2 = 1.4
+ k = KGauss(sigma2=sigma2)
+
+ # n x n
+ modG = np.zeros((n, n))
+ K = k.eval(X, X)
+ for i in range(n):
+ for j in range(n):
+ diffi2 = np.sum((X[i, :] - X[j, :]) ** 2)
+ modG[i, j] = K[i, j] / sigma2 * (d - diffi2 / sigma2)
+
+ # check correctness of function that calculates sum of the gradient across x_i and y_i in Y
+ G = k.gradXY_sum(X, X)
+
+ testing.assert_equal(G.shape, modG.shape)
+ testing.assert_almost_equal(G, modG)
diff --git a/hyppo/ksample/__init__.py b/hyppo/ksample/__init__.py
index bfc1d0335..41537b8d2 100644
--- a/hyppo/ksample/__init__.py
+++ b/hyppo/ksample/__init__.py
@@ -1,3 +1,26 @@
+from ._utils import k_sample_transform
+from .disco import DISCO
+from .energy import Energy
+from .hotelling import Hotelling
from .ksamp import KSample
+from .manova import MANOVA
+from .mmd import MMD
+from .ksamplehhg import KSampleHHG
+
+from .smoothCF import SmoothCFTest, smooth_cf_distance
+from .mean_embedding import MeanEmbeddingTest, mean_embed_distance
__all__ = [s for s in dir()] # add imported tests to __all__
+
+
+KSAMP_TESTS = {
+ "disco": DISCO,
+ "energy": Energy,
+ "hotelling": Hotelling,
+ "ksample": KSample,
+ "manova": MANOVA,
+ "mmd": MMD,
+ "smoothCF": SmoothCFTest,
+ "mean_embedding": MeanEmbeddingTest,
+ "ksamplehhg": KSampleHHG,
+}
diff --git a/hyppo/ksample/_utils.py b/hyppo/ksample/_utils.py
index 9cf82a64d..2236e6951 100644
--- a/hyppo/ksample/_utils.py
+++ b/hyppo/ksample/_utils.py
@@ -1,11 +1,10 @@
import numpy as np
from ..tools import contains_nan
-from ..independence import CCA, Dcorr, HHG, RV, Hsic, MGC, KMERF
class _CheckInputs:
- def __init__(self, inputs, indep_test, reps=None):
+ def __init__(self, inputs, indep_test=None, reps=None):
self.inputs = inputs
self.reps = reps
self.indep_test = indep_test
@@ -57,8 +56,8 @@ def _convert_inputs_float64(self):
return [np.asarray(i).astype(np.float64) for i in self.inputs]
def _check_indep_test(self):
- tests = [CCA, Dcorr, HHG, RV, Hsic, MGC, KMERF]
- if self.indep_test.__class__ not in tests and self.indep_test:
+ tests = ["cca", "dcorr", "hhg", "rv", "hsic", "mgc", "kmerf"]
+ if self.indep_test not in tests and self.indep_test is not None:
raise ValueError("indep_test must be in {}".format(tests))
def _check_min_samples(self):
@@ -68,6 +67,36 @@ def _check_min_samples(self):
def k_sample_transform(inputs, test_type="normal"):
+ """
+ Computes a `k`-sample transform of the inputs.
+
+ For :math:`k` groups, this creates two matrices, the first vertically stacks the
+ inputs.
+ In order to use this function, the inputs must have the same number of dimensions
+ :math:`p` and can have varying number of samples :math:`n`. The second output is a
+ label
+ matrix the one-hoc encodes the groups. The outputs are thus ``(N, p)`` and
+ ``(N, k)`` where `N` is the total number of samples. In the case where the test
+ a random forest based tests, it creates a ``(N, 1)`` where the entries are
+ varlues from 1 to :math:`k` based on the number of samples.
+
+ Parameters
+ ----------
+ inputs : list of ndarray
+ A list of the inputs. All inputs must be ``(n, p)`` where `n` is the number
+ of samples and `p` is the number of dimensions. `n` can vary between samples,
+ but `p` must be the same among all the samples.
+ test_type : {"normal", "rf"}, default: "normal"
+ Whether to one-hoc encode the inputs ("normal") or use a one-dimensional
+ categorical encoding ("rf").
+
+ Returns
+ -------
+ u : ndarray
+ The matrix of concatenated inputs of shape ``(N, p)``.
+ v : ndarray
+ The label matrix of shape ``(N, k)`` ("normal") or ``(N, 1)`` ("rf").
+ """
n_inputs = len(inputs)
u = np.vstack(inputs)
if np.var(u) == 0:
diff --git a/hyppo/ksample/base.py b/hyppo/ksample/base.py
index 7bd0c3710..9d03380df 100644
--- a/hyppo/ksample/base.py
+++ b/hyppo/ksample/base.py
@@ -1,56 +1,105 @@
from abc import ABC, abstractmethod
+from typing import NamedTuple
+
+
+class KSampleTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
class KSampleTest(ABC):
"""
- A base class for a k-sample test.
+ A base class for a *k*-sample test.
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
- A function that computes the distance or similarity among the samples
- within each data matrix. Set to `None` if `x` and `y` are already
- distance matrices. To call a custom function, either create the
- distance matrix before-hand or create a function of the form
- ``compute_distance(x)`` where `x` is the data matrix for which
- pairwise distances are calculated.
+ compute_distance : str, callable, or None, default: "euclidean" or "gaussian"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Alternatively, this function computes the kernel similarity among the
+ samples within each data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
bias : bool (default: False)
Whether or not to use the biased or unbiased test statistics. Only
applies to ``Dcorr`` and ``Hsic``.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distkern``.
"""
- def __init__(self, bias=False, **kwargs):
+ def __init__(self, compute_distance=None, bias=False, **kwargs):
# set statistic and p-value
self.stat = None
self.pvalue = None
self.bias = bias
+ self.compute_distance = compute_distance
self.kwargs = kwargs
super().__init__()
@abstractmethod
- def _statistic(self, inputs):
+ def statistic(self, *args):
r"""
Calulates the *k*-sample test statistic.
Parameters
----------
- inputs : ndarray
- Input data matrices.
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of dimensions. That is, the shapes must be `(n, p)` and
+ `(m, p)`, ... where `n`, `m`, ... are the number of samples and `p` is
+ the number of dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed *k*-Sample statistic.
"""
@abstractmethod
- def test(self, inputs, reps=1000, workers=1):
+ def test(self, *args, reps=1000, workers=1, random_state=None):
r"""
- Calulates the k-sample test p-value.
+ Calculates the *k*-sample test statistic and p-value.
Parameters
----------
- inputs : list of ndarray
- Input data matrices.
- reps : int, optional
- The number of replications used in permutation, by default 1000.
- workers : int, optional (default: 1)
- Evaluates method using `multiprocessing.Pool `).
- Supply `-1` to use all cores available to the Process.
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of dimensions. That is, the shapes must be `(n, p)` and
+ `(m, p)`, ... where `n`, `m`, ... are the number of samples and `p` is
+ the number of dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+
+ Returns
+ -------
+ stat : float
+ The computed *k*-sample statistic.
+ pvalue : float
+ The computed *k*-sample p-value.
"""
diff --git a/hyppo/ksample/disco.py b/hyppo/ksample/disco.py
new file mode 100644
index 000000000..097f2e244
--- /dev/null
+++ b/hyppo/ksample/disco.py
@@ -0,0 +1,202 @@
+import numpy as np
+
+from ..independence.dcorr import _dcov
+from ..tools import compute_dist
+from ._utils import _CheckInputs, k_sample_transform
+from .base import KSampleTest, KSampleTestOutput
+from .ksamp import KSample
+
+
+class DISCO(KSampleTest):
+ r"""
+ Distance Components (DISCO) test statistic and p-value.
+
+ DISCO is a powerful multivariate `k`-sample test. It leverages distance matrix
+ capabilities (similar to tests like distance correlation or Dcorr). In fact, DISCO
+ statistic is equivalent to our 2-sample formulation nonparametric MANOVA via
+ independence testing, i.e. :class:`hyppo.ksample.KSample`,
+ and to
+ :class:`hyppo.independence.Dcorr`,
+ :class:`hyppo.ksample.Energy`,
+ :class:`hyppo.independence.Hsic`, and
+ :class:`hyppo.ksample.MMD`
+ :footcite:p:`pandaNonparMANOVAIndependence2021`
+ :footcite:p:`shenExactEquivalenceDistance2020`.
+
+ Parameters
+ ----------
+ compute_distance : str, callable, or None, default: "euclidean"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
+ matrices. To call a custom function, either create the distance matrix
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ bias : bool, default: False
+ Whether or not to use the biased or unbiased test statistics.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
+
+ Notes
+ -----
+ Traditionally, the formulation for the DISCO statistic
+ is as follows :footcite:p:`szekelyTestingEqualDistributions`:
+
+ Define
+ :math:`\{ u^i_1 \stackrel{iid}{\sim} F_{U_1},\ i = 1, ..., n_1 \}` up to
+ :math:`\{ u^j_k \stackrel{iid}{\sim} F_{V_1},\ j = 1, ..., n_k \}` as `k` groups
+ of samples deriving from different distributions with the same
+ dimensionality. If :math:`d(\cdot, \cdot)` is a distance metric (i.e. Euclidean),
+ :math:`N = \sum_{i = 1}^k n_k`,
+ and :math:`\mathrm{Energy}` is the Energy test statistic from
+ :class:`hyppo.ksample.Energy`
+ then,
+
+ .. math::
+
+ \mathrm{DISCO}_N(\mathbf{u}_1, \ldots, \mathbf{u}_k) =
+ \sum_{1 \leq k < l \leq K} \frac{n_k n_l}{2N}
+ \mathrm{Energy}_{n_k + n_l} (\mathbf{u}_k, \mathbf{u}_l)
+
+ The implementation in the :class:`hyppo.ksample.KSample` class (using
+ :class:`hyppo.independence.Dcorr`) is in
+ fact equivalent to this implementation (for p-values) and statistics are
+ equivalent up to a scaling factor :footcite:p:`pandaNonparMANOVAIndependence2021`.
+
+ The p-value returned is calculated using a permutation test uses
+ :meth:`hyppo.tools.perm_test`.
+ The fast version of the test uses :meth:`hyppo.tools.chi2_approx`.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, compute_distance="euclidean", bias=False, **kwargs):
+ KSampleTest.__init__(
+ self, compute_distance=compute_distance, bias=bias, **kwargs
+ )
+
+ def statistic(self, *args):
+ r"""
+ Calulates the DISCO test statistic.
+
+ Parameters
+ ----------
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples and dimensions. That is, the shapes must be `(n, p)`
+ where `n` are the number of samples and `p` is
+ the number of dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed DISCO statistic.
+ """
+ inputs = list(args)
+ N = [i.shape[0] for i in inputs]
+
+ if len(set(N)) > 1:
+ raise ValueError(
+ "Shape mismatch, inputs must have same sample size, "
+ "currently {}".format(len(set(N)))
+ )
+ u, v = k_sample_transform(inputs)
+
+ distu, distv = compute_dist(u, v, metric=self.compute_distance, **self.kwargs)
+
+ # exact equivalence transformation Dcov and DISCO
+ stat = _dcov(distu, distv, self.bias) * np.sum(N) * len(N) / 2
+ self.stat = stat
+
+ return stat
+
+ def test(self, *args, reps=1000, workers=1, auto=True, random_state=None):
+ r"""
+ Calculates the DISCO test statistic and p-value.
+
+ Parameters
+ ----------
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples and dimensions. That is, the shapes must be `(n, p)`
+ where `n` is the number of samples and `p` is
+ the number of dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
+
+ Returns
+ -------
+ stat : float
+ The computed DISCO statistic.
+ pvalue : float
+ The computed DISCO p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import DISCO
+ >>> x = np.arange(7)
+ >>> y = x
+ >>> stat, pvalue = DISCO().test(x, y)
+ >>> '%.3f, %.1f' % (stat, pvalue)
+ '-1.566, 1.0'
+ """
+ inputs = list(args)
+ check_input = _CheckInputs(
+ inputs=inputs,
+ indep_test="dcorr",
+ )
+ inputs = check_input()
+ N = [i.shape[0] for i in inputs]
+
+ if len(set(N)) > 1:
+ raise ValueError(
+ "Shape mismatch, inputs must have same sample size, "
+ "currently {}".format(len(set(N)))
+ )
+
+ # observed statistic
+ stat = self.statistic(*inputs)
+
+ # since stat transformation is invariant under permutation, k-sample Dcorr
+ # pvalue is identical to DISCO
+ _, pvalue = KSample(
+ indep_test="Dcorr",
+ compute_distkern=self.compute_distance,
+ bias=self.bias,
+ **self.kwargs
+ ).test(
+ *inputs, reps=reps, workers=workers, auto=auto, random_state=random_state
+ )
+
+ return KSampleTestOutput(stat, pvalue)
diff --git a/hyppo/ksample/energy.py b/hyppo/ksample/energy.py
new file mode 100644
index 000000000..6abc64a4d
--- /dev/null
+++ b/hyppo/ksample/energy.py
@@ -0,0 +1,184 @@
+from ..independence.dcorr import _dcov
+from ..tools import compute_dist
+from ._utils import _CheckInputs
+from .base import KSampleTest, KSampleTestOutput
+from .ksamp import KSample
+
+
+class Energy(KSampleTest):
+ r"""
+ Energy test statistic and p-value.
+
+ Energy is a powerful multivariate 2-sample test. It leverages distance matrix
+ capabilities (similar to tests like distance correlation or Dcorr). In fact, Energy
+ statistic is equivalent to our 2-sample formulation nonparametric MANOVA via
+ independence testing, i.e. :class:`hyppo.ksample.KSample`,
+ and to
+ :class:`hyppo.independence.Dcorr`,
+ :class:`hyppo.ksample.DISCO`,
+ :class:`hyppo.independence.Hsic`, and
+ :class:`hyppo.ksample.MMD`
+ :footcite:p:`pandaNonparMANOVAIndependence2021`
+ :footcite:p:`shenExactEquivalenceDistance2020`.
+
+ Parameters
+ ----------
+ compute_distance : str, callable, or None, default: "euclidean"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
+ matrices. To call a custom function, either create the distance matrix
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ bias : bool, default: False
+ Whether or not to use the biased or unbiased test statistics.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
+
+ Notes
+ -----
+ Traditionally, the formulation for the 2-sample Energy statistic
+ is as follows :footcite:p:`szekelyTestingEqualDistributions`:
+
+ Define
+ :math:`\{ u_i \stackrel{iid}{\sim} F_U,\ i = 1, ..., n \}` and
+ :math:`\{ v_j \stackrel{iid}{\sim} F_V,\ j = 1, ..., m \}` as two groups
+ of samples deriving from different distributions with the same
+ dimensionality. If :math:`d(\cdot, \cdot)` is a distance metric (i.e. Euclidean)
+ then,
+
+ .. math::
+
+ \mathrm{Energy}_{n, m}(\mathbf{u}, \mathbf{v}) = \frac{1}{n^2 m^2}
+ \left( 2nm \sum_{i = 1}^n \sum_{j = 1}^m d(u_i, v_j) - m^2
+ \sum_{i,j=1}^n d(u_i, u_j) - n^2 \sum_{i, j=1}^m d(v_i, v_j) \right)
+
+ The implementation in the :class:`hyppo.ksample.KSample` class (using
+ :class:`hyppo.independence.Dcorr` using 2 samples) is in
+ fact equivalent to this implementation (for p-values) and statistics are
+ equivalent up to a scaling factor :footcite:p:`pandaNonparMANOVAIndependence2021`.
+
+ The p-value returned is calculated using a permutation test uses
+ :meth:`hyppo.tools.perm_test`.
+ The fast version of the test uses :meth:`hyppo.tools.chi2_approx`.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, compute_distance="euclidean", bias=False, **kwargs):
+ KSampleTest.__init__(
+ self, compute_distance=compute_distance, bias=bias, **kwargs
+ )
+
+ def statistic(self, x, y):
+ r"""
+ Calulates the Energy test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed Energy statistic.
+ """
+ distx = x
+ disty = y
+ n = x.shape[0]
+ m = y.shape[0]
+
+ distx, disty = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
+
+ # exact equivalence transformation Dcorr and Energy
+ stat = (
+ _dcov(distx, disty, self.bias) * (2 * (n**2) * (m**2)) / ((n + m) ** 4)
+ )
+ self.stat = stat
+
+ return stat
+
+ def test(self, x, y, reps=1000, workers=1, auto=True, random_state=None):
+ r"""
+ Calculates the Energy test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
+
+ Returns
+ -------
+ stat : float
+ The computed Energy statistic.
+ pvalue : float
+ The computed Energy p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import Energy
+ >>> x = np.arange(7)
+ >>> y = x
+ >>> stat, pvalue = Energy().test(x, y)
+ >>> '%.3f, %.1f' % (stat, pvalue)
+ '0.267, 1.0'
+ """
+ check_input = _CheckInputs(
+ inputs=[x, y],
+ indep_test="dcorr",
+ )
+ x, y = check_input()
+
+ # observed statistic
+ stat = self.statistic(x, y)
+
+ # since stat transformation is invariant under permutation, 2-sample Dcorr
+ # pvalue is identical to Energy
+ _, pvalue = KSample(
+ indep_test="Dcorr",
+ compute_distkern=self.compute_distance,
+ bias=self.bias,
+ **self.kwargs
+ ).test(x, y, reps=reps, workers=workers, auto=auto, random_state=random_state)
+
+ return KSampleTestOutput(stat, pvalue)
diff --git a/hyppo/ksample/hotelling.py b/hyppo/ksample/hotelling.py
new file mode 100644
index 000000000..7164421de
--- /dev/null
+++ b/hyppo/ksample/hotelling.py
@@ -0,0 +1,143 @@
+import numpy as np
+from hyppo.ksample._utils import _CheckInputs
+from hyppo.ksample.base import KSampleTest, KSampleTestOutput
+from scipy.stats import f
+
+
+class Hotelling(KSampleTest):
+ r"""
+ Hotelling :math:`T^2` test statistic and p-value.
+
+ Hotelling :math:`T^2` is 2-sample multivariate analysis of variance (MANOVA)
+ and generalization of Student's t-test in arbitary dimension
+ :footcite:p:`hotellingGeneralizationStudentRatio1931`.
+
+ Notes
+ -----
+ The test statistic is formulated as below
+ :footcite:p:`pandaNonparMANOVAIndependence2021`:
+
+ Consider input samples :math:`u_i \stackrel{iid}{\sim} F_U` for
+ :math:`i \in \{ 1, \ldots, n \}` and :math:`v_i \stackrel{iid}{\sim} F_V` for
+ :math:`i \in \{ 1, \ldots, m \}`. Let :math:`\bar{u}` refer to the columnwise means
+ of :math:`u`; that is, :math:`\bar{u} = (1/n) \sum_{i=1}^{n} u_i` and let
+ :math:`\bar{v}` be the same for :math:`v`. Calculate sample covariance matrices
+ :math:`\hat{\Sigma}_{uv} = u^T v` and sample variance matrices
+ :math:`\hat{\Sigma}_{uu} = u^T u` and :math:`\hat{\Sigma}_{vv} = v^T v`.
+ Denote pooled covariance matrix :math:`\hat{\Sigma}` as
+
+ .. math::
+
+ \hat{\Sigma} = \frac{(n - 1) \hat{\Sigma}_{uu} + (m - 1) \hat{\Sigma}_{vv} }
+ {n + m - 2}
+
+ Then,
+
+ .. math::
+
+ \text{Hotelling}_{n, m} (u, v) = \frac{n m}{n + m}
+ (\bar{u} - \bar{v})^T \hat{\Sigma}^{-1} (\bar{u} - \bar{v})
+
+ Since it is a multivariate generalization of Student's t-tests, it suffers from
+ some of the same assumptions as Student's t-tests. That is, the validity of MANOVA
+ depends on the assumption that random variables are normally distributed within
+ each group and each with the same covariance matrix. Distributions of input data
+ are generally not known and cannot always be reasonably modeled as Gaussian
+ :footcite:p:`micceriUnicornNormalCurve1989`
+ :footcite:p:`stiglerRobustEstimatorsWork1977`
+ and having the same covariance across groups is also generally not true of
+ real data.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self):
+ KSampleTest.__init__(self)
+
+ def statistic(self, x, y):
+ r"""
+ Calulates the Hotelling :math:`T^2` test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed Hotelling :math:`T^2` statistic.
+ """
+ # ported from Hotteling packge in R
+ nx, p = x.shape
+ ny = y.shape[0]
+
+ meanx = np.mean(x, axis=0)
+ meany = np.mean(y, axis=0)
+
+ covx = np.cov(x, rowvar=False)
+ covy = np.cov(y, rowvar=False)
+
+ covs = ((nx - 1) * covx + (ny - 1) * covy) / (nx + ny - 2)
+ m = (nx + ny - p - 1) / (p * (nx + ny - 2))
+ if p > 1:
+ inv_covs = np.linalg.pinv(covs)
+ stat = (
+ m * (meanx - meany).T @ inv_covs @ (meanx - meany) * nx * ny / (nx + ny)
+ )
+ else:
+ inv_covs = 1 / p
+ stat = m * (meanx - meany) ** 2 * inv_covs * nx * ny / (nx + ny)
+
+ self.stat = stat
+
+ return stat
+
+ def test(self, x, y):
+ r"""
+ Calculates the Hotelling :math:`T^2` test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed Hotelling :math:`T^2` statistic.
+ pvalue : float
+ The computed Hotelling :math:`T^2` p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import Hotelling
+ >>> x = np.arange(7)
+ >>> y = x
+ >>> stat, pvalue = Hotelling().test(x, y)
+ >>> '%.3f, %.1f' % (stat, pvalue)
+ '0.000, 1.0'
+ """
+ check_input = _CheckInputs(
+ inputs=[x, y],
+ )
+ x, y = check_input()
+
+ stat = self.statistic(x, y)
+ nx, p = x.shape
+ ny = y.shape[0]
+ pvalue = f.sf(stat, p, nx + ny - p - 1)
+ self.stat = stat
+ self.pvalue = pvalue
+ self.null_dist = None
+
+ return KSampleTestOutput(stat, pvalue)
diff --git a/hyppo/ksample/ksamp.py b/hyppo/ksample/ksamp.py
index 1b89bbe7b..7249e4e4c 100644
--- a/hyppo/ksample/ksamp.py
+++ b/hyppo/ksample/ksamp.py
@@ -1,11 +1,11 @@
-from .base import KSampleTest
-from ..independence import CCA, Dcorr, HHG, RV, Hsic, MGC, KMERF
+from ..independence import INDEP_TESTS
from ._utils import _CheckInputs, k_sample_transform
+from .base import KSampleTest
class KSample(KSampleTest):
r"""
- Class for calculating the *k*-sample test statistic and p-value.
+ Nonparametric `K`-Sample Testing test statistic and p-value.
A *k*-sample test tests equality in distribution among groups. Groups
can be of different sizes, but generally have the same dimensionality.
@@ -15,23 +15,49 @@ class KSample(KSampleTest):
Parameters
----------
- indep_test : {"CCA", "Dcorr", "HHG", "RV", "Hsic", "MGC"}
+ indep_test : "CCA", "Dcorr", "HHG", "RV", "Hsic", "MGC", "KMERF", or list
A string corresponding to the desired independence test from
- ``mgc.independence``. This is not case sensitive.
- compute_distance : callable(), optional (default: euclidean)
+ :mod:`hyppo.independence`. This is not case sensitive. If using ``"MaxMargin"``
+ then this must be a list containing ``"MaxMargin"`` in the first index and
+ another ``indep_test`` in the second index.
+ compute_distkern : str, callable, or None, default: "euclidean" or "gaussian"
A function that computes the distance among the samples within each
- data matrix. Set to `None` if `x` and `y` are already distance
- matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_distance(x)``
- where `x` is the data matrix for which pairwise distances are
- calculated.
- bias : bool (default: False)
- Whether or not to use the biased or unbiased test statistics. Only
- applies to ``Dcorr`` and ``Hsic``.
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Alternatively, this function computes the kernel similarity among the
+ samples within each data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ bias : bool, default: False
+ Whether or not to use the biased or unbiased test statistics (for
+ ``indep_test="Dcorr"`` and ``indep_test="Hsic"``).
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distkern``.
Notes
-----
- The formulation for this implementation is as follows [#1Ksamp]_:
+ The formulation for this implementation is as follows
+ :footcite:p:`pandaNonparMANOVAIndependence2021`:
The *k*-sample testing problem can be thought of as a generalization of
the two sample testing problem. Define
@@ -47,8 +73,8 @@ class KSample(KSampleTest):
The closely related independence testing problem can be generalized
similarly: Given a set of paired data
- :math:`\{\left(x_i, y_i \right) \stackrel{iid}{\sim} F_{XY},
- \ i = 1, ..., N\}`, the problem that we are testing is,
+ :math:`\{\left(x_i, y_i \right) \stackrel{iid}{\sim} F_{XY}, \ i = 1, ..., N\}`,
+ the problem that we are testing is,
.. math::
@@ -58,7 +84,7 @@ class KSample(KSampleTest):
By manipulating the inputs of the *k*-sample test, we can create
concatenated versions of the inputs and another label matrix which are
necessarily paired. Then, any nonparametric test can be performed on
- this data. That is,
+ this data.
Letting :math:`n = \sum_{i=1}^k n_i`, define new data matrices
:math:`\mathbf{x}` and :math:`\mathbf{y}` such that,
@@ -122,80 +148,85 @@ class KSample(KSampleTest):
proportional to how many labels (ways) samples differ by, a hierarchy of distances
between samples thought to be true if the null hypothesis is rejected.
- Performing a multilevel test involves constructing :math:x` and :math:`y` using
- either of the methods above and then performing a block permutation [#2Ksamp]_.
+ Performing a multilevel test involves constructing :math:`x` and :math:`y` using
+ either of the methods above and then performing a block permutation
+ :footcite:p:`winklerMultilevelBlockPermutation2015`.
Essentially, the permutation is striated, where permutation is limited to be within
a block of samples or between blocks of samples, but not both. This is done because
the data is not freely exchangeable, so it is necessary to block the permutation to
- preserve the joint distribution [#2Ksamp]_.
+ preserve the joint distribution :footcite:p:`winklerMultilevelBlockPermutation2015`.
- The p-value returned is calculated using a permutation test using a
- `permutation test `_.
- The fast version of the test (for :math:`k`-sample Dcorr and Hsic) uses a
- `chi squared approximation `_.
+ The p-value returned is calculated using a permutation test uses
+ :meth:`hyppo.tools.perm_test`.
+ The fast version of the test uses :meth:`hyppo.tools.chi2_approx`.
References
----------
- .. [#1Ksamp] Panda, S., Shen, C., Perry, R., Zorn, J., Lutz, A., Priebe, C. E., &
- Vogelstein, J. T. (2019). Nonparametric MANOVA via Independence
- Testing. arXiv e-prints, arXiv-1910.
- .. [#2Ksamp] Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E., &
- Smith, S. M. (2015). Multi-level block permutation. Neuroimage, 123,
- 253-268.
+ .. footbibliography::
"""
- def __init__(self, indep_test, compute_distance="euclidean", bias=False, **kwargs):
- indep_test = indep_test.lower()
- test_names = {
- "rv": RV,
- "cca": CCA,
- "hhg": HHG,
- "hsic": Hsic,
- "dcorr": Dcorr,
- "mgc": MGC,
- "kmerf": KMERF,
- }
- if indep_test not in test_names.keys():
- raise ValueError("Test is not a valid independence test")
- if indep_test == "hsic" and compute_distance == "euclidean":
- compute_distance = "gaussian"
- self.indep_test_name = indep_test
- indep_test = test_names[indep_test]
-
- if self.indep_test_name in ["dcorr", "hhg", "hsic", "mgc"]:
- if self.indep_test_name == "hsic":
- self.indep_test = indep_test(
- compute_kernel=compute_distance, bias=bias, **kwargs
- )
- elif self.indep_test_name == "dcorr":
- self.indep_test = indep_test(
- compute_distance=compute_distance, bias=bias, **kwargs
+ def __init__(self, indep_test, compute_distkern="euclidean", bias=False, **kwargs):
+ if type(indep_test) is list:
+ indep_test = [test.lower() for test in indep_test]
+ self.indep_test_name = indep_test[1]
+ else:
+ indep_test = indep_test.lower()
+ if indep_test not in INDEP_TESTS.keys():
+ raise ValueError(
+ "Test {} is not in {}".format(indep_test, INDEP_TESTS.keys())
)
+ if indep_test == "hsic" and compute_distkern == "euclidean":
+ compute_distkern = "gaussian"
+ self.indep_test_name = indep_test
+
+ indep_kwargs = {
+ "dcorr": {"bias": bias, "compute_distance": compute_distkern},
+ "hsic": {"bias": bias, "compute_kernel": compute_distkern},
+ "hhg": {"compute_distance": compute_distkern},
+ "mgc": {"compute_distance": compute_distkern},
+ "kmerf": {"forest": "classifier"},
+ "rv": {},
+ "cca": {},
+ }
+
+ if type(indep_test) is list:
+ if indep_test[0] == "maxmargin" and indep_test[1] in INDEP_TESTS.keys():
+ if indep_test[1] == "hsic" and compute_distkern == "euclidean":
+ compute_distkern = "gaussian"
+ indep_kwargs["maxmargin"] = {
+ "indep_test": indep_test[1],
+ "compute_distkern": compute_distkern,
+ "bias": bias,
+ }
+ indep_test = "maxmargin"
else:
- self.indep_test = indep_test(
- compute_distance=compute_distance, **kwargs
+ raise ValueError(
+ "Test 1 must be Maximal Margin, currently {}; Test 2 must be an "
+ "independence test, currently {}".format(*indep_test)
)
- elif self.indep_test_name == "kmerf":
- self.indep_test = indep_test(forest_type="classifier", **kwargs)
- else:
- self.indep_test = indep_test()
+
+ self.indep_test = INDEP_TESTS[indep_test](**indep_kwargs[indep_test], **kwargs)
KSampleTest.__init__(
- self, compute_distance=compute_distance, bias=bias, **kwargs
+ self, compute_distance=compute_distkern, bias=bias, **kwargs
)
- def _statistic(self, *args):
+ def statistic(self, *args):
r"""
Calulates the *k*-sample test statistic.
Parameters
----------
- *args : ndarrays
+ *args : ndarray of float
Variable length input data matrices. All inputs must have the same
- number of samples. That is, the shapes must be `(n, p)` and
- `(m, p)` where `n` and `m` are the number of samples and `p` are
- the number of dimensions. Alternatively, inputs can be distance
- matrices, where the shapes must all be `(n, n)`.
+ number of dimensions. That is, the shapes must be `(n, p)` and
+ `(m, p)`, ... where `n`, `m`, ... are the number of samples and `p` is
+ the number of dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed *k*-sample statistic.
"""
inputs = list(args)
if self.indep_test_name == "kmerf":
@@ -203,38 +234,43 @@ def _statistic(self, *args):
else:
u, v = k_sample_transform(inputs)
- return self.indep_test._statistic(u, v)
+ return self.indep_test.statistic(u, v)
- def test(self, *args, reps=1000, workers=1, auto=True):
+ def test(self, *args, reps=1000, workers=1, auto=True, random_state=None):
r"""
Calculates the *k*-sample test statistic and p-value.
Parameters
----------
- *args : ndarrays
+ *args : ndarray of float
Variable length input data matrices. All inputs must have the same
- number of samples. That is, the shapes must be `(n, p)` and
- `(m, p)` where `n` and `m` are the number of samples and `p` are
- the number of dimensions. Alternatively, inputs can be distance
- matrices, where the shapes must all be `(n, n)`.
- reps : int, optional (default: 1000)
+ number of dimensions. That is, the shapes must be `(n, p)` and
+ `(m, p)`, ... where `n`, `m`, ... are the number of samples and `p` is
+ the number of dimensions.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- auto : bool (default: True)
- Automatically uses fast approximation when sample size and size of array
- is greater than 20. If True, and sample size is greater than 20, a fast
- chi2 approximation will be run. Parameters ``reps`` and ``workers`` are
- irrelevant in this case. Only applies to ``Dcorr`` and ``Hsic``.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Only applies to ``"Dcorr"`` and ``"Hsic"``.
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
Returns
-------
stat : float
- The computed *k*-Sample statistic.
+ The computed *k*-sample statistic.
pvalue : float
- The computed *k*-Sample p-value.
+ The computed *k*-sample p-value.
+ dict
+ A dictionary containing optional parameters for tests that return them.
+ See the relevant test in :mod:`hyppo.independence`.
Examples
--------
@@ -250,7 +286,7 @@ def test(self, *args, reps=1000, workers=1, auto=True):
inputs = list(args)
check_input = _CheckInputs(
inputs=inputs,
- indep_test=self.indep_test,
+ indep_test=self.indep_test_name,
)
inputs = check_input()
if self.indep_test_name == "kmerf":
@@ -262,4 +298,6 @@ def test(self, *args, reps=1000, workers=1, auto=True):
if self.indep_test_name in ["dcorr", "hsic"]:
kwargs = {"auto": auto}
- return self.indep_test.test(u, v, reps, workers, **kwargs)
+ return self.indep_test.test(
+ u, v, reps, workers, **kwargs, random_state=random_state
+ )
diff --git a/hyppo/ksample/ksamplehhg.py b/hyppo/ksample/ksamplehhg.py
new file mode 100644
index 000000000..c6d95ae51
--- /dev/null
+++ b/hyppo/ksample/ksamplehhg.py
@@ -0,0 +1,167 @@
+import numpy as np
+from numba import jit
+from hyppo.ksample.base import KSampleTest
+from hyppo.ksample._utils import _CheckInputs
+from sklearn.metrics import pairwise_distances
+from scipy.stats import ks_2samp
+
+
+class KSampleHHG(KSampleTest):
+ r"""
+ HHG 2-Sample test statistic.
+
+ This is a 2-sample multivariate test based on univariate test statistics.
+ It inherits the computational complexity from the unvariate tests to achieve
+ faster speeds than classic multivariate tests.
+ The univariate test used is the Kolmogorov-Smirnov 2-sample test.
+ :footcite:p:`hellerMultivariateTestsOfAssociation2016`.
+
+ Parameters
+ ----------
+ compute_distance : str, callable, or None, default: "euclidean"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
+ matrices. To call a custom function, either create the distance matrix
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
+
+ Notes
+ -----
+ The statistic can be derived as follows:
+ :footcite:p:`hellerMultivariateTestsOfAssociation2016`.
+
+ Let :math:`x`, :math:`y` be :math:`(n, p)`, :math:`(m, p)` samples of random variables
+ :math:`X` and :math:`Y \in \R^p` . Let there be a center point
+ :math:`\in \R^p`.
+ For every sample :math:`i`, calculate the distances from the center point
+ in :math:`x` and :math:`y` and denote this as :math:`d_x(x_i)`
+ and :math:`d_y(y_i)`. This will create a 1D collection of distances for each
+ sample group.
+
+ Then apply the KS 2-sample test on these center-point distances. This classic test
+ compares the empirical distribution function of the two samples and takes
+ the supremum of the difference between them. See Notes under scipy.stats.ks_2samp
+ for more details.
+
+ To achieve better power, the above process is repeated with each sample point
+ :math:`x_i` and :math:`y_i` as center points. The resultant :math:`n+m` p-values
+ are then pooled for use in the Bonferroni test of the global null hypothesis.
+ The HHG statistic is the KS stat associated with the smallest p-value from the pool,
+ while the HHG p-value is the smallest p-value multipled by the number of sample points.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, compute_distance="euclidean", **kwargs):
+ self.compute_distance = compute_distance
+ KSampleTest.__init__(self, compute_distance=compute_distance, **kwargs)
+
+ def statistic(self, x, y):
+ """
+ Calculates K-Sample HHG test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` and are the number of samples and `p` is the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed KS test statistic associated with the lowest p-value.
+ """
+ xy = np.concatenate((x, y), axis=0)
+ distxy = _centerpoint_dist(xy, self.compute_distance, 1)
+ distx = distxy[:, 0 : len(x)]
+ disty = distxy[:, len(x) : len(x) + len(y)]
+ stats, pvalues = _distance_score(distx, disty)
+ minP = min(pvalues)
+ stat = stats[pvalues.index(minP)]
+ self.minP = minP
+ self.stat = stat
+ return self.stat
+
+ def test(self, x, y):
+ """
+ Calculates K-Sample HHG test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` and `m` are the number of samples and `p` is the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed KS test statistic associated with the lowest p-value.
+ pvalue : float
+ The computed HHG pvalue. Equivalent to the lowest p-value multiplied by the total number
+ of samples.
+ """
+ check_input = _CheckInputs(inputs=[x, y],)
+ x, y = check_input()
+ N = x.shape[0] + y.shape[0]
+
+ stat = self.statistic(x, y)
+ pvalue = self.minP * N
+ return stat, pvalue
+
+
+def _centerpoint_dist(xy, metric, workers=1, **kwargs):
+ """Gives pairwise distances - each row corresponds to center-point distances
+ where one sample point is the center point"""
+ distxy = pairwise_distances(xy, metric=metric, n_jobs=workers, **kwargs)
+ return distxy
+
+
+def _distance_score(distx, disty):
+ dist1, dist2 = _group_distances(distx, disty)
+ stats = []
+ pvalues = []
+ for i in range(len(distx)):
+ stat, pvalue = ks_2samp(dist1[i], dist2[i])
+ stats.append(stat)
+ pvalues.append(pvalue)
+ return stats, pvalues
+
+
+@jit(nopython=True, cache=True)
+def _group_distances(distx, disty): # pragma: no cover
+ dist1 = []
+ dist2 = []
+ for i in range(len(distx)):
+ distancex = np.delete(distx[i], 0)
+ distancey = np.delete(disty[i], 0)
+ distancex = distancex.reshape(-1)
+ distancey = distancey.reshape(-1)
+ dist1.append(distancex)
+ dist2.append(distancey)
+ return dist1, dist2
diff --git a/hyppo/ksample/manova.py b/hyppo/ksample/manova.py
new file mode 100644
index 000000000..24a866ad0
--- /dev/null
+++ b/hyppo/ksample/manova.py
@@ -0,0 +1,199 @@
+import numpy as np
+from numba import jit
+from scipy.stats import f
+
+from ._utils import _CheckInputs
+from .base import KSampleTest, KSampleTestOutput
+
+
+class MANOVA(KSampleTest):
+ r"""
+ Multivariate analysis of variance (MANOVA) test statistic and p-value.
+
+ MANOVA is the current standard for multivariate `k`-sample testing.
+
+ Notes
+ -----
+ The test statistic is formulated as below
+ :footcite:p:`pandaNonparMANOVAIndependence2021`:
+
+ In MANOVA, we are testing if the mean vectors of each of the `k`-samples are the
+ same. Define
+ :math:`\{ {x_1}_i \stackrel{iid}{\sim} F_{X_1},\ i = 1, ..., n_1 \}`,
+ :math:`\{ {x_2}_j \stackrel{iid}{\sim} F_{X_2},\ j = 1, ..., n_2 \}`, ... as `k`
+ groups
+ of samples deriving from different a multivariate Gaussian distribution with the
+ same dimensionality and same covariance matrix.
+ That is, the null and alternate hypotheses are,
+
+ .. math::
+
+ H_0 &: \mu_1 = \mu_2 = \cdots = \mu_k, \\
+ H_A &: \exists \ j \neq j' \text{ s.t. } \mu_j \neq \mu_{j'}
+
+ Let :math:`\bar{x}_{i \cdot}` refer to the columnwise means of :math:`x_i`; that is,
+ :math:`\bar{x}_{i \cdot} = (1/n_i) \sum_{j=1}^{n_i} x_{ij}`. The pooled sample
+ covariance of each group, :math:`W`, is
+
+ .. math::
+
+ W = \sum_{i=1}^k \sum_{j=1}^{n_i} (x_{ij} - \bar{x}_{i\cdot}
+ (x_{ij} - \bar{x}_{i\cdot})^T
+
+ Next, define :math:`B` as the sample covariance matrix of the means. If
+ :math:`n = \sum_{i=1}^k n_i` and the grand mean is
+ :math:`\bar{x}_{\cdot \cdot} = (1/n) \sum_{i=1}^k \sum_{j=1}^{n} x_{ij}`,
+
+ .. math::
+
+ B = \sum_{i=1}^k n_i (\bar{x}_{i \cdot} - \bar{x}_{\cdot \cdot})
+ (\bar{x}_{i \cdot} - \bar{x}_{\cdot \cdot})^T
+
+ Some of the most common statistics used when performing MANOVA include the Wilks'
+ Lambda, the Lawley-Hotelling trace, Roy's greatest root, and
+ Pillai-Bartlett trace (PBT)
+ :footcite:p:`bartlettNoteTestsSignificance1939`
+ :footcite:p:`raoTestsSignificanceMultivariate1948`
+ (PBT was chosen to be the best of these
+ as it is the most conservative
+ :footcite:p:`warnePrimerMultivariateAnalysis2019`) and
+ :footcite:p:`everittMonteCarloInvestigation1979`
+ has shown that there are
+ minimal differences in statistical power among these statistics.
+ Let :math:`\lambda_1, \lambda_2, \ldots, \lambda_s` refer to the eigenvalues of
+ :math:`W^{-1} B`. Here :math:`s = \min(\nu_{B}, p)` is the minimum between the
+ degrees of freedom of :math:`B`, :math:`\nu_{B}` and :math:`p`. So, the PBT
+ MANOVA test statistic can be written as,
+
+ .. math::
+
+ \mathrm{MANOVA}_{n_1, \ldots, n_k} (x, y) = \sum_{i=1}^s
+ \frac{\lambda_i}{1 + \lambda_i} = \mathrm{tr} (B (B + W)^{-1})
+
+ The p-value analytically by using the F statitic. In the case of PBT, given
+ :math:`m = (|p - \nu_{B}| - 1) / 2` and :math:`r = (\nu_{W} - p - 1) / 2`, this is:
+
+ .. math::
+
+ F_{s(2m + s + 1), s(2r + s + 1)} = \frac{(2r + s + 1)
+ \mathrm{MANOVA}_{n_1, n_2} (x, y)}{(2m + s + 1) (s -
+ \mathrm{MANOVA}_{n_1, n_2} (x, y))}
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self):
+ KSampleTest.__init__(self)
+
+ def statistic(self, *args):
+ r"""
+ Calulates the MANOVA test statistic.
+
+ Parameters
+ ----------
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of dimensions. That is, the shapes must be `(n, p)` and
+ `(m, p)`, ... where `n`, `m`, ... are the number of samples and `p` is
+ the number of dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed MANOVA statistic.
+ """
+ cmean = tuple(i.mean(axis=0) for i in args)
+ gmean = np.vstack(args).mean(axis=0)
+ W = _compute_w(args, cmean)
+ B = _compute_b(args, cmean, gmean)
+
+ stat = np.trace(B @ np.linalg.pinv(B + W))
+ self.stat = stat
+
+ return stat
+
+ def test(self, *args):
+ r"""
+ Calculates the MANOVA test statistic and p-value.
+
+ Parameters
+ ----------
+ *args : ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of dimensions. That is, the shapes must be `(n, p)` and
+ `(m, p)`, ... where `n`, `m`, ... are the number of samples and `p` is
+ the number of dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed MANOVA statistic.
+ pvalue : float
+ The computed MANOVA p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import MANOVA
+ >>> x = np.arange(7)
+ >>> y = x
+ >>> stat, pvalue = MANOVA().test(x, y)
+ >>> '%.3f, %.1f' % (stat, pvalue)
+ '0.000, 1.0'
+ """
+ inputs = list(args)
+ check_input = _CheckInputs(
+ inputs=inputs,
+ )
+ inputs = check_input()
+
+ N = np.sum([i.shape[0] for i in inputs])
+ p = inputs[0].shape[1]
+ nu_w = N - len(inputs)
+
+ if nu_w < p:
+ raise ValueError("Test cannot be run, degree of freedoms is off")
+
+ stat = self.statistic(*inputs)
+ nu_b = len(inputs) - 1
+ s = np.min([p, nu_b])
+ m = (np.abs(p - nu_b) - 1) / 2
+ n = (nu_w - p - 1) / 2
+ num = 2 * n + s + 1
+ denom = 2 * m + s + 1
+ pvalue = f.sf(num / denom * stat / (s - stat), s * denom, s * num)
+ self.stat = stat
+ self.pvalue = pvalue
+ self.null_dist = None
+
+ return KSampleTestOutput(stat, pvalue)
+
+
+@jit(nopython=True, cache=True)
+def _compute_w(inputs, cmean): # pragma: no cover
+ """Calculate the W matrix"""
+
+ p = list(inputs)[0].shape[1]
+ W = np.zeros((p, p))
+
+ for i in range(len(inputs)):
+ for j in range(inputs[i].shape[0]):
+ W += (inputs[i][j, :] - cmean[i]) @ (inputs[i][j, :] - cmean[i]).T
+
+ return W
+
+
+@jit(nopython=True, cache=True)
+def _compute_b(inputs, cmean, gmean): # pragma: no cover
+ """Calculate the B matrix"""
+
+ p = list(inputs)[0].shape[1]
+ B = np.zeros((p, p))
+
+ for i in range(len(inputs)):
+ n = inputs[i].shape[0]
+ B += n * (cmean[i] - gmean) @ (cmean[i] - gmean).T
+
+ return B
diff --git a/hyppo/ksample/mean_embedding.py b/hyppo/ksample/mean_embedding.py
new file mode 100644
index 000000000..fc8552084
--- /dev/null
+++ b/hyppo/ksample/mean_embedding.py
@@ -0,0 +1,173 @@
+import numpy as np
+from scipy.stats import chi2
+from ._utils import _CheckInputs
+from .base import KSampleTestOutput, KSampleTest
+
+
+class MeanEmbeddingTest(KSampleTest):
+ r"""
+ Mean Embedding test statistic and p-value.
+
+ The Mean Embedding test is a two-sample test that uses
+ differences in (analytic) mean embeddings of two data
+ distributions in a reproducing kernel Hilbert space.
+ :footcite:p:`chwialkowski2015fast`.
+
+ Parameters
+ ----------
+ num_randfreq: int
+ Used to construct random array with size ``(p, q)`` where `p` is the number of
+ dimensions of the data and `q` is the random frequency at which the
+ test is performed. These are the random test points at which test occurs (see notes).
+
+ Notes
+ -----
+ The test statistic, like the Smooth CF statistic, takes on the following form:
+
+ .. math::
+
+ W_n\Sigma_n^{-1}W_n
+
+ As seen in the above formulation, this test-statistic takes the same form as
+ the Hotelling :math:`T^2` statistic found in :class:`hyppo.ksample.Hotelling`.
+ However, the components are defined differently in this case. Given data sets
+ X and Y, define the following as :math:`Z_i`, the vector of differences:
+
+ .. math::
+
+ Z_i = (k(X_i, T_1) - k(Y_i, T_1), \ldots,
+ k(X_i, T_J) - k(Y_i, T_J)) \in \mathbb{R}^J
+
+ The above is the vector of differences between kernels at test points,
+ :math:`T_j`. The kernel maps into the reproducing kernel Hilbert space.
+ This same formulation is used in the Mean Embedding Test.
+ Moving forward, :math:`W_n` can be defined:
+
+ .. math::
+
+ W_n = \frac{1}{n} \sum_{i = 1}^n Z_i
+
+ This leaves :math:`\Sigma_n`, the covariance matrix as:
+
+ .. math::
+
+ \Sigma_n = \frac{1}{n}ZZ^T
+
+ Once :math:`S_n` is calculated, a threshold :math:`r_{\alpha}` corresponding to the
+ :math:`1 - \alpha` quantile of a Chi-squared distribution w/ J degrees of freedom
+ is chosen. Null is rejected if :math:`S_n` is larger than this threshold.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, num_randfreq=5):
+ self.num_randfreq = num_randfreq
+ KSampleTest.__init__(self)
+
+ def statistic(self, x, y, random_state):
+ r"""
+ Calculates the mean embedding test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ random_state: int
+ Set random seed for generation of test points
+
+ Returns
+ -------
+ stat : float
+ The computed mean embedding statistic.
+ """
+ _, p = np.shape(x)
+
+ if random_state:
+ np.random.seed(random_state)
+ points = np.random.randn(self.num_randfreq, p)
+
+ ra = np.zeros((self.num_randfreq, x.shape[0]))
+ for idx, point in enumerate(points):
+
+ ra[idx] = np.exp(-(np.linalg.norm(x - point, axis=1) ** 2) / 2) - np.exp(
+ -(np.linalg.norm(y - point, axis=1) ** 2) / 2
+ )
+
+ obs = ra.T
+ return mean_embed_distance(obs, self.num_randfreq)
+
+ def test(self, x, y, random_state=None):
+ r"""
+ Calculates the mean embedding test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ random_state: int
+ Set random seed for generation of test points
+
+ Returns
+ -------
+ stat : float
+ The computed mean embedding statistic.
+ pvalue : float
+ The computed mean embedding p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import MeanEmbeddingTest
+ >>> np.random.seed(1234)
+ >>> x = np.random.randn(500, 10)
+ >>> y = np.random.randn(500, 10)
+ >>> stat, pvalue = MeanEmbeddingTest().test(x, y, random_state=1234)
+ >>> '%.2f, %.3f' % (stat, pvalue)
+ '5.33, 0.377'
+ """
+ check_input = _CheckInputs(inputs=[x, y], indep_test=None)
+ x, y = check_input()
+
+ stat = self.statistic(x, y, random_state)
+ pvalue = chi2.sf(stat, self.num_randfreq)
+ self.stat = stat
+ self.pvalue = pvalue
+
+ return KSampleTestOutput(stat, pvalue)
+
+
+def mean_embed_distance(difference, num_randfeatures):
+ r"""
+ Calculates the Mean Embedding test statistic using the vector of differences.
+
+ Parameters
+ ----------
+ difference : ndarray of float
+ The vector of differences which indicates distance between mean embeddings.
+ num_randfeatures : int
+ The number of test frequencies
+
+ Returns
+ -------
+ stat : float
+ The computed mean embedding statistic.
+ """
+ num_samples, _ = np.shape(difference)
+ sigma = np.cov(np.transpose(difference))
+
+ mu = np.mean(difference, 0)
+
+ if num_randfeatures == 1:
+ stat = float(num_samples * mu**2) / float(sigma)
+ else:
+ stat = num_samples * mu.dot(np.linalg.solve(sigma, np.transpose(mu)))
+
+ return stat
diff --git a/hyppo/ksample/mmd.py b/hyppo/ksample/mmd.py
new file mode 100644
index 000000000..ec5fc8d34
--- /dev/null
+++ b/hyppo/ksample/mmd.py
@@ -0,0 +1,182 @@
+from ._utils import _CheckInputs
+from .base import KSampleTest, KSampleTestOutput
+from .ksamp import KSample
+
+
+class MMD(KSampleTest):
+ r"""
+ Maximum Mean Discrepency (MMD) test statistic and p-value.
+
+ MMD is a powerful multivariate 2-sample test. It leverages kernel similarity
+ matrices
+ capabilities (similar to tests like distance correlation or Dcorr). In fact, MMD
+ statistic is equivalent to our 2-sample formulation nonparametric MANOVA via
+ independence testing, i.e. :class:`hyppo.ksample.KSample`,
+ and to
+ :class:`hyppo.independence.Dcorr`,
+ :class:`hyppo.ksample.DISCO`,
+ :class:`hyppo.independence.Hsic`, and
+ :class:`hyppo.ksample.Energy`
+ :footcite:p:`pandaNonparMANOVAIndependence2021`
+ :footcite:p:`shenExactEquivalenceDistance2020`.
+
+ Parameters
+ ----------
+ compute_kernel : str, callable, or None, default: "gaussian"
+ A function that computes the kernel similarity among the samples within each
+ data matrix.
+ Valid strings for ``compute_kernel`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already similarity
+ matrices. To call a custom function, either create the similarity matrix
+ before-hand or create a function of the form :func:`metric(x, **kwargs)`
+ where ``x`` is the data matrix for which pairwise kernel similarity matrices are
+ calculated and kwargs are extra arguements to send to your custom function.
+ bias : bool, default: False
+ Whether or not to use the biased or unbiased test statistics.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_kernel``.
+
+ Notes
+ -----
+ Traditionally, the formulation for the 2-sample MMD statistic
+ is as follows :footcite:p:`grettonKernelTwoSampleTest2012`:
+
+ Define
+ :math:`\{ u_i \stackrel{iid}{\sim} F_U,\ i = 1, ..., n \}` and
+ :math:`\{ v_j \stackrel{iid}{\sim} F_V,\ j = 1, ..., m \}` as two groups
+ of samples deriving from different distributions with the same
+ dimensionality. If :math:`k(\cdot, \cdot)` is a kernel metric (i.e. Gaussian)
+ then,
+
+ .. math::
+
+ \mathrm{MMD}_{n, m}(\mathbf{u}, \mathbf{v}) =
+ \frac{1}{m(m - 1)} \sum_{i = 1}^m \sum_{j \neq i}^m k(u_i, u_j)
+ + \frac{1}{n(n - 1)} \sum_{i = 1}^n \sum_{j \neq i}^n k(v_i, v_j)
+ - \frac{2}{mn} \sum_{i = 1}^n \sum_{j \neq i}^n k(v_i, v_j)
+
+ The implementation in the :class:`hyppo.ksample.KSample` class (using
+ :class:`hyppo.independence.Hsic` using 2 samples) is in
+ fact equivalent to this implementation (for p-values) and statistics are
+ equivalent up to a scaling factor :footcite:p:`shenExactEquivalenceDistance2020`.
+
+ The p-value returned is calculated using a permutation test uses
+ :meth:`hyppo.tools.perm_test`.
+ The fast version of the test uses :meth:`hyppo.tools.chi2_approx`.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, compute_kernel="gaussian", bias=False, **kwargs):
+ self.compute_kernel = compute_kernel
+ self.bias = bias
+ KSampleTest.__init__(self, compute_distance=None, bias=bias, **kwargs)
+
+ def statistic(self, x, y):
+ r"""
+ Calulates the MMD test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+
+ Returns
+ -------
+ stat : float
+ The computed MMD statistic.
+ """
+ n = x.shape[0]
+ m = y.shape[0]
+
+ # exact equivalence transformation Hsic and MMD
+ stat = (
+ KSample(
+ indep_test="Hsic",
+ compute_distkern=self.compute_kernel,
+ bias=self.bias,
+ **self.kwargs
+ ).statistic(
+ x,
+ y,
+ )
+ * (2 * (n**2) * (m**2))
+ / ((n + m) ** 4)
+ )
+ self.stat = stat
+
+ return stat
+
+ def test(self, x, y, reps=1000, workers=1, auto=True, random_state=None):
+ r"""
+ Calculates the MMD test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
+
+ Returns
+ -------
+ stat : float
+ The computed MMD statistic.
+ pvalue : float
+ The computed MMD p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import MMD
+ >>> x = np.arange(7)
+ >>> y = x
+ >>> stat, pvalue = MMD().test(x, y)
+ >>> '%.3f, %.1f' % (stat, pvalue)
+ '-0.015, 1.0'
+ """
+ check_input = _CheckInputs(
+ inputs=[x, y],
+ indep_test="hsic",
+ )
+ x, y = check_input()
+
+ # observed statistic
+ stat = self.statistic(x, y)
+
+ # since stat transformation is invariant under permutation, 2-sample Hsic
+ # pvalue is identical to MMD
+ _, pvalue = KSample(
+ indep_test="Hsic",
+ compute_distkern=self.compute_kernel,
+ bias=self.bias,
+ **self.kwargs
+ ).test(x, y, reps=reps, workers=workers, auto=auto, random_state=random_state)
+
+ return KSampleTestOutput(stat, pvalue)
diff --git a/hyppo/ksample/smoothCF.py b/hyppo/ksample/smoothCF.py
new file mode 100644
index 000000000..7beaf5305
--- /dev/null
+++ b/hyppo/ksample/smoothCF.py
@@ -0,0 +1,183 @@
+import numpy as np
+from scipy.stats import chi2
+from ._utils import _CheckInputs
+from hyppo.ksample.base import KSampleTest, KSampleTestOutput
+
+
+class SmoothCFTest(KSampleTest):
+ r"""
+ Smooth Characteristic Function test statistic and p-value
+
+ The Smooth Characteristic Function test is a two-sample test that uses
+ differences in the smoothed (analytic) characteristic function of
+ two data distributions in order to determine how different
+ the two data are :footcite:p:`chwialkowski2015fast`.
+
+ Parameters
+ ----------
+ num_randfreq: int
+ Used to construct random array with size ``(p, q)`` where `p` is the number of
+ dimensions of the data and `q` is the random frequency at which the
+ test is performed. These are the random test points at which test occurs (see notes).
+
+ Notes
+ -----
+ The test statistic takes on the following form:
+
+ .. math::
+
+ nW_n\Sigma_n^{-1}W_n
+
+ As seen in the above formulation, this test-statistic takes the same form as
+ the Hotelling :math:`T^2` statistic. However, the components are
+ defined differently in this case. Given data sets
+ X and Y, define the following as :math:`Z_i`, the vector of differences:
+
+ .. math::
+
+ Z_i = (k(X_i, T_1) - k(Y_i, T_1), \ldots,
+ k(X_i, T_J) - k(Y_i, T_J)) \in \mathbb{R}^J
+
+ The above is the vector of differences between kernels at test points, :math:`T_j`.
+ This same formulation is used in the Mean Embedding Test.
+ Moving forward, :math:`W_n` can be defined:
+
+ .. math::
+
+ W_n = \frac{1}{n} \sum_{i = 1}^n Z_i
+
+ This leaves :math:`\Sigma_n`, the covariance matrix as:
+
+ .. math::
+
+ \Sigma_n = \frac{1}{n}ZZ^T
+
+ In the specific case of the Smooth Characteristic function test,
+ the vector of differences can be defined as follows:
+
+ .. math::
+
+ Z_i = (f(X_i)\sin(X_iT_1) - f(Y_i)\sin(Y_iT_1),
+ f(X_i)\cos(X_iT_1) - f(Y_i)\cos(Y_iT_1),\cdots) \in \mathbb{R}^{2J}
+
+ Once :math:`S_n` is calculated, a threshold :math:`r_{\alpha}` corresponding to the
+ :math:`1 - \alpha` quantile of a Chi-squared distribution w/ J degrees of freedom
+ is chosen. Null is rejected if :math:`S_n` is larger than this threshold.
+
+ References
+ ----------
+ .. footbibliography::
+ """
+
+ def __init__(self, num_randfreq=5):
+
+ self.num_randfreq = num_randfreq
+ KSampleTest.__init__(self)
+
+ def statistic(self, x, y, random_state):
+ r"""
+ Calculates the smooth CF test statistic.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ random_state: int
+ Set random seed for generation of test points
+
+ Returns
+ -------
+ stat : float
+ The computed Smooth CF statistic.
+ """
+ _, p = np.shape(x)
+ if random_state:
+ np.random.seed(random_state)
+ random_frequencies = np.random.randn(p, self.num_randfreq)
+
+ x_smooth = np.exp(-np.linalg.norm(x, axis=1) ** 2 / 2).reshape(-1, 1)
+ y_smooth = np.exp(-np.linalg.norm(y, axis=1) ** 2 / 2).reshape(-1, 1)
+
+ x_mat = x.dot(random_frequencies)
+ y_mat = y.dot(random_frequencies)
+
+ x_smooth_cf = np.concatenate(
+ (np.sin(x_mat) * x_smooth, np.cos(x_mat) * x_smooth), 1
+ )
+ y_smooth_cf = np.concatenate(
+ (np.sin(y_mat) * y_smooth, np.cos(y_mat) * y_smooth), 1
+ )
+
+ difference = x_smooth_cf - y_smooth_cf
+ return smooth_cf_distance(difference)
+
+ def test(self, x, y, random_state=None):
+ r"""
+ Calculates the smooth CF test statistic and p-value.
+
+ Parameters
+ ----------
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ dimensions. That is, the shapes must be ``(n, p)`` and ``(m, p)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions.
+ random_state: int
+ Set random seed for generation of test points
+
+ Returns
+ -------
+ stat : float
+ The computed Smooth CF statistic.
+ pvalue : float
+ The computed smooth CF p-value.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from hyppo.ksample import SmoothCFTest
+ >>> np.random.seed(1234)
+ >>> x = np.random.randn(500, 10)
+ >>> y = np.random.randn(500, 10)
+ >>> stat, pvalue = SmoothCFTest().test(x, y, random_state=1234)
+ >>> '%.2f, %.3f' % (stat, pvalue)
+ '4.70, 0.910'
+ """
+ check_input = _CheckInputs(inputs=[x, y], indep_test=None)
+ x, y = check_input()
+
+ stat = self.statistic(x, y, random_state)
+ pvalue = chi2.sf(stat, 2 * self.num_randfreq)
+ self.stat = stat
+ self.pvalue = pvalue
+
+ return KSampleTestOutput(stat, pvalue)
+
+
+def smooth_cf_distance(difference):
+ r"""
+ Calculates the Smooth CF test statistic using the vector of differences.
+
+ Parameters
+ ----------
+ difference : ndarray of float
+ The vector of differences which indicates distance between mean embeddings.
+ num_randfeatures : int
+ The number of test frequencies
+
+ Returns
+ -------
+ stat : float
+ The computed smooth CF statistic.
+ """
+ num_samples, _ = np.shape(difference)
+ sigma = np.cov(np.transpose(difference))
+
+ mu = np.mean(difference, 0)
+
+ stat = num_samples * mu.dot(np.linalg.solve(sigma, np.transpose(mu)))
+
+ return stat
diff --git a/hyppo/ksample/tests/test_disco.py b/hyppo/ksample/tests/test_disco.py
new file mode 100644
index 000000000..a8ef82d5d
--- /dev/null
+++ b/hyppo/ksample/tests/test_disco.py
@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises
+from sklearn.metrics import pairwise_distances
+
+from ...tools import power, rot_ksamp
+from .. import DISCO
+
+
+class TestDISCO:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(200, 6.621905272534802, 0.001), (100, 2.675357570989666, 0.001)],
+ )
+ def test_disco_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = DISCO().test(x, y, auto=False)
+
+ assert_almost_equal(stat, obs_stat, decimal=1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(100, 2.675357570989666, 0.001)],
+ )
+ def test_rep(self, n, obs_stat, obs_pvalue):
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = DISCO().test(x, y, auto=False, random_state=99)
+ stat2, pvalue2 = DISCO().test(x, y, auto=False, random_state=99)
+
+ assert stat == stat2
+ assert pvalue == pvalue2
+
+
+class TestDISCOErrorWarn:
+ """Tests errors and warnings derived from MGC."""
+
+ def test_diffshape(self):
+ # raises error if not indep test
+ x = np.arange(20)
+ y = np.arange(10)
+ assert_raises(ValueError, DISCO().statistic, x, y)
+ assert_raises(ValueError, DISCO().test, x, y)
+
+
+class TestDISCOTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "DISCO",
+ sim_type="ksamp",
+ sim="multimodal_independence",
+ k=2,
+ n=100,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/ksample/tests/test_energy.py b/hyppo/ksample/tests/test_energy.py
new file mode 100644
index 000000000..1c4f33b7f
--- /dev/null
+++ b/hyppo/ksample/tests/test_energy.py
@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from ...tools import power, rot_ksamp
+from .. import Energy
+
+
+class TestEnergy:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(200, 4.28e-7, 0.001), (100, 8.24e-5, 0.001)],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = Energy().test(x, y, auto=False)
+
+ assert_almost_equal(stat, obs_stat, decimal=1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(100, 8.24e-5, 0.001)],
+ )
+ def test_rep(self, n, obs_stat, obs_pvalue):
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = Energy().test(x, y, auto=False, random_state=2)
+ stat2, pvalue2 = Energy().test(x, y, auto=False, random_state=2)
+
+ assert stat == stat2
+ assert pvalue == pvalue2
+
+
+class TestEnergyTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Energy",
+ sim_type="ksamp",
+ sim="multimodal_independence",
+ k=2,
+ n=100,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/ksample/tests/test_hhg.py b/hyppo/ksample/tests/test_hhg.py
new file mode 100644
index 000000000..68e0f088e
--- /dev/null
+++ b/hyppo/ksample/tests/test_hhg.py
@@ -0,0 +1,46 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from ...tools import rot_ksamp
+from .. import KSampleHHG
+
+
+class TestKSampleHHG:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue", [(100, 0.515, 4.912e-10), (10, 0.777, 0.125874),],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2, noise=False)
+ stat, pvalue = KSampleHHG().test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=3)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=3)
+
+ @pytest.mark.parametrize(
+ "n", [(100)],
+ )
+ def test_rep(self, n):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2, noise=False)
+ MPstat1 = KSampleHHG().statistic(x, y)
+ MPstat2 = KSampleHHG().statistic(x, y)
+
+ assert MPstat1 == MPstat2
+
+
+class TestKSampleHHGTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ rejections = 0
+ for i in range(1000):
+ x, y = rot_ksamp(
+ "multimodal_independence", n=100, p=1, noise=True, degree=90
+ )
+ stat, pvalue = KSampleHHG().test(x, y)
+ if pvalue < 0.05:
+ rejections += 1
+ est_power = rejections / 1000
+
+ assert_almost_equal(est_power, 0, decimal=2)
diff --git a/hyppo/ksample/tests/test_hotelling.py b/hyppo/ksample/tests/test_hotelling.py
new file mode 100644
index 000000000..efb4d817f
--- /dev/null
+++ b/hyppo/ksample/tests/test_hotelling.py
@@ -0,0 +1,39 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from ...tools import power, rot_ksamp
+from .. import Hotelling
+
+
+class TestHotelling:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [
+ (1000, 0.00253015392620975, 0.997473047413008),
+ (100, 8.24e-5, 0.953158769593477),
+ ],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2, noise=False)
+ stat, pvalue = Hotelling().test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+
+
+class TestHotellingTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "Hotelling",
+ sim_type="ksamp",
+ sim="multimodal_independence",
+ k=2,
+ n=100,
+ p=1,
+ alpha=0.05,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/ksample/tests/test_ksamp.py b/hyppo/ksample/tests/test_ksamp.py
index 5cf8bb8d1..324519c7e 100644
--- a/hyppo/ksample/tests/test_ksamp.py
+++ b/hyppo/ksample/tests/test_ksamp.py
@@ -1,54 +1,76 @@
-import pytest
import numpy as np
+import pytest
from numpy.testing import assert_almost_equal, assert_raises
-from ...tools import linear, rot_2samp
+from ...tools import power, rot_ksamp
from .. import KSample
-from ...independence import CCA, Dcorr
class TestKSample:
@pytest.mark.parametrize(
"n, obs_stat, obs_pvalue, indep_test",
- [(1000, 4.28e-7, 1.0, CCA.__name__), (100, 8.24e-5, 0.001, Dcorr.__name__)],
+ [(1000, 4.28e-7, 1.0, "CCA"), (100, 8.24e-5, 0.001, "Dcorr")],
)
def test_twosamp_linear_oned(self, n, obs_stat, obs_pvalue, indep_test):
np.random.seed(123456789)
- x, y = rot_2samp(linear, n, 1)
+ x, y = rot_ksamp("linear", n, 1, k=2)
stat, pvalue = KSample(indep_test).test(x, y)
assert_almost_equal(stat, obs_stat, decimal=1)
assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+ def test_rf(self):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", 50, 1, k=2)
+ stat, _, _ = KSample("KMERF").test(x, y, reps=0)
+
+ assert_almost_equal(stat, 0.2714, decimal=1)
+
+ def test_maxmargin(self):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", 50, 1, k=2)
+ stat, _ = KSample(["MaxMargin", "Dcorr"]).test(x, y, reps=0)
+
+ assert_almost_equal(stat, 0.0317, decimal=1)
+
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue, indep_test",
+ [(100, 8.24e-5, 0.001, "Dcorr")],
+ )
+ def test_rep(self, n, obs_stat, obs_pvalue, indep_test):
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = KSample(indep_test).test(x, y)
+ stat2, pvalue2 = KSample(indep_test).test(x, y)
+
+ assert stat == stat2
+ assert pvalue == pvalue2
+
class TestKSampleErrorWarn:
"""Tests errors and warnings derived from MGC."""
- def test_error_notndarray(self):
- # raises error if x or y is not a ndarray
- x = np.arange(20)
- y = [5] * 20
- z = np.arange(5)
- assert_raises(ValueError, KSample(Dcorr.__name__).test, x, y, z)
-
- def test_error_shape(self):
- # raises error if number of samples different (n)
- x = np.arange(100).reshape(25, 4)
- y = x.reshape(10, 10)
- z = x
- assert_raises(ValueError, KSample(Dcorr.__name__).test, x, y, z)
-
- def test_error_lowsamples(self):
- # raises error if samples are low (< 3)
- x = np.arange(3)
- y = np.arange(3)
- assert_raises(ValueError, KSample(CCA.__name__).test, x, y)
-
- def test_error_nans(self):
- # raises error if inputs contain NaNs
- x = np.arange(20, dtype=float)
- x[0] = np.nan
- assert_raises(ValueError, KSample(CCA.__name__).test, x, x)
-
- y = np.arange(20)
- assert_raises(ValueError, KSample(CCA.__name__).test, x, y)
+ def test_no_indeptest(self):
+ # raises error if not indep test
+ assert_raises(ValueError, KSample, "abcd")
+
+ def test_no_second_maxmargin(self):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", 50, 1, k=2)
+ assert_raises(ValueError, KSample, ["MaxMargin", "abcd"])
+
+
+class TestKSampleTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "CCA",
+ sim_type="ksamp",
+ sim="multimodal_independence",
+ k=2,
+ n=100,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/ksample/tests/test_manova.py b/hyppo/ksample/tests/test_manova.py
new file mode 100644
index 000000000..8b5b220f6
--- /dev/null
+++ b/hyppo/ksample/tests/test_manova.py
@@ -0,0 +1,45 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal, assert_raises
+
+from ...tools import power, rot_ksamp
+from .. import MANOVA
+
+
+class TestManova:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(1000, 0.005062841807278008, 1.0), (100, 8.24e-5, 0.9762956529114515)],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2, noise=False)
+ stat, pvalue = MANOVA().test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+
+
+class TestManovaErrorWarn:
+ """Tests errors and warnings derived from MGC."""
+
+ def test_no_indeptest(self):
+ # raises error if not indep test
+ x = np.arange(100).reshape(5, 20)
+ y = np.arange(50, 150).reshape(5, 20)
+ assert_raises(ValueError, MANOVA().test, x, y)
+
+
+class TestManovaTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "MANOVA",
+ sim_type="gauss",
+ sim="multimodal_independence",
+ case=1,
+ n=100,
+ alpha=0.05,
+ )
+
+ assert est_power <= 0.05
diff --git a/hyppo/ksample/tests/test_mean_embedding.py b/hyppo/ksample/tests/test_mean_embedding.py
new file mode 100644
index 000000000..d8fba751f
--- /dev/null
+++ b/hyppo/ksample/tests/test_mean_embedding.py
@@ -0,0 +1,52 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from hyppo.tools import rot_ksamp
+from hyppo.ksample import MeanEmbeddingTest
+
+
+class TestMeanEmbedding:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [
+ (200, 77.452, 2.862e-15),
+ (700, 245.478, 5.132e-51),
+ ],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = MeanEmbeddingTest().test(x, y, random_state=1234)
+ print(stat, pvalue)
+ assert_almost_equal(stat, obs_stat, decimal=2)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=10)
+
+ def test_null(self):
+ np.random.seed(120)
+ x = np.random.randn(500, 10)
+ y = np.random.randn(500, 10)
+ _, pval = MeanEmbeddingTest().test(x, y)
+ assert pval > 0.05
+
+ def test_alternative(self):
+ np.random.seed(120)
+ x = np.random.randn(500, 10)
+ x[:, 1] *= 3
+ y = np.random.randn(500, 10)
+ _, pval = MeanEmbeddingTest().test(x, y)
+ assert pval < 0.05
+
+ @pytest.mark.parametrize(
+ "obs_stat, obs_pval",
+ [
+ (0.0164, 0.8980),
+ ],
+ )
+ def test_one_frequency(self, obs_stat, obs_pval):
+ np.random.seed(123456789)
+ x = np.random.randn(500, 10)
+ y = np.random.randn(500, 10)
+ stat, pvalue = MeanEmbeddingTest(num_randfreq=1).test(x, y, random_state=1234)
+ assert_almost_equal(stat, obs_stat, decimal=2)
+ assert_almost_equal(pvalue, obs_pval, decimal=2)
diff --git a/hyppo/ksample/tests/test_mmd.py b/hyppo/ksample/tests/test_mmd.py
new file mode 100644
index 000000000..b5702ff2a
--- /dev/null
+++ b/hyppo/ksample/tests/test_mmd.py
@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from ...tools import power, rot_ksamp
+from .. import MMD
+
+
+class TestMMD:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(200, 4.28e-7, 0.001), (100, 8.24e-5, 0.001)],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = MMD().test(x, y, auto=False)
+
+ assert_almost_equal(stat, obs_stat, decimal=1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=1)
+
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [(100, 8.24e-5, 0.001)],
+ )
+ def test_rep(self, n, obs_stat, obs_pvalue):
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = MMD().test(x, y, auto=False, random_state=2)
+ stat2, pvalue2 = MMD().test(x, y, auto=False, random_state=2)
+
+ assert stat == stat2
+ assert pvalue == pvalue2
+
+
+class TestMMDTypeIError:
+ def test_oned(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "MMD",
+ sim_type="ksamp",
+ sim="multimodal_independence",
+ k=2,
+ n=100,
+ p=1,
+ alpha=0.05,
+ auto=True,
+ )
+
+ assert_almost_equal(est_power, 0.05, decimal=2)
diff --git a/hyppo/ksample/tests/test_smoothCF.py b/hyppo/ksample/tests/test_smoothCF.py
new file mode 100644
index 000000000..72f1f3e54
--- /dev/null
+++ b/hyppo/ksample/tests/test_smoothCF.py
@@ -0,0 +1,52 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from hyppo.tools import rot_ksamp
+from hyppo.ksample import SmoothCFTest
+
+
+class TestSmoothCF:
+ @pytest.mark.parametrize(
+ "n, obs_stat, obs_pvalue",
+ [
+ (2000, 1317.0159, 8.134e-277),
+ (1000, 546.279, 5.608e-111),
+ ],
+ )
+ def test_linear_oned(self, n, obs_stat, obs_pvalue):
+ np.random.seed(123456789)
+ x, y = rot_ksamp("linear", n, 1, k=2)
+ stat, pvalue = SmoothCFTest().test(x, y)
+
+ assert_almost_equal(stat, obs_stat, decimal=1)
+ assert_almost_equal(pvalue, obs_pvalue, decimal=100)
+
+ def test_null(self):
+ np.random.seed(120)
+ x = np.random.randn(500, 10)
+ y = np.random.randn(500, 10)
+ _, pval = SmoothCFTest().test(x, y)
+ assert pval > 0.05
+
+ def test_alternative(self):
+ np.random.seed(120)
+ x = np.random.randn(500, 10)
+ x[:, 1] *= 3
+ y = np.random.randn(500, 10)
+ _, pval = SmoothCFTest().test(x, y)
+ assert pval < 0.05
+
+ @pytest.mark.parametrize(
+ "obs_stat, obs_pval",
+ [
+ (0.2707, 0.8734),
+ ],
+ )
+ def test_one_frequency(self, obs_stat, obs_pval):
+ np.random.seed(123456789)
+ x = np.random.randn(500, 10)
+ y = np.random.randn(500, 10)
+ stat, pvalue = SmoothCFTest(num_randfreq=1).test(x, y, random_state=1234)
+ assert_almost_equal(stat, obs_stat, decimal=2)
+ assert_almost_equal(pvalue, obs_pval, decimal=2)
diff --git a/hyppo/time_series/_utils.py b/hyppo/time_series/_utils.py
index 2e64e1784..896f5a850 100644
--- a/hyppo/time_series/_utils.py
+++ b/hyppo/time_series/_utils.py
@@ -2,14 +2,14 @@
import numpy as np
+from ..independence import MGC
from ..tools import (
- contains_nan,
check_ndarray_xy,
- convert_xy_float64,
check_reps,
compute_dist,
+ contains_nan,
+ convert_xy_float64,
)
-from ..independence import MGC
class _CheckInputs:
@@ -59,7 +59,7 @@ def _check_nd_indeptest(self):
if nx != ny:
raise ValueError(
- "Shape mismatch, x and y must have shape " "[n, p] and [n, q]."
+ "Shape mismatch, x and y must have shape [n, p] and [n, q]."
)
def _check_max_lag(self):
@@ -79,14 +79,12 @@ def _check_min_samples(self):
def compute_stat(x, y, indep_test, compute_distance, max_lag, **kwargs):
"""Compute time series test statistic"""
# calculate distance matrices
- if not compute_distance:
- compute_distance = "precomputed"
distx, disty = compute_dist(x, y, metric=compute_distance, **kwargs)
# calculate dep_lag when max_lag is 0
dep_lag = []
- indep_test = indep_test(compute_distance=compute_distance)
- indep_test_stat = indep_test._statistic(x, y)
+ indep_test = indep_test(compute_distance=compute_distance, **kwargs)
+ indep_test_stat = indep_test.statistic(x, y)
dep_lag.append(indep_test_stat)
# loop over time points and find max test statistic
@@ -94,7 +92,7 @@ def compute_stat(x, y, indep_test, compute_distance, max_lag, **kwargs):
for j in range(1, max_lag + 1):
slice_distx = distx[j:n, j:n]
slice_disty = disty[0 : (n - j), 0 : (n - j)]
- stat = indep_test._statistic(slice_distx, slice_disty)
+ stat = indep_test.statistic(slice_distx, slice_disty)
dep_lag.append((n - j) * stat / n)
# calculate optimal lag and test statistic
diff --git a/hyppo/time_series/base.py b/hyppo/time_series/base.py
index b318619b9..38d6b2717 100644
--- a/hyppo/time_series/base.py
+++ b/hyppo/time_series/base.py
@@ -4,32 +4,46 @@
from joblib import Parallel, delayed
from ..tools import compute_dist
+from sklearn.utils import check_random_state
class TimeSeriesTest(ABC):
"""
- Base class for time series in hyppo.
+ A base class for a time-series test.
Parameters
----------
- compute_distance : callable, optional
- Function indicating distance metric (or alternatively the kernel) to
- use. Calculates the pairwise distance for each input, by default
- euclidean.
-
- Attributes
- ----------
- stat : float
- The computed independence test statistic.
- pvalue : float
- The computed independence test p-value.
- compute_distance : callable, optional
- Function indicating distance metric (or alternatively the kernel) to
- use. Calculates the pairwise distance for each input, by default
- euclidean.
+ compute_distance : str, callable, or None, default: "euclidean"
+ A function that computes the distance among the samples within each
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
+ matrices. To call a custom function, either create the distance matrix
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ max_lag : float, default: 0
+ The maximium lag to consider when computing the test statistics and p-values.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
"""
- def __init__(self, compute_distance="euclidean", max_lag=0, **kwargs):
+ def __init__(self, compute_distance=None, max_lag=0, **kwargs):
# set statistic and p-value
self.stat = None
self.pvalue = None
@@ -37,84 +51,92 @@ def __init__(self, compute_distance="euclidean", max_lag=0, **kwargs):
self.kwargs = kwargs
# set compute_distance kernel
- if not compute_distance:
- compute_distance = None
self.compute_distance = compute_distance
super().__init__()
@abstractmethod
- def _statistic(self, x, y):
+ def statistic(self, x, y):
"""
- Calulates the independence test statistic.
+ Calulates the time-series test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices that have shapes depending on the particular
- independence tests (check desired test class for specifics).
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
"""
@abstractmethod
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
"""
- Calulates the independece test p-value.
+ Calulates the time-series test test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices that have shapes depending on the particular
- independence tests (check desired test class for specifics).
- reps : int, optional
- The number of replications used in permutation, by default 1000.
- workers : int, optional
- Evaluates method using `multiprocessing.Pool `).
- Supply `-1` to use all cores available to the Process.
- random_state : int or np.random.RandomState instance, optional
- If already a RandomState instance, use it.
- If seed is an int, return a new RandomState instance seeded with seed.
- If None, use np.random.RandomState. Default is None.
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
+ `n` is the number of samples and `p` and `q` are the number of
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
+ stat : float
+ The discriminability test statistic.
pvalue : float
- The pvalue obtained via permutation.
+ The discriminability p-value.
null_dist : list
The null distribution of the permuted test statistics.
"""
- self.distx, self.disty = compute_dist(
- x, y, metric=self.compute_distance, **self.kwargs
- )
+ distx, disty = compute_dist(x, y, metric=self.compute_distance, **self.kwargs)
# calculate observed test statistic
- stat_list = self._statistic(x, y)
+ stat_list = self.statistic(x, y)
stat = stat_list[0]
+ # make RandomState seeded array
+ if random_state is not None:
+ rng = check_random_state(random_state)
+ random_state = rng.randint(np.iinfo(np.int32).max, size=reps)
+
+ # make random array
+ else:
+ random_state = np.random.randint(np.iinfo(np.int32).max, size=reps)
+
# calculate null distribution
null_dist = np.array(
Parallel(n_jobs=workers)(
[
- delayed(_perm_stat)(self._statistic, self.distx, self.disty)
- for rep in range(reps)
+ delayed(_perm_stat)(self.statistic, distx, disty, rng)
+ for rng in random_state
]
)
)
pvalue = (1 + (null_dist >= stat).sum()) / (1 + reps)
-
- # correct for a p-value of 0. This is because, with bootstrapping
- # permutations, a p-value of 0 is incorrect
- if pvalue == 0:
- pvalue = 1 / reps
self.pvalue = pvalue
+ self.null_dist = null_dist
return stat, pvalue, stat_list
-def _perm_stat(calc_stat, distx, disty):
+def _perm_stat(calc_stat, distx, disty, random_state=None):
+ """Permutes the test statistics."""
n = distx.shape[0]
block_size = int(np.ceil(np.sqrt(n)))
+ rng = check_random_state(random_state)
perm_index = np.r_[
- [np.arange(t, t + block_size) for t in np.random.choice(n, n // block_size + 1)]
+ [np.arange(t, t + block_size) for t in rng.choice(n, n // block_size + 1)]
].flatten()[:n]
perm_index = np.mod(perm_index, n)
permy = disty[np.ix_(perm_index, perm_index)]
diff --git a/hyppo/time_series/dcorrx.py b/hyppo/time_series/dcorrx.py
index 272dc7f3a..8fc0b147c 100644
--- a/hyppo/time_series/dcorrx.py
+++ b/hyppo/time_series/dcorrx.py
@@ -1,38 +1,62 @@
-from .base import TimeSeriesTest
-from ._utils import _CheckInputs, compute_stat
+from typing import NamedTuple
+
from ..independence import Dcorr
+from ._utils import _CheckInputs, compute_stat
+from .base import TimeSeriesTest
+
+
+class DcorrXTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+ dcorrx_dict: dict
class DcorrX(TimeSeriesTest):
r"""
- Class for running the DcorrX test for independence of time series.
+ Cross Distance Correlation (DcorrX) test statistic and p-value.
DcorrX is an independence test between two (paired) time series of
not necessarily equal dimensions. The population parameter is 0 if and only if the
time series are independent. It is based upon energy distance between distributions.
+ .. _[1]: https://arxiv.org/abs/1908.06486
+
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
+ compute_distance : str, callable, or None, default: "euclidean"
A function that computes the distance among the samples within each
- data matrix. Set to `None` if `x` and `y` are already distance
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_distance(x)``
- where `x` is the data matrix for which pairwise distances are
- calculated.
-
- max_lag : int, optional (default: 0)
- The maximum number of lags in the past to check dependence between `x` and the
- shifted `y`. Also the :math:`M` hyperparmeter below.
-
- See Also
- --------
- Dcorr: Distance correlation test statistic and p-value.
- MGCX : Cross multiscale graph correlation test statistic and p-value.
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ max_lag : int, default: 0
+ The maximum number of lags in the past to check dependence between ``x`` and the
+ shifted ``y``. Also the ``M`` hyperparmeter below.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
Notes
-----
- The statistic can be derived as follows:
+ The statistic can be derived as follows
+ :footcite:p:`mehtaIndependenceTestingMultivariate2020`:
Let :math:`x` and :math:`y` be :math:`(n, p)` and :math:`(n, q)` series
respectively, which each contain :math:`y` observations of the series
@@ -43,14 +67,14 @@ class DcorrX(TimeSeriesTest):
.. math::
- \mathrm{DcorrX}_n (x, y) = \sum_{j=0}^M frac{n-j}{n}
- \mathrm{Dcorr}_n (x[j:n], y[0:(n-j)])
+ \mathrm{DcorrX}_n (x, y) = \sum_{j=0}^M \frac{n-j}{n}
+ Dcorr_n (x[j:n], y[0:(n-j)])
+
+ The p-value returned is calculated using a permutation test.
References
----------
- .. [#1DcorX] Mehta, R., Chung, J., Shen C., Xu T., Vogelstein, J. T. (2019).
- A Consistent Independence Test for Multivariate Time-Series.
- *ArXiv*
+ .. footbibliography::
"""
def __init__(self, compute_distance="euclidean", max_lag=0, **kwargs):
@@ -58,18 +82,18 @@ def __init__(self, compute_distance="euclidean", max_lag=0, **kwargs):
self, compute_distance=compute_distance, max_lag=max_lag, **kwargs
)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the DcorrX test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
@@ -86,29 +110,24 @@ def _statistic(self, x, y):
return stat, opt_lag
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the DcorrX test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- auto : bool (default: True)
- Automatically uses fast approximation when sample size and size of array
- is greater than 20. If True, and sample size is greater than 20, a fast
- chi2 approximation will be run. Parameters ``reps`` and ``workers`` are
- irrelevant in this case.
+ Supply ``-1`` to use all cores available to the Process.
Returns
-------
@@ -131,18 +150,7 @@ def test(self, x, y, reps=1000, workers=1):
>>> y = x
>>> stat, pvalue, dcorrx_dict = DcorrX().test(x, y, reps = 100)
>>> '%.1f, %.2f, %d' % (stat, pvalue, dcorrx_dict['opt_lag'])
- '1.0, 0.01, 0'
-
- The increasing the max_lag can increase the ability to identify dependence.
-
- >>> import numpy as np
- >>> from hyppo.time_series import DcorrX
- >>> np.random.seed(1234)
- >>> x = np.random.permutation(10)
- >>> y = np.roll(x, -1)
- >>> stat, pvalue, dcorrx_dict = DcorrX(max_lag=1).test(x, y, reps=1000)
- >>> '%.1f, %.2f, %d' % (stat, pvalue, dcorrx_dict['opt_lag'])
- '1.1, 0.01, 1'
+ '1.0, 0.04, 0'
"""
check_input = _CheckInputs(
x,
@@ -151,6 +159,8 @@ def test(self, x, y, reps=1000, workers=1):
)
x, y = check_input()
- stat, pvalue, stat_list = super(DcorrX, self).test(x, y, reps, workers)
+ stat, pvalue, stat_list = super(DcorrX, self).test(
+ x, y, reps, workers, random_state
+ )
dcorrx_dict = {"opt_lag": stat_list[1]}
- return stat, pvalue, dcorrx_dict
+ return DcorrXTestOutput(stat, pvalue, dcorrx_dict)
diff --git a/hyppo/time_series/mgcx.py b/hyppo/time_series/mgcx.py
index 33fa0f37e..51034916e 100644
--- a/hyppo/time_series/mgcx.py
+++ b/hyppo/time_series/mgcx.py
@@ -1,38 +1,62 @@
-from .base import TimeSeriesTest
-from ._utils import _CheckInputs, compute_stat, compute_scale_at_lag
+from typing import NamedTuple
+
from ..independence import MGC
+from ._utils import _CheckInputs, compute_scale_at_lag, compute_stat
+from .base import TimeSeriesTest
+
+
+class MGCXTestOutput(NamedTuple):
+ stat: float
+ pvalue: float
+ mgcx_dict: dict
class MGCX(TimeSeriesTest):
r"""
- Class for running the MGCX test for independence of time series.
+ Cross Multiscale Graph Correlation (MGCX) test statistic and p-value.
MGCX is an independence test between two (paired) time series of
not necessarily equal dimensions. The population parameter is 0 if and only if the
time series are independent. It is based upon energy distance between distributions.
+ .. _[1]: https://arxiv.org/abs/1908.06486
+
Parameters
----------
- compute_distance : callable(), optional (default: euclidean)
+ compute_distance : str, callable, or None, default: "euclidean"
A function that computes the distance among the samples within each
- data matrix. Set to `None` if `x` and `y` are already distance
+ data matrix.
+ Valid strings for ``compute_distance`` are, as defined in
+ :func:`sklearn.metrics.pairwise_distances`,
+
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
+ on these metrics.
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``compute_distance(x)``
- where `x` is the data matrix for which pairwise distances are
- calculated.
-
- max_lag : int, optional (default: 0)
- The maximum number of lags in the past to check dependence between `x` and the
- shifted `y`. Also the :math:`M` hyperparmeter below.
-
- See Also
- --------
- MGC: Multiscale graph correlation test statistic and p-value.
- DcorrX: Cross distance correlation test statistic and p-value.
+ before-hand or create a function of the form ``metric(x, **kwargs)``
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ max_lag : int, default: 0
+ The maximum number of lags in the past to check dependence between ``x`` and the
+ shifted ``y``. Also the ``M`` hyperparmeter below.
+ **kwargs
+ Arbitrary keyword arguments for ``compute_distance``.
Notes
-----
- The statistic can be derived as follows:
+ The statistic can be derived as follows
+ :footcite:p:`mehtaIndependenceTestingMultivariate2020`:
Let :math:`x` and :math:`y` be :math:`(n, p)` and :math:`(n, q)` series
respectively, which each contain :math:`y` observations of the series :math:`(X_t)`
@@ -43,14 +67,12 @@ class MGCX(TimeSeriesTest):
.. math::
- \mathrm{MGCX}_n (x, y) = \sum_{j=0}^M frac{n-j}{n}
- \mathrm{MGC}_n (x[j:n], y[0:(n-j)])
+ \mathrm{MGCX}_n (x, y) = \sum_{j=0}^M \frac{n-j}{n}
+ MGC_n (x[j:n], y[0:(n-j)])
References
----------
- .. [#1DcorX] Mehta, R., Chung, J., Shen C., Xu T., Vogelstein, J. T. (2019).
- A Consistent Independence Test for Multivariate Time-Series.
- *ArXiv*
+ .. footbibliography::
"""
def __init__(self, compute_distance="euclidean", max_lag=0, **kwargs):
@@ -58,18 +80,18 @@ def __init__(self, compute_distance="euclidean", max_lag=0, **kwargs):
self, compute_distance=compute_distance, max_lag=max_lag, **kwargs
)
- def _statistic(self, x, y):
+ def statistic(self, x, y):
r"""
Helper function that calculates the MGCX test statistic.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
@@ -77,7 +99,7 @@ def _statistic(self, x, y):
The computed MGCX statistic.
opt_lag : int
The computed optimal lag.
- opt_scale : tuple
+ opt_scale : (int, int)
The computed optimal scale as a pair of two elements.
"""
stat, opt_lag = compute_stat(
@@ -94,29 +116,31 @@ def _statistic(self, x, y):
return stat, opt_lag, opt_scale
- def test(self, x, y, reps=1000, workers=1):
+ def test(self, x, y, reps=1000, workers=1, random_state=None):
r"""
Calculates the MGCX test statistic and p-value.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- auto : bool (default: True)
- Automatically uses fast approximation when sample size and size of array
- is greater than 20. If True, and sample size is greater than 20, a fast
- chi2 approximation will be run. Parameters ``reps`` and ``workers`` are
- irrelevant in this case.
+ Supply ``-1`` to use all cores available to the Process.
+ auto : bool, default: True
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20. If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. Parameters ``reps`` and
+ ``workers`` are
+ irrelevant in this case. Otherwise, :class:`hyppo.tools.perm_test` will be
+ run.
Returns
-------
@@ -130,13 +154,13 @@ def test(self, x, y, reps=1000, workers=1):
- opt_lag : int
The optimal lag that maximizes the strength of the relationship
with respect to lag.
- - opt_scale : tuple
+ - opt_scale : (int, int)
The optimal scale that maximizes the strength of the relationship
with respect to scale.
Examples
--------
- The optimal scale should be global `[n, n]` for cases of linear correlation.
+ The optimal scale should be global ``(n, n)`` for cases of linear correlation.
>>> import numpy as np
>>> from hyppo.time_series import MGCX
@@ -146,7 +170,7 @@ def test(self, x, y, reps=1000, workers=1):
>>> stat, pvalue, mgcx_dict = MGCX().test(x, y, reps = 100)
>>> '%.1f, %.2f, [%d, %d]' % (stat, pvalue, mgcx_dict['opt_scale'][0],
... mgcx_dict['opt_scale'][1])
- '1.0, 0.03, [7, 7]'
+ '1.0, 0.05, [7, 7]'
The increasing the max_lag can increase the ability to identify dependence.
@@ -166,7 +190,9 @@ def test(self, x, y, reps=1000, workers=1):
)
x, y = check_input()
- stat, pvalue, stat_list = super(MGCX, self).test(x, y, reps, workers)
+ stat, pvalue, stat_list = super(MGCX, self).test(
+ x, y, reps, workers, random_state
+ )
mgcx_dict = {"opt_lag": stat_list[1], "opt_scale": stat_list[2]}
- return stat, pvalue, mgcx_dict
+ return MGCXTestOutput(stat, pvalue, mgcx_dict)
diff --git a/hyppo/time_series/tests/test_dcorrx.py b/hyppo/time_series/tests/test_dcorrx.py
index f135ff0e4..e85ec9033 100644
--- a/hyppo/time_series/tests/test_dcorrx.py
+++ b/hyppo/time_series/tests/test_dcorrx.py
@@ -1,10 +1,10 @@
-import pytest
import numpy as np
+import pytest
from numpy.testing import assert_almost_equal
from ...independence import Dcorr
-from .. import DcorrX
from ...tools import cross_corr_ar, nonlinear_process
+from .. import DcorrX
class TestDcorrXStat:
@@ -17,7 +17,7 @@ def test_zero_var(self):
def test_multiple_lags(self):
x = np.ones(6)
y = np.arange(1, 7)
- stat = DcorrX(max_lag=3).test(x, y)[0]
+ stat = DcorrX(max_lag=3).test(x, y, reps=0)[0]
assert_almost_equal(stat, 0.0)
@pytest.mark.parametrize("n", [100, 200])
@@ -33,32 +33,33 @@ def test_correlated_oned(self, n, obs_pvalue, obs_opt_lag):
assert_almost_equal(pvalue, obs_pvalue, decimal=2)
assert_almost_equal(opt_lag, obs_opt_lag, decimal=0)
+ @pytest.mark.parametrize("n", [100, 200])
+ @pytest.mark.parametrize("obs_pvalue", [1 / 1000])
+ @pytest.mark.parametrize("obs_opt_lag", [1])
+ def test_rep(self, n, obs_pvalue, obs_opt_lag):
+ x, y = cross_corr_ar(n, lag=1, phi=0.9, sigma=0.1)
+ _, pvalue, dcorrx_dict = DcorrX(max_lag=1).test(x, y, random_state=2)
+ opt_lag = dcorrx_dict["opt_lag"]
+
+ _, pvalue2, dcorr_dict2 = DcorrX(max_lag=1).test(x, y, random_state=2)
+ opt_lag2 = dcorrx_dict["opt_lag"]
+
+ assert pvalue == pvalue2
+ assert opt_lag == opt_lag2
+
def test_lag0(self):
# Dependent
x, y = nonlinear_process(100, lag=1)
- stat1 = Dcorr().test(x, y)[0]
- stat2 = DcorrX(max_lag=0).test(x, y)[0]
+ stat1 = Dcorr().test(x, y, auto=False, reps=0)[0]
+ stat2 = DcorrX(max_lag=0).test(x, y, reps=0)[0]
- assert_almost_equal(stat1, stat2, decimal=0)
+ assert_almost_equal(stat1, stat2, decimal=2)
# Independent
x = np.array([[0.95476444], [0.36741182], [0.37473209], [-0.62469378]])
y = np.array([[0.86515308], [-0.01985114], [0.21544295], [1.56443374]])
- stat, p = Dcorr().test(x, y)
- statx, px, _ = DcorrX(max_lag=0).test(x, y)
-
- assert stat == statx
-
- def test_distance(self):
- n = 6
- x = np.ones(n)
- y = np.arange(1, n + 1)
-
- distx = np.zeros((n, n))
- disty = np.fromfunction(lambda i, j: np.abs(i - j), (n, n))
-
- stat1 = DcorrX(max_lag=1).test(x, y)[0]
- stat2 = DcorrX(max_lag=1).test(distx, disty)[0]
+ stat = Dcorr().test(x, y, auto=False, reps=0)[0]
+ statx = DcorrX(max_lag=0).test(x, y, reps=0)[0]
- assert_almost_equal(stat1, stat2, decimal=0)
+ assert_almost_equal(stat, statx, decimal=2)
diff --git a/hyppo/time_series/tests/test_mgcx.py b/hyppo/time_series/tests/test_mgcx.py
index 172f60716..7bd7167b9 100644
--- a/hyppo/time_series/tests/test_mgcx.py
+++ b/hyppo/time_series/tests/test_mgcx.py
@@ -1,8 +1,8 @@
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_less
-from .. import MGCX
from ...tools import nonlinear_process
+from .. import MGCX
class TestMGCXStat:
@@ -32,19 +32,6 @@ def test_nonlinear(self):
# assert_almost_equal(stat1, stat2, decimal=0)
- def test_distance(self):
- n = 10
- x = np.arange(1, n + 1)
- y = np.arange(1, n + 1)
-
- distx = np.fromfunction(lambda i, j: np.abs(i - j), (n, n))
- disty = np.fromfunction(lambda i, j: np.abs(i - j), (n, n))
-
- stat1 = MGCX(max_lag=1).test(x, y)[0]
- stat2 = MGCX(max_lag=1).test(distx, disty)[0]
-
- assert_almost_equal(stat1, stat2, decimal=0)
-
def test_optimal_scale_linear(self):
n = 10
x = np.arange(n)
@@ -57,8 +44,30 @@ def test_optimal_scale_linear(self):
def test_optimal_scale_nonlinear(self):
n = 7
x = np.arange(n)
- y = x ** 2
+ y = x**2
mgcx_dict = MGCX().test(x, y, reps=100)[2]
opt_scale = mgcx_dict["opt_scale"]
assert_array_less(opt_scale[0], n)
assert_array_less(opt_scale[1], n)
+
+ def test_rep_linear(self):
+ n = 10
+ x = np.arange(n)
+ y = x
+ mgcx_dict = MGCX().test(x, y, reps=100, random_state=2)[2]
+ opt_scale = mgcx_dict["opt_scale"]
+ mgcx_dict2 = MGCX().test(x, y, reps=100, random_state=2)[2]
+ opt_scale2 = mgcx_dict2["opt_scale"]
+
+ assert opt_scale == opt_scale2
+
+ def test_rep_nonlinear(self):
+ n = 7
+ x = np.arange(n)
+ y = x**2
+ mgcx_dict = MGCX().test(x, y, reps=100, random_state=2)[2]
+ opt_scale = mgcx_dict["opt_scale"]
+ mgcx_dict2 = MGCX().test(x, y, reps=100, random_state=2)[2]
+ opt_scale2 = mgcx_dict2["opt_scale"]
+
+ assert opt_scale == opt_scale2
diff --git a/hyppo/time_series/tests/test_utils.py b/hyppo/time_series/tests/test_utils.py
new file mode 100644
index 000000000..08dd42c0e
--- /dev/null
+++ b/hyppo/time_series/tests/test_utils.py
@@ -0,0 +1,45 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_raises
+
+from .._utils import _CheckInputs
+
+
+class TestErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_error_notndarray(self):
+ # raises error if x or y is not a ndarray
+ x = np.arange(20)
+ y = [5] * 20
+ assert_raises(TypeError, _CheckInputs(x, y, max_lag=1))
+ assert_raises(TypeError, _CheckInputs(y, x, max_lag=1))
+
+ def test_error_shape(self):
+ # raises error if number of samples different (n)
+ x = np.arange(100).reshape(25, 4)
+ y = x.reshape(10, 10)
+ assert_raises(ValueError, _CheckInputs(x, y, max_lag=1))
+
+ def test_error_lowsamples(self):
+ # raises error if samples are low (< 3)
+ x = np.arange(3)
+ y = np.arange(3)
+ assert_raises(ValueError, _CheckInputs(x, y, max_lag=1))
+
+ def test_error_nans(self):
+ # raises error if inputs contain NaNs
+ x = np.arange(20, dtype=float)
+ x[0] = np.nan
+ assert_raises(ValueError, _CheckInputs(x, x, max_lag=1))
+
+ y = np.arange(20)
+ assert_raises(ValueError, _CheckInputs(x, y, max_lag=1))
+
+ @pytest.mark.parametrize(
+ "reps", [-1, "1"] # reps is negative # reps is not integer
+ )
+ def test_error_reps(self, reps):
+ # raises error if reps is negative
+ x = np.arange(20)
+ assert_raises(ValueError, _CheckInputs(x, x, reps=reps, max_lag=1))
diff --git a/hyppo/tools/__init__.py b/hyppo/tools/__init__.py
index f0be4fdd7..f0146c3b1 100755
--- a/hyppo/tools/__init__.py
+++ b/hyppo/tools/__init__.py
@@ -1,6 +1,7 @@
+from .common import *
from .indep_sim import *
from .ksample_sim import *
+from .power import *
from .time_series_sim import *
-from .common import *
__all__ = [s for s in dir() if not s.startswith("_")] # remove dunders
diff --git a/hyppo/tools/common.py b/hyppo/tools/common.py
index 2d18cf503..8e47ed591 100644
--- a/hyppo/tools/common.py
+++ b/hyppo/tools/common.py
@@ -1,42 +1,48 @@
import warnings
-from joblib import Parallel, delayed
import numpy as np
+from joblib import Parallel, delayed
from scipy.stats.distributions import chi2
from sklearn.metrics import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
+from sklearn.utils import check_random_state
-def contains_nan(a): # from scipy
- """Check if inputs contains NaNs"""
+# Explicitly copying private function from scipy 1.7.3
+# Modified to only use nan_policy 'raise'
+# REF: https://github.com/scipy/scipy/blob/59e6539cf80dc04b16b0f0ab52343381f0a7a2fa/scipy/stats/stats.py#L79
+def contains_nan(a):
+ nan_policy = "raise"
try:
# Calling np.sum to avoid creating a huge array into memory
# e.g. np.isnan(a).any()
with np.errstate(invalid="ignore"):
- contains_nan = np.isnan(np.sum(a))
+ contains_nan_var = np.isnan(np.sum(a))
except TypeError:
# This can happen when attempting to sum things which are not
# numbers (e.g. as in the function `mode`). Try an alternative method:
try:
- contains_nan = np.nan in set(a.ravel())
+ contains_nan_var = np.nan in set(a.ravel())
except TypeError:
# Don't know what to do. Fall back to omitting nan values and
# issue a warning.
- contains_nan = False
- msg = (
- "The input array could not be properly checked for nan "
- "values. nan values will be ignored."
+ contains_nan_var = False
+ nan_policy = "omit"
+ warnings.warn(
+ "The input array could not be properly "
+ "checked for nan values. nan values "
+ "will be ignored.",
+ RuntimeWarning,
)
- warnings.warn(msg, RuntimeWarning)
- if contains_nan:
- raise ValueError("Input contains NaNs. Please omit and try again")
+ if contains_nan_var:
+ raise ValueError("The input contains nan values")
- return contains_nan
+ return contains_nan_var, nan_policy
def check_ndarray_xy(x, y):
- """Check if x or y is an ndarray"""
+ """Check if x or y is an ndarray of float"""
if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray):
raise TypeError("x and y must be ndarrays")
@@ -69,8 +75,8 @@ def check_reps(reps):
def _check_distmat(x, y):
"""Check if x and y are distance matrices."""
if (
- not np.array_equal(x, x.T)
- or not np.array_equal(y, y.T)
+ not np.allclose(x, x.T)
+ or not np.allclose(y, y.T)
or not np.all((x.diagonal() == 0))
or not np.all((y.diagonal() == 0))
):
@@ -94,13 +100,14 @@ def _check_distmat(x, y):
def _check_kernmat(x, y):
"""Check if x and y are similarity matrices."""
if (
- not np.array_equal(x, x.T)
- or not np.array_equal(y, y.T)
+ not np.allclose(x, x.T)
+ or not np.allclose(y, y.T)
or not np.all((x.diagonal() == 1))
or not np.all((y.diagonal() == 1))
):
raise ValueError(
- "x and y must be distance matrices, {is_sym} symmetric and {one_diag} "
+ "x and y must be kernel similarity matrices, "
+ "{is_sym} symmetric and {one_diag} "
"ones along the diagonal".format(
is_sym="x is not"
if not np.array_equal(x, x.T)
@@ -118,54 +125,60 @@ def _check_kernmat(x, y):
def compute_kern(x, y, metric="gaussian", workers=1, **kwargs):
"""
- Compute kernel similarity matrix for the input matrices.
+ Kernel similarity matrices for the inputs.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, if `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`, no kernel will be computed.
- metric : str, optional (default: "gaussian")
- A function that computes the distance among the samples within each
+ dimensions. Alternatively, ``x`` and ``y`` can be kernel similarity matrices,
+ where the shapes must both be ``(n, n)``.
+ metric : str, callable, or None, default: "gaussian"
+ A function that computes the kernel similarity among the samples within each
data matrix.
Valid strings for ``metric`` are, as defined in
- ``sklearn.metrics.pairwise.pairwise_kernels``,
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
- ['additive_chi2', 'chi2', 'linear', 'poly', 'polynomial', 'gaussian',
- 'laplacian', 'sigmoid', 'cosine']
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
- Set to `None` or `precomputed` if `x` and `y` are already distance
- matrices. To call a custom function, either create the distance matrix
- before-hand or create a function of the form ``metric(x, **kwargs)``
- where `x` is the data matrix for which pairwise distances are
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already similarity
+ matrices. To call a custom function, either create the similarity matrix
+ before-hand or create a function of the form :func:`metric(x, **kwargs)`
+ where ``x`` is the data matrix for which pairwise kernel similarity matrices are
calculated and kwargs are extra arguements to send to your custom function.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- **kwargs : optional
- Optional arguments provided to ``sklearn.metrics.pairwise.pairwise_kernels``
+ Supply ``-1`` to use all cores available to the Process.
+ **kwargs
+ Arbitrary keyword arguments provided to
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`
or a custom kernel function.
Returns
-------
- simx, simy : ndarray
+ simx, simy : ndarray of float
Similarity matrices based on the metric provided by the user.
"""
if not metric:
metric = "precomputed"
- if metric == "gaussian":
+ if metric in ["gaussian", "rbf"]:
if "gamma" not in kwargs:
- l1 = pairwise_distances(x, metric="l1", n_jobs=workers)
- n = l1.shape[0]
+ l2 = pairwise_distances(x, metric="l2", n_jobs=workers)
+ n = l2.shape[0]
+ # compute median of off diagonal elements
med = np.median(
np.lib.stride_tricks.as_strided(
- l1, (n - 1, n + 1), (l1.itemsize * (n + 1), l1.itemsize)
+ l2, (n - 1, n + 1), (l2.itemsize * (n + 1), l2.itemsize)
)[:, 1:]
)
- kwargs["gamma"] = 1.0 / (2 * (med ** 2))
+ # prevents division by zero when used on label vectors
+ med = med if med else 1
+ kwargs["gamma"] = 1.0 / (2 * (med**2))
metric = "rbf"
if callable(metric):
simx = metric(x, **kwargs)
@@ -179,48 +192,142 @@ def compute_kern(x, y, metric="gaussian", workers=1, **kwargs):
return simx, simy
-def compute_dist(x, y, metric="euclidean", workers=None, **kwargs):
+def _multi_check_kernmat(*args):
+ """Check if every input is a similarity matrix."""
+ for x in args:
+ if not np.allclose(x, x.T) or not np.all((x.diagonal() == 1)):
+ raise ValueError(
+ "x must be a kernel similarity matrix, "
+ "{is_sym} symmetric and {one_diag} "
+ "ones along the diagonal".format(
+ is_sym="is not" if not np.array_equal(x, x.T) else "is",
+ one_diag="doesn't have"
+ if not np.all((x.diagonal() == 1))
+ else "has",
+ )
+ )
+
+
+def multi_compute_kern(*args, metric="gaussian", workers=1, **kwargs):
+ """
+ Kernel similarity matrices for the input matrices.
+
+ Parameters
+ ----------
+ *args: ndarray of float
+ Variable length input data matrices. All inputs must have the same
+ number of samples. That is, the shapes must be ``(n, p)``, ``(n, q)``,
+ etc., where `n` is the number of samples and `p` and `q` are the
+ number of dimensions.
+ metric: str, callable, or None, default="gaussian"
+ A function that computes the kernel similarity among the samples within each
+ data matrix.
+ Valid strings for ``metric`` are, as defined in
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`,
+
+ [``"additive_chi2"``, ``"chi2"``, ``"linear"``, ``"poly"``,
+ ``"polynomial"``, ``"rbf"``,
+ ``"laplacian"``, ``"sigmoid"``, ``"cosine"``]
+
+ Note ``"rbf"`` and ``"gaussian"`` are the same metric.
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already similarity
+ matrices. To call a custom function, either create the similarity matrix
+ before-hand or create a function of the form :func:`metric(x, **kwargs)`
+ where ``x`` is the data matrix for which pairwise kernel similarity matrices are
+ calculated and kwargs are extra arguements to send to your custom function.
+ workers: int, default=1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+ **kwargs
+ Arbitrary keyword arguments provided to
+ :func:`sklearn.metrics.pairwise.pairwise_kernels`
+ or a custom kernel function.
+
+ Returns
+ -------
+ sim_matrices: ndarray of float
+ Similarity matrices based on the metric provided by the user.
+ Must be same shape as ''args''.
+ """
+ if not metric:
+ metric = "precomputed"
+ if metric in ["gaussian", "rbf"]:
+ if "gamma" not in kwargs:
+ l2 = pairwise_distances(args[0], metric="l2", n_jobs=workers)
+ n = l2.shape[0]
+ # compute median of off diagonal elements
+ med = np.median(
+ np.lib.stride_tricks.as_strided(
+ l2, (n - 1, n + 1), (l2.itemsize * (n + 1), l2.itemsize)
+ )[:, 1:]
+ )
+ # prevents division by zero when used on label vectors
+ med = med if med else 1
+ kwargs["gamma"] = 1.0 / (2 * (med**2))
+ metric = "rbf"
+ if callable(metric):
+ sim_mats = []
+ for mat in args:
+ sim_mat = metric(mat, **kwargs)
+ sim_mats.append(sim_mat)
+ sim_matrices = tuple(sim_mats)
+ _multi_check_kernmat(*sim_matrices)
+ else:
+ sim_mats = []
+ for mat in args:
+ sim_mat = pairwise_kernels(mat, metric=metric, n_jobs=workers, **kwargs)
+ sim_mats.append(sim_mat)
+ sim_matrices = tuple(sim_mats)
+ return sim_matrices
+
+
+def compute_dist(x, y, metric="euclidean", workers=1, **kwargs):
"""
- Compute kernel similarity matrix for the input matrices.
+ Distance matrices for the inputs.
Parameters
----------
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, if `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`, no kernel will be computed.
- metric : str, optional (default: "gaussian")
+ dimensions. Alternatively, ``x`` and ``y`` can be distance matrices,
+ where the shapes must both be ``(n, n)``.
+ metric : str, callable, or None, default: "euclidean"
A function that computes the distance among the samples within each
data matrix.
Valid strings for ``metric`` are, as defined in
- ``sklearn.metrics.pairwise_distances``,
+ :func:`sklearn.metrics.pairwise_distances`,
- - From scikit-learn: [βcityblockβ, βcosineβ, βeuclideanβ, βl1β, βl2β,
- βmanhattanβ] See the documentation for scipy.spatial.distance for details
+ - From scikit-learn: [``"euclidean"``, ``"cityblock"``, ``"cosine"``,
+ ``"l1"``, ``"l2"``, ``"manhattan"``] See the documentation for
+ :mod:`scipy.spatial.distance` for details
on these metrics.
- - From scipy.spatial.distance: [βbraycurtisβ, βcanberraβ, βchebyshevβ,
- βcorrelationβ, βdiceβ, βhammingβ, βjaccardβ, βkulsinskiβ, βmahalanobisβ,
- βminkowskiβ, βrogerstanimotoβ, βrussellraoβ, βseuclideanβ,
- βsokalmichenerβ, βsokalsneathβ, βsqeuclideanβ, βyuleβ] See the
- documentation for scipy.spatial.distance for details on these metrics.
-
- Set to `None` or `precomputed` if `x` and `y` are already distance
+ - From scipy.spatial.distance: [``"braycurtis"``, ``"canberra"``,
+ ``"chebyshev"``, ``"correlation"``, ``"dice"``, ``"hamming"``,
+ ``"jaccard"``, ``"kulsinski"``, ``"mahalanobis"``, ``"minkowski"``,
+ ``"rogerstanimoto"``, ``"russellrao"``, ``"seuclidean"``,
+ ``"sokalmichener"``, ``"sokalsneath"``, ``"sqeuclidean"``,
+ ``"yule"``] See the documentation for :mod:`scipy.spatial.distance` for
+ details on these metrics.
+
+ Set to ``None`` or ``"precomputed"`` if ``x`` and ``y`` are already distance
matrices. To call a custom function, either create the distance matrix
before-hand or create a function of the form ``metric(x, **kwargs)``
- where `x` is the data matrix for which pairwise distances are
- calculated and kwargs are extra arguements to send to your custom function.
- workers : int, optional (default: 1)
+ where ``x`` is the data matrix for which pairwise distances are
+ calculated and ``**kwargs`` are extra arguements to send to your custom
+ function.
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- **kwargs : optional
- Optional arguments provided to ``sklearn.metrics.pairwise_distances`` or a
- custom kernel function.
+ Supply ``-1`` to use all cores available to the Process.
+ **kwargs
+ Arbitrary keyword arguments provided to
+ :func:`sklearn.metrics.pairwise_distances` or a
+ custom distance function.
Returns
-------
- distx, disty : ndarray
+ distx, disty : ndarray of float
Distance matrices based on the metric provided by the user.
"""
if not metric:
@@ -326,7 +433,9 @@ def _add_levels(self, root: _PermNode, perm_blocks, indices):
root.add_child(child_node)
self._add_levels(child_node, perm_blocks[idxs, 1:], indices[idxs])
- def _permute_level(self, node):
+ def _permute_level(self, node, rng=None):
+ if rng is None:
+ rng = np.random
if len(node.get_children()) == 0:
return [node.index]
else:
@@ -339,13 +448,11 @@ def _permute_level(self, node):
shuffle_children = [i for i, label in enumerate(labels) if label >= 0]
indices = np.asarray(indices)
if len(shuffle_children) > 1:
- indices[shuffle_children] = indices[
- np.random.permutation(shuffle_children)
- ]
+ indices[shuffle_children] = indices[rng.permutation(shuffle_children)]
return np.concatenate(indices)
- def permute_indices(self):
- return self._permute_level(self.root)[self._index_order]
+ def permute_indices(self, rng=None):
+ return self._permute_level(self.root, rng)[self._index_order]
def original_indices(self):
return np.arange(len(self._index_order))
@@ -362,22 +469,25 @@ def __init__(self, y, perm_blocks=None):
else:
self.perm_tree = _PermTree(perm_blocks)
- def __call__(self):
+ def __call__(self, rng=None):
+ if rng is None:
+ rng = np.random
if self.perm_tree is None:
- order = np.random.permutation(self.n)
+ order = rng.permutation(self.n)
else:
- order = self.perm_tree.permute_indices()
+ order = self.perm_tree.permute_indices(rng)
return order
# p-value computation
-def _perm_stat(calc_stat, x, y, is_distsim=True, permuter=None):
+def _perm_stat(calc_stat, x, y, is_distsim=True, permuter=None, random_state=None):
"""Permute the test statistic"""
- if not permuter:
- order = np.random.permutation(y.shape[0])
+ rng = check_random_state(random_state)
+ if permuter is None:
+ order = rng.permutation(y.shape[0])
else:
- order = permuter()
+ order = permuter(rng)
if is_distsim:
permy = y[order][:, order]
@@ -389,9 +499,18 @@ def _perm_stat(calc_stat, x, y, is_distsim=True, permuter=None):
return perm_stat
-def perm_test(calc_stat, x, y, reps=1000, workers=1, is_distsim=True, perm_blocks=None):
+def perm_test(
+ calc_stat,
+ x,
+ y,
+ reps=1000,
+ workers=1,
+ is_distsim=True,
+ perm_blocks=None,
+ random_state=None,
+):
"""
- Calculate the p-value for a nonparametric test via permutation.
+ Permutation test for the p-value of a nonparametric test.
This process is completed by first randomly permuting :math:`y` to estimate the null
distribution and then calculating the probability of observing a test
@@ -400,43 +519,61 @@ def perm_test(calc_stat, x, y, reps=1000, workers=1, is_distsim=True, perm_block
Parameters
----------
- calc_stat : callable()
- The method used to calculate the test statistic (must use hyppo API)
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ calc_stat : callable
+ The method used to calculate the test statistic (must use hyppo API).
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
- reps : int, optional (default: 1000)
+ dimensions. Alternatively, ``x`` and ``y`` can be distance or similarity
+ matrices,
+ where the shapes must both be ``(n, n)``.
+ reps : int, default: 1000
The number of replications used to estimate the null distribution
when using the permutation test used to calculate the p-value.
- workers : int, optional (default: 1)
+ workers : int, default: 1
The number of cores to parallelize the p-value computation over.
- Supply -1 to use all cores available to the Process.
- is_distsim : bool, optional (default: True)
- Whether or not `x` and `y` are distance or similarity matrices. Changes the
- permutation style of `y`.
-
+ Supply ``-1`` to use all cores available to the Process.
+ is_distsim : bool, default: True
+ Whether or not ``x`` and ``y`` are distance or similarity matrices.
+ perm_blocks : ndarray, default: None
+ Defines blocks of exchangeable samples during the permutation test.
+ If ``None``, all samples can be permuted with one another. Requires `n`
+ rows. Constructs a tree graph with all samples initially at
+ the root node. Each column partitions samples from the same leaf with
+ shared column label into a child of that leaf. During the permutation
+ test, samples within the same final leaf node are exchangeable
+ and blocks of samples with a common parent node are exchangeable. If a
+ column value is negative, the resulting block is unexchangeable.
Returns
-------
stat : float
The computed test statistic.
pvalue : float
The computed p-value.
- pvalue : float
- The approximated null distribution of shape `(reps,)`.
+ null_dist : list of float
+ The approximated null distribution of shape ``(reps,)``.
"""
# calculate observed test statistic
stat = calc_stat(x, y)
+ # make RandomState seeded array
+ if random_state is not None:
+ rng = check_random_state(random_state)
+ random_state = rng.randint(np.iinfo(np.int32).max, size=reps)
+
+ # make random array
+ else:
+ random_state = np.random.randint(np.iinfo(np.int32).max, size=reps)
+
# calculate null distribution
permuter = _PermGroups(y, perm_blocks)
+
null_dist = np.array(
Parallel(n_jobs=workers)(
[
- delayed(_perm_stat)(calc_stat, x, y, is_distsim, permuter)
- for rep in range(reps)
+ delayed(_perm_stat)(calc_stat, x, y, is_distsim, permuter, rng)
+ for rng in random_state
]
)
)
@@ -445,25 +582,88 @@ def perm_test(calc_stat, x, y, reps=1000, workers=1, is_distsim=True, perm_block
return stat, pvalue, null_dist
+def _multi_perm_stat(calc_stat, *args):
+ """Permute every entry and calculate test statistic"""
+ # permute each row
+ comb_matrix = np.concatenate(args, axis=1)
+ perm_matrix = np.zeros(np.shape(comb_matrix))
+ for j in range(comb_matrix.shape[1]):
+ order = np.random.permutation(comb_matrix.shape[0])
+ perm_matrix[:, j] = comb_matrix[order, j]
+ perm_args = tuple(np.split(perm_matrix, len(args), axis=1))
+
+ # calculate test statistic using permuted matrices
+ perm_stat = calc_stat(*perm_args)
+
+ return perm_stat
+
+
+def multi_perm_test(calc_stat, *args, reps=1000, workers=1):
+ """
+ Permutation test for the p-value of a nonparametric test with multiple variables.
+
+ Parameters
+ ----------
+ calc_stat : callable
+ The method used to calculate the test statistic (must use hyppo API).
+ *args : ndarray
+ Variable length input data matrices. All inputs must have the same
+ number of samples. That is, the shapes must be ``(n, p)``, ``(n, q)``,
+ etc., where `n` is the number of samples and `p` and `q` are the
+ number of dimensions.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ workers : int, default: 1
+ The number of cores to parallelize the p-value computation over.
+ Supply ``-1`` to use all cores available to the Process.
+
+ Returns
+ -------
+ stat : float
+ The computed test statistic.
+ pvalue : float
+ The computed p-value.
+ null_dist : list of float
+ The approximated null distribution of shape ``(reps,)``.
+ """
+ # calculate observed test statistic
+ stat = calc_stat(*args)
+
+ # calculate null distribution
+ null_dist = np.array(
+ Parallel(n_jobs=workers)(
+ [delayed(_multi_perm_stat)(calc_stat, *args) for _ in range(reps)]
+ )
+ )
+ pvalue = (1 + (null_dist >= stat).sum()) / (1 + reps)
+
+ return stat, pvalue, null_dist
+
+
def chi2_approx(calc_stat, x, y):
"""
- Calculate the p-value for Dcorr and Hsic via a chi-squared approximation.
+ Fast chi-squared approximation for the p-value.
In the case of distance and kernel methods, Dcorr (and by extension Hsic
- [#2ChiSq]_) can be approximated via a chi-squared distribution [#1ChiSq].
+ :footcite:p:`shenExactEquivalenceDistance2020`)
+ can be approximated via a chi-squared distribution
+ :footcite:p:`shenChiSquareTestDistance2021`.
This approximation is also applicable for the nonparametric MANOVA via
- independence testing method in our package [#3ChiSq]_.
+ independence testing method in our package
+ :footcite:p:`pandaNonparMANOVAIndependence2021`.
Parameters
----------
- calc_stat : callable()
+ calc_stat : callable
The method used to calculate the test statistic (must use hyppo API).
- x, y : ndarray
- Input data matrices. `x` and `y` must have the same number of
- samples. That is, the shapes must be `(n, p)` and `(n, q)` where
+ x,y : ndarray of float
+ Input data matrices. ``x`` and ``y`` must have the same number of
+ samples. That is, the shapes must be ``(n, p)`` and ``(n, q)`` where
`n` is the number of samples and `p` and `q` are the number of
- dimensions. Alternatively, `x` and `y` can be distance matrices,
- where the shapes must both be `(n, n)`.
+ dimensions. Alternatively, ``x`` and ``y`` can be distance or similarity
+ matrices,
+ where the shapes must both be ``(n, n)``.
Returns
-------
@@ -474,14 +674,7 @@ def chi2_approx(calc_stat, x, y):
References
----------
- .. [#1ChiSq] Shen, C., & Vogelstein, J. T. (2019). The Chi-Square Test of Distance
- Correlation. arXiv preprint arXiv:1912.12150.
- .. [#2ChiSq] Shen, C., & Vogelstein, J. T. (2018). The exact equivalence of
- distance and kernel methods for hypothesis testing. arXiv preprint
- arXiv:1806.05514.
- .. [#3ChiSq] Panda, S., Shen, C., Perry, R., Zorn, J., Lutz, A., Priebe, C. E., &
- Vogelstein, J. T. (2019). Nonparametric MANOVA via Independence
- Testing. arXiv e-prints, arXiv-1910.
+ .. footbibliography::
"""
n = x.shape[0]
stat = calc_stat(x, y)
diff --git a/hyppo/tools/indep_sim.py b/hyppo/tools/indep_sim.py
index 356e03bbc..7fa57f9d0 100644
--- a/hyppo/tools/indep_sim.py
+++ b/hyppo/tools/indep_sim.py
@@ -2,87 +2,88 @@
class _CheckInputs:
- """ Check if additional arguments are correct """
+ """Check if additional arguments are correct."""
def __init__(self, n, p):
self.n = n
self.p = p
- def __call__(self, *args):
+ def __call__(self, **kwargs):
if type(self.n) is not int or type(self.p) is not int:
- raise ValueError("n and p must be ints")
-
- if self.n < 5 or self.p < 1:
raise ValueError(
- "n must be greater than or equal to 5 and p "
- "must be greater than or equal to than 1"
+ "Expected n and p of type int, got {} and {}".format(
+ type(self.n), type(self.p)
+ )
)
- for arg in args:
- if arg[1] is float and type(arg[0]) is int:
+ if self.n < 5:
+ raise ValueError("n must be >= 5, got {}".format(self.n))
+
+ if self.p < 1:
+ raise ValueError("p must be >= 1, got {}".format(self.p))
+
+ for key, value in kwargs.items():
+ if value[1] is float and type(value[0]) is int:
continue
- if type(arg[0]) is not arg[1]:
- raise ValueError("Incorrect input variable type")
+ if type(value[0]) is not value[1]:
+ raise ValueError(
+ "Expected {} type {} got {}".format(key, value[1], type(value[0]))
+ )
def _gen_coeffs(p):
- """Calculates coefficients polynomials"""
+ """Calculates coefficients polynomials."""
return np.array([1 / (i + 1) for i in range(p)]).reshape(-1, 1)
def _random_uniform(n, p, low=-1, high=1):
- """Generate random uniform data"""
+ """Generate random uniform data."""
return np.array(np.random.uniform(low, high, size=(n, p)))
def _calc_eps(n):
- """Calculate noise"""
+ """Calculate noise."""
return np.random.normal(0, 1, size=(n, 1))
def linear(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate linear data.
+ Linear simulation.
+
+ Linear :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= w^T X + \kappa \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Linear :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= w^T X + \kappa \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import linear
- >>> x, y = linear(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
coeffs = _gen_coeffs(p)
@@ -94,47 +95,42 @@ def linear(n, p, noise=False, low=-1, high=1):
def exponential(n, p, noise=False, low=0, high=3):
r"""
- Simulates univariate or multivariate exponential data.
+ Exponential simulation.
+
+ Exponential :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(0, 3)^p \\
+ Y &= \exp (w^T X) + 10 \kappa \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: 0)
+ low : float, default: 0
The lower limit of the uniform distribution simulated from.
- high : float, (default: 3)
+ high : float, default: 3
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Exponential :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
-
- .. math::
-
- X &\sim \mathcal{U}(0, 3)^p \\
- Y &= \exp (w^T X) + 10 \kappa \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import exponential
- >>> x, y = exponential(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
coeffs = _gen_coeffs(p)
@@ -146,61 +142,52 @@ def exponential(n, p, noise=False, low=0, high=3):
def cubic(n, p, noise=False, low=-1, high=1, cubs=[-12, 48, 128], scale=1 / 3):
r"""
- Simulates univariate or multivariate cubic data.
+ Cubic simulation.
+
+ Cubic :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= 128 \left( w^T X - \frac{1}{3} \right)^3
+ + 48 \left( w^T X - \frac{1}{3} \right)^2
+ - 12 \left( w^T X - \frac{1}{3} \right)
+ + 80 \kappa \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
- cubs : list of ints (default: [-12, 48, 128])
+ cubs : list of ints, default: [-12, 48, 128]
Coefficients of the cubic function where each value corresponds to the
order of the cubic polynomial.
- scale : float (default: 1/3)
+ scale : float, default: 1/3
Scaling center of the cubic.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Cubic :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= 128 \left( w^T X - \frac{1}{3} \right)^3
- + 48 \left( w^T X - \frac{1}{3} \right)^2
- - 12 \left( w^T X - \frac{1}{3} \right)
- + 80 \kappa \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import cubic
- >>> x, y = cubic(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [
- (noise, bool),
- (low, float),
- (high, float),
- (cubs, list),
- (scale, float),
- ]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ "cubs": (cubs, list),
+ "scale": (scale, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
coeffs = _gen_coeffs(p)
@@ -208,9 +195,9 @@ def cubic(n, p, noise=False, low=-1, high=1, cubs=[-12, 48, 128], scale=1 / 3):
x_coeffs = x @ coeffs - scale
y = (
- cubs[2] * x_coeffs ** 3
- + cubs[1] * x_coeffs ** 2
- + cubs[0] * x_coeffs ** 3
+ cubs[2] * x_coeffs**3
+ + cubs[1] * x_coeffs**2
+ + cubs[0] * x_coeffs**3
+ 80 * noise * eps
)
@@ -219,26 +206,8 @@ def cubic(n, p, noise=False, low=-1, high=1, cubs=[-12, 48, 128], scale=1 / 3):
def joint_normal(n, p, noise=False):
r"""
- Simulates univariate or multivariate joint-normal data.
+ Joint Normal simulation.
- Parameters
- ----------
- n : int
- The number of samples desired by the simulation.
- p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
- Whether or not to include noise in the simulation.
-
- Returns
- -------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
- where `n` is the number of samples and `p` is the number of
- dimensions.
-
- Notes
- -----
Joint Normal :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`: Let
:math:`\rho = \frac{1}{2} p`, :math:`I_p` be the identity matrix of size
:math:`p \times p`, :math:`J_p` be the matrix of ones of size
@@ -250,19 +219,30 @@ def joint_normal(n, p, noise=False):
(X, Y) \sim \mathcal{N}(0, \Sigma)
- Examples
- --------
- >>> from hyppo.tools import joint_normal
- >>> x, y = joint_normal(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
+ Parameters
+ ----------
+ n : int
+ The number of samples desired by the simulation (>= 5).
+ p : int
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
+ Whether or not to include noise in the simulation.
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
+ where `n` is the number of samples and `p` is the number of
+ dimensions.
"""
if p > 10:
- raise ValueError("Covariance matrix for p>10 is not positive" "semi-definite")
+ raise ValueError("Covariance matrix for p > 10 is not positive semi-definite")
- extra_args = [(noise, bool)]
+ extra_args = {
+ "noise": (noise, bool),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
rho = 1 / (2 * p)
cov1 = np.concatenate((np.identity(p), rho * np.ones((p, p))), axis=1)
@@ -279,49 +259,44 @@ def joint_normal(n, p, noise=False):
def step(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate step data.
+ Step simulation.
+
+ Step :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= \mathbb{1}_{w^T X > 0} + \epsilon
+
+ where :math:`\mathbb{1}` is the indicator function.
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Step :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= \mathbb{1}_{w^T X > 0} + \epsilon
-
- where :math:`\mathbb{1}` is the indicator function.
-
- Examples
- --------
- >>> from hyppo.tools import step
- >>> x, y = step(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
if p > 1:
noise = True
@@ -337,103 +312,93 @@ def step(n, p, noise=False, low=-1, high=1):
def quadratic(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate quadratic data.
+ Quadratic simulation.
+
+ Quadratic :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= (w^T X)^2 + 0.5 \kappa \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Quadratic :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= (w^T X)^2 + 0.5 \kappa \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import quadratic
- >>> x, y = quadratic(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
coeffs = _gen_coeffs(p)
eps = _calc_eps(n)
x_coeffs = x @ coeffs
- y = x_coeffs ** 2 + 0.5 * noise * eps
+ y = x_coeffs**2 + 0.5 * noise * eps
return x, y
def w_shaped(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate quadratic data.
+ W-Shaped simulation.
+
+ W-Shaped :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+ :math:`\mathcal{U}(-1, 1)^p`,
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= \left[ \left( (w^T X)^2 - \frac{1}{2} \right)^2
+ + \frac{w^T U}{500} \right] + 0.5 \kappa \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- W-Shaped :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
- :math:`\mathcal{U}(-1, 1)^p`,
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= \left[ \left( (w^T X)^2 - \frac{1}{2} \right)^2
- + \frac{w^T U}{500} \right] + 0.5 \kappa \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import w_shaped
- >>> x, y = w_shaped(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
u = _random_uniform(n, p, 0, 1)
@@ -442,56 +407,51 @@ def w_shaped(n, p, noise=False, low=-1, high=1):
x_coeffs = x @ coeffs
u_coeffs = u @ coeffs
- y = 4 * ((x_coeffs ** 2 - 0.5) ** 2 + u_coeffs / 500) + 0.5 * noise * eps
+ y = 4 * ((x_coeffs**2 - 0.5) ** 2 + u_coeffs / 500) + 0.5 * noise * eps
return x, y
def spiral(n, p, noise=False, low=0, high=5):
r"""
- Simulates univariate or multivariate spiral data.
+ Spiral simulation.
+
+ Spiral :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+ :math:`U \sim \mathcal{U}(0, 5)`, :math:`\epsilon \sim \mathcal{N}(0, 1)`
+
+ .. math::
+
+ X_{|d|} &= U \sin(\pi U) \cos^d(\pi U)\ \mathrm{for}\ d = 1,...,p-1 \\
+ X_{|p|} &= U \cos^p(\pi U) \\
+ Y &= U \sin(\pi U) + 0.4 p \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: 0)
+ low : float, default: 0
The lower limit of the uniform distribution simulated from.
- high : float, (default: 5)
+ high : float, default: 5
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Spiral :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
- :math:`U \sim \mathcal{U}(0, 5)`, :math:`\epsilon \sim \mathcal{N}(0, 1)`
-
- .. math::
-
- X_{|d|} &= U \sin(\pi U) \cos^d(\pi U)\ \mathrm{for}\ d = 1,...,p-1 \\
- X_{|p|} &= U \cos^p(\pi U) \\
- Y &= U \sin(\pi U) + 0.4 p \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import spiral
- >>> x, y = spiral(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
if p > 1:
noise = True
@@ -515,47 +475,41 @@ def spiral(n, p, noise=False, low=0, high=5):
def uncorrelated_bernoulli(n, p, noise=False, prob=0.5):
r"""
- Simulates univariate or multivariate uncorrelated Bernoulli data.
+ Uncorrelated Bernoulli simulation.
+
+ Uncorrelated Bernoulli :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+ :math:`U \sim \mathcal{B}(0.5)`, :math:`\epsilon_1 \sim \mathcal{N}(0, I_p)`,
+ :math:`\epsilon_2 \sim \mathcal{N}(0, 1)`,
+
+ .. math::
+
+ X &= \mathcal{B}(0.5)^p + 0.5 \epsilon_1 \\
+ Y &= (2U - 1) w^T X + 0.5 \epsilon_2
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- prob : float, (default: 0.5)
+ prob : float, default: 0.5
The probability of the bernoulli distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Uncorrelated Bernoulli :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
- :math:`U \sim \mathcal{B}(0.5)`, :math:`\epsilon_1 \sim \mathcal{N}(0, I_p)`,
- :math:`\epsilon_2 \sim \mathcal{N}(0, 1)`,
-
- .. math::
-
- X &= \mathcal{B}(0.5)^p + 0.5 \epsilon_1 \\
- Y &= (2U - 1) w^T X + 0.5 \epsilon_2
-
- Examples
- --------
- >>> from hyppo.tools import uncorrelated_bernoulli
- >>> x, y = uncorrelated_bernoulli(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (prob, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "prob": (prob, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
binom = np.random.binomial(1, prob, size=(n, 1))
sig = np.identity(p)
@@ -574,98 +528,86 @@ def uncorrelated_bernoulli(n, p, noise=False, prob=0.5):
def logarithmic(n, p, noise=False):
r"""
- Simulates univariate or multivariate logarithmic data.
+ Logarithmic simulation.
+
+ Logarithmic :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
+ :math:`\epsilon \sim \mathcal{N}(0, I_p)`,
+
+ .. math::
+
+ X &\sim \mathcal{N}(0, I_p) \\
+ Y_{|d|} &= 2 \log_2 (|X_{|d|}|) + 3 \kappa \epsilon_{|d|}
+ \ \mathrm{for}\ d = 1, ..., p
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Logarithmic :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
- :math:`\epsilon \sim \mathcal{N}(0, I_p)`,
-
- .. math::
-
- X &\sim \mathcal{N}(0, I_p) \\
- Y_{|d|} &= 2 \log_2 (|X_{|d|}|) + 3 \kappa \epsilon_{|d|}
- \ \mathrm{for}\ d = 1, ..., p
-
- Examples
- --------
- >>> from hyppo.tools import logarithmic
- >>> x, y = logarithmic(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
"""
- extra_args = [(noise, bool)]
+ extra_args = {
+ "noise": (noise, bool),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
sig = np.identity(p)
x = np.random.multivariate_normal(np.zeros(p), sig, size=n)
eps = _calc_eps(n)
- y = np.log(x ** 2) + 3 * noise * eps
+ y = np.log(x**2) + 3 * noise * eps
return x, y
def fourth_root(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate fourth root data.
+ Fourth Root simulation.
+
+ Fourth Root :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= |w^T X|^\frac{1}{4} + \frac{\kappa}{4} \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Fourth Root :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}`:
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= |w^T X|^\frac{1}{4} + \frac{\kappa}{4} \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import fourth_root
- >>> x, y = fourth_root(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 1)
"""
- extra_args = [(noise, bool), (low, float), (high, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
eps = _calc_eps(n)
@@ -679,9 +621,14 @@ def fourth_root(n, p, noise=False, low=-1, high=1):
def _sin(n, p, noise=False, low=-1, high=1, period=4 * np.pi):
"""Helper function to calculate sine simulation"""
- extra_args = [(noise, bool), (low, float), (high, float), (period, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ "period": (period, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
if p > 1 or noise:
@@ -702,99 +649,86 @@ def _sin(n, p, noise=False, low=-1, high=1, period=4 * np.pi):
def sin_four_pi(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate sine 4 :math:`\pi` data.
+ Sine 4\ :math:`\pi` simulation.
+
+ Sine 4\ :math:`\pi` :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
+ :math:`U \sim \mathcal{U}(-1, 1)`, :math:`V \sim \mathcal{N}(0, 1)^p`,
+ :math:`\theta = 4 \pi`,
+
+ .. math::
+
+ X_{|d|} &= U + 0.02 p V_{|d|}\ \mathrm{for}\ d = 1, ..., p \\
+ Y &= \sin (\theta X) + \kappa \epsilon
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
+ """
+ return _sin(n, p, noise=noise, low=low, high=high, period=4 * np.pi)
+
+
+def sin_sixteen_pi(n, p, noise=False, low=-1, high=1):
+ r"""
+ Sine 16\ :math:`\pi` simulation.
- Notes
- -----
- Sine 4:math:`\pi` :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
+ Sine 16\ :math:`\pi` :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
:math:`U \sim \mathcal{U}(-1, 1)`, :math:`V \sim \mathcal{N}(0, 1)^p`,
- :math:`\theta = 4 \pi`,
+ :math:`\theta = 16 \pi`,
.. math::
X_{|d|} &= U + 0.02 p V_{|d|}\ \mathrm{for}\ d = 1, ..., p \\
Y &= \sin (\theta X) + \kappa \epsilon
- Examples
- --------
- >>> from hyppo.tools import sin_four_pi
- >>> x, y = sin_four_pi(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
- """
- return _sin(n, p, noise=noise, low=low, high=high, period=4 * np.pi)
-
-
-def sin_sixteen_pi(n, p, noise=False, low=-1, high=1):
- r"""
- Simulates univariate or multivariate sine 16 :math:`\pi` data.
-
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: 1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Sine 16:math:`\pi` :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
- :math:`U \sim \mathcal{U}(-1, 1)`, :math:`V \sim \mathcal{N}(0, 1)^p`,
- :math:`\theta = 16 \pi`,
-
- .. math::
-
- X_{|d|} &= U + 0.02 p V_{|d|}\ \mathrm{for}\ d = 1, ..., p \\
- Y &= \sin (\theta X) + \kappa \epsilon
-
- Examples
- --------
- >>> from hyppo.tools import sin_sixteen_pi
- >>> x, y = sin_sixteen_pi(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
"""
return _sin(n, p, noise=noise, low=low, high=high, period=16 * np.pi)
def _square_diamond(n, p, noise=False, low=-1, high=1, period=-np.pi / 2):
"""Helper function to calculate square/diamond simulation"""
- extra_args = [(noise, bool), (low, float), (high, float), (period, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ "period": (period, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
u = _random_uniform(n, p, low, high)
v = _random_uniform(n, p, low, high)
@@ -809,95 +743,82 @@ def _square_diamond(n, p, noise=False, low=-1, high=1, period=-np.pi / 2):
def square(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate square data.
+ Square simulation.
+
+ Square :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
+ :math:`U \sim \mathcal{U}(-1, 1)`, :math:`V \sim \mathcal{N}(0, 1)^p`,
+ :math:`\theta = -\frac{\pi}{8}`,
+
+ .. math::
+
+ X_{|d|} &= U \cos(\theta) + V \sin(\theta) + 0.05 p \epsilon_{|d|}
+ \ \mathrm{for}\ d = 1, ..., p \\
+ Y_{|d|} &= -U \sin(\theta) + V \cos(\theta)
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Square :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
- :math:`U \sim \mathcal{U}(-1, 1)`, :math:`V \sim \mathcal{N}(0, 1)^p`,
- :math:`\theta = -\frac{\pi}{8}`,
-
- .. math::
-
- X_{|d|} &= U \cos(\theta) + V \sin(\theta) + 0.05 p \epsilon_{|d|}
- \ \mathrm{for}\ d = 1, ..., p \\
- Y_{|d|} &= -U \sin(\theta) + V \cos(\theta)
-
- Examples
- --------
- >>> from hyppo.tools import square
- >>> x, y = square(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
"""
return _square_diamond(n, p, noise=noise, low=low, high=high, period=-np.pi / 8)
def two_parabolas(n, p, noise=False, low=-1, high=1, prob=0.5):
r"""
- Simulates univariate or multivariate two parabolas data.
+ Two Parabolas simulation.
+
+ Two Parabolas :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
+
+ .. math::
+
+ X &\sim \mathcal{U}(-1, 1)^p \\
+ Y &= ((w^T X)^2 + 2 \kappa \epsilon) \times \left( U = \frac{1}{2} \right)
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
- prob : float, (default: 0.5)
+ prob : float, default: 0.5
The probability of the bernoulli distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, 1)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, 1)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Two Parabolas :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
-
- .. math::
-
- X &\sim \mathcal{U}(-1, 1)^p \\
- Y &= ((w^T X)^2 + 2 \kappa \epsilon) \times \left( U = \frac{1}{2} \right)
-
- Examples
- --------
- >>> from hyppo.tools import two_parabolas
- >>> x, y = two_parabolas(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
"""
- extra_args = [(noise, bool), (low, float), (high, float), (prob, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ "prob": (prob, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
x = _random_uniform(n, p, low, high)
coeffs = _gen_coeffs(p)
@@ -905,16 +826,21 @@ def two_parabolas(n, p, noise=False, low=-1, high=1, prob=0.5):
rand_noise = _random_uniform(n, p, low=0, high=1)
x_coeffs = x @ coeffs
- y = (x_coeffs ** 2 + 2 * noise * rand_noise) * (u - 0.5)
+ y = (x_coeffs**2 + 2 * noise * rand_noise) * (u - 0.5)
return x, y
def _circle_ellipse(n, p, noise=False, low=-1, high=1, radius=1):
"""Helper function to calculate circle/ellipse simulation"""
- extra_args = [(noise, bool), (low, float), (high, float), (radius, float)]
+ extra_args = {
+ "noise": (noise, bool),
+ "low": (low, float),
+ "high": (high, float),
+ "radius": (radius, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
if p > 1:
noise = True
@@ -938,30 +864,8 @@ def _circle_ellipse(n, p, noise=False, low=-1, high=1, radius=1):
def circle(n, p, noise=False, low=-1, high=1):
r"""
- Simulates univariate or multivariate circle data.
+ Circle simulation.
- Parameters
- ----------
- n : int
- The number of samples desired by the simulation.
- p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
- Whether or not to include noise in the simulation.
- low : float, (default: -1)
- The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
- The upper limit of the uniform distribution simulated from.
-
- Returns
- -------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
- where `n` is the number of samples and `p` is the number of
- dimensions.
-
- Notes
- -----
Circle :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
:math:`U \sim \mathcal{U}(-1, 1)^p`, :math:`\epsilon \sim \mathcal{N}(0, I_p)`,
:math:`r = 1`,
@@ -974,42 +878,33 @@ def circle(n, p, noise=False, low=-1, high=1):
+ 0.4 \epsilon_{|p|} \right) \\
Y_{|d|} &= \sin(\pi U_{|1|})
- Examples
- --------
- >>> from hyppo.tools import circle
- >>> x, y = circle(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
- """
- return _circle_ellipse(n, p, noise=noise, low=low, high=high, radius=1)
-
-
-def ellipse(n, p, noise=False, low=-1, high=1):
- r"""
- Simulates univariate or multivariate ellipse data.
-
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
+ """
+ return _circle_ellipse(n, p, noise=noise, low=low, high=high, radius=1)
+
+
+def ellipse(n, p, noise=False, low=-1, high=1):
+ r"""
+ Ellipse simulation.
- Notes
- -----
Ellipse :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
:math:`U \sim \mathcal{U}(-1, 1)^p`, :math:`\epsilon \sim \mathcal{N}(0, I_p)`,
:math:`r = 5`,
@@ -1022,42 +917,33 @@ def ellipse(n, p, noise=False, low=-1, high=1):
+ 0.4 \epsilon_{|p|} \right) \\
Y_{|d|} &= \sin(\pi U_{|1|})
- Examples
- --------
- >>> from hyppo.tools import ellipse
- >>> x, y = ellipse(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
- """
- return _circle_ellipse(n, p, noise=noise, low=low, high=high, radius=5)
-
-
-def diamond(n, p, noise=False, low=-1, high=1):
- r"""
- Simulates univariate or multivariate diamond data.
-
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
Whether or not to include noise in the simulation.
- low : float, (default: -1)
+ low : float, default: -1
The lower limit of the uniform distribution simulated from.
- high : float, (default: -1)
+ high : float, default: 1
The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
+ """
+ return _circle_ellipse(n, p, noise=noise, low=low, high=high, radius=5)
+
+
+def diamond(n, p, noise=False, low=-1, high=1):
+ r"""
+ Diamond simulation.
- Notes
- -----
Diamond :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
:math:`U \sim \mathcal{U}(-1, 1)`, :math:`V \sim \mathcal{N}(0, 1)^p`,
:math:`\theta = -\frac{\pi}{4}`,
@@ -1068,36 +954,33 @@ def diamond(n, p, noise=False, low=-1, high=1):
+ 0.05 p \epsilon_{|d|}\ \mathrm{for}\ d = 1, ..., p \\
Y_{|d|} &= -U \sin(\theta) + V \cos(\theta)
- Examples
- --------
- >>> from hyppo.tools import diamond
- >>> x, y = diamond(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
- """
- return _square_diamond(n, p, noise=noise, low=low, high=high, period=-np.pi / 4)
-
-
-def multiplicative_noise(n, p):
- r"""
- Simulates univariate or multivariate multiplicative noise data.
-
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
+ The number of dimensions desired by the simulation (>= 1).
+ noise : bool, default: False
+ Whether or not to include noise in the simulation.
+ low : float, default: -1
+ The lower limit of the uniform distribution simulated from.
+ high : float, default: 1
+ The upper limit of the uniform distribution simulated from.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
+ """
+ return _square_diamond(n, p, noise=noise, low=low, high=high, period=-np.pi / 4)
+
+
+def multiplicative_noise(n, p):
+ r"""
+ Multiplicative Noise simulation.
- Notes
- -----
Multiplicative Noise :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
:math:`\U \sim \mathcal{N}(0, I_p)`,
@@ -1106,16 +989,22 @@ def multiplicative_noise(n, p):
X &\sim \mathcal{N}(0, I_p) \\
Y_{|d|} &= U_{|d|} X_{|d|}\ \mathrm{for}\ d = 1, ..., p
- Examples
- --------
- >>> from hyppo.tools import multiplicative_noise
- >>> x, y = multiplicative_noise(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
+ Parameters
+ ----------
+ n : int
+ The number of samples desired by the simulation (>= 5).
+ p : int
+ The number of dimensions desired by the simulation (>= 1).
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
+ where `n` is the number of samples and `p` is the number of
+ dimensions.
"""
- extra_args = []
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in()
sig = np.identity(p)
x = np.random.multivariate_normal(np.zeros(p), sig, size=n)
@@ -1127,47 +1016,42 @@ def multiplicative_noise(n, p):
def multimodal_independence(n, p, prob=0.5, sep1=3, sep2=2):
r"""
- Simulates univariate or multimodal independence data.
+ Multimodal Independence data.
+
+ Multimodal Independence :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
+ :math:`U \sim \mathcal{N}(0, I_p)`, :math:`V \sim \mathcal{N}(0, I_p)`,
+ :math:`U^\prime \sim \mathcal{B}(0.5)^p`, :math:`V^\prime \sim \mathcal{B}(0.5)^p`,
+
+ .. math::
+
+ X &= \frac{U}{3} + 2 U^\prime - 1 \\
+ Y &= \frac{V}{3} + 2 V^\prime - 1
Parameters
----------
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by the simulation (>= 5).
p : int
- The number of dimensions desired by the simulation.
- prob : float, (default: 0.5)
+ The number of dimensions desired by the simulation (>= 1).
+ prob : float, default: 0.5
The probability of the bernoulli distribution simulated from.
- sep1, sep2: float, (default: 3, 2)
+ sep1, sep2: float, default: 3, 2
The separation between clusters of normally distributed data.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n, p)` and `(n, p)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x` and ``y`` have shapes ``(n, p)`` and ``(n, p)``
where `n` is the number of samples and `p` is the number of
dimensions.
-
- Notes
- -----
- Multimodal Independence :math:`(X, Y) \in \mathbb{R}^p \times \mathbb{R}^p`:
- :math:`U \sim \mathcal{N}(0, I_p)`, :math:`V \sim \mathcal{N}(0, I_p)`,
- :math:`U^\prime \sim \mathcal{B}(0.5)^p`, :math:`V^\prime \sim \mathcal{B}(0.5)^p`,
-
- .. math::
-
- X &= \frac{U}{3} + 2 U^\prime - 1 \\
- Y &= \frac{V}{3} + 2 V^\prime - 1
-
- Examples
- --------
- >>> from hyppo.tools import multimodal_independence
- >>> x, y = multimodal_independence(100, 2)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
"""
- extra_args = [(prob, float), (sep1, float), (sep2, float)]
+ extra_args = {
+ "prob": (prob, float),
+ "sep1": (sep1, float),
+ "sep2": (sep2, float),
+ }
check_in = _CheckInputs(n, p)
- check_in(*extra_args)
+ check_in(**extra_args)
sig = np.identity(p)
u = np.random.multivariate_normal(np.zeros(p), sig, size=n)
@@ -1179,3 +1063,61 @@ def multimodal_independence(n, p, prob=0.5, sep1=3, sep2=2):
y = v / sep1 + sep2 * v_2 - 1
return x, y
+
+
+SIMULATIONS = {
+ "linear": linear,
+ "exponential": exponential,
+ "cubic": cubic,
+ "joint_normal": joint_normal,
+ "step": step,
+ "quadratic": quadratic,
+ "w_shaped": w_shaped,
+ "spiral": spiral,
+ "uncorrelated_bernoulli": uncorrelated_bernoulli,
+ "logarithmic": logarithmic,
+ "fourth_root": fourth_root,
+ "sin_four_pi": sin_four_pi,
+ "sin_sixteen_pi": sin_sixteen_pi,
+ "square": square,
+ "two_parabolas": two_parabolas,
+ "circle": circle,
+ "ellipse": ellipse,
+ "diamond": diamond,
+ "multiplicative_noise": multiplicative_noise,
+ "multimodal_independence": multimodal_independence,
+}
+
+
+def indep_sim(sim, n, p, **kwargs):
+ r"""
+ Independence simulation generator.
+
+ Takes a simulation and the required parameters, and outputs the simulated
+ data matrices.
+
+ Parameters
+ ----------
+ sim : str
+ The name of the simulation (from the :mod:`hyppo.tools` module) that is to be
+ rotated.
+ n : int
+ The number of samples desired by the simulation (>= 5).
+ p : int
+ The number of dimensions desired by the simulation (>= 1).
+ **kwargs
+ Additional keyword arguements for the desired simulation.
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices.
+ """
+ if sim not in SIMULATIONS.keys():
+ raise ValueError(
+ "sim_name must be one of the following: {}".format(list(SIMULATIONS.keys()))
+ )
+ else:
+ sim = SIMULATIONS[sim]
+
+ return sim(n, p, **kwargs)
diff --git a/hyppo/tools/ksample_sim.py b/hyppo/tools/ksample_sim.py
index 600d048a4..4d9bc2cca 100644
--- a/hyppo/tools/ksample_sim.py
+++ b/hyppo/tools/ksample_sim.py
@@ -1,61 +1,10 @@
import numpy as np
-from .indep_sim import (
- linear,
- spiral,
- exponential,
- cubic,
- joint_normal,
- step,
- quadratic,
- w_shaped,
- uncorrelated_bernoulli,
- logarithmic,
- fourth_root,
- sin_four_pi,
- sin_sixteen_pi,
- two_parabolas,
- circle,
- ellipse,
- diamond,
- multiplicative_noise,
- square,
- multimodal_independence,
-)
-
-
-_SIMS = [
- linear,
- spiral,
- exponential,
- cubic,
- joint_normal,
- step,
- quadratic,
- w_shaped,
- uncorrelated_bernoulli,
- logarithmic,
- fourth_root,
- sin_four_pi,
- sin_sixteen_pi,
- two_parabolas,
- circle,
- ellipse,
- diamond,
- multiplicative_noise,
- square,
- multimodal_independence,
-]
-
-
-def _normalize(x, y):
- """Normalize input data matricies."""
- x[:, 0] = x[:, 0] / np.max(np.abs(x[:, 0]))
- y[:, 0] = y[:, 0] / np.max(np.abs(y[:, 0]))
- return x, y
+from .indep_sim import SIMULATIONS
def _2samp_rotate(sim, x, y, p, degree=90, pow_type="samp"):
+ """Generate an independence simulation, rotate it to produce another."""
angle = np.radians(degree)
data = np.hstack([x, y])
same_shape = [
@@ -71,23 +20,22 @@ def _2samp_rotate(sim, x, y, p, degree=90, pow_type="samp"):
"multiplicative_noise",
"multimodal_independence",
]
- if sim.__name__ in same_shape:
+ if sim in same_shape:
rot_shape = 2 * p
else:
rot_shape = p + 1
rot_mat = np.identity(rot_shape)
if pow_type == "dim":
- if sim.__name__ not in [
+ if sim not in [
"exponential",
"cubic",
"spiral",
"uncorrelated_bernoulli",
"fourth_root",
- "circle",
]:
for i in range(rot_shape):
mat = np.random.normal(size=(rot_shape, 1))
- mat = mat / np.sqrt(np.sum(mat ** 2))
+ mat = mat / np.sqrt(np.sum(mat**2))
if i == 0:
rot = mat
else:
@@ -107,7 +55,7 @@ def _2samp_rotate(sim, x, y, p, degree=90, pow_type="samp"):
raise ValueError("pow_type not a valid flag ('dim', 'samp')")
rot_data = (rot_mat @ data.T).T
- if sim.__name__ in same_shape:
+ if sim in same_shape:
x_rot, y_rot = np.hsplit(rot_data, 2)
else:
x_rot, y_rot = np.hsplit(rot_data, [-p])
@@ -115,107 +63,79 @@ def _2samp_rotate(sim, x, y, p, degree=90, pow_type="samp"):
return x_rot, y_rot
-def rot_2samp(sim, n, p, noise=True, degree=90):
+def rot_ksamp(sim, n, p, k=2, noise=True, degree=90, pow_type="samp", **kwargs):
r"""
- Rotates input simulations to produce a 2-sample simulation.
+ Rotates input simulations to produce a `k`-sample simulation.
Parameters
----------
- sim : callable()
- The simulation (from the ``hyppo.tools`` module) that is to be rotated.
+ sim : str
+ The name of the simulation (from the :mod:`hyppo.tools` module) that is to be
+ rotated.
n : int
- The number of samples desired by the simulation.
+ The number of samples desired by ``sim`` (>= 5).
p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: True)
+ The number of dimensions desired by ``sim`` (>= 1).
+ k : int, default: 2
+ The number of groups to simulate.
+ noise : bool, default: True
Whether or not to include noise in the simulation.
- degree : float, (default: 90)
+ degree : float or list of float, default: 90
The number of degrees to rotate the input simulation by (in first dimension).
+ The list must be the same size as ``k - 1``.
+ pow_type : "samp", "dim", default: "samp"
+ Simulation type, (increasing sample size or dimension).
+ **kwargs
+ Additional keyword arguments for the independence simulation.
Returns
-------
- samp1, samp2 : ndarray
- Rotated data matrices. `samp1` and `samp2` have shapes `(n, p+1)` and `(n, p+1)`
- or `(n, 2p)` and `(n, 2p)` depending on the independence simulation. Here, `n`
+ sims : list of ndarray
+ Rotated data matrices. ``sims`` is a list of arrays of shape ``(n, p+1)``
+ or ``(n, 2p)`` depending on the independence simulation. Here, `n`
is the number of samples and `p` is the number of dimensions.
-
- Examples
- --------
- >>> from hyppo.tools import rot_2samp, linear
- >>> x, y = rot_2samp(linear, 100, 1)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
"""
- if sim not in _SIMS:
- raise ValueError("Not valid simulation")
+ if sim not in SIMULATIONS.keys():
+ raise ValueError(
+ "Not valid simulation, must be one of {}".format(SIMULATIONS.keys())
+ )
- if sim.__name__ == "multimodal_independence":
- x, y = sim(n, p)
- x_rot, y_rot = sim(n, p)
- else:
- if sim.__name__ == "multiplicative_noise":
- x, y = sim(n, p)
+ if (k - 1) > 1:
+ if type(degree) is list:
+ if (k - 1) != len(degree):
+ raise ValueError(
+ "k={}, so length of degree must be {}, got {}".format(
+ k, k - 1, len(degree)
+ )
+ )
else:
- x, y = sim(n, p, noise=noise)
- x_rot, y_rot = _2samp_rotate(sim, x, y, p, degree=degree, pow_type="samp")
- samp1 = np.hstack([x, y])
- samp2 = np.hstack([x_rot, y_rot])
-
- return samp1, samp2
-
-
-def trans_2samp(sim, n, p, noise=True, degree=90, trans=0.3):
- r"""
- Translates and rotates input simulations to produce a 2-sample
- simulation.
-
- Parameters
- ----------
- n : int
- The number of samples desired by the simulation.
- p : int
- The number of dimensions desired by the simulation.
- noise : bool, (default: False)
- Whether or not to include noise in the simulation.
- degree : float, (default: 90)
- The number of degrees to rotate the input simulation by (in first dimension).
- trans : float, (default: 0.3)
- The amount to translate the second simulation by (in first dimension).
-
- Returns
- -------
- samp1, samp2 : ndarray
- Translated/rotated data matrices. `samp1` and `samp2` have shapes `(n, p+1)` and
- `(n, p+1)` or `(n, 2p)` and `(n, 2p)` depending on the independence simulation.
- Here, `n` is the number of samples and `p` is the number of dimensions.
-
- Examples
- --------
- >>> from hyppo.tools import trans_2samp, linear
- >>> x, y = trans_2samp(linear, 100, 1)
- >>> print(x.shape, y.shape)
- (100, 2) (100, 2)
- """
- if sim not in _SIMS:
- raise ValueError("Not valid simulation")
-
- if sim.__name__ == "multimodal_independence":
- x, y = sim(n, p)
- x_trans, y_trans = sim(n, p)
+ if (k - 1) != 1:
+ raise ValueError(
+ "k={}, so degree must be type int or float, got {}".format(
+ k, type(degree)
+ )
+ )
+
+ if sim == "multimodal_independence":
+ sims = [np.hstack(SIMULATIONS[sim](n, p, **kwargs)) for _ in range(k)]
else:
- if sim.__name__ == "multiplicative_noise":
- x, y = sim(n, p)
+ if sim != "multiplicative_noise":
+ kwargs["noise"] = noise
+ x, y = SIMULATIONS[sim](n, p, **kwargs)
+ if (k - 1) == 1:
+ sims = [
+ np.hstack([x, y]),
+ np.hstack(
+ _2samp_rotate(sim, x, y, p, degree=degree, pow_type=pow_type)
+ ),
+ ]
else:
- x, y = sim(n, p, noise=noise)
- x, y = _normalize(x, y)
- x_trans, y_trans = _2samp_rotate(sim, x, y, p, degree=degree, pow_type="dim")
- x_trans[:, 0] += trans
- y_trans[:, 0] = y_trans[:, -1]
-
- samp1 = np.hstack([x, y])
- samp2 = np.hstack([x_trans, y_trans])
+ sims = [np.hstack([x, y])] + [
+ np.hstack(_2samp_rotate(sim, x, y, p, degree=deg, pow_type=pow_type))
+ for deg in degree
+ ]
- return samp1, samp2
+ return sims
def gaussian_3samp(n, epsilon=1, weight=0, case=1):
@@ -225,13 +145,13 @@ def gaussian_3samp(n, epsilon=1, weight=0, case=1):
Parameters
----------
n : int
- The number of samples desired by the simulation.
- epsilon : float, (default: 1)
+ The number of samples desired by the simulation (>= 5).
+ epsilon : float, default: 1
The amount to translate simulation by (amount depends on case).
- weight : float, (default: False)
+ weight : float, default: False
Number between 0 and 1 corresponding to weight of the second Gaussian
- (used in case 4 and 5 to produce a mixture of Gaussians)
- case : {1, 2, 3, 4, 5}, (default: 1)
+ (used in case 4 and 5 to produce a mixture of Gaussians).
+ case : 1, 2, 3, 4, 5, default: 1
The case in which to evaluate statistical power for each test.
Returns
@@ -239,14 +159,10 @@ def gaussian_3samp(n, epsilon=1, weight=0, case=1):
sims : list of ndarray
List of 3 2-dimensional multivariate Gaussian each
corresponding to the desired case.
-
- Examples
- --------
- >>> from hyppo.tools import gaussian_3samp
- >>> sims = gaussian_3samp(100)
- >>> print(sims[0].shape, sims[1].shape, sims[2].shape)
- (100, 2) (100, 2) (100, 2)
"""
+ if n < 5:
+ raise ValueError("n must be >= 5, got {}".format(n))
+
old_case = case
if old_case == 4:
case = 2
@@ -284,3 +200,40 @@ def gaussian_3samp(n, epsilon=1, weight=0, case=1):
]
return sims
+
+
+KSAMP_SIMS = {
+ "rot_ksamp": rot_ksamp,
+ "gaussian_3samp": gaussian_3samp,
+}
+
+
+def ksamp_sim(ksim, n, **kwargs):
+ r"""
+ `K`-sample simulation generator.
+
+ Takes a simulation and the required parameters, and outputs the simulated
+ data matrices.
+
+ Parameters
+ ----------
+ sim : str
+ The name of the simulation (from the :mod:`hyppo.tools module).
+ n : int
+ The number of samples desired by the simulation (>= 3).
+ **kwargs
+ Additional keyword arguements for the desired simulation.
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices.
+ """
+ if ksim not in KSAMP_SIMS.keys():
+ raise ValueError(
+ "sim must be one of the following: {}".format(list(KSAMP_SIMS.keys()))
+ )
+ else:
+ ksim = KSAMP_SIMS[ksim]
+
+ return ksim(n=n, **kwargs)
diff --git a/hyppo/tools/power.py b/hyppo/tools/power.py
new file mode 100644
index 000000000..64c8bb16d
--- /dev/null
+++ b/hyppo/tools/power.py
@@ -0,0 +1,211 @@
+from math import ceil
+
+import numpy as np
+
+from ..independence import INDEP_TESTS
+from ..ksample import KSAMP_TESTS, KSample, k_sample_transform
+from ..d_variate import MULTI_TESTS
+from .indep_sim import indep_sim
+from .ksample_sim import gaussian_3samp, rot_ksamp
+
+_ALL_SIMS = {
+ "indep": indep_sim,
+ "multi": indep_sim,
+ "ksamp": rot_ksamp,
+ "gauss": gaussian_3samp,
+}
+
+_NONPERM_TESTS = {
+ "dcorr": "fast",
+ "hsic": "fast",
+ "dhsic": "fast",
+ "energy": "fast",
+ "mmd": "fast",
+ "disco": "fast",
+ "manova": "analytical",
+ "hotelling": "analytical",
+}
+
+
+def _sim_gen(sim_type, **kwargs):
+ """
+ Generate ``sims`` for the desired simulations.
+ """
+ if sim_type in ["indep", "ksamp"]:
+ if (
+ kwargs["sim"] in ["multiplicative_noise", "multimodal_independence"]
+ and "noise" in kwargs.keys()
+ ):
+ kwargs.pop("noise")
+
+ sims = _ALL_SIMS[sim_type](**kwargs)
+
+ return sims
+
+
+def _indep_perm_stat(test, sim_type, **kwargs):
+ """
+ Generates null and alternate distributions for the independence test.
+ """
+ x, y = _sim_gen(sim_type=sim_type, **kwargs)
+ obs_stat = test.statistic(x, y)
+ permy = np.random.permutation(y)
+ perm_stat = test.statistic(x, permy)
+
+ return obs_stat, perm_stat
+
+
+def _ksamp_perm_stat(test, sim_type, **kwargs):
+ """
+ Generates null and alternate distributions for the k-sample test.
+ """
+ sims = _sim_gen(sim_type=sim_type, **kwargs)
+ u, v = k_sample_transform(sims)
+ obs_stat = test.statistic(u, v)
+ permv = np.random.permutation(v)
+ perm_stat = test.statistic(u, permv)
+
+ return obs_stat, perm_stat
+
+
+def _multi_perm_stat(test, sim_type, **kwargs):
+ """
+ Generates null and alternate distributions for the d_variate independence test.
+ """
+ x, y = _sim_gen(sim_type=sim_type, **kwargs)
+ obs_stat = test.statistic(*(x, y))
+ [permx, permy] = np.split(np.random.permutation(np.append(x, y)), 2)
+ permx = permx.reshape(permx.shape[0], 1)
+ permy = permy.reshape(permy.shape[0], 1)
+ perm_stat = test.statistic(*(permx, permy))
+
+ return obs_stat, perm_stat
+
+
+_PERM_STATS = {
+ "indep": _indep_perm_stat,
+ "ksamp": _ksamp_perm_stat,
+ "multi": _multi_perm_stat,
+}
+
+
+def _nonperm_pval(test, sim_type, **kwargs):
+ """
+ Generates fast permutation pvalues
+ """
+ sims = _sim_gen(sim_type=sim_type, **kwargs)
+ pvalue = test.test(*sims)[1]
+
+ return pvalue
+
+
+def power(test, sim_type, sim=None, n=100, alpha=0.05, reps=1000, auto=False, **kwargs):
+ """
+ Computes empircal power for hypothesis tests
+
+ Parameters
+ ----------
+ test : str or list
+ The name of the independence test (from the :mod:`hyppo.independence` module
+ or the :mod:`hyppo.d_variate` module) that is to be tested. If MaxMargin,
+ accepts list with first entry "MaxMargin" and second entry the name of
+ another independence test.
+ For :class:`hyppo.ksample.KSample` put the name of the independence test.
+ For other tests in :mod:`hyppo.ksample` just use the name of the class.
+ sim_type : "indep", "ksamp", "gauss", "multi"
+ Type of power method to calculate. Depends on the type of ``sim``.
+ sim : str, default: None
+ The name of the independence simulation (from the :mod:`hyppo.tools` module).
+ that is to be used. Set to ``None`` if using Gaussian simulation curve.
+ n : int, default: 100
+ The number of samples desired by the simulation (>= 5).
+ alpha : float, default: 0.05
+ The alpha level of the test.
+ reps : int, default: 1000
+ The number of replications used to estimate the null distribution
+ when using the permutation test used to calculate the p-value.
+ auto : bool, default: False
+ Automatically uses fast approximation when `n` and size of array
+ is greater than 20 or test has a non-permutation based p-value.
+ If ``True``, and sample size is greater than 20, then
+ :class:`hyppo.tools.chi2_approx` will be run. ``reps`` is
+ irrelevant in this case.
+ See documentation for ``test`` if this parameter applies.
+ **kwargs
+ Additional keyword arguements for ``sim``.
+
+ Returns
+ -------
+ empirical_power : ndarray of float
+ Estimated empirical power for the test.
+ """
+ if sim_type not in _ALL_SIMS.keys():
+ raise ValueError(
+ "sim_type {}, must be in {}".format(sim_type, list(_ALL_SIMS.keys()))
+ )
+
+ if type(test) is list:
+ test_name = [t.lower() for t in test]
+ if test_name[0] != "maxmargin" or test_name[1] not in INDEP_TESTS.keys():
+ raise ValueError(
+ "Test 1 must be Maximal Margin, currently {}; Test 2 must be an "
+ "independence test, currently {}".format(*test)
+ )
+ test = INDEP_TESTS[test_name[0]](indep_test=test_name[1])
+ test_name = test_name[1]
+ else:
+ test_name = test.lower()
+ if test_name in INDEP_TESTS.keys():
+ test = INDEP_TESTS[test_name]()
+ elif test_name in KSAMP_TESTS.keys():
+ test = KSAMP_TESTS[test_name]()
+ elif test_name in MULTI_TESTS.keys():
+ test = MULTI_TESTS[test_name]()
+ else:
+ raise ValueError(
+ "Test {} not in {}".format(
+ test_name,
+ list(INDEP_TESTS.keys())
+ + list(KSAMP_TESTS.keys())
+ + list(MULTI_TESTS.keys()),
+ )
+ )
+
+ kwargs["n"] = n
+ perm_type = "indep"
+ if sim_type in ["ksamp", "gauss"]:
+ perm_type = "ksamp"
+ if sim_type == "multi":
+ perm_type = "multi"
+ if sim_type != "gauss":
+ kwargs["sim"] = sim
+
+ if test_name in _NONPERM_TESTS.keys() and (
+ auto or _NONPERM_TESTS[test_name] == "analytical"
+ ):
+ if test_name in INDEP_TESTS.keys() and perm_type == "ksamp":
+ test = KSample(test_name)
+ if n < 20 and _NONPERM_TESTS[test_name] == "fast":
+ empirical_power = np.nan
+ else:
+ pvals = np.array(
+ [
+ _nonperm_pval(test=test, sim_type=sim_type, **kwargs)
+ for _ in range(reps)
+ ]
+ )
+ empirical_power = (1 + (pvals <= alpha).sum()) / (1 + reps)
+ else:
+ alt_dist, null_dist = map(
+ np.float64,
+ zip(
+ *[
+ _PERM_STATS[perm_type](test=test, sim_type=sim_type, **kwargs)
+ for _ in range(reps)
+ ]
+ ),
+ )
+ cutoff = np.sort(null_dist)[ceil(reps * (1 - alpha))]
+ empirical_power = (1 + (alt_dist >= cutoff).sum()) / (1 + reps)
+
+ return empirical_power
diff --git a/hyppo/tools/tests/test_common.py b/hyppo/tools/tests/test_common.py
new file mode 100644
index 000000000..bab2618f9
--- /dev/null
+++ b/hyppo/tools/tests/test_common.py
@@ -0,0 +1,290 @@
+import numpy as np
+import pytest
+from numpy.testing import (
+ assert_allclose,
+ assert_almost_equal,
+ assert_array_equal,
+ assert_array_less,
+ assert_equal,
+ assert_raises,
+ assert_warns,
+)
+from sklearn.metrics import pairwise_distances
+from sklearn.metrics.pairwise import pairwise_kernels
+
+from ...independence import Dcorr
+from ..common import (
+ _check_distmat,
+ _check_kernmat,
+ _multi_check_kernmat,
+ _PermTree,
+ check_ndarray_xy,
+ check_perm_blocks,
+ check_perm_blocks_dim,
+ check_reps,
+ chi2_approx,
+ compute_dist,
+ compute_kern,
+ contains_nan,
+ convert_xy_float64,
+ perm_test,
+ multi_compute_kern,
+ multi_perm_test,
+)
+from ..indep_sim import linear
+
+
+class TestPermTree:
+ """
+ Tests that permutations are restricted correctly
+ """
+
+ def test_within_permutatins(self):
+ np.random.seed(1)
+ # i.e. case: y = np.asarray([0,1,0,1,0,1])
+ blocks = np.vstack(
+ (
+ [-1, 1],
+ [-1, 2],
+ [-2, 1],
+ [-2, 2],
+ [-3, 1],
+ [-3, 2],
+ )
+ )
+ perm_tree = _PermTree(blocks)
+ original_indices = perm_tree.original_indices()
+ perms = np.asarray([perm_tree.permute_indices() for _ in range(10)])
+ assert_array_less(np.abs(original_indices - perms), 2)
+ assert_allclose(
+ np.mean(perms, axis=0), [0.5, 0.5, 2.5, 2.5, 4.5, 4.5], rtol=0, atol=0.2
+ )
+
+ def test_across_permutations(self):
+ np.random.seed(0)
+ # i.e. case: y = np.asarray([0,0,1,1,2,2])
+ blocks = np.vstack(
+ (
+ [1, -1],
+ [1, -2],
+ [2, -1],
+ [2, -2],
+ [3, -1],
+ [3, -2],
+ )
+ )
+ perm_tree = _PermTree(blocks)
+ _ = perm_tree.original_indices()
+ perms = np.asarray([perm_tree.permute_indices() for _ in range(100)])
+ assert_equal(perms[0][1::2] - perms[0][::2], [1, 1, 1])
+ assert_allclose(np.mean(perms, axis=0), [2, 3, 2, 3, 2, 3], rtol=0, atol=0.2)
+
+ def test_fixed_permutation(self):
+ np.random.seed(0)
+ blocks = [-1, -2, -3, -4]
+ perm_tree = _PermTree(blocks)
+ assert_equal(perm_tree.permute_indices(), perm_tree.original_indices())
+
+ def test_semifixed(self):
+ np.random.seed(1)
+ blocks = [1, 2, -3, -4]
+ perm_tree = _PermTree(blocks)
+ perms = np.asarray([perm_tree.permute_indices() for _ in range(10)])
+ assert_equal(perms[0][2:], perm_tree.original_indices()[2:])
+ assert_allclose(np.mean(perms, axis=0)[:2], [0.5, 0.5], rtol=0, atol=0.2)
+
+ def test_non_int_inputs(self):
+ blocks = ["a", "b", "c"]
+ _ = _PermTree(blocks)
+
+ def test_block_sizes(self):
+ blocks = [1, 1, 2]
+ assert_raises(ValueError, _PermTree, blocks)
+
+ def test_noblock(self):
+ perm_blocks = check_perm_blocks(None)
+ assert_equal(perm_blocks, None)
+
+ def test_not_ndarray(self):
+ perm_blocks = (1, 2, 3)
+ assert_raises(TypeError, check_perm_blocks, perm_blocks)
+
+ def test_incorrect_dim(self):
+ perm_blocks = np.arange(125).reshape(5, 5, 5)
+ assert_raises(ValueError, check_perm_blocks, perm_blocks)
+
+ def test_permblock_dim(self):
+ perm_blocks = np.arange(100).reshape(50, 2)
+ y = np.arange(100).reshape(10, 10)
+ assert_raises(ValueError, check_perm_blocks_dim, perm_blocks, y)
+
+
+class TestErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_error_notndarray(self):
+ # raises error if x or y is not a ndarray
+ x = np.arange(20)
+ y = [5] * 20
+ assert_raises(TypeError, check_ndarray_xy, x, y)
+ assert_raises(TypeError, check_ndarray_xy, y, x)
+
+ def test_conv_float(self):
+ # raises error if number of samples different (n)
+ x = np.arange(20)
+ y = np.arange(20)
+ x, y = convert_xy_float64(x, y)
+ assert_equal(x.dtype, np.float64)
+ assert_equal(y.dtype, np.float64)
+
+ def test_error_distkern(self):
+ # raises error if samples are low (< 3)
+ x = np.arange(10).reshape(-1, 1)
+ y = np.arange(10).reshape(1, -1)
+ assert_raises(ValueError, _check_distmat, x, y)
+ assert_raises(ValueError, _check_kernmat, x, y)
+
+ def test_error_multidistkern(self):
+ # raises error if samples are low (< 3)
+ x = np.arange(10).reshape(-1, 1)
+ y = np.arange(10).reshape(1, -1)
+ assert_raises(ValueError, _multi_check_kernmat, x, y)
+
+ def test_error_nans(self):
+ # raises error if inputs contain NaNs
+ x = np.arange(20, dtype=float)
+ x[0] = np.nan
+ assert_raises(ValueError, contains_nan, x)
+
+ @pytest.mark.parametrize(
+ "reps", [-1, "1"] # reps is negative # reps is not integer
+ )
+ def test_error_reps(self, reps):
+ # raises error if reps is negative
+ assert_raises(ValueError, check_reps, reps)
+
+ def test_warn_reps(self):
+ # raises error if reps is negative
+ reps = 100
+ assert_warns(RuntimeWarning, check_reps, reps)
+
+
+class TestHelper:
+ """Tests errors and warnings derived from MGC."""
+
+ def test_diskern(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ distx = pairwise_distances(x, x)
+ disty = pairwise_distances(y, y)
+
+ l1 = pairwise_distances(x, metric="l1")
+ n = l1.shape[0]
+ med = np.median(
+ np.lib.stride_tricks.as_strided(
+ l1, (n - 1, n + 1), (l1.itemsize * (n + 1), l1.itemsize)
+ )[:, 1:]
+ )
+ gamma = 1.0 / (2 * (med**2))
+
+ kernx = pairwise_kernels(x, x, metric="rbf", gamma=gamma)
+ kerny = pairwise_kernels(y, y, metric="rbf", gamma=gamma)
+
+ distx, disty = compute_dist(distx, disty, metric=None)
+ kernx, kerny = compute_kern(kernx, kerny, metric=None)
+ distx_comp, disty_comp = compute_dist(x, y)
+ kernx_comp, kerny_comp = compute_kern(x, y)
+ kernx_comp1, kerny_comp1 = compute_kern(x, y, metric="rbf")
+
+ assert_array_equal(distx, distx_comp)
+ assert_array_equal(disty, disty_comp)
+ assert_array_equal(kernx, kernx_comp)
+ assert_array_equal(kerny, kerny_comp)
+ assert_array_equal(kernx, kerny_comp1)
+ assert_array_equal(kerny, kerny_comp1)
+
+ def gaussian(x, **kwargs):
+ return pairwise_kernels(x, x, metric="rbf", **kwargs)
+
+ def euclidean(x, **kwargs):
+ return pairwise_distances(x, x, metric="euclidean", **kwargs)
+
+ distx, disty = compute_dist(x, y, metric=euclidean)
+ kernx, kerny = compute_kern(x, y, metric=gaussian, gamma=gamma)
+
+ assert_array_equal(distx, distx_comp)
+ assert_array_equal(disty, disty_comp)
+ assert_array_equal(kernx, kernx_comp)
+ assert_array_equal(kerny, kerny_comp)
+
+ def test_multidiskern(self):
+ np.random.seed(123456789)
+ x, y = linear(100, 1)
+ distx = pairwise_distances(x, x)
+ disty = pairwise_distances(y, y)
+
+ l1 = pairwise_distances(x, metric="l1")
+ n = l1.shape[0]
+ med = np.median(
+ np.lib.stride_tricks.as_strided(
+ l1, (n - 1, n + 1), (l1.itemsize * (n + 1), l1.itemsize)
+ )[:, 1:]
+ )
+ gamma = 1.0 / (2 * (med**2))
+
+ kernx = pairwise_kernels(x, x, metric="rbf", gamma=gamma)
+ kerny = pairwise_kernels(y, y, metric="rbf", gamma=gamma)
+
+ distx, disty = compute_dist(distx, disty, metric=None)
+ kernx, kerny = multi_compute_kern(*(kernx, kerny), metric=None)
+ distx_comp, disty_comp = compute_dist(x, y)
+ kernx_comp, kerny_comp = multi_compute_kern(*(x, y))
+ kernx_comp1, kerny_comp1 = multi_compute_kern(*(x, y), metric="rbf")
+
+ assert_array_equal(distx, distx_comp)
+ assert_array_equal(disty, disty_comp)
+ assert_array_equal(kernx, kernx_comp)
+ assert_array_equal(kerny, kerny_comp)
+ assert_array_equal(kernx, kerny_comp1)
+ assert_array_equal(kerny, kerny_comp1)
+
+ def gaussian(x, **kwargs):
+ return pairwise_kernels(x, x, metric="rbf", **kwargs)
+
+ def euclidean(x, **kwargs):
+ return pairwise_distances(x, x, metric="euclidean", **kwargs)
+
+ distx, disty = compute_dist(x, y, metric=euclidean)
+ kernx, kerny = multi_compute_kern(*(x, y), metric=gaussian, gamma=gamma)
+
+ assert_array_equal(distx, distx_comp)
+ assert_array_equal(disty, disty_comp)
+ assert_array_equal(kernx, kernx_comp)
+ assert_array_equal(kerny, kerny_comp)
+
+ def test_permtest(self):
+ x, y = linear(100, 1)
+
+ stat, pvalue, _ = perm_test(Dcorr().statistic, x, y, is_distsim=False)
+ assert_almost_equal(stat, 1.0, decimal=1)
+ assert_almost_equal(pvalue, 1 / 1000, decimal=1)
+
+ x = pairwise_distances(x, x)
+ y = pairwise_distances(y, y)
+ stat, pvalue, _ = perm_test(Dcorr().statistic, x, y, is_distsim=True)
+ assert_almost_equal(stat, 1.0, decimal=1)
+ assert_almost_equal(pvalue, 1 / 1000, decimal=1)
+
+ def test_multipermtest(self):
+ x, y = linear(100, 1)
+
+ stat, pvalue, _ = multi_perm_test(Dcorr().statistic, *(x, y))
+ assert_almost_equal(stat, 1.0, decimal=1)
+ assert_almost_equal(pvalue, 1 / 1000, decimal=1)
+
+ def test_chi2(self):
+ x, y = linear(100, 1)
+
+ stat, pvalue = chi2_approx(Dcorr().statistic, x, y)
+ assert_almost_equal(stat, 1.0, decimal=1)
+ assert_almost_equal(pvalue, 1 / 1000, decimal=1)
diff --git a/hyppo/tools/tests/test_indep_sim.py b/hyppo/tools/tests/test_indep_sim.py
index 62101616c..cb1a9182b 100644
--- a/hyppo/tools/tests/test_indep_sim.py
+++ b/hyppo/tools/tests/test_indep_sim.py
@@ -1,66 +1,27 @@
-import pytest
import numpy as np
-from numpy.testing import assert_equal
+import pytest
+from numpy.testing import assert_equal, assert_raises
-from .. import (
- linear,
- spiral,
- exponential,
- cubic,
- joint_normal,
- step,
- quadratic,
- w_shaped,
- uncorrelated_bernoulli,
- logarithmic,
- fourth_root,
- sin_four_pi,
- sin_sixteen_pi,
- two_parabolas,
- circle,
- ellipse,
- diamond,
- multiplicative_noise,
- square,
- multimodal_independence,
-)
+from .. import SIMULATIONS, indep_sim
-class TestIndepShape:
+class TestIndepSimShape:
@pytest.mark.parametrize("n", [100, 1000])
@pytest.mark.parametrize("p", [1, 5])
@pytest.mark.parametrize(
"sim",
- [
- linear,
- spiral,
- exponential,
- cubic,
- joint_normal,
- step,
- quadratic,
- w_shaped,
- uncorrelated_bernoulli,
- logarithmic,
- fourth_root,
- sin_four_pi,
- sin_sixteen_pi,
- two_parabolas,
- circle,
- ellipse,
- diamond,
- multiplicative_noise,
- square,
- multimodal_independence,
- ],
+ SIMULATIONS.keys(),
)
def test_shapes(self, n, p, sim):
np.random.seed(123456789)
- x, y = sim(n, p)
+ x, y = SIMULATIONS[sim](n, p)
+ x1, y1 = indep_sim(sim, n, p)
nx, px = x.shape
ny, py = y.shape
+ nx1, px1 = x1.shape
+ ny1, py1 = y1.shape
- if sim.__name__ in [
+ if sim in [
"joint_normal",
"logarithmic",
"sin_four_pi",
@@ -74,6 +35,32 @@ def test_shapes(self, n, p, sim):
"multimodal_independence",
]:
assert_equal(px, py)
+ assert_equal(px1, py1)
else:
assert_equal(px, py * p)
+ assert_equal(px1, py1 * p)
assert_equal(nx, ny)
+ assert_equal(nx1, ny1)
+
+
+class TestIndepSimErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_np_inctype(self):
+ assert_raises(ValueError, indep_sim, n="a", p=1, sim="linear")
+ assert_raises(ValueError, indep_sim, n=10, p=7.0, sim="linear")
+
+ def test_low_n(self):
+ assert_raises(ValueError, indep_sim, n=3, p=1, sim="linear")
+
+ def test_low_p(self):
+ assert_raises(ValueError, indep_sim, n=5, p=0, sim="linear")
+
+ def test_extra_inctype(self):
+ assert_raises(ValueError, indep_sim, n=5, p=0, sim="linear", low="a")
+
+ def test_wrong_sim(self):
+ assert_raises(ValueError, indep_sim, n=100, p=1, sim="abcd")
+
+ def test_joint_lowcov(self):
+ assert_raises(ValueError, indep_sim, n=100, p=20, sim="joint_normal")
diff --git a/hyppo/tools/tests/test_ksample_sim.py b/hyppo/tools/tests/test_ksample_sim.py
index df88d040d..5a6a25d27 100644
--- a/hyppo/tools/tests/test_ksample_sim.py
+++ b/hyppo/tools/tests/test_ksample_sim.py
@@ -1,70 +1,25 @@
-import pytest
import numpy as np
-from numpy.testing import assert_equal
+import pytest
+from numpy.testing import assert_equal, assert_raises
-from .. import (
- linear,
- spiral,
- exponential,
- cubic,
- joint_normal,
- step,
- quadratic,
- w_shaped,
- uncorrelated_bernoulli,
- logarithmic,
- fourth_root,
- sin_four_pi,
- sin_sixteen_pi,
- two_parabolas,
- circle,
- ellipse,
- diamond,
- multiplicative_noise,
- square,
- multimodal_independence,
- rot_2samp,
- trans_2samp,
-)
+from .. import SIMULATIONS, gaussian_3samp, ksamp_sim, rot_ksamp
-class TestKSampleShape:
+class TestKSampleSimShape:
@pytest.mark.parametrize("n", [100, 1000])
@pytest.mark.parametrize("p", [1, 5])
@pytest.mark.parametrize(
"indep_sim",
- [
- linear,
- spiral,
- exponential,
- cubic,
- joint_normal,
- step,
- quadratic,
- w_shaped,
- uncorrelated_bernoulli,
- logarithmic,
- fourth_root,
- sin_four_pi,
- sin_sixteen_pi,
- two_parabolas,
- circle,
- ellipse,
- diamond,
- multiplicative_noise,
- square,
- multimodal_independence,
- ],
+ SIMULATIONS.keys(),
)
- @pytest.mark.parametrize("sim", [rot_2samp, trans_2samp])
- def test_shapes(self, indep_sim, n, p, sim):
+ @pytest.mark.parametrize("k, degree", [(2, 90), (3, [90, 90])])
+ def test_shapes(self, indep_sim, k, n, p, degree):
np.random.seed(123456789)
- x, y = sim(indep_sim, n, p)
- nx, px = x.shape
- ny, py = y.shape
+ sims = rot_ksamp(indep_sim, n, p, k=k, degree=degree)
+ sims1 = rot_ksamp(indep_sim, n, p, k=k, degree=degree, pow_type="dim")
+ sims2 = ksamp_sim("rot_ksamp", n, sim=indep_sim, p=p, k=k, degree=degree)
- sim_name = indep_sim.__name__
- if sim_name in [
+ if indep_sim in [
"joint_normal",
"logarithmic",
"sin_four_pi",
@@ -77,9 +32,44 @@ def test_shapes(self, indep_sim, n, p, sim):
"multiplicative_noise",
"multimodal_independence",
]:
- assert_equal(px, p * 2)
- assert_equal(py, p * 2)
+ [assert_equal(sim.shape[1], p * 2) for sim in sims]
+ [assert_equal(sim.shape[1], p * 2) for sim in sims1]
+ [assert_equal(sim.shape[1], p * 2) for sim in sims2]
else:
- assert_equal(px, p + 1)
- assert_equal(py, p + 1)
- assert_equal(nx, ny)
+ [assert_equal(sim.shape[1], p + 1) for sim in sims]
+ [assert_equal(sim.shape[1], p + 1) for sim in sims1]
+ [assert_equal(sim.shape[1], p + 1) for sim in sims2]
+
+
+class TestGaussianSimShape:
+ @pytest.mark.parametrize("n", [100, 1000])
+ @pytest.mark.parametrize(
+ "case",
+ [1, 2, 3, 4, 5],
+ )
+ def test_shapes(self, n, case):
+ np.random.seed(123456789)
+ sims = gaussian_3samp(n, case=case)
+
+ [assert_equal(sim.shape[0], n) for sim in sims]
+
+
+class TestKSampleSimErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_wrong_powtype(self):
+ assert_raises(ValueError, rot_ksamp, sim="linear", n=100, p=1, pow_type="abcd")
+
+ def test_wrong_k(self):
+ assert_raises(
+ ValueError, rot_ksamp, sim="linear", k=3, n=100, p=1, pow_type="abcd"
+ )
+ assert_raises(
+ ValueError,
+ rot_ksamp,
+ sim="linear",
+ degree=[90, 90],
+ n=100,
+ p=1,
+ pow_type="abcd",
+ )
diff --git a/hyppo/tools/tests/test_power.py b/hyppo/tools/tests/test_power.py
new file mode 100644
index 000000000..26c7b1070
--- /dev/null
+++ b/hyppo/tools/tests/test_power.py
@@ -0,0 +1,57 @@
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_raises
+
+from .. import power
+
+
+class TestPower:
+ def test_power_perm(self):
+ np.random.seed(123456789)
+ est_power = power("RV", "indep", sim="linear", p=1)
+ assert_almost_equal(est_power, 1.0, decimal=1)
+
+ def test_power_fast(self):
+ np.random.seed(123456789)
+ est_power = power("Dcorr", "indep", sim="linear", p=1, auto=True)
+ assert_almost_equal(est_power, 1.0, decimal=1)
+
+ def test_power_fast_nless20(self):
+ np.random.seed(123456789)
+ est_power = power("Dcorr", "indep", sim="linear", n=19, p=1, auto=True)
+ assert_almost_equal(est_power, np.nan, decimal=1)
+
+ def test_ksamp(self):
+ np.random.seed(123456789)
+ est_power = power("Dcorr", "ksamp", sim="linear", p=1, auto=True)
+ assert_almost_equal(est_power, 1.0, decimal=1)
+
+ def test_gaussian(self):
+ np.random.seed(123456789)
+ est_power = power("Dcorr", "gauss", auto=True, case=2)
+ assert_almost_equal(est_power, 1.0, decimal=1)
+
+ def test_multi(self):
+ np.random.seed(123456789)
+ est_power = power(
+ "dHsic",
+ sim_type="multi",
+ sim="multimodal_independence",
+ n=100,
+ p=1,
+ auto=False,
+ )
+ assert_almost_equal(est_power, 0.05, decimal=1)
+
+
+class TestPowerErrorWarn:
+ def test_power_nosim(self):
+ np.random.seed(123456789)
+ assert_raises(ValueError, power, "CCA", "abcd")
+
+ def test_power_noindep(self):
+ np.random.seed(123456789)
+ assert_raises(ValueError, power, "abcd", "indep")
+
+ def test_power_nomaxmargin(self):
+ np.random.seed(123456789)
+ assert_raises(ValueError, power, ["MaxMargin", "abcd"], "indep")
diff --git a/hyppo/tools/tests/test_time_series_sim.py b/hyppo/tools/tests/test_time_series_sim.py
new file mode 100644
index 000000000..51c984830
--- /dev/null
+++ b/hyppo/tools/tests/test_time_series_sim.py
@@ -0,0 +1,33 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_equal, assert_raises
+
+from .. import ts_sim
+
+
+class TestTSSimShape:
+ @pytest.mark.parametrize("n", [100, 1000])
+ @pytest.mark.parametrize(
+ "sim",
+ ["indep_ar", "cross_corr_ar", "nonlinear_process"],
+ )
+ def test_shapes(self, n, sim):
+ np.random.seed(123456789)
+ x, y = ts_sim(sim, n)
+ assert_equal(x.shape, y.shape)
+
+
+class TestTSSimErrorWarn:
+ """Tests errors and warnings."""
+
+ def test_np_inctype(self):
+ assert_raises(ValueError, ts_sim, "indep_ar", n="a")
+
+ def test_low_n(self):
+ assert_raises(ValueError, ts_sim, "indep_ar", n=1)
+
+ def test_extra_inctype(self):
+ assert_raises(ValueError, ts_sim, "indep_ar", n=10, phi="a")
+
+ def test_wrong_sim(self):
+ assert_raises(ValueError, ts_sim, "abcd", n=10, phi=1)
diff --git a/hyppo/tools/tests/test_utils.py b/hyppo/tools/tests/test_utils.py
deleted file mode 100644
index 42261fef6..000000000
--- a/hyppo/tools/tests/test_utils.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from ..common import _PermTree
-from numpy.testing import (
- assert_array_less,
- assert_allclose,
- assert_equal,
- assert_raises,
-)
-import numpy as np
-
-
-class TestPermTree:
- """
- Tests that permutations are restricted correctly
- """
-
- def test_within_permutatins(self):
- np.random.seed(1)
- # i.e. case: y = np.asarray([0,1,0,1,0,1])
- blocks = np.vstack(
- (
- [-1, 1],
- [-1, 2],
- [-2, 1],
- [-2, 2],
- [-3, 1],
- [-3, 2],
- )
- )
- perm_tree = _PermTree(blocks)
- original_indices = perm_tree.original_indices()
- perms = np.asarray([perm_tree.permute_indices() for _ in range(10)])
- assert_array_less(np.abs(original_indices - perms), 2)
- assert_allclose(
- np.mean(perms, axis=0), [0.5, 0.5, 2.5, 2.5, 4.5, 4.5], rtol=0, atol=0.2
- )
-
- def test_across_permutations(self):
- np.random.seed(0)
- # i.e. case: y = np.asarray([0,0,1,1,2,2])
- blocks = np.vstack(
- (
- [1, -1],
- [1, -2],
- [2, -1],
- [2, -2],
- [3, -1],
- [3, -2],
- )
- )
- perm_tree = _PermTree(blocks)
- _ = perm_tree.original_indices()
- perms = np.asarray([perm_tree.permute_indices() for _ in range(100)])
- assert_equal(perms[0][1::2] - perms[0][::2], [1, 1, 1])
- assert_allclose(np.mean(perms, axis=0), [2, 3, 2, 3, 2, 3], rtol=0, atol=0.2)
-
- def test_fixed_permutation(self):
- np.random.seed(0)
- blocks = [-1, -2, -3, -4]
- perm_tree = _PermTree(blocks)
- assert_equal(perm_tree.permute_indices(), perm_tree.original_indices())
-
- def test_semifixed(self):
- np.random.seed(1)
- blocks = [1, 2, -3, -4]
- perm_tree = _PermTree(blocks)
- perms = np.asarray([perm_tree.permute_indices() for _ in range(10)])
- assert_equal(perms[0][2:], perm_tree.original_indices()[2:])
- assert_allclose(np.mean(perms, axis=0)[:2], [0.5, 0.5], rtol=0, atol=0.2)
-
- def test_non_int_inputs(self):
- blocks = ["a", "b", "c"]
- _ = _PermTree(blocks)
-
- def test_block_sizes(self):
- blocks = [1, 1, 2]
- assert_raises(ValueError, _PermTree, blocks)
diff --git a/hyppo/tools/time_series_sim.py b/hyppo/tools/time_series_sim.py
index 63e8b9f81..18b68cb6d 100644
--- a/hyppo/tools/time_series_sim.py
+++ b/hyppo/tools/time_series_sim.py
@@ -2,48 +2,31 @@
class _CheckInputs:
- """ Check if additional arguments are correct """
+ """Check if additional arguments are correct"""
def __init__(self, n):
self.n = n
- def __call__(self, *args):
+ def __call__(self, **kwargs):
if type(self.n) is not int:
- raise ValueError("n must be int")
+ raise ValueError("Expected n of type int, got {}".format(type(self.n)))
if self.n < 5:
- raise ValueError("n must be greater than or equal to 5")
+ raise ValueError("n must be >= 5, got {}".format(self.n))
- for arg in args:
- if arg[1] is float and type(arg[0]) is int:
+ for key, value in kwargs.items():
+ if value[1] is float and type(value[0]) is int:
continue
- if type(arg[0]) is not arg[1]:
- raise ValueError("Incorrect input variable type")
+ if type(value[0]) is not value[1]:
+ raise ValueError(
+ "Expected {} type {} got {}".format(key, value[1], type(value[0]))
+ )
def indep_ar(n, lag=1, phi=0.5, sigma=1):
r"""
- Simulates two independent, stationary, autoregressive time series.
+ 2 independent, stationary, autoregressive time series simulation.
- Parameters
- ----------
- n : int
- The number of samples desired by the simulation.
- lag : float, optional (default: 1)
- The maximum time lag considered between `x` and `y`.
- phi : float, optional (default: 0.5)
- The AR coefficient.
- sigma : float, optional (default: 1)
- The variance of the noise.
-
- Returns
- -------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n,)` and `(n,)`
- where `n` is the number of samples.
-
- Notes
- -----
:math:`X_t` and :math:`Y_t` are univarite AR(``1ag``) with
:math:`\phi = 0.5` for both series. Noise follows
:math:`\mathcal{N}(0, \sigma)`. With lag (1), this is
@@ -55,20 +38,30 @@ def indep_ar(n, lag=1, phi=0.5, sigma=1):
\begin{bmatrix} X_{t - 1} \\ Y_{t - 1} \end{bmatrix} +
\begin{bmatrix} \epsilon_t \\ \eta_t \end{bmatrix}
- Examples
- --------
- >>> from hyppo.tools import indep_ar
- >>> x, y = indep_ar(100)
- >>> print(x.shape, y.shape)
- (100,) (100,)
+ Parameters
+ ----------
+ n : int
+ The number of samples desired by the simulation (>= 3).
+ lag : float, default: 1
+ The maximum time lag considered between ``x`` and ``y``.
+ phi : float, default: 0.5
+ The AR coefficient.
+ sigma : float, default: 1
+ The variance of the noise.
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices. ``x`` and ``y`` have shape ``(n,)``
+ where `n` is the number of samples.
"""
- extra_args = [
- (lag, float),
- (phi, float),
- (sigma, float),
- ]
+ extra_args = {
+ "lag": (lag, float),
+ "phi": (phi, float),
+ "sigma": (sigma, float),
+ }
check_in = _CheckInputs(n)
- check_in(*extra_args)
+ check_in(**extra_args)
epsilons = np.random.normal(0, sigma, n)
etas = np.random.normal(0, sigma, n)
@@ -86,27 +79,8 @@ def indep_ar(n, lag=1, phi=0.5, sigma=1):
def cross_corr_ar(n, lag=1, phi=0.5, sigma=1):
r"""
- Simulates two linearly dependent time series.
-
- Parameters
- ----------
- n : int
- The number of samples desired by the simulation.
- lag : float, optional (default: 1)
- The maximum time lag considered between `x` and `y`.
- phi : float, optional (default: 0.5)
- The AR coefficient.
- sigma : float, optional (default: 1)
- The variance of the noise.
+ 2 linearly dependent time series simulation.
- Returns
- -------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n,)` and `(n,)`
- where `n` is the number of samples.
-
- Notes
- -----
:math:`X_t` and :math:`Y_t` are together a bivariate univarite AR(``1ag``) with
:math:`\phi = \begin{bmatrix} 0 & 0.5 \\ 0.5 & 0 \end{bmatrix}` for both series.
Noise follows :math:`\mathcal{N}(0, \sigma)`. With lag (1), this is
@@ -118,20 +92,30 @@ def cross_corr_ar(n, lag=1, phi=0.5, sigma=1):
\begin{bmatrix} X_{t - 1} \\ Y_{t - 1} \end{bmatrix} +
\begin{bmatrix} \epsilon_t \\ \eta_t \end{bmatrix}
- Examples
- --------
- >>> from hyppo.tools import cross_corr_ar
- >>> x, y = cross_corr_ar(100)
- >>> print(x.shape, y.shape)
- (100,) (100,)
+ Parameters
+ ----------
+ n : int
+ The number of samples desired by the simulation (>= 3).
+ lag : float, default: 1
+ The maximum time lag considered between ``x`` and ``y``.
+ phi : float, default: 0.5
+ The AR coefficient.
+ sigma : float, default: 1
+ The variance of the noise.
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices. ``x`` and ``y`` have shape ``(n,)``
+ where `n` is the number of samples.
"""
- extra_args = [
- (lag, float),
- (phi, float),
- (sigma, float),
- ]
+ extra_args = {
+ "lag": (lag, float),
+ "phi": (phi, float),
+ "sigma": (sigma, float),
+ }
check_in = _CheckInputs(n)
- check_in(*extra_args)
+ check_in(**extra_args)
epsilons = np.random.normal(0, sigma, n)
etas = np.random.normal(0, sigma, n)
@@ -148,49 +132,40 @@ def cross_corr_ar(n, lag=1, phi=0.5, sigma=1):
def nonlinear_process(n, lag=1, phi=1, sigma=1):
r"""
- Simulates two nonlinearly dependent time series.
+ 2 nonlinearly dependent time series simulation.
+
+ :math:`X_t` and :math:`Y_t` are together a bivariate nonlinear process.
+ Noise follows :math:`\mathcal{N}(0, \sigma)`. With lag (1), this is
+
+ .. math::
+
+ \begin{bmatrix} X_t \\ Y_t \end{bmatrix} =
+ \begin{bmatrix} \phi \epsilon_t Y_{t - 1} \\ \eta_t \end{bmatrix}
Parameters
----------
n : int
- The number of samples desired by the simulation.
- lag : float, optional (default: 1)
+ The number of samples desired by the simulation (>= 3).
+ lag : float, default: 1
The maximum time lag considered between `x` and `y`.
- phi : float, optional (default: 1)
+ phi : float, default: 1
The AR coefficient.
- sigma : float, optional (default: 1)
+ sigma : float, default: 1
The variance of the noise.
Returns
-------
- x, y : ndarray
- Simulated data matrices. `x` and `y` have shapes `(n,)` and `(n,)`
+ x,y : ndarray of float
+ Simulated data matrices. ``x`` and ``y`` have shape ``(n,)``
where `n` is the number of samples.
-
- Notes
- -----
- :math:`X_t` and :math:`Y_t` are together a bivariate nonlinear process.
- Noise follows :math:`\mathcal{N}(0, \sigma)`. With lag (1), this is
-
- .. math::
-
- \begin{bmatrix} X_t \\ Y_t \end{bmatrix} =
- \begin{bmatrix} \phi \epsilon_t Y_{t - 1} \\ \eta_t \end{bmatrix}
-
- Examples
- --------
- >>> from hyppo.tools import cross_corr_ar
- >>> x, y = cross_corr_ar(100)
- >>> print(x.shape, y.shape)
- (100,) (100,)
"""
- extra_args = [
- (lag, float),
- (phi, float),
- (sigma, float),
- ]
+ extra_args = {
+ "lag": (lag, float),
+ "phi": (phi, float),
+ "sigma": (sigma, float),
+ }
check_in = _CheckInputs(n)
- check_in(*extra_args)
+ check_in(**extra_args)
epsilons = np.random.normal(0, sigma, n)
etas = np.random.normal(0, sigma, n)
@@ -202,3 +177,42 @@ def nonlinear_process(n, lag=1, phi=1, sigma=1):
x[t] = phi * epsilons[t] * y[t - lag]
return x, y
+
+
+TS_SIMS = {
+ "indep_ar": indep_ar,
+ "cross_corr_ar": cross_corr_ar,
+ "nonlinear_process": nonlinear_process,
+}
+
+
+def ts_sim(sim, n, **kwargs):
+ r"""
+ Time-series simulation generator.
+
+ Takes a simulation and the required parameters, and outputs the simulated
+ data matrices.
+
+ Parameters
+ ----------
+ sim : str
+ The name of the simulation (from the :mod:`hyppo.tools` module) that is to be
+ rotated.
+ n : int
+ The number of samples desired by the simulation (>= 3).
+ **kwargs
+ Additional keyword arguements for the desired simulation.
+
+ Returns
+ -------
+ x,y : ndarray of float
+ Simulated data matrices.
+ """
+ if sim not in TS_SIMS.keys():
+ raise ValueError(
+ "sim_name must be one of the following: {}".format(list(TS_SIMS.keys()))
+ )
+ else:
+ sim = TS_SIMS[sim]
+
+ return sim(n, **kwargs)
diff --git a/netlify.toml b/netlify.toml
index 6baf3846b..df785609b 100644
--- a/netlify.toml
+++ b/netlify.toml
@@ -11,7 +11,7 @@
publish = "docs/_build/html/"
# Default build command.
- command = "pip install -r docs/requirements.txt; cd docs; make html; cd .."
+ command = "pip install -r docs/requirements.txt; pip install -r requirements.txt; pip install -e .; cd docs; make all; cd .."
# Directory with the serverless Lambda functions to deploy to AWS.
# functions = "project/functions/"
\ No newline at end of file
diff --git a/pytest.ini b/pytest.ini
index acb8f8d5d..c6411fa94 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,6 +1,5 @@
[pytest]
-addopts = --cov=hyppo --doctest-modules
-deselect = "hyppo/independence/kmerf.py"
+addopts = --doctest-modules
filterwarnings =
diff --git a/requirements.txt b/requirements.txt
index aed0a9632..a583edf26 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,6 @@
-numpy>=1.17
+numpy>=1.17, <=1.23.5
scipy>=1.4.0
numba>=0.46
-scikit-learn>=0.19.1
\ No newline at end of file
+scikit-learn>=0.22
+joblib>=0.17.0
+autograd>=1.3
diff --git a/runtime.txt b/runtime.txt
index 548d71365..98fccd6d0 100644
--- a/runtime.txt
+++ b/runtime.txt
@@ -1 +1 @@
-3.7
\ No newline at end of file
+3.8
\ No newline at end of file
diff --git a/sample_data/README.txt b/sample_data/README.txt
new file mode 100644
index 000000000..4d517ad91
--- /dev/null
+++ b/sample_data/README.txt
@@ -0,0 +1,7 @@
+.. _sample_data:
+
+Simulations
+=============
+
+hyppo provides some sample data and ways of generating synthetic data through
+the :mod:`hyppo.tools` module. These are the simulations currently available:
\ No newline at end of file
diff --git a/sample_data/indep_sims.py b/sample_data/indep_sims.py
new file mode 100644
index 000000000..77573834b
--- /dev/null
+++ b/sample_data/indep_sims.py
@@ -0,0 +1,96 @@
+"""
+Independence Sims
+===========================
+
+Independence simulations are found in :mod:`hyppo.tools`. Here, we visualize what these
+simulations look like. Noise-free simulations are overlaid over the noisy simulation.
+Note that the last 2 simulations have no noise parameter. These simulations were chosen
+as an aggregate of many popularly tested equations in the literature.
+"""
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from hyppo.tools import SIMULATIONS
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=2)
+PALETTE = sns.color_palette("Greys", n_colors=9)
+sns.set_palette(PALETTE[2::2])
+
+# constants
+NOISY = 100 # sample size of noisy simulation
+NO_NOISE = 1000 # sample size of noise-free simulation
+
+# simulation titles
+SIM_TITLES = [
+ "Linear",
+ "Exponential",
+ "Cubic",
+ "Joint Normal",
+ "Step",
+ "Quadratic",
+ "W-Shaped",
+ "Spiral",
+ "Bernoulli",
+ "Logarithmic",
+ "Fourth Root",
+ "Sine 4\u03C0",
+ "Sine 16\u03C0",
+ "Square",
+ "Two Parabolas",
+ "Circle",
+ "Ellipse",
+ "Diamond",
+ "Noise",
+ "Independence",
+]
+
+
+# make a function that runs the code depending on the simulation
+def plot_sims():
+ """Plot simulations"""
+ fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28, 24))
+
+ plt.suptitle("Independence Simulations", y=0.93, va="baseline")
+
+ for i, row in enumerate(ax):
+ for j, col in enumerate(row):
+ count = 5 * i + j
+ sim_title = SIM_TITLES[count]
+ sim = list(SIMULATIONS.values())[count]
+
+ # the multiplicative noise and independence simulation don't have a noise
+ # parameter
+ if sim_title in ["Noise", "Independence"]:
+ x, y = sim(NO_NOISE, 1)
+ x_no_noise, y_no_noise = x, y
+ else:
+ x, y = sim(NOISY, 1, noise=True)
+ x_no_noise, y_no_noise = sim(NO_NOISE, 1)
+
+ # plot the noise and noise-free sims
+ col.scatter(x, y, label="Noisy")
+ col.scatter(x_no_noise, y_no_noise, label="No Noise")
+
+ # make the plot look pretty
+ col.set_title("{}".format(sim_title))
+ col.set_xticks([])
+ col.set_yticks([])
+ if count == 16:
+ col.set_ylim([-1, 1])
+ sns.despine(left=True, bottom=True, right=True)
+
+ leg = plt.legend(
+ bbox_to_anchor=(0.5, 0.1),
+ bbox_transform=plt.gcf().transFigure,
+ ncol=5,
+ loc="upper center",
+ )
+ leg.get_frame().set_linewidth(0.0)
+ for legobj in leg.legendHandles:
+ legobj.set_linewidth(5.0)
+ plt.subplots_adjust(hspace=0.75)
+
+
+# run the created function for the simultions
+plot_sims()
diff --git a/sample_data/ksamp_gaussian_sims.py b/sample_data/ksamp_gaussian_sims.py
new file mode 100644
index 000000000..567fdb9a0
--- /dev/null
+++ b/sample_data/ksamp_gaussian_sims.py
@@ -0,0 +1,76 @@
+"""
+Guassian Sims
+===========================
+
+Gaussian `k`-sample simulations are found in :mod:`hyppo.tools`. Here, we visualize
+what these
+simulations look like. We use these Gaussian simulations when comparing our
+algorithms against multivariate analysis of variance (MANOVA).
+"""
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from hyppo.tools import gaussian_3samp
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=2)
+PALETTE = sns.color_palette("Greys", n_colors=9)
+sns.set_palette(PALETTE[2::2])
+
+# constants
+N = 500
+CASES = [1, 2, 3, 4, 5]
+
+
+# make a function to plot the guassian simulations
+def plot_gaussian_sims():
+ """Plot simulations"""
+ fig, ax = plt.subplots(nrows=1, ncols=5, figsize=(28, 6))
+
+ sim_titles = [
+ "None Different",
+ "One Different",
+ "All Different",
+ "One Not Gaussian",
+ "None Gaussian",
+ ]
+
+ # plt.suptitle("Gaussian Simulations", y=0.93, va="baseline")
+
+ for i, col in enumerate(ax):
+ sim_title = sim_titles[i]
+
+ # rotated k-sample simulation
+ sims = gaussian_3samp(N, epsilon=4, weight=0.9, case=CASES[i])
+
+ # plot the nose and noise-free sims
+ for index in range(len(sims)):
+ col.scatter(
+ sims[index][:, 0],
+ sims[index][:, 1],
+ label="Sample {}".format(index + 1),
+ )
+
+ # make the plot look pretty
+ col.set_title("{}".format(sim_title))
+ col.set_xticks([])
+ col.set_yticks([])
+ col.set_xlim(-5, 5)
+ if CASES[i] not in [2, 4]:
+ col.set_ylim(-5, 5)
+ sns.despine(left=True, bottom=True, right=True)
+
+ leg = plt.legend(
+ bbox_to_anchor=(0.5, 0.17),
+ bbox_transform=plt.gcf().transFigure,
+ ncol=3,
+ loc="upper center",
+ )
+ leg.get_frame().set_linewidth(0.0)
+ for legobj in leg.legendHandles:
+ legobj.set_linewidth(5.0)
+ plt.subplots_adjust(hspace=0.75)
+
+
+# run the created function for the guassian simulations
+plot_gaussian_sims()
diff --git a/sample_data/ksamp_sims.py b/sample_data/ksamp_sims.py
new file mode 100644
index 000000000..63706c3ed
--- /dev/null
+++ b/sample_data/ksamp_sims.py
@@ -0,0 +1,96 @@
+"""
+`K`-Sample Sims
+===========================
+
+`K`-sample simulations are found in :mod:`hyppo.tools`. Here, we visualize what these
+simulations look like. The original simulation and rotated simulation are shown. Note
+that since these are noise-free, we do not see any rotation due to the rotational
+symmetry of the simulation.
+"""
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from hyppo.tools import SIMULATIONS, rot_ksamp
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=2)
+PALETTE = sns.color_palette("Greys", n_colors=9)
+sns.set_palette(PALETTE[2::2])
+
+# constants
+N = 500 # sample size
+P = 1 # dimensionality
+DEGREE = [5, -5] # angle
+
+# simulation titles
+SIM_TITLES = [
+ "Linear",
+ "Exponential",
+ "Cubic",
+ "Joint Normal",
+ "Step",
+ "Quadratic",
+ "W-Shaped",
+ "Spiral",
+ "Bernoulli",
+ "Logarithmic",
+ "Fourth Root",
+ "Sine 4\u03C0",
+ "Sine 16\u03C0",
+ "Square",
+ "Two Parabolas",
+ "Circle",
+ "Ellipse",
+ "Diamond",
+ "Noise",
+ "Independence",
+]
+
+
+# make a function that runs the code depending on the simulation
+def plot_sims(k=2, degree=DEGREE):
+ """Plot simulations"""
+ fig, ax = plt.subplots(nrows=4, ncols=5, figsize=(28, 24))
+
+ plt.suptitle("{}-Sample Simulations".format(k), y=0.93, va="baseline")
+
+ count = 0
+ for i, row in enumerate(ax):
+ for j, col in enumerate(row):
+ count = 5 * i + j
+ sim_title = SIM_TITLES[count]
+ sim = list(SIMULATIONS.keys())[count]
+
+ # rotated k-sample simulation
+ sims = rot_ksamp(sim, N, P, k=k, degree=degree, noise=False)
+
+ # plot the nose and noise-free sims
+ for index in range(len(sims)):
+ col.scatter(
+ sims[index][:, 0],
+ sims[index][:, 1],
+ label="Sample {}".format(index + 1),
+ )
+
+ # make the plot look pretty
+ col.set_title("{}".format(sim_title))
+ col.set_xticks([])
+ col.set_yticks([])
+ sns.despine(left=True, bottom=True, right=True)
+
+ leg = plt.legend(
+ bbox_to_anchor=(0.5, 0.1),
+ bbox_transform=plt.gcf().transFigure,
+ ncol=5,
+ loc="upper center",
+ )
+ leg.get_frame().set_linewidth(0.0)
+ for legobj in leg.legendHandles:
+ legobj.set_linewidth(5.0)
+ plt.subplots_adjust(hspace=0.75)
+
+
+# run the created function for the simultions for 2 sample and 3 sample
+# and run for the guassian simulations
+plot_sims(k=2, degree=DEGREE[0])
+plot_sims(k=3, degree=DEGREE)
diff --git a/sample_data/time_series_sim.py b/sample_data/time_series_sim.py
new file mode 100644
index 000000000..2385b5b8b
--- /dev/null
+++ b/sample_data/time_series_sim.py
@@ -0,0 +1,72 @@
+"""
+Time-Series Sims
+===========================
+
+Time-series simulations are found in :mod:`hyppo.tools`. Here, we visualize
+what these
+simulations look like.
+"""
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from hyppo.tools import cross_corr_ar, indep_ar, nonlinear_process
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=2)
+PALETTE = sns.color_palette("Greys", n_colors=9)
+sns.set_palette(PALETTE[2::2])
+
+# constants
+N = 100
+
+# dictionary mapping of simulations
+SIMULATIONS = [
+ (indep_ar, "Independent"),
+ (cross_corr_ar, "Cross Correlation"),
+ (nonlinear_process, "Nonlinear"),
+]
+
+
+# make a function to plot the guassian simulations
+def plot_time_series_sims():
+ """Plot simulations"""
+ fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(17, 12))
+
+ plt.suptitle("Time-Series Simulations", y=0.95, va="baseline")
+
+ for i, row in enumerate(ax):
+ for j, col in enumerate(row):
+ sim, sim_title = SIMULATIONS[j]
+
+ # time-series simulation
+ x, y = sim(N)
+ n = x.shape[0]
+ t = range(1, n + 1)
+
+ if i == 0:
+ col.plot(t, x, label="X_t")
+ col.plot(t, y, label="Y_t")
+
+ else:
+ col.scatter(x, y)
+
+ # make the plot look pretty
+ col.set_yticks([])
+ if j == 0:
+ col.set_yticks([-5, 0, 5])
+ if i == 1:
+ col.set_xticks([-3, 0, 3])
+ col.set_xlim(-3, 3)
+ if i == 0:
+ col.set_title("{}".format(sim_title))
+ col.set_xticks([0, 100])
+ col.set_ylim(-5, 5)
+
+ plt.subplots_adjust(hspace=0.5)
+ fig.text(0.5, 0.02, r"$X_t$", ha="center")
+ fig.text(0.5, 0.5, r"$t$", ha="center")
+ fig.text(0.05, 0.25, r"$Y_t$", va="center", rotation="vertical")
+
+
+# run the created function for the time-series simulations
+plot_time_series_sims()
diff --git a/setup.py b/setup.py
index bf6d29602..e402a85ad 100644
--- a/setup.py
+++ b/setup.py
@@ -1,12 +1,26 @@
import os
import sys
-from setuptools import setup, find_packages
-from sys import platform
+
+from setuptools import find_packages, setup
+from setuptools.command.install import install
PACKAGE_NAME = "hyppo"
DESCRIPTION = "A comprehensive independence testing package"
-with open("README.md", "r") as f:
- LONG_DESCRIPTION = f.read()
+LONG_DESCRIPTION = """
+hyppo (**HYP**othesis Testing in **P**yth**O**n, pronounced "Hippo") is an open-source
+software package for multivariate hypothesis testing. We decided to develop hyppo for
+the following reasons:
+
+* With the increase in the amount of data in many fields, hypothesis testing for high
+dimensional and nonlinear data is important
+* Libraries in R exist, but their interfaces are inconsistent and most are not available
+in Python
+
+hyppo intends to be a comprehensive multivariate hypothesis testing package and runs on
+all major versions of operating systems. It also includes novel tests not found in other
+packages. It is quick to install and free of charge. If you need to use multivariate
+hypothesis testing, be sure to give hyppo a try!"
+"""
AUTHOR = ("Sambit Panda",)
AUTHOR_EMAIL = "spanda3@jhu.edu"
URL = "https://github.com/neurodata/hyppo"
@@ -16,6 +30,7 @@
"scipy>=1.4.0",
"numba>=0.46",
"scikit-learn>=0.19.1",
+ "autograd>=1.3",
]
# Find mgc version.
@@ -33,6 +48,23 @@ def check_python_version():
check_python_version()
+
+class VerifyVersionCommand(install):
+ """Custom command to verify that the git tag matches our version"""
+
+ description = "verify that the git tag matches our version"
+
+ def run(self):
+ tag = os.getenv("CIRCLE_TAG")
+ version = "v{}".format(VERSION)
+
+ if tag != version:
+ info = "Git tag: {0} does not match the version of this app: {1}".format(
+ tag, version
+ )
+ sys.exit(info)
+
+
setup(
name=PACKAGE_NAME,
version=VERSION,
@@ -43,18 +75,22 @@ def check_python_version():
author_email=AUTHOR_EMAIL,
install_requires=REQUIRED_PACKAGES,
url=URL,
- license="Apache 2.0",
+ license="MIT",
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Mathematics",
- "License :: OSI Approved :: Apache Software License",
+ "License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
+ "Programming Language :: Python :: 3.9",
],
packages=find_packages(),
include_package_data=True,
test_suite="tests",
+ cmdclass={
+ "verify": VerifyVersionCommand,
+ },
)
diff --git a/tutorials/README.txt b/tutorials/README.txt
new file mode 100644
index 000000000..48020555e
--- /dev/null
+++ b/tutorials/README.txt
@@ -0,0 +1,4 @@
+.. _tutorials:
+
+Tutorials
+=========
diff --git a/tutorials/conditional.py b/tutorials/conditional.py
new file mode 100644
index 000000000..3b4583a13
--- /dev/null
+++ b/tutorials/conditional.py
@@ -0,0 +1,96 @@
+r"""
+.. _conditional:
+
+Conditional Independence Testing
+********************************
+
+Conditional independence testing is similar to independence testing but introduces
+the presence of a third conditioning variable. Consider random variables :math:`X`,
+:math:`Y`, and :math:`Z` with distributions :math:`F_X`, :math:`F_Y`, and :math:`F_Z`.
+When performing conditional independence testing, we are evaluating whether
+:math:`F_{X, Y|Z} = F_{X|Z}F_{Y|Z}`. Specifically, we are testing
+
+.. math::
+
+ H_0 &: X \perp \!\!\! \perp Y \mid Z \\
+ H_A &: X \not\!\perp\!\!\!\perp Y \mid Z
+
+Like all the other tests within hyppo, each method has a :func:`statistic` and
+:func:`test` method. The :func:`test` method is the one that returns the test statistic
+and p-values, among other outputs, and is the one that is used most often in the
+examples, tutorials, etc.
+
+Specifics about how the test statistics are calculated for each in
+:class:`hyppo.conditional` can be found the docstring of the respective test. Here,
+we overview subsets of the types of conditional tests we offer in hyppo, and special
+parameters unique to those tests.
+
+Now, let's look at unique properties of some of the tests in :mod:`hyppo.conditional`:
+"""
+
+########################################################################################
+# Fast Conditional Independence Test (FCIT)
+# ---------------------------------------------
+#
+# The **Fast Conditional Independence Test (FCIT)** is a non-parametric conditional
+# independence test. The test is based on a weak assumption that if the conditional
+# independence alternative hypothesis is true, then prediction of the independent
+# variable with only the conditioning variable should be just as accurate as
+# prediction of the independent variable using the dependent variable conditioned on
+# the conditioning variable.
+# More details can be found in :class:`hyppo.conditional.FCIT`
+#
+# .. note::
+#
+# :Pros: - Very fast due on high-dimensional data due to parallel processes
+# :Cons: - Heuristic method; above assumption, though weak, is not always true
+#
+# The test uses a regression model to construct predictors for the indendent variable.
+# By default, the regressor used is the decision tree regressor but the user can also
+# specify other forms of regressors to use along with a set of hyperparameters to be
+# tuned using cross-validation. Below is an example where the null hypothesis is true:
+
+import numpy as np
+from hyppo.conditional import FCIT
+from sklearn.tree import DecisionTreeRegressor
+np.random.seed(1234)
+dim = 2
+n = 100000
+z1 = np.random.multivariate_normal(mean=np.zeros(dim), cov=np.eye(dim), size=(n))
+A1 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+B1 = np.random.normal(loc=0, scale=1, size=dim * dim).reshape(dim, dim)
+x1 = (A1 @ z1.T + np.random.multivariate_normal(mean=np.zeros(dim), cov=np.eye(dim), size=(n)).T)
+y1 = (B1 @ z1.T + np.random.multivariate_normal(mean=np.zeros(dim), cov=np.eye(dim), size=(n)).T)
+model = DecisionTreeRegressor()
+cv_grid = {"min_samples_split": [2, 8, 64, 512, 1e-2, 0.2, 0.4]}
+stat, pvalue = FCIT(model=model, cv_grid=cv_grid).test(x1.T, y1.T, z1)
+print("Statistic: ", stat)
+print("p-value: ", pvalue)
+
+########################################################################################
+# Kernel Conditional Independence Test (KCI)
+# ---------------------------------------------
+#
+# The Kernel Conditional Independence Test (KCI) is a conditional independence test
+# that works based on calculating the RBF kernels of distinct samples of data.
+# The respective kernels are then normalized and multiplied together to determine
+# the test statistic via the trace of the matrix product. The test then employs
+# a gamma approximation based on the mean and variance of the independent
+# sample kernel values to determine the p-value of the test.
+# More details can be found in :class:`hyppo.conditional.kci
+#
+# .. note::
+#
+# :Pros: - Very fast on high-dimensional data due to simplicity and approximation
+# :Cons: - Dispute in literature as to ideal theta value, loss of accuracy on very large datasets
+#
+# Below is a linear example where we fail to reject the null hypothesis:
+
+import numpy as np
+from hyppo.conditional import KCI
+from hyppo.tools import linear
+np.random.seed(123456789)
+x, y = linear(100, 1)
+stat, pvalue = KCI().test(x, y)
+print("Statistic: ", stat)
+print("p-value: ", pvalue)
diff --git a/tutorials/discrim.py b/tutorials/discrim.py
new file mode 100644
index 000000000..b5feb0bc2
--- /dev/null
+++ b/tutorials/discrim.py
@@ -0,0 +1,48 @@
+r"""
+.. _discrim:
+
+Discriminability Testing
+***************************
+
+If you have repeated measures from the same subject, and want to see if these are
+different than those from other subjects. Let's look at the mathematical formulations:
+
+With :math:`D_x` as the sample discriminability of :math:`x`, one sample test performs
+the following test:
+
+.. math::
+
+ H_0 &: D_x = D_0 \\
+ H_A &: D_x > D_0
+
+where :math:`D_0` is the discriminability that would be observed by random chance.
+
+This can also be formulated as a two-sample test. Let :math:`\hat{D}_x` denote the
+sample discriminability of one approach, and :math:`\hat{D}_y` denote the sample
+discriminability of another approach. Then,
+
+.. math::
+
+ H_0 &: D_x = D_y \\
+ H_A &: D_x > D_y
+
+Alternative tests can be done for :math:`D_x < D_y` and :math:`D_x \neq D_y`.
+
+Like all the other tests within hyppo, each method has a :func:`statistic` and
+:func:`test` method. The :func:`test` method is the one that returns the test statistic
+and p-values, among other outputs, and is the one that is used most often in the
+examples, tutorials, etc.
+The p-value returned is calculated using a permutation test.
+"""
+
+########################################################################################
+# **Discrimnability one-sample** and
+# **Discrimnability two-sample** are time series tests of independence.
+# More details can be found in :class:`hyppo.discrim.DiscrimOneSample` and
+# :class:`hyppo.discrim.DiscrimTwoSample`.
+#
+# Each class has a ``is_dist`` parameter that indicates whether or not inputs are
+# distance matrices. These distances must be Euclidean distance.
+# Also, ``remove_isolates`` indicates whether or not to remove measurements with a single
+# instance.
+# Otherwise, these tests runs like :ref:`any other test`.
diff --git a/tutorials/dvariate.py b/tutorials/dvariate.py
new file mode 100644
index 000000000..6c1dd5c01
--- /dev/null
+++ b/tutorials/dvariate.py
@@ -0,0 +1,61 @@
+r"""
+.. _d_var:
+
+D-Variate Independence Testing
+*******************************
+
+Here, we consider joint independence testing of :math:`d` random variables. This
+is a more difficult task than pairwise independence testing, but this can be very
+useful when we are asking the question of whether three or more groups are affecting
+one another. Joint independence can be tested by combining pairwise independence
+tests, but using a :math:`d`-variate independence test is generally faster.
+
+The :math:`d`-variate independence test can be found in :mod:`hyppo.d_variate`, and
+will be explained in detail below. Like all the other tests within hyppo, each
+method has a :func:`statistic` and :func:`test` method. The :func:`test` method is
+the one that returns the test statistic and p-values, among other outputs, and is
+the one that is used most often in the examples, tutorials, etc. The p-value returned
+is calculated using a permutation test using :meth:`hyppo.tools.multi_perm_test`.
+
+Specifics about how the statistic is calculated in :class:`hyppo.d_variate` can be
+found in the docstring of the test. Here, we give an overview of the :math:`d`-variate
+independence test we offer in hyppo and some of its properties compared to those
+in :mod:`hyppo.independence`.
+"""
+
+########################################################################################
+# D-variable Hilbert Schmidt Independence Criterion (dHsic)
+# ---------------------------------------------------------
+#
+# **dHsic** is an extension of :class:`hyppo.independence.Hsic`, and it uses the
+# reproducing kernel Hilbert space to test for the joint independence of :math:`d`
+# random variables. More details can be found in :class:`hyppo.d_variate.dHsic`.
+# Note that unlike :class:`hyppo.independence.Hsic`, there is no fast version of
+# the test. It always uses the permutation method to compute its p-value.
+#
+# .. note::
+#
+# :Pros: - Highly accurate independence test for d random variables
+# - Much faster than constructing a joint independence test from multiple
+# pairwise independence tests
+# :Cons: - Is not always more powerful than pairwise Hsic, depends on simulation
+# and the dependence structure of the variables
+#
+# dHsic is often computationally less expensive than using pairwise Hsic, and if
+# dimension :math:`d` is too large, a pairwise Hsic approach may fail to reject
+# the null hypothesis.
+#
+# The following is a general use case of dHsic using data points that simulate a
+# 1D linear relationship between random variables :math:`X`, :math:`Y`, :math:`U`,
+# and :math:`V`. Note that here we use the default gaussian kernel with a gamma
+# value of 0.5. For a full list of parameters, see :class:`hyppo.d_variate.dHsic`.
+
+from hyppo.d_variate import dHsic
+from hyppo.tools import linear
+
+x, y = linear(100, 1)
+u, v = linear(100, 1)
+stat, pvalue = dHsic(gamma=0.5).test(x, y, u, v)
+print(stat, pvalue)
+
+########################################################################################
diff --git a/tutorials/images/fr_full_tree.png b/tutorials/images/fr_full_tree.png
new file mode 100644
index 000000000..6313abdfb
Binary files /dev/null and b/tutorials/images/fr_full_tree.png differ
diff --git a/tutorials/images/fr_runs_tree.png b/tutorials/images/fr_runs_tree.png
new file mode 100644
index 000000000..4164c0e29
Binary files /dev/null and b/tutorials/images/fr_runs_tree.png differ
diff --git a/tutorials/independence.py b/tutorials/independence.py
new file mode 100644
index 000000000..1cd8c3eee
--- /dev/null
+++ b/tutorials/independence.py
@@ -0,0 +1,379 @@
+r"""
+.. _indep:
+
+Independence Testing
+*********************
+
+Finding trends in data is pretty hard. Independence testing is a fundamental tool that
+helps us find trends in data and make further decisions based on these results. This
+testing is desirable to answer many question such as: does brain connectivity effect
+creativity? Does gene expression effect cancer? Does something effect something else?
+
+If you are interested in questions of this mold, this module of the package is for you!
+All our tests can be found in :mod:`hyppo.independence`, and will be elaborated in
+detail below. But before that, let's look at the mathematical formulations:
+
+Consider random variables :math:`X` and :math:`Y` with distributions :math:`F_X` and
+:math:`F_Y` respectively, and joint distribution is :math:`F_{XY} = F_{Y|X} F_X`.
+When performing independence testing, we are seeing whether or not
+:math:`F_{Y|X} = F_Y`. That is, we are testing
+
+.. math::
+
+ H_0 &: F_{XY} = F_X F_Y \\
+ H_A &: F_{XY} \neq F_X F_Y
+
+Like all the other tests within hyppo, each method has a :func:`statistic` and
+:func:`test` method. The :func:`test` method is the one that returns the test statistic
+and p-values, among other outputs, and is the one that is used most often in the
+examples, tutorials, etc.
+The p-value returned is calculated using a permutation test using
+:meth:`hyppo.tools.perm_test` unless otherwise specified.
+
+Specifics about how the test statistics are calculated for each in
+:class:`hyppo.independence` can be found the docstring of the respective test. Here,
+we overview subsets of the types of independence tests we offer in hyppo, and special
+parameters unique to those tests.
+
+Now, let's look at unique properties of some of the tests in :mod:`hyppo.independence`:
+"""
+
+########################################################################################
+# Pearson's Correlation Multivariate Variants
+# ---------------------------------------------
+#
+# **Cannonical correlation (CCA)** and **Rank value (RV)** are multivariate analogues
+# of Pearson's correlation.
+# More details can be found in :class:`hyppo.independence.CCA` and
+# :class:`hyppo.independence.RV`.
+# The following applies to both:
+#
+# .. note::
+#
+# :Pros: - Very fast
+# - Similar to tests found in scientific literature
+# :Cons: - Not accurate when compared to other tests in most situations
+# - Makes dangerous variance assumptions about the data, among others
+# (similar assumptions to Pearson's correlation)
+#
+# Neither of these test are distance based, and so do not have a ``compute_distance``
+# parameter.
+# Otherwise, these tests runs like :ref:`any other test`.
+
+########################################################################################
+# Distance (and Kernel) Based Tests
+# -----------------------------------
+#
+# A number of tests within :mod:`hyppo.independence` use the concept of inter-sample
+# distance, or kernel similarity, to generate powerful indpendence tests.
+#
+# **Heller Heller Gorfine (HHG)** is a powerful multivariate independence test that
+# compares intersample distance, and computes a Pearson statistic.
+# More details can be found in :class:`hyppo.independence.HHG`.
+#
+# .. note::
+#
+# :Pros: - Very accurate in certain settings
+# - Has fast implementation
+# :Cons: - Very slow (computationally complex)
+# - Test statistic not very interpretable, not between (-1, 1)
+#
+# This test runs like :ref:`any other test` and can be implemented
+# as below:
+
+import timeit
+
+import numpy as np
+
+setup_code = """
+from hyppo.independence import HHG
+from hyppo.tools import w_shaped
+x, y = w_shaped(n=100, p=3, noise=True)
+"""
+
+t_hhg = timeit.Timer(stmt="HHG().test(x, y, auto=False)", setup=setup_code)
+t_fast_hhg = timeit.Timer(stmt="HHG().test(x, y, auto=True)", setup=setup_code)
+
+hhg_time = np.array(t_hhg.timeit(number=1)) # original HHG
+fast_hhg_time = np.array(t_fast_hhg.timeit(number=5)) / 5 # fast HHG
+
+print("Original HHG time: {0:.3g}s".format(hhg_time))
+print("Fast HHG time: {0:.3g}s".format(fast_hhg_time))
+
+########################################################################################
+# ------------
+#
+# **Distance Correlation (Dcorr)** is a powerful multivariate independence test based on
+# energy distance.
+# **Hilbert Schmidt Independence Criterion (Hsic)** is a kernel-based analogue to Dcorr
+# that uses the a Gaussian median kernel by default `[1]`_.
+# More details can be found in :class:`hyppo.independence.Dcorr` and
+# :class:`hyppo.independence.Hsic`.
+# The following applies to both:
+#
+# .. note::
+#
+# :Pros: - Accurate, powerful independence test for multivariate and nonlinear
+# data
+# - Has enticing empiral properties (foundation of some of the other tests in
+# the package)
+# - Has fast implementations (fastest test in the package)
+# :Cons: - Slightly less accurate as the above tests
+#
+# For Hsic, kernels are used instead of distances with the ``compute_kernel`` parameter.
+# Any addition, if the bias variant of the test statistic is required, then the ``bias``
+# parameter can be set to ``True``. In general, we do not recommend doing this.
+# Otherwise, these tests runs like :ref:`any other test`.
+# Since Dcorr and Hsic are implemented similarly, let's look at Dcorr.
+
+import timeit
+
+import numpy as np
+
+setup_code = """
+from hyppo.independence import Dcorr
+from hyppo.tools import w_shaped
+x1, y1 = w_shaped(n=100, p=3, noise=True)
+x2, y2 = w_shaped(n=100, p=1, noise=True)
+"""
+
+t_perm = timeit.Timer(stmt="Dcorr().test(x1, y1, auto=False)", setup=setup_code)
+t_chisq = timeit.Timer(stmt="Dcorr().test(x1, y1, auto=True)", setup=setup_code)
+t_fast_perm = timeit.Timer(stmt="Dcorr().test(x2, y2, auto=True)", setup=setup_code)
+t_fast_chisq = timeit.Timer(stmt="Dcorr().test(x2, y2, auto=True)", setup=setup_code)
+
+perm_time = np.array(t_perm.timeit(number=1)) # permutation Dcorr
+chisq_time = np.array(t_chisq.timeit(number=1000)) / 1000 # fast Dcorr
+fast_perm_time = np.array(t_fast_perm.timeit(number=1)) # permutation Dcorr
+fast_chisq_time = np.array(t_fast_chisq.timeit(number=1000)) / 1000 # fast Dcorr
+
+print("Permutation time: {0:.3g}s".format(perm_time))
+print("Fast time (chi-square): {0:.3g}s".format(chisq_time))
+print("Permutation time (fast statistic): {0:.3g}s".format(fast_perm_time))
+print("Fast time (fast statistic chi-square): {0:.3g}s".format(fast_chisq_time))
+
+########################################################################################
+# Look at the time increases when using the fast test!
+# The fast test approximates the null distribution as a chi-squared random variable,
+# and so is far faster than the permutation method.
+# To call it, simply set ``auto`` to ``True``, which is the default, and if your data
+# has a sample size greater than 20, then the test will run.
+# In the case where the data is 1 dimensional and Euclidean, an even faster version is
+# run.
+
+########################################################################################
+# ------------
+#
+# **Multiscale graph correlation (MGC)** is a powerful independence test the uses the
+# power of Dcorr
+# and `k`-nearest neighbors to create an efficient and powerful independence test.
+# More details can be found in :class:`hyppo.independence.MGC`.
+#
+# .. note::
+#
+# We recently added the majority of the source code of this algorithm to
+# :func:`scipy.stats.multiscale_graphcorr`.
+# This class serves as a wrapper for that implementation.
+#
+# .. note::
+#
+# :Pros: - Highly accurate, powerful independence test for multivariate and nonlinear
+# data
+# - Gives information about geometric nature of the dependency
+# :Cons: - Slightly slower than similar tests in this section
+#
+# MGC has some specific differences outlined below, but creating the instance of the
+# class was demonstrated in the :ref:`general indep` in the Overview.
+
+from hyppo.independence import MGC
+from hyppo.tools import w_shaped
+
+# 100 samples, 1D x and 1D y, noise
+x, y = w_shaped(n=100, p=1, noise=True)
+
+# get the MGC map and optimal scale (only need the statistic)
+_, _, mgc_dict = MGC().test(x, y, reps=0)
+
+########################################################################################
+# A unique property of MGC is a MGC map, which is a ``(n, n)`` map (or ``(n, k)`` where
+# `k` is less than `n` if there are repeated values). This shows you the test statistics
+# at all these different "scales". The optimal scale is an ordered pair that indicates
+# nearest neighbors where the test statistic is maximized and is marked by a red "X".
+# This optimal scale is the location of the test statistic.
+# The heat map gives insights into the nature of the dependency between `x` and `y`.
+# Otherwise, this test runs like :ref:`any other test`.
+# We can plot it below:
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import seaborn as sns
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=1)
+
+mgc_map = mgc_dict["mgc_map"]
+opt_scale = mgc_dict["opt_scale"] # i.e. maximum smoothed test statistic
+print("Optimal Scale:", opt_scale)
+
+# create figure
+fig, (ax, cax) = plt.subplots(
+ ncols=2, figsize=(9, 8.5), gridspec_kw={"width_ratios": [1, 0.05]}
+)
+
+# draw heatmap and colorbar
+ax = sns.heatmap(mgc_map, cmap="YlGnBu", ax=ax, cbar=False)
+fig.colorbar(ax.get_children()[0], cax=cax, orientation="vertical")
+ax.invert_yaxis()
+
+# optimal scale
+ax.scatter(opt_scale[1], opt_scale[0], marker="X", s=200, color="red")
+
+# make plots look nice
+ax.set_title("MGC Map")
+ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
+ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
+ax.yaxis.set_major_locator(ticker.MultipleLocator(10))
+ax.yaxis.set_major_formatter(ticker.ScalarFormatter())
+ax.set_xlabel("Neighbors for x")
+ax.set_ylabel("Neighbors for y")
+ax.set_xticks([0, 50, 100])
+ax.set_yticks([0, 50, 100])
+ax.xaxis.set_tick_params()
+ax.yaxis.set_tick_params()
+cax.xaxis.set_tick_params()
+cax.yaxis.set_tick_params()
+plt.show()
+
+########################################################################################
+# For linear data, we would expect the optimal scale to be at the maximum nearest
+# neighbor pair. Since we consider nonlinear data, this is not the case.
+
+########################################################################################
+# Random Forest Based Tests
+# --------------------------------------------
+#
+# Random-forest based tests exploit the theoretical properties of decision tree based
+# classifiers to create highly accurate tests (especially in cases of high dimensional
+# data sets).
+#
+# **Kernel mean embedding random forest (KMERF)** is one such test, which uses
+# similarity matrices as a result of random forest to generate a test statistic and
+# p-value. More details can be found in :class:`hyppo.independence.KMERF`.
+#
+# .. note::
+#
+# :Pros: - Highly accurate, powerful independence test for multivariate and nonlinear
+# data
+# - Gives information about releative dimension (or feature) importance
+# :Cons: - Very slow (requires training a random forest for each permutation)
+#
+# Unlike other tests, there is no ``compute_distance``
+# parameter. Instead, the number of trees can be set explicityly, and the type of
+# classifier can be set ("classifier" in the case where the return value :math:`y` is
+# categorical, and "regressor" when that is not the case). Check out
+# :class:`sklearn.ensemble.RandomForestClassifier` and
+# :class:`sklearn.ensemble.RandomForestRegressor` for additional parameters to change.
+# Otherwise, this test runs like :ref:`any other test`.
+
+from hyppo.independence import KMERF
+from hyppo.tools import cubic
+
+# 100 samples, 5D sim
+x, y = cubic(n=100, p=5)
+
+# get the feature importances (only need the statistic)
+_, _, kmerf_dict = KMERF(ntrees=5000).test(x, y, reps=0)
+
+########################################################################################
+# Because this test is random-forest based, we can get feature importances. This gives
+# us relative importances value of dimension (i.e. feature). KMERF returns a normalized
+# version of this parameter, and we can plot these importances:
+
+importances = kmerf_dict["feat_importance"]
+dims = range(1, 6) # range of dimensions of simulation
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=1)
+
+# plot the feature importances
+plt.figure(figsize=(9, 6.5))
+plt.plot(dims, importances)
+plt.xlim([1, 5])
+plt.xticks([1, 2, 3, 4, 5])
+plt.xlabel("Dimensions")
+plt.ylim([0, 1])
+plt.yticks([])
+plt.ylabel("Normalized Feature Importance")
+plt.title("Feature Importances")
+plt.show()
+
+########################################################################################
+# We see that feature importances decreases as dimension increases. This is true for
+# most of the simulations in :mod:`hyppo.tools`.
+#
+# Maximal Margin Correlation
+# ---------------------------------------------
+#
+# **Maximial Margin Correlation** takes the independence tests in
+# :mod:`hyppo.independence`, and compute the maximal correlation of pairwise comparisons
+# between each dimension of `x` and `y`.
+# More details can be found in :class:`hyppo.independence.MaxMargin`.
+#
+# .. note::
+#
+# :Pros: - As powerful as some of the tests within this module
+# - Minimal decrease in testing power as dimension increases
+# :Cons: - Adds computational complexity, so can be slow
+#
+# These tests have an ``indep_test`` parameter corresponding to the desired independence
+# test to be run. All the parameters from the above tests can also be modified, and see
+# the relevant section of reference documentation in :mod:`hyppo.independence` for more
+# information.
+# These tests runs like :ref:`any other test`.
+
+########################################################################################
+# Friedman Rafsky Test for Randomness
+# --------------------------------------------
+#
+# This notebook will introduce the usage of the Friedman Rafsky test, a multivariate
+# extension of the Wald-Wolfowitz runs test to test for randomness between two multivariate
+# samples. More specifically, the function tests whether two multivariate samples were
+# independently drawn from the same distribution.
+#
+# The question proposed in 'Multivariate Generalizations of the Wald-Wolfowitz and Smirnov Two-Sample Tests'
+# is that of how to extend the univariate Wald-Wolfowitz runs test to a multivariate setting.
+#
+# The univariate Wald-Wolfowitz runs test is a non-parametric statistical test that checks a randomness hypothesis
+# for a two-valued data sequence. More specifically, it can be used to test the hypothesis that the elements of a sequence
+# are mutually independent. For a data sequence with identifiers of two groups,
+# say: :math:`X , Y` we begin by sorting the combined data set
+# :math: `W` in numerical ascending order. The number of runs is then defined by the number
+# of maximal, non-empty segments of the sequence consisting of adjacent and equal elements.
+# So if we designate every :math:`X = +, Y = -` an example assortment of the 15 element long sequence
+# of both sets could be given as :math:`+++----++---+++` which contains 5 runs, 3 of which positive and 2 of which negative.
+# By randomly permuting the labels of our data a large number of times, we can compare our true number of runs to that of the
+# random permutations to determine the test statistic and p-value.
+#
+# For extension to the multivariate case, we determine a neighbor not by proximity in numerical order,
+# but instead by euclidean distance between each point. The data is then 'sorted' via a calculation of
+# the minimum spanning tree in which each point is connected to each other point with edge weight equal
+# to that of the euclidean distance between the two points. The number of runs is then determined by severing
+# each edge of the MST for which the points connected do not belong to the same family. Similarly to the univariate
+# case, the labels associated with each node are then permuted randomly a large number of times, we can
+# compare our number of true runs to the random distribution of runs to determine the multivariate test
+# statistic and p-value.
+#
+# As such, we see that this set of data contains 3 such runs, and again by randomizing the
+# labels of each data point we can determine the test statistic and p-value for the hypothesis
+# that to determine the randomness of our combined sample.
+# It's worth noting that X and Y need not have the same number of samples, just that they posess the same number of
+# multivariate features. Lastly, labels for X and Y need not be 0 and 1, they just need be consistent across samples.
+# These tests runs like :ref:`any other test`.
+#
+# Test statistic range: :math:`[2, m+n]`
+#
+# .. _[1]: https://link.springer.com/article/10.1007/s10182-020-00378-1
diff --git a/tutorials/kgof.py b/tutorials/kgof.py
new file mode 100644
index 000000000..d256ea55e
--- /dev/null
+++ b/tutorials/kgof.py
@@ -0,0 +1,112 @@
+r"""
+.. _kgof:
+
+Kernel Goodness-of-Fit Testing
+******************************
+
+Figuring out the origin of a particular set of data is difficult. However, insights gained from
+the origin of a set of samples (i.e., its target density function) can be instrumental in understanding
+its characteristics. This sort of testing is desirable in practice to answer questions such as: does this
+sample correctly represent this population? Can a certain statistical phenomena be attributed to a given
+distribution?
+
+For anyone interested in questions like the above, this module of the package is perfect!
+The goal of goodness-of-fit testing is to determine, given a set of samples,
+how likely it is that these were generated from a target density function. The tests can be found in
+:mod:`hyppo.kgof`, and will be elaborated upon in detail below. But let's take a look
+at some of the mathematical theory behind the test formation:
+
+Consider a known probability density :math:`p` and a sample :math:`\{ \mathbf{x}_i \}_{i=1}^n \sim q`
+where :math:`q` is an unknown density, a goodness-of-fit test proposes null and alternative hypotheses:
+
+.. math::
+
+ H_0 &: p = q \\
+ H_1 &: p \neq q
+
+In other words, the GoF test tests whether or not the sample :math:`\{ \mathbf{x}_i \}_{i=1}^n`
+is distributed according to a known :math:`p`.
+
+The implemented test relies on a new test statistic called The Finite-Set Stein Discrepancy (FSSD)
+which is a discrepancy measure between a density and a sample. Unique features of the new goodness-of-fit test are:
+
+It makes only a few mild assumptions on the distributions :math:`p` and :math:`q`. The model :math:`p`
+can take almost any form. The normalizer of :math:`p` is not assumed known. The test only assesses the goodness of
+:math:`p` through :math:`\nabla_{\mathbf{x}} \log p(\mathbf{x})` i.e., the first derivative of the log density.
+
+It returns a set of points (features) which indicate where :math:`p` fails to fit the data.
+
+"""
+
+########################################################################################
+# .. _gauss fssd:
+#
+# A simple Gaussian model
+# ---------------------------------------------
+#
+# Assume that :math:`p(\mathbf{x}) = \mathcal{N}(\mathbf{0}, \mathbf{I})`
+# in :math:`\mathbb{R}^2` (two-dimensional space).
+# The data :math:`\{ \mathbf{x}_i \}_{i=1}^n \sim q = \mathcal{N}([m, 0], \mathbf{I})`
+# where :math:`m` specifies the mean of the first coordinate of :math:`q`.
+# From this setting, if :math:`m\neq 0`, then :math:`H_1` is true and the test should
+# reject :math:`H_0`.
+# First construct the log density function for the model
+
+from hyppo.kgof import DataSource
+from hyppo.kgof import KGauss
+from hyppo.kgof import IsotropicNormal
+from hyppo.kgof import fit_gaussian_draw, meddistance
+from hyppo.kgof import FSSD, FSSDH0SimCovObs
+import matplotlib.pyplot as plt
+import autograd.numpy as np
+from numpy.random import default_rng
+
+
+def isogauss_log_den(X):
+ mean = np.zeros(2)
+ variance = 1
+ unden = -np.sum((X - mean) ** 2, 1) / (2.0 * variance)
+ return unden
+
+
+########################################################################################
+# This function computes the log of an unnormalized density. This works fine as this test
+# only requires a :math:`\nabla_{\mathbf{x}} \log p(\mathbf{x})` which does not depend on
+# the normalizer. The gradient :math:`\nabla_{\mathbf{x}} \log p(\mathbf{x})` will be
+# automatically computed by autograd. In this kgof package, a model :math:`p` can be
+# specified by implementing the class :class:`hyppo.kgof.density.UnnormalizedDensity`. Implementing
+# this directly is a bit tedious, however. Construct an :class:`UnnormalizedDensity` which will
+# represent a Gaussian model. All the implemented goodness-of-fit tests take this object as input.
+
+# Next, draw a sample from q.
+# Drawing n points from q
+m = 1 # If m = 0, p = q and H_0 is true
+
+seed = 4
+rng = default_rng(seed)
+n = 400
+X = rng.standard_normal(size=(n, 2)) + np.array([m, 0])
+
+# Plot the data from q
+plt.plot(X[0], X[1], "ko", label="Data from $q$")
+plt.legend()
+
+########################################################################################
+# Conduct an FSSD kernel goodness-of-fit test for an isotropic normal density with mean
+# 0 and variance 1.
+
+mean = 0
+variance = 1
+
+J = 1
+sig2 = meddistance(X, subsample=1000) ** 2
+k = KGauss(sig2)
+isonorm = IsotropicNormal(mean, variance)
+
+# random test locations
+V = fit_gaussian_draw(X, J, seed=seed + 1)
+null_sim = FSSDH0SimCovObs(n_simulate=200, seed=3)
+fssd = FSSD(isonorm, k, V, null_sim=null_sim, alpha=0.01)
+
+tresult = fssd.test(X, return_simulated_stats=True)
+tresult
diff --git a/tutorials/ksample.py b/tutorials/ksample.py
new file mode 100644
index 000000000..a7af7f976
--- /dev/null
+++ b/tutorials/ksample.py
@@ -0,0 +1,272 @@
+r"""
+.. _ksample:
+
+`K`-Sample Testing
+*********************
+
+A common problem experienced in research is the `k`-sample testing problem.
+Conceptually, it can be described as follows: consider `k` groups of data where each
+group had a different treatment. We can ask, are these groups the similar to one
+another or statistically different? More specifically, supposing that each group has
+a distribution, are these distributions equivalent to one another, or is one of them
+different?
+
+If you are interested in questions of this mold, this module of the package is for you!
+All our tests can be found in :mod:`hyppo.ksample`, and will be elaborated in
+detail below. But before that, let's look at the mathematical formulations:
+
+Consider random variables :math:`U_1, U_2, \ldots, U_k` with distributions
+:math:`F_{U_1}, F_{U_2}, \ldots F_{U_k}`.
+When performing `k`-sample testing, we are seeing whether or not
+these distributions are equivalent. That is, we are testing
+
+.. math::
+
+ H_0 &: F_{U_1} = F_{U_2} = \cdots = F_{U_k} \\
+ H_A &: \exists \, i \neq j \text{ s.t. } F_{U_i} \neq F_{U_j}
+
+Like all the other tests within hyppo, each method has a :func:`statistic` and
+:func:`test` method. The :func:`test` method is the one that returns the test statistic
+and p-values, among other outputs, and is the one that is used most often in the
+examples, tutorials, etc.
+The p-value returned is calculated using a permutation test using
+:meth:`hyppo.tools.perm_test` unless otherwise specified.
+
+Specifics about how the test statistics are calculated for each in
+:class:`hyppo.ksample` can be found the docstring of the respective test.
+let's look at unique properties of some of these tests:
+"""
+
+########################################################################################
+# Multivariate Analysis of Variance (MANOVA) and Hotelling
+# ---------------------------------------------------------
+#
+# **MANOVA** is the current standard for `k`-sample testing in the literature.
+# More details can be found in :class:`hyppo.ksample.MANOVA`.
+# **Hotelling** is 2-sample MANOVA.
+# More details can be found in :class:`hyppo.ksample.Hotelling`.
+#
+# .. note::
+#
+# :Pros: - Very fast
+# - Similar to tests found in scientific literature
+# :Cons: - Not accurate when compared to other tests in most situations
+# - Assumes data is derived from a multivariate Gaussian
+# - Assumes data is has same covariance matrix
+#
+# Neither of these test are distance based, and so do not have a ``compute_distance``
+# parameter and are not nonparametric, so they don't have ``reps`` nor ``workers``.
+# Otherwise, these test runs like :ref:`any other test`.
+
+########################################################################################
+# .. _nonpar manova:
+#
+# `K`-Sample Testing via Independence Testing
+# ---------------------------------------------
+#
+# **Nonparametric MANOVA via Independence Testing** is a `k`-sample test that addresses
+# the aforementioned `k`-sample testing problem as follow: reduce the `k`-sample testing
+# problem to the independence testing problem (see :ref:`indep`).
+# To solve this, we create a new matrix of concatenated inputs and a matrix that labels
+# which of the concatenated data comes from which input `[2]`_.
+# Because independence tests have high finite sample testing power in some cases, this
+# method has a number of advantages.
+# More details can be found in :class:`hyppo.ksample.KSample`.
+# The following applies to both:
+#
+# .. note::
+#
+# If you want use 2-sample MGC, we have added that functionality to SciPy!
+# Please see :func:`scipy.stats.multiscale_graphcorr`.
+#
+# .. note::
+#
+# :Pros: - Highly accurate
+# - No additional computation complexity added
+# - Not many assumptions of the data (only must be i.i.d.)
+# - Has fast implementations (for ``indep_test="Dcorr"`` and
+# ``indep_test="Hsic"``)
+# :Cons: - Can be a little slower than some of the other tests in the package
+#
+# The ``indep_test`` parameter accepts a string corresponding to the name of the class
+# in the :mod:`hyppo.independence`.
+# Other parameters are those in the corresponding independence test.
+# Since this this process is nearly the same for all independence tests, we are going
+# to use :class:`hyppo.independence.MGC` as the example independence test.
+
+from hyppo.ksample import KSample
+from hyppo.tools import rot_ksamp
+
+# 100 samples, 1D cubic independence simulation, 3 groups sim, 60 degree rotation, no
+# noise
+sims = rot_ksamp("linear", n=100, p=1, k=3, degree=[60, -60], noise=True)
+
+########################################################################################
+# The data are points simulating a 1D linear relationship between random variables
+# :math:`X` and :math:`Y`. It the concatenates these two matrices, and then rotates
+# the simulation by 60 degrees, generating the second and, in this case, the third
+# sample. It returns realizations as :class:`numpy.ndarray`.
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# make plots look pretty
+sns.set(color_codes=True, style="white", context="talk", font_scale=1)
+
+# look at the simulation
+plt.figure(figsize=(5, 5))
+for sim in sims:
+ plt.scatter(sim[:, 0], sim[:, 1])
+plt.xticks([])
+plt.yticks([])
+sns.despine(left=True, bottom=True, right=True)
+plt.show()
+
+# run k-sample test on the provided simulations. Note that *sims just unpacks the list
+# we got containing our simulated data
+stat, pvalue = KSample(indep_test="Dcorr").test(*sims)
+print(stat, pvalue)
+
+########################################################################################
+# This was a general use case for the test, but there are a number of intricacies that
+# depend on the type of independence test chosen. Those same parameters can be modified
+# in this class. For a full list of the parameters, see the desired test in
+# :mod:`hyppo.independence` and for examples on how to use it, see :ref:`indep`.
+
+########################################################################################
+# Distance (and Kernel) Equivalencies
+# --------------------------------------------
+#
+# It turns out that a number of test statistics are multiples of one another and so,
+# their p-values are equivalent to the above :ref:`nonpar manova`. `[1]`_ goes through
+# the distance and kernel equivalencies and `[2]`_ goes through the independence and
+# two-sample (and by extension `k`-sample) equivalences in far more detail.
+#
+# **Energy** is a powerful distance-based two sample test,
+# **Distance components (DISCO)** is the `k`-sample analogue to Energy,
+# and **Maximal mean discrepency (MMD)** is a powerful kernel-based two sample test,
+# These are equivalent to :class:`hyppo.ksample.KSample` using ``indep_test="Dcorr"``
+# for Energy and DISCO and ``indep_test="Hsic"`` for MMD.
+# More information can be found at :class:`hyppo.ksample.Energy`,
+# :class:`hyppo.ksample.DISCO`, and
+# :class:`hyppo.ksample.MMD`.
+# However, the test statistics have been modified to make it more in tune with other
+# implementations.
+#
+# .. note::
+#
+# :Pros: - Highly accurate
+# - Has similar test statistics to the literature
+# - Has fast implementations
+# :Cons: - Lower power than more computationally complex algorithms
+#
+# For MMD, kernels are used instead of distances with the ``compute_kernel`` parameter.
+# Any addition, if the bias variant of the test statistic is required, then the ``bias``
+# parameter can be set to ``True``. In general, we do not recommend doing this.
+# Otherwise, these tests runs like :ref:`any other test`.
+
+########################################################################################
+# Smooth Characteristic Function Test
+# --------------------------------------------
+#
+# The **Smooth Characteristic Function Test** (Smooth CF), is a form of non-parametric two-sample
+# tests. The Smooth CF test utilizes smoothed empirical characteristic functions to represent
+# two data distributions. Characteristic functions completely define the probability distribution
+# of a random variable. In hypothesis testing, it is useful to estimate characteristic functions
+# for given data. However, empirical characteristic functions can be very complex and therefore
+# expensive to compute. The smooth characteristic function can serve as a heuristic in place of
+# the empirical function which is much faster w.r.t. computation times. More information can be
+# found at :class:`hyppo.ksample.SmoothCFTest`.
+#
+# .. note::
+#
+# :Pros: - Very fast computation time
+# - Faster than current, state-of-the-art quadratic-time kernel-based tests
+# :Cons: - Heuristic method, checking more frequencies will give more power.
+#
+# This test is also initialized with the ``num_randfreq`` parameter. This parameter can be
+# thought of as the degrees of freedom associated with the test and also dictates the number
+# of test points used in the test (see :class:`hyppo.ksample.SmoothCFTest`). If data
+# is kept constant, increasing the magnitude of this parameter will generally result in
+# larger magnitude test statistics while magnitude of the p-value will fluctuate:
+
+import numpy as np
+from hyppo.ksample import SmoothCFTest
+
+np.random.seed(1234)
+x = np.random.randn(500, 10)
+y = np.random.randn(500, 10)
+
+stat1, pvalue1 = SmoothCFTest(num_randfreq=5).test(x, y, random_state=1234)
+stat2, pvalue2 = SmoothCFTest(num_randfreq=10).test(x, y, random_state=1234)
+
+print("5 degrees of freedom (stat, pval):\n", stat1, pvalue1)
+print("10 degrees of freedom (stat, pval):\n", stat2, pvalue2)
+
+########################################################################################
+# Mean Embedding Test
+# --------------------------------------------
+#
+# The **Mean Embedding Test** is another non-parametric two-sample statistical test. This test
+# is based on analytic mean embeddings of data distributions in a reproducing kernel hilbert
+# space (RKHS). Hilbert spaces allow the representation of functions as points; thus, if
+# mean embeddings can be determined for two data distributions then the distance between
+# these two distributions in the hilbert space can be determined. In other words, the RKHS
+# allows the mapping of probability measures into a finite dimensional Euclidean space. More
+# details can be found at :class:`hyppo.ksample.MeanEmbeddingTest`.
+#
+# .. note::
+#
+# :Pros: - Very fast computation time
+# - Faster than current, state-of-the-art quadratic-time kernel-based tests
+# :Cons: - Heuristic method, checking more frequencies will give more power.
+#
+# This test is also initialized with the ``num_randfreq`` parameter. This parameter can be
+# thought ofas the degrees of freedom associated with the test and also dictates the number
+# of test points used in the test (see :class:`hyppo.ksample.MeanEmbeddingTest`). If data
+# is kept constant, increasing the magnitude of this parameter will generally result in
+# larger magnitude test statistics while magnitude of the p-value will fluctuate:
+
+from hyppo.ksample import MeanEmbeddingTest
+
+np.random.seed(1234)
+x = np.random.randn(500, 10)
+y = np.random.randn(500, 10)
+
+stat1, pval1 = MeanEmbeddingTest(num_randfreq=5).test(x, y, random_state=1234)
+stat2, pval2 = MeanEmbeddingTest(num_randfreq=10).test(x, y, random_state=1234)
+
+print("5 degrees of freedom (stat, pval):\n", stat1, pval1)
+print("10 degrees of freedom (stat, pval):\n", stat2, pval2)
+
+########################################################################################
+# Univariate-Based Test
+# --------------------------------------------
+# The **Heller Heller Gorfine (HHG) 2-Sample Test** is a non-parametric two-sample
+# statistical test. This test is based on testing the independence of the distances of sample vectors
+# from a center point by a univariate K-sample test. If the distribution of samples differs
+# across categories, then so does the distribution of distances of the vectors from almost every
+# point z. The univariate test used is the Kolmogorov-Smirnov 2-sample Test, which looks
+# at the largest absolute deviation between the cumulative distribution functions of
+# the samples.
+# More info can found at :class:`hyppo.ksample.KSampleHHG`.
+#
+# .. note::
+#
+# :Pros: - Very fast computation time
+# :Cons: - Lower power than more computationally complex algorithms
+# - Inherits the assumptions of the KS univariate test
+
+from hyppo.ksample import KSampleHHG
+
+np.random.seed(1234)
+
+x, y = rot_ksamp("linear", n=100, p=1, k=2, noise=False)
+
+stat, pvalue = KSampleHHG().test(x, y)
+print(stat, pvalue)
+
+
+########################################################################################
+# .. _[1]: https://link.springer.com/article/10.1007/s10182-020-00378-1
+# .. _[2]: https://arxiv.org/abs/1910.08883
diff --git a/tutorials/overview.py b/tutorials/overview.py
new file mode 100644
index 000000000..1968ca4a7
--- /dev/null
+++ b/tutorials/overview.py
@@ -0,0 +1,125 @@
+"""
+.. _overview:
+
+Overview
+========
+
+hyppo is a multivariate hypothesis testing package, dealing with problems such as
+independence testing, *k*-sample testing, time series independence testing, etc. It
+includes algorithms developed by the `neurodata lab 
+
+
+
+
+
+
+
+
+