diff --git a/.gitignore b/.gitignore index 902cf039..78671e1e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,6 @@ # Compiled source # ################### *.pyc -*.ipynb # Packages # ############ @@ -36,3 +35,11 @@ Thumbs.db dist .cache +# Documentation +############### +_build/ +.ipynb_checkpoints/ + +# PyCharm +######### +.idea/ diff --git a/README.md b/README.md index 9b64d662..bd677738 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Generalized Additive Models in Python. -## Tutorial +## Documentation [pyGAM: Getting started with Generalized Additive Models in Python](https://medium.com/@jpoberhauser/pygam-getting-started-with-generalized-additive-models-in-python-457df5b4705f) ## Installation @@ -72,359 +72,6 @@ GAMs extend generalized linear models by allowing non-linear functions of featur The result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting. - -## Regression -For **regression** problems, we can use a **linear GAM** which models: - -![alt tag](http://latex.codecogs.com/svg.latex?\mathbb{E}[y|X]=\beta_0+f_1(X_1)+f_2(X_2)+\dots+f_p(X_p)) - -```python -from pygam import LinearGAM, s, f -from pygam.datasets import wage - -X, y = wage(return_X_y=True) - -gam = LinearGAM(s(0) + s(1) + f(2)).gridsearch(X, y) - -fig, axs = plt.subplots(1, 3) -titles = ['year', 'age', 'education'] - -for i, ax in enumerate(axs): - XX = gam.generate_X_grid(term=i) - pdep, confi = gam.partial_dependence(term=i, width=.95) - - ax.plot(XX[:, i], pdep) - ax.plot(XX[:, i], confi, c='r', ls='--') - ax.set_title(titles[i]) -``` - - -Even though we allowed **n_splines=20** per numerical feature, our **smoothing penalty** reduces us to just 19 **effective degrees of freedom**: - -``` -gam.summary() - -LinearGAM -=============================================== ========================================================== -Distribution: NormalDist Effective DoF: 19.2602 -Link Function: IdentityLink Log Likelihood: -24116.7451 -Number of Samples: 3000 AIC: 48274.0107 - AICc: 48274.2999 - GCV: 1250.3656 - Scale: 1235.9245 - Pseudo R-Squared: 0.2945 -========================================================================================================== -Feature Function Lambda Rank EDoF P > x Sig. Code -================================= ==================== ============ ============ ============ ============ -s(0) [15.8489] 20 6.9 5.52e-03 ** -s(1) [15.8489] 20 8.5 1.11e-16 *** -f(2) [15.8489] 5 3.8 1.11e-16 *** -intercept 0 1 0.0 1.11e-16 *** -========================================================================================================== -Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -``` - - -With **LinearGAMs**, we can also check the **prediction intervals**: - -```python -from pygam import LinearGAM -from pygam.datasets import mcycle - -X, y = mcycle(return_X_y=True) - -gam = LinearGAM().gridsearch(X, y) -XX = gam.generate_X_grid(term=0) - -plt.plot(XX, gam.predict(XX), 'r--') -plt.plot(XX, gam.prediction_intervals(XX, width=.95), color='b', ls='--') - -plt.scatter(X, y, facecolor='gray', edgecolors='none') -plt.title('95% prediction interval') -``` - - -And simulate from the posterior: - -```python -# continuing last example with the mcycle dataset -for response in gam.sample(X, y, quantity='y', n_draws=50, sample_at_X=XX): - plt.scatter(XX, response, alpha=.03, color='k') -plt.plot(XX, gam.predict(XX), 'r--') -plt.plot(XX, gam.prediction_intervals(XX, width=.95), color='b', ls='--') -plt.title('draw samples from the posterior of the coefficients') -``` - - - -## Classification -For **binary classification** problems, we can use a **logistic GAM** which models: - -![alt tag](http://latex.codecogs.com/svg.latex?log\left(\frac{P(y=1|X)}{P(y=0|X)}\right)=\beta_0+f_1(X_1)+f_2(X_2)+\dots+f_p(X_p)) - -```python -from pygam import LogisticGAM, s, f -from pygam.datasets import default - -X, y = default(return_X_y=True) - -gam = LogisticGAM(f(0) + s(1) + s(2)).gridsearch(X, y) - -fig, axs = plt.subplots(1, 3) -titles = ['student', 'balance', 'income'] - -for i, ax in enumerate(axs): - XX = gam.generate_X_grid(term=i) - pdep, confi = gam.partial_dependence(term=i, width=.95) - - ax.plot(XX[:, i], pdep) - ax.plot(XX[:, i], confi, c='r', ls='--') - ax.set_title(titles[i]) - -# and check the accuracy -gam.accuracy(X, y) -``` - - -Since the **scale** of the **Binomial distribution** is known, our gridsearch minimizes the **Un-Biased Risk Estimator** (UBRE) objective: - -``` -gam.summary() - -LogisticGAM -=============================================== ========================================================== -Distribution: BinomialDist Effective DoF: 3.8047 -Link Function: LogitLink Log Likelihood: -788.877 -Number of Samples: 10000 AIC: 1585.3634 - AICc: 1585.369 - UBRE: 2.1588 - Scale: 1.0 - Pseudo R-Squared: 0.4598 -========================================================================================================== -Feature Function Lambda Rank EDoF P > x Sig. Code -================================= ==================== ============ ============ ============ ============ -f(0) [1000.] 2 1.7 4.61e-03 ** -s(1) [1000.] 20 1.2 0.00e+00 *** -s(2) [1000.] 20 0.8 3.29e-02 * -intercept 0 1 0.0 0.00e+00 *** -========================================================================================================== -Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -``` - - -## Poisson and Histogram Smoothing -We can intuitively perform **histogram smoothing** by modeling the counts in each bin -as being distributed Poisson via **PoissonGAM**. - -```python -from pygam import PoissonGAM -from pygam.datasets import faithful - -X, y = faithful(return_X_y=True) - -gam = PoissonGAM().gridsearch(X, y) - -plt.hist(faithful(return_X_y=False)['eruptions'], bins=200, color='k'); -plt.plot(X, gam.predict(X), color='r') -plt.title('Best Lambda: {0:.2f}'.format(gam.lam[0][0])); -``` - - -## Terms and Interactions - -pyGAM can also fit interactions using tensor products via `te()` -```python -from pygam import LinearGAM, s, te -from pygam.datasets import chicago - -X, y = chicago(return_X_y=True) - -gam = PoissonGAM(s(0, n_splines=200) + te(3, 1) + s(2)).fit(X, y) -``` - -and plot a 3D surface: - -```python -XX = gam.generate_X_grid(term=0, meshgrid=True) -Z = gam.partial_dependence(term=0, X=XX, meshgrid=True) - -from mpl_toolkits import mplot3d -ax = plt.axes(projection='3d') -ax.plot_surface(XX[0], XX[1], Z, cmap='viridis') -``` - - - -For simple interactions it is sometimes useful to add a by-variable to a term - -```python -from pygam import LinearGAM, s -from pygam.datasets import toy_interaction - -X, y = toy_interaction(return_X_y=True) - -gam = LinearGAM(s(0, by=1)).fit(X, y) -gam.summary() -``` - -#### Available Terms -- `l()` linear terms -- `s()` spline terms -- `f()` factor terms -- `te()` tensor products -- `intercept` - -## Custom Models -It's also easy to build custom models, by using the base **GAM** class and specifying the **distribution** and the **link function**. - -```python -from pygam import GAM -from pygam.datasets import trees - -X, y = trees(return_X_y=True) - -gam = GAM(distribution='gamma', link='log') -gam.gridsearch(X, y) - -plt.scatter(y, gam.predict(X)) -plt.xlabel('true volume') -plt.ylabel('predicted volume') -``` - - -We can check the quality of the fit by looking at the `Pseudo R-Squared`: - -``` -gam.summary() - -GAM -=============================================== ========================================================== -Distribution: GammaDist Effective DoF: 25.3616 -Link Function: LogLink Log Likelihood: -26.1673 -Number of Samples: 31 AIC: 105.0579 - AICc: 501.5549 - GCV: 0.0088 - Scale: 0.001 - Pseudo R-Squared: 0.9993 -========================================================================================================== -Feature Function Lambda Rank EDoF P > x Sig. Code -================================= ==================== ============ ============ ============ ============ -s(0) [0.001] 20 2.04e-08 *** -s(1) [0.001] 20 7.36e-06 *** -intercept 0 1 4.39e-13 *** -========================================================================================================== -Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -``` - -## Penalties / Constraints -With GAMs we can encode **prior knowledge** and **control overfitting** by using penalties and constraints. - -#### Available penalties: -- second derivative smoothing (default on numerical features) -- L2 smoothing (default on categorical features) - -#### Availabe constraints: -- monotonic increasing/decreasing smoothing -- convex/concave smoothing -- periodic smoothing [soon...] - - -We can inject our intuition into our model by using **monotonic** and **concave** constraints: - -```python -from pygam import LinearGAM, s -from pygam.datasets import hepatitis - -X, y = hepatitis(return_X_y=True) - -gam1 = LinearGAM(s(0, constraints='monotonic_inc')).fit(X, y) -gam2 = LinearGAM(s(0, constraints='concave')).fit(X, y) - -fig, ax = plt.subplots(1, 2) -ax[0].plot(X, y, label='data') -ax[0].plot(X, gam1.predict(X), label='monotonic fit') -ax[0].legend() - -ax[1].plot(X, y, label='data') -ax[1].plot(X, gam2.predict(X), label='concave fit') -ax[1].legend() -``` - - -## API -pyGAM is intuitive, modular, and adheres to a familiar API: - -```python -from pygam import LogisticGAM -from pygam.datasets import toy_classification - -X, y = toy_classification(return_X_y=True) - -gam = LogisticGAM(s(0) + s(1) + s(2) + s(3) + s(4) + f(5)) -gam.fit(X, y) -``` - -Since GAMs are additive, it is also super easy to visualize each individual **feature function**, `f_i(X_i)`. These feature functions describe the effect of each `X_i` on `y` individually while marginalizing out all other predictors: - -```python -pdeps = gam.partial_dependence(X) -plt.plot(pdeps) -``` - - -## Current Features -### Models -pyGAM comes with many models out-of-the-box: - -- GAM (base class for constructing custom models) -- LinearGAM -- LogisticGAM -- GammaGAM -- PoissonGAM -- InvGaussGAM -- ExpectileGAM - -You can mix and match distributions with link functions to create custom models! - -```python -gam = GAM(distribution='gamma', link='inverse') -``` - -### Distributions - -- Normal -- Binomial -- Gamma -- Poisson -- Inverse Gaussian - -### Link Functions -Link functions take the distribution mean to the linear prediction. These are the canonical link functions for the above distributions: - -- Identity -- Logit -- Inverse -- Log -- Inverse-squared - -### Callbacks -Callbacks are performed during each optimization iteration. It's also easy to write your own. - -- deviance - model deviance -- diffs - differences of coefficient norm -- accuracy - model accuracy for LogisticGAM -- coef - coefficient logging - -You can check a callback by inspecting: - -```python -plt.plot(gam.logs_['deviance']) -``` - - -### Linear Extrapolation - - ## Citing pyGAM Please consider citing pyGAM if it has helped you in your research or work: diff --git a/doc/Makefile b/doc/Makefile new file mode 100644 index 00000000..558d8ec3 --- /dev/null +++ b/doc/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = pyGAM +SOURCEDIR = source +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/doc/make.bat b/doc/make.bat new file mode 100644 index 00000000..3b02f276 --- /dev/null +++ b/doc/make.bat @@ -0,0 +1,36 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build +set SPHINXPROJ=pyGAM + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/doc/source/api/api.rst b/doc/source/api/api.rst new file mode 100644 index 00000000..be834c91 --- /dev/null +++ b/doc/source/api/api.rst @@ -0,0 +1,38 @@ +.. Top level package + +User API +======== + +Generalized Additive Model Classes +---------------------------------- + +.. toctree:: + :maxdepth: 2 + + gam + lineargam + gammagam + invgaussgam + logisticgam + poissongam + expectilegam + + +Terms +------ + +Linear Term +++++++++++++ +.. autofunction:: pygam.terms.l + +Spline Term +++++++++++++ +.. autofunction:: pygam.terms.s + +Factor Term +++++++++++++ +.. autofunction:: pygam.terms.f + +Tensor Term +++++++++++++ +.. autofunction:: pygam.terms.te diff --git a/doc/source/api/expectilegam.rst b/doc/source/api/expectilegam.rst new file mode 100644 index 00000000..e947570d --- /dev/null +++ b/doc/source/api/expectilegam.rst @@ -0,0 +1,48 @@ +.. Expectile GAM class documentation + +ExpectileGAM +============ +:: + + from pygam import ExpectileGAM + from pygam.datasets import mcycle + + X, y = mcycle(return_X_y=True) + + # lets fit the mean model first by CV + gam50 = ExpectileGAM(expectile=0.5).gridsearch(X, y) + + # and copy the smoothing to the other models + lam = gam50.lam + + # now fit a few more models + gam95 = ExpectileGAM(expectile=0.95, lam=lam).fit(X, y) + gam75 = ExpectileGAM(expectile=0.75, lam=lam).fit(X, y) + gam25 = ExpectileGAM(expectile=0.25, lam=lam).fit(X, y) + gam05 = ExpectileGAM(expectile=0.05, lam=lam).fit(X, y) + + +:: + + from matplotlib import pyplot as plt + + XX = gam50.generate_X_grid(term=0, n=500) + + plt.scatter(X, y, c='k', alpha=0.2) + plt.plot(XX, gam95.predict(XX), label='0.95') + plt.plot(XX, gam75.predict(XX), label='0.75') + plt.plot(XX, gam50.predict(XX), label='0.50') + plt.plot(XX, gam25.predict(XX), label='0.25') + plt.plot(XX, gam05.predict(XX), label='0.05') + plt.legend() + + +.. image:: ../../../imgs/pygam_expectiles.png + :alt: pyGAM expectiles + :align: center + +.. autoclass:: pygam.pygam.ExpectileGAM + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/gam.rst b/doc/source/api/gam.rst new file mode 100644 index 00000000..e81f7771 --- /dev/null +++ b/doc/source/api/gam.rst @@ -0,0 +1,9 @@ +.. Base GAM class documentation + +GAM +=== + +.. autoclass:: pygam.pygam.GAM + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/gammagam.rst b/doc/source/api/gammagam.rst new file mode 100644 index 00000000..c56219cd --- /dev/null +++ b/doc/source/api/gammagam.rst @@ -0,0 +1,10 @@ +.. Gamma GAM class documentation + +GammaGAM +======== + +.. autoclass:: pygam.pygam.GammaGAM + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/invgaussgam.rst b/doc/source/api/invgaussgam.rst new file mode 100644 index 00000000..d6b817f4 --- /dev/null +++ b/doc/source/api/invgaussgam.rst @@ -0,0 +1,10 @@ +.. Inverse Gauss GAM class documentation + +InvGaussGAM +=========== + +.. autoclass:: pygam.pygam.InvGaussGAM + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/lineargam.rst b/doc/source/api/lineargam.rst new file mode 100644 index 00000000..a3e971d8 --- /dev/null +++ b/doc/source/api/lineargam.rst @@ -0,0 +1,10 @@ +.. Linear GAM class documentation + +LinearGAM +========= + +.. autoclass:: pygam.pygam.LinearGAM + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/logisticgam.rst b/doc/source/api/logisticgam.rst new file mode 100644 index 00000000..2d6df7f1 --- /dev/null +++ b/doc/source/api/logisticgam.rst @@ -0,0 +1,10 @@ +.. Logistic GAM class documentation + +LogisticGAM +=========== + +.. autoclass:: pygam.pygam.LogisticGAM + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/poissongam.rst b/doc/source/api/poissongam.rst new file mode 100644 index 00000000..9a4355e8 --- /dev/null +++ b/doc/source/api/poissongam.rst @@ -0,0 +1,10 @@ +.. Poisson GAM class documentation + +PoissonGAM +========== + +.. autoclass:: pygam.pygam.PoissonGAM + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/api/terms.rst b/doc/source/api/terms.rst new file mode 100644 index 00000000..a004a9b5 --- /dev/null +++ b/doc/source/api/terms.rst @@ -0,0 +1,20 @@ +.. Terms documentation + +Terms +========= + +Linear Term +------------ +.. autofunction:: pygam.terms.l + +Spline Term +------------ +.. autofunction:: pygam.terms.s + +Factor Term +------------ +.. autofunction:: pygam.terms.f + +Tensor Term +------------ +.. autofunction:: pygam.terms.te diff --git a/doc/source/conf.py b/doc/source/conf.py new file mode 100644 index 00000000..766c2048 --- /dev/null +++ b/doc/source/conf.py @@ -0,0 +1,188 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# 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 +# sys.path.insert(0, os.path.abspath('.')) + + +# -- Project information ----------------------------------------------------- + +project = 'pyGAM' +copyright = '2018, Daniel Servén and Charlie Brummitt' +author = 'Daniel Servén and Charlie Brummitt' + + +import pygam + +# The short X.Y version +version = pygam.__version__ +# The full version, including alpha/beta/rc tags +release = pygam.__version__ + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.napoleon', + 'nbsphinx' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path . +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints'] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' +# html_theme = 'classic' +# +nbsphinx_prompt_width = 0 + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = { +# "fixed_sidebar": "false", +# "description": "Generailzed Additive Models in Python" +# } + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# 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 = {} + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +html_logo = '../../imgs/pygam_tensor.png' + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'pyGAMdoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'pyGAM.tex', 'pyGAM Documentation', + 'Daniel Servén', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'pygam', 'pyGAM Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'pyGAM', 'pyGAM Documentation', + author, 'pyGAM', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Extension configuration ------------------------------------------------- + +# -- Options for intersphinx extension --------------------------------------- + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = {'https://docs.python.org/': None} + +# -- Options for todo extension ---------------------------------------------- + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True diff --git a/doc/source/dev-api/api.rst b/doc/source/dev-api/api.rst new file mode 100644 index 00000000..037c8675 --- /dev/null +++ b/doc/source/dev-api/api.rst @@ -0,0 +1,13 @@ +.. Top level package + +Developer API +============== + +.. toctree:: + :maxdepth: 2 + + terms + distributions + link + callbacks + penalties diff --git a/doc/source/dev-api/callbacks.rst b/doc/source/dev-api/callbacks.rst new file mode 100644 index 00000000..0da953f9 --- /dev/null +++ b/doc/source/dev-api/callbacks.rst @@ -0,0 +1,37 @@ +.. Callbacks documentation + +Callbacks +========= + +.. autoclass:: pygam.callbacks.CallBack + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.callbacks.Accuracy + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.callbacks.Coef + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.callbacks.Deviance + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.callbacks.Diffs + :members: + :undoc-members: + :show-inheritance: + +.. autofunction:: pygam.callbacks.validate_callback + +.. autofunction:: pygam.callbacks.validate_callback_data diff --git a/doc/source/dev-api/distributions.rst b/doc/source/dev-api/distributions.rst new file mode 100644 index 00000000..e8d6acbf --- /dev/null +++ b/doc/source/dev-api/distributions.rst @@ -0,0 +1,9 @@ +.. Base Link class documentation + +Distributions +============= + +.. automodule:: pygam.distributions + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/dev-api/link.rst b/doc/source/dev-api/link.rst new file mode 100644 index 00000000..56952dfd --- /dev/null +++ b/doc/source/dev-api/link.rst @@ -0,0 +1,33 @@ +.. Base Link class documentation + +Links +===== + +.. autoclass:: pygam.links.Link + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.links.IdentityLink + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.links.InvSquaredLink + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.links.LogitLink + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.links.LogLink + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/dev-api/penalties.rst b/doc/source/dev-api/penalties.rst new file mode 100644 index 00000000..e202da8e --- /dev/null +++ b/doc/source/dev-api/penalties.rst @@ -0,0 +1,9 @@ +.. Base Link class documentation + +Penalties +========= + +.. automodule:: pygam.penalties + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/source/dev-api/terms.rst b/doc/source/dev-api/terms.rst new file mode 100644 index 00000000..e85daa7e --- /dev/null +++ b/doc/source/dev-api/terms.rst @@ -0,0 +1,43 @@ +.. Terms documentation + +Terms +========= + +.. autoclass:: pygam.terms.Term + :members: + :undoc-members: + :show-inheritance: + + +.. autoclass:: pygam.terms.LinearTerm + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + + +.. autoclass:: pygam.terms.SplineTerm + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + + +.. autoclass:: pygam.terms.FactorTerm + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + + +.. autoclass:: pygam.terms.TensorTerm + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + +.. autoclass:: pygam.terms.TermList + :members: + :undoc-members: + :show-inheritance: + :inherited-members: diff --git a/doc/source/index.rst b/doc/source/index.rst new file mode 100644 index 00000000..80cbbb07 --- /dev/null +++ b/doc/source/index.rst @@ -0,0 +1,112 @@ +.. pyGAM documentation master file, created by + sphinx-quickstart on Sat Aug 18 15:42:53 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to pyGAM's documentation! +================================= + +.. image:: ../../imgs/pygam_tensor.png + :height: 300px + :alt: pyGAM logo + :align: center + +|Build Status| |Coverage| |PyPi Version| |Py27| |Py36| |Zenodo| |Open Source| + +pyGAM is a package for building Generalized Additive Models in Python, +with an emphasis on modularity and performance. The API will be immediately familiar to anyone with experience +of scikit-learn or scipy. + +Installation +============ + +pyGAM is on pypi, and can be installed using ``pip``: :: + + pip install pygam + +Or via ``conda-forge``, however this is typically less up-to-date: :: + + conda install -c conda-forge pyGAM + +You can install the bleeding edge from github using ``flit``. +First clone the repo, ``cd`` into the main directory and do: :: + + pip install flit + flit install + + +Optional +""""""""" +To speed up optimization on large models with constraints, it helps to +have ``scikit-sparse`` installed because it contains a slightly faster, +sparse version of Cholesky factorization. The import from +``scikit-sparse`` references ``nose``, so you'll need that too. + +The easiest way is to use Conda: :: + + conda install -c conda-forge scikit-sparse nose + + +More information is available in the `scikit-sparse docs +`_. + + +Dependencies +============= +pyGAM is tested on Python 2.7 and 3.6 and depends on ``NumPy``, ``SciPy``, and ``progressbar2`` (see ``requirements.txt`` for version information). + +Optional: ``scikit-sparse``. + +In addtion to the above dependencies, the ``datasets`` submodule relies on ``Pandas``. + +Citing pyGAM +============ + + Servén D., Brummitt C. (2018). pyGAM: Generalized Additive Models in Python. Zenodo. `DOI: 10.5281/zenodo.1208723 `_ + +Contact +======= +To report an issue with pyGAM please use the `issue tracker `_. + +License +======= +GNU General Public License v3.0 + + +Getting Started +=============== +If you're new to pyGAM, read :ref:`the Tour of pyGAM ` +for an introduction to the package. + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + notebooks/quick_start.ipynb + notebooks/tour_of_pygam.ipynb + api/api + dev-api/api + + +Indices and tables +================== +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + + + +.. |Build Status| image:: https://travis-ci.org/dswah/pyGAM.svg?branch=master + :target: https://travis-ci.org/dswah/pyGAM +.. |Coverage| image:: https://codecov.io/gh/dswah/pygam/branch/master/graph/badge.svg + :target: https://codecov.io/gh/dswah/pygam +.. |PyPi Version| image:: https://badge.fury.io/py/pygam.svg + :target: https://badge.fury.io/py/pygam +.. |Py27| image:: https://img.shields.io/badge/python-2.7-blue.svg + :target: https://badge.fury.io/py/pygam +.. |Py36| image:: https://img.shields.io/badge/python-3.6-blue.svg + :target: https://badge.fury.io/py/pygam +.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1208723.svg + :target: https://doi.org/10.5281/zenodo.1208723 +.. |Open Source| image:: https://img.shields.io/badge/powered%20by-Open%20Source-orange.svg?style=flat&colorA=E1523D&colorB=007D8A + :target: https://github.com/dswah/pyGAM diff --git a/doc/source/notebooks/pygam_basis.png b/doc/source/notebooks/pygam_basis.png new file mode 100644 index 00000000..3159fd32 Binary files /dev/null and b/doc/source/notebooks/pygam_basis.png differ diff --git a/doc/source/notebooks/quick_start.ipynb b/doc/source/notebooks/quick_start.ipynb new file mode 100644 index 00000000..ee0921f7 --- /dev/null +++ b/doc/source/notebooks/quick_start.ipynb @@ -0,0 +1,532 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quick Start\n", + "\n", + "This quick start will show how to do the following:\n", + "\n", + "- `Install` everything needed to use pyGAM.\n", + "- `fit a regression model` with custom terms\n", + "- search for the `best smoothing parameters`\n", + "- plot `partial dependence` functions\n", + "\n", + "\n", + "## Install pyGAM\n", + "#### Pip\n", + "\n", + " pip install pygam\n", + "\n", + "\n", + "#### Conda\n", + "pyGAM is on conda-forge, however this is typically less up-to-date:\n", + "\n", + " conda install -c conda-forge pygam\n", + " \n", + "\n", + "#### Bleeding edge\n", + "You can install the bleeding edge from github using `flit`.\n", + "First clone the repo, ``cd`` into the main directory and do:\n", + "\n", + " pip install flit\n", + " flit install" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get `pandas` and `matplotlib`\n", + "\n", + " pip install pandas matplotlib\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit a Model\n", + "\n", + "Let's get to it. First we need some data:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dswah/miniconda3/envs/pygam36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", + " return f(*args, **kwds)\n" + ] + } + ], + "source": [ + "from pygam.datasets import wage\n", + "\n", + "X, y = wage()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's import a GAM that's made for regression problems.\n", + "\n", + "Let's fit a spline term to the first 2 features, and a factor term to the 3rd feature." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pygam import LinearGAM, s, f\n", + "\n", + "gam = LinearGAM(s(0) + s(1) + f(2)).fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the model fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearGAM \n", + "=============================================== ==========================================================\n", + "Distribution: NormalDist Effective DoF: 25.1911\n", + "Link Function: IdentityLink Log Likelihood: -24118.6847\n", + "Number of Samples: 3000 AIC: 48289.7516\n", + " AICc: 48290.2307\n", + " GCV: 1255.6902\n", + " Scale: 1236.7251\n", + " Pseudo R-Squared: 0.2955\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [0.6] 20 7.1 5.95e-03 ** \n", + "s(1) [0.6] 20 14.1 1.11e-16 *** \n", + "f(2) [0.6] 5 4.0 1.11e-16 *** \n", + "intercept 1 0.0 1.11e-16 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we have 3 terms with a total of `(20 + 20 + 5) = 45` free variables, the default smoothing penalty (`lam=0.6`) reduces the effective degrees of freedom to just ~25." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, the spline terms, `s(...)`, use 20 basis functions. This is a good starting point. The rule of thumb is to use a fairly large amount of flexibility, and then let the smoothing penalty regularize the model.\n", + "\n", + "However, we can always use our expert knowledge to add flexibility where it is needed, or remove basis functions, and make fitting easier:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "gam = LinearGAM(s(0, n_splines=5) + s(1) + f(2)).fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Automatically tune the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, spline terms, `s()` have a penalty on their 2nd derivative, which encourages the functions to be smoother, while factor terms, `f()` and linear terms `l()`, have a l2, ie ridge penalty, which encourages them to take on smaller values.\n", + "\n", + "`lam`, short for $\\lambda$, controls the strength of the regularization penalty on each term. Terms can have multiple penalties, and therefore multiple `lam`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.6], [0.6], [0.6]]\n" + ] + } + ], + "source": [ + "print(gam.lam)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our model has 3 `lam` parameters, currently just one per term.\n", + "\n", + "Let's perform a grid-search over multiple `lam` values to see if we can improve our model. \n", + "We will seek the model with the lowest generalized cross-validation (GCV) score.\n", + "\n", + "Our search space is 3-dimensional, so we have to be conservative with the number of points we consider per dimension.\n", + "\n", + "Let's try 5 values for each smoothing parameter, resulting in a total of `5*5*5 = 125` points in our grid." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (125 of 125) |######################| Elapsed Time: 0:00:07 Time: 0:00:07\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearGAM \n", + "=============================================== ==========================================================\n", + "Distribution: NormalDist Effective DoF: 9.2948\n", + "Link Function: IdentityLink Log Likelihood: -24119.7277\n", + "Number of Samples: 3000 AIC: 48260.0451\n", + " AICc: 48260.1229\n", + " GCV: 1244.089\n", + " Scale: 1237.1528\n", + " Pseudo R-Squared: 0.2915\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [100000.] 5 2.0 7.54e-03 ** \n", + "s(1) [1000.] 20 3.3 1.11e-16 *** \n", + "f(2) [0.1] 5 4.0 1.11e-16 *** \n", + "intercept 1 0.0 1.11e-16 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "lam = np.logspace(-3, 5, 5)\n", + "lams = [lam] * 3\n", + "\n", + "gam.gridsearch(X, y, lam=lams)\n", + "gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is quite a bit better. Even though the in-sample $R^2$ value is lower, we can expect our model to generalize better because the GCV error is lower.\n", + "\n", + "We could be more rigorous by using a train/test split, and checking our model's error on the test set. We were also quite lazy and only tried 125 values in our hyperopt. We might find a better model if we spent more time searching across more points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For high-dimensional search-spaces, it is sometimes a good idea to try a **randomized search**. \n", + "We can acheive this by using numpy's `random` module:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "lams = np.random.rand(100, 3) # random points on [0, 1], with shape (100, 3)\n", + "lams = lams * 8 - 3 # shift values to -3, 3\n", + "lams = np.exp(lams) # transforms values to 1e-3, 1e3" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (100 of 100) |######################| Elapsed Time: 0:00:07 Time: 0:00:07\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearGAM \n", + "=============================================== ==========================================================\n", + "Distribution: NormalDist Effective DoF: 15.6683\n", + "Link Function: IdentityLink Log Likelihood: -24115.6727\n", + "Number of Samples: 3000 AIC: 48264.6819\n", + " AICc: 48264.8794\n", + " GCV: 1247.2011\n", + " Scale: 1235.4817\n", + " Pseudo R-Squared: 0.2939\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [137.6336] 20 6.3 7.08e-03 ** \n", + "s(1) [128.3511] 20 5.4 1.11e-16 *** \n", + "f(2) [0.3212] 5 4.0 1.11e-16 *** \n", + "intercept 1 0.0 1.11e-16 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "random_gam = LinearGAM(s(0) + s(1) + f(2)).gridsearch(X, y, lam=lams)\n", + "random_gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, our deterministic search found a better model:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gam.statistics_['GCV'] < random_gam.statistics_['GCV']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `statistics_` attribute is populated after the model has been fitted.\n", + "There are lots of interesting model statistics to check out, although many are automatically reported in the model summary:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['n_samples',\n", + " 'm_features',\n", + " 'edof_per_coef',\n", + " 'edof',\n", + " 'scale',\n", + " 'cov',\n", + " 'se',\n", + " 'AIC',\n", + " 'AICc',\n", + " 'pseudo_r2',\n", + " 'GCV',\n", + " 'UBRE',\n", + " 'loglikelihood',\n", + " 'deviance',\n", + " 'p_values']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(gam.statistics_.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partial Dependence Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the most attractive properties of GAMs is that we can decompose and inspect the contribution of each feature to the overall prediction. \n", + "\n", + "This is done via **partial dependence** functions.\n", + "\n", + "Let's plot the partial dependence for each term in our model, along with a 95% confidence interval for the estimated function." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, term in enumerate(gam.terms):\n", + " if term.isintercept:\n", + " continue\n", + " \n", + " XX = gam.generate_X_grid(term=i)\n", + " pdep, confi = gam.partial_dependence(term=i, X=XX, width=0.95)\n", + " \n", + " plt.figure()\n", + " plt.plot(XX[:, term.feature], pdep)\n", + " plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n", + " plt.title(repr(term))\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: we skip the intercept term because it has nothing interesting to plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pygam36", + "language": "python", + "name": "pygam36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/source/notebooks/tour_of_pygam.ipynb b/doc/source/notebooks/tour_of_pygam.ipynb new file mode 100644 index 00000000..c6e0dd05 --- /dev/null +++ b/doc/source/notebooks/tour_of_pygam.ipynb @@ -0,0 +1,1248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A Tour of pyGAM\n", + "\n", + "## Introduction\n", + "\n", + "Generalized Additive Models (GAMs) are smooth semi-parametric models of the form:\n", + "\n", + "$$\n", + " g(\\mathbb{E}[y|X]) = \\beta_0 + f_1(X_1) + f_2(X_2, X3) + \\ldots + f_M(X_N)\n", + "$$\n", + "\n", + "where `X.T = [X_1, X_2, ..., X_N]` are independent variables, `y` is the dependent variable, and `g()` is the link function that relates our predictor variables to the expected value of the dependent variable.\n", + "\n", + "The feature functions `f_i()` are built using **penalized B splines**, which allow us to **automatically model non-linear relationships** without having to manually try out many different transformations on each variable.\n", + "\n", + "\n", + "![Basis splines](pygam_basis.png)\n", + "\n", + "GAMs extend generalized linear models by allowing non-linear functions of features while maintaining additivity. Since the model is additive, it is easy to examine the effect of each `X_i` on `Y` individually while holding all other predictors constant.\n", + "\n", + "The result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Additive Models, in general" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "y \\sim ExponentialFamily(\\mu|X)\n", + "$$\n", + "\n", + "where \n", + "$$\n", + "g(\\mu|X) = \\beta_0 + f_1(X_1) + f_2(X_2, X3) + \\ldots + f_M(X_N)\n", + "$$\n", + "\n", + "So we can see that a GAM has 3 components:\n", + "\n", + "- ``distribution`` from the exponential family\n", + "- ``link function`` $g(\\cdot)$\n", + "- ``functional form`` with an additive structure $\\beta_0 + f_1(X_1) + f_2(X_2, X3) + \\ldots + f_M(X_N)$\n", + "\n", + "### Distribution: \n", + "Specified via: ``GAM(distribution='...')``\n", + "\n", + "Currently you can choose from the following:\n", + "\n", + "- `'normal'`\n", + "- `'binomial'`\n", + "- `'poisson'`\n", + "- `'gamma'`\n", + "- `'inv_gauss'`\n", + "\n", + "### Link function: \n", + "We specify this using: ``GAM(link='...')``\n", + "\n", + "Link functions take the distribution mean to the linear prediction. So far, the following are available:\n", + "\n", + "- `'identity'`\n", + "- `'logit'`\n", + "- `'inverse'`\n", + "- `'log'`\n", + "- `'inverse-squared'`\n", + "\n", + "\n", + "### Functional Form: \n", + "Speficied in ``GAM(terms=...)`` or more simply ``GAM(...)``\n", + "\n", + "In pyGAM, we specify the functional form using terms:\n", + "\n", + "- `l()` linear terms: for terms like $X_i$\n", + "- `s()` spline terms\n", + "- `f()` factor terms\n", + "- `te()` tensor products\n", + "- `intercept` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these, we can quickly and compactly build models like:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GAM(callbacks=['deviance', 'diffs'], distribution='poisson', \n", + " fit_intercept=True, link='log', max_iter=100, \n", + " terms=s(0) + te(3, 1) + s(2), tol=0.0001, verbose=False)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pygam import GAM, s, te\n", + "\n", + "GAM(s(0, n_splines=200) + te(3,1) + s(2), distribution='poisson', link='log')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which specifies that we want a:\n", + "\n", + "- spline function on feature 0, with 200 basis functions\n", + "- tensor spline interaction on features 1 and 3\n", + "- spline function on feature 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note:\n", + "\n", + "``GAM(..., intercept=True)`` so models include an intercept by default." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### in Practice...\n", + "in **pyGAM** you can build custom models by specifying these 3 elements, **or** you can choose from **common models**:\n", + "\n", + "- `LinearGAM` identity link and normal distribution\n", + "- `LogisticGAM` logit link and binomial distribution\n", + "- `PoissonGAM` log link and poission distribution\n", + "- `GammaGAM` log link and gamma distribution\n", + "- `InvGauss` log link and inv_gauss distribution\n", + "\n", + "The benefit of the common models is that they have some extra features, apart from reducing boilerplate code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Terms and Interactions\n", + "\n", + "pyGAM can also fit interactions using tensor products via `te()`" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "from pygam import PoissonGAM, s, te\n", + "from pygam.datasets import chicago\n", + "\n", + "X, y = chicago(return_X_y=True)\n", + "\n", + "gam = PoissonGAM(s(0, n_splines=200) + te(3, 1) + s(2)).fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and plot a 3D surface:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits import mplot3d\n", + "\n", + "plt.ion()\n", + "plt.rcParams['figure.figsize'] = (12, 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "XX = gam.generate_X_grid(term=1, meshgrid=True)\n", + "Z = gam.partial_dependence(term=1, X=XX, meshgrid=True)\n", + "\n", + "ax = plt.axes(projection='3d')\n", + "ax.plot_surface(XX[0], XX[1], Z, cmap='viridis')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simple interactions it is sometimes useful to add a by-variable to a term" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearGAM \n", + "=============================================== ==========================================================\n", + "Distribution: NormalDist Effective DoF: 20.8449\n", + "Link Function: IdentityLink Log Likelihood: -2317525.6219\n", + "Number of Samples: 50000 AIC: 4635094.9336\n", + " AICc: 4635094.9536\n", + " GCV: 0.01\n", + " Scale: 0.01\n", + " Pseudo R-Squared: 0.9976\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [0.6] 20 19.8 1.11e-16 *** \n", + "intercept 1 1.0 1.79e-01 \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "from pygam import LinearGAM, s\n", + "from pygam.datasets import toy_interaction\n", + "\n", + "X, y = toy_interaction(return_X_y=True)\n", + "\n", + "gam = LinearGAM(s(0, by=1)).fit(X, y)\n", + "gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression\n", + "\n", + "For **regression** problems, we can use a **linear GAM** which models:\n", + "\n", + "$$\n", + " \\mathbb{E}[y|X]=\\beta_0+f_1(X_1)+f_2(X_2, X3)+\\dots+f_M(X_N)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:01 Time: 0:00:01\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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1y0IIUR5nzsDPP5tEMNiH4w4XXghXXmnqBb/+uuzHaw1vvmlGpo8dM5Nz3JkoF1i+HC6/HLKz3X9uIYTP6Xwr+cpCsCx3LYQQAjAr2GVlmUU4fMliMSvqDR4Mt90G337r+rFnz5oykscfh86doYb7bls6vNavv5pYhRABR1utWC0Wp+sVVWYB+C0JIYQXrFtnRmC92QXDmfBwU2Pct69ZsCQpqfRjtm0zdcQTJpjuGpMnm84ennLhhWZZ7AkTPHcNIYTv5OdjU0EEWdwwd8LPSLIshBDl8a9/wYEDnk0wy6JaNbPk9tSpZpQYYPdu58tNP/+8aef244/w4ovOl692F6XMcuDLlsHmzZ69lhDC6/JDw0iJrE6wJMtCCCH+Eutnk1hCQ80S02AS0latTKeOW24x5Rb9+5tkFUxLt40bYeRI78V3662mFZ2MLgsRcLb+/XH63fsFFnd05fEzkiwLIURZffKJmVSXk+PrSJxLTITx483EvaVL4bffzKTEggl2TZtC3brejSkhAR5+GNq08e51hRAeZ7WZu1iBWIYhreOEEKKsfvjBlGC4ow+xp0RFwR13mA9/8vrrvo5ACOEBjad+xeu/z8fyxCBfh+J2MrIshBBlceaM6W08YoSvI6m8MjNhyRJfRyGEcKMaWzcwYO8agoMCb2RZkmUhhCiL5cshNxeGDPF1JJXXE0/AsGGm9Z4QIjBYrVhVEEFSsyyEEFXcggWms0Pfvr6OpPK6/npTO/3rr76ORAjhLlYrNosFSwDWLEuyLIQQZdGwIYwZU64FPHLyrGw+nMbyPSlsOZxOvtXmgQArgQsvhHr1ZIESIQKJ1Uq+xRKQreNkgp8QQpTFnXeajzI4np7D/+btYvq6w6Sdyfvr+cjQIIa3q8NdFzahXb0Yd0fqv4KCzMqHn3xi6perVfN1REKICsqpXoPT1ROIl2RZCCGqsPR008u4DF0wFuw4wSNT1pGRm8+IdnUY1q42sZGhnMjIZcXeVH5ed4gfkw5xdef6PHt5W2pEhnrwG/AjV10F770H8+fDZZf5OhohRAUtfuBZXp2xjc0BWLMsybIQQrjqvffg5Zfh+HGIji519183HOaBSUm0rBXN5LG9aFH7/GNGdq7PUyNaM37Rbj5ZsIeFO0/ywc2d6dk03lPfgf/o188sitKuna8jEUK4gVUHbp9lqVkWQghXLVwILVq4lCivSk7lkSnr6doolp/u61ssUS4QExnC48NbM/3+vlSPCGbUhBVMWbXf3ZH7n+BgaN/eTJYUQlR63T97m//M+sgky8uXQ5cuxT+WLjU7z53rePu6dWb7L7843r5jh9k+ZYrj7YcOnTu/G8nIshBVjFKqIfAVUBvQwHit9TilVBwwBUgEkoHrtdanfBWn37FazR+AUaNK3TUlM5e//d8a6sdG8Omt3YgIDSr1mHb1Yvjx7325/9u1PPn9RjJy8rnrwqbuiNx/HT4MTz8NY8dKdxEhKrkGa5bQacdmgtasMaVqDRoU3ykszDw62x5qL0OLjCx5e1SU4+3B9rQ2IqLs30AJJFkWourJBx7VWq9VSkUDa5RSs4HbgDla69eUUk8BTwFP+jBO/7JtG2RkQK9epe768m9bycjJY9LdvYiNcr0GOSYihM9v686Dk5N46bet2LRmbP9mFYnav8XEwNSpprOIJMtCVGqW/HxCbFbQNujUCX7+2fnOffqUvH3IkJJ72V92WclzHXr3Lj3gMpAyDCGqGK31Ea31WvvnGcBWoD5wJTDRvttEYKRvIvRTK1aYx549S9xt8c6T/JB0iHsGNKNVndLLNYoKCbLw3o2duaxjXV75fRvTVh8oT7SVQ1SU+YP422++jkQIUUHKajWfBJV+J62ykWRZiCpMKZUIdAZWALW11kfsm45iyjQcHTNWKbVaKbX6xIkTXonTL/TtC2+9ZWqWnbDaNM//spkmNaO4b1Dzcl8qOMjC29d3ol/zmjz1w0YW7Ajgn/PFF8Pu3bBrl68jEUJUwF/JcnDgFS1IsixEFaWUqgZ8DzyktU4vvE1rrTH1zMVorcdrrbtprbslJCR4IVI/0aoVPPIIWJz/2vxj01F2Hc/k0WEtCQ+p2OhKaLCFj0Z1oWXtaO7/di27T2RW6Hx+a8QI8zhjhm/j8FNKqXCl1Eql1Hql1Gal1Av255sopVYopXYppaYopapIz0Hhr7JiYs0nMrIshAgESqkQTKL8jdb6B/vTx5RSde3b6wLHfRWf38nONvV1p0873UVrzftzd9I0IYqL29d1y2Wjw0P49NauhARZuPur1aTn5JV+UGXTrBkMGwYhIb6OxF/lAoO11hcAnYARSqlewOvAO1rr5sApoGwr5QjhZnNGP8yMdgMgNtbXobidJMtCVDFKKQVMALZqrd8utOlnYIz98zHAdG/H5rdWrYIrr4QlS5zuMmfrcbYdzeC+gc3d2me0QWwkH97ShX0p2TwxbQNaOxzwr9xmzoR77vF1FH5JGwW3FULsHxoYDHxnf17mGAif29+yA09d90/HXSoqOUmWhah6+gKjgcFKqXX2j0uA14ChSqmdwEX2rwW4NLnv8yV7qV8jgis71XP75Xs1jefJEa34Y/NRJi5Ndvv5/YLValZIFMUopYKUUuswd3tmA7uB01rrfPsuBzGTdB0dWzXnGAivG/nuP3ls1qe+DsMjAq8KWwhRIq31YsDZ0GcJvXqqsJUroWlTqFnT4eZ9KVks3Z3CY8NaEhzkmTGIu/o1ZcWeVF7+fStdG8fRoUGMR67jqoIRbuWORUWsVkhMhGuugXffrfj5AozW2gp0UkrVAH4EWpfh2PHAeIBu3boF4G0J4S8SN62iQ8px2L8fGjXydThuJcmyEEKUZs0a6NHD6eYpqw5gUXBt14YeC8FiUbx1/QVcPG4R/5icxK8P9CMqzLu/wrNy85m0cj9zth5n/cHT2LSmVnQ4F3eowx19m1C7enj5ThwUBK1bw59/ujfgAKO1Pq2Umgf0BmoopYLto8sNgEO+jU5UdRablWBtK3ESdGUVeN+REEK4U0oKJCdD164ON+dZbUxbc5DBrWtRJ6acyaKLakSG8s4NnUhOyeK5nzd79FqF2WyaL5bspc9rc3npt62k5+RxbdcGjO7VmBa1qvHpwj1c+N95/JRUgXztootg82Y4cqT0fasQpVSCfUQZpVQEMBTTG30ecK19N5ljIHxOWW3mkwBsHRd435EQQrhTbCxs2WJWmXNg/vYTnMjI5Ybu3rnt2KtpPPcPas77c3dxYYuaXNnJYamq2xw+fYaHp6xjxd5U+rdM4JGhLenU8Pyfxf6UbB7/bj0PTVnH0fQc7hlQjlUHhw6Fp54yo8ujR7sp+oBQF5iolArCDHBN1Vr/qpTaAkxWSr0EJGEm7QrhM0rbk+UAbB0nybIQQpTEYoE2bZxu/nXDYWIjQxjYyns9px8c0oKlu1N45sdNdG4YS6P4SI9cZ1VyKvd+vYacPBv/vbYj13Vt4LBGuVF8JF/d2YNHp67ntRnbaBwXycUdytg+r1MnUxMuyfJ5tNYbMAsHFX1+D+C8NkgILzsdW4vo9FMBmSxLGYYQQpTk009h2jSHm3LyrMzZepxhbesQ4qGJfY4EB1kYd2MnlIIHJq0lN9/q9mv8lHSImz9dTnR4CD/d14fruzUscTJfWHAQb1/fiQsaxPDE9xs4kJpdtgtaLPD223CntAsWojL68aYH+b3npRDu2XI0X5BkWQghSvLqqzB1qsNNi3eeJDM3n4s71PFyUKb/8hvXXsD6g2m8+OsWt51Xa81H83fz0JR1dG0cy09/70vzWtEuHRsabOH9m7qAhkenrS97T+jRo6F//3JELYTwtS1tezDuhicg0jN3unypwsmyUqqhUmqeUmqLfSnOB+3PxymlZiuldtofA29JFyFEYEtNhb17nU7u+33TEaqHB9OnmeOWcp42on0dxvZvytfL9/P9moMVPp/Vpnnhly28/sc2Lr+gHhPv6EFMZNlW1msUH8lTl7Rm5d5U5mwt4yKQWsPChef6WgshKo2HX76bUTO/9HUYHuGOkeV84FGtdVugF3CfUqot8BQwR2vdAphj/1oIISqPtWvNo4Nk+Wy+jdlbjjG0bR1Cg313k+6J4a3o3TSep3/YyPI9KeU+T06elQcmreXLpcnc2a8J427oRFhw+WoPr+/WkCY1o/jvzG1YbWUYXVYKbrsNXpP1cISobBL3bmX0zC8gP7/0nSuZCv+G11of0VqvtX+egWlpUx+4ErMEJ8hSnEKIyqggWe7SpdimFXtTyMjJZ0R775dgFBYcZOHjUV1pFB/J2K9Ws/1oRpnPcTwjhxvGL2fGpqM8c0kb/n1ZWywVWLI7JMjCY8NaseNYJj+WtZ3ckCEwf75ZqEQIUWmogrIrmeBXMqVUImbW7gqgtta6oGHmUaC2k2NkKU4hhH/atw8aN4b4+GKbFu44QWiQhb7Ni2/ztpjIEL68vTsRoUHcMH4ZSftPuXzs4p0nuWTcYnYczeDjUV25u39Tt8R0cfs6tK1bnU8W7C5b7fLgwXD6NKxb55Y4hBDeobQNDeYOUYBxW7KslKoGfA88pLVOL7xNm9+UDn9baq3Ha627aa27JSR4r/WSEEKU6oMPTI9lBxbsOEH3JrFEhvpHB84GsZFM+1sfYiJCuOWzFUxZtb/EJDUlM5dnftzI6M9XUCMyhB/v68Pwdu4bJbdYFLf3TWTn8UyW7i5DecigQeZx7ly3xSKE8DylNZrAS5TBTcmyUioEkyh/o7X+wf70MaVUXfv2ukAZZ3oIIYQfcDCz+/DpM+w4lsmAlv71Br9RfCTT7ulN+/oxPPn9Rm4Yv5zp6w6Rlp2H1pqs3HxWJ6fy7582MfCN+UxedYAxvRP5+f6+tK5T3e3xXH5BPeKiQvlyabLrB9WpA23bwqJFbo9HCOE5J+LqYPNiC01vqvCQiDKNNycAW7XWbxfa9DNmCc7XkKU4hRCVzZo18OKL8MYb0KLFeZsW7TQlYwNa1vJFZCWqFR3O5Lt7MWnVft6fs4sHJ5tyhpAgRZ7VjDSHBVsY0b4O9w9qTovarrWFK4/wkCBu6tGQj+bv5kBqNg3jXGwp9euvUN+zKxMKIdzry2seoF3yJq7wdSAe4I77h32B0cBGpVRBkdk/MUnyVKXUncA+4Ho3XEsIIbxj6VKYPt2UYhSxYMcJ6lQPp2Xtaj4IrHQWi+KWno25qXsjViansv7AaU5l5xEVGkSL2tH0bhpf5pZw5TWqV2M+mr+bKasO8NjwVq4d1KSJZ4MSQrjdivZ92dJtgCTLjmitF4PTIpUhFT2/EEL4RFISJCRAvXrnPW21aRbvPMmI9nVKXNHOH1gsil5N4+nV1HeTEOvGRNCvRQI/Jh3ikaEtXeuyoTU88YQpx7j9ds8HKYSoGK357JmrmDv0erizp6+jcbvALC4RQoiKSkoyLeOKJMRbj6STnpNP3+a+WYikMrqqcz0OnT7D6n0udulQCmbPhq+/9mxgQgj3sFqpmXaCK3753NeReIQky0IIUVRuLmzaBJ07F9u0Ym8qAD2axHk7qkpreLs6RIYG8WNSGVYZHDTIlMLk5nouMCGEe9j7oms/v9tWXpIsCyFEUSdOQLdu0LP47cSVe1NoFBdJ3ZiIsp1Ta1i1CsaNg9dfh4kT4VAZF+yopCJDgxnerg6/bjhCTp6Li40MHAg5ObB8uUdjE0K4wV/JcmCmlf7RIFQIIfxJgwawbFmxp7XWrNybypA2DtdYKtn06XDVVcWfnzPHLMQR4EZ2rs+PSYdYsOOEa/2c+/c35Rjz58OAAR6PTwhRAfYlrrUlMJPlwPyuhBCiIpws5rHreCansvNcL8HQGg7aSw+GD4ePP4ajRyEry6xQN27cuUTwt98gObnisfupPs3iiYkIYebmo64dEBsLQ4c6/bcQQviRoCCOxdQiP8Q7XXa8TUaWhRCiqKFDoXlzk9wWUlCv3NPVZPmDD+CFF2DDBqhbF/72t3PbLrjAfACcPQtjx5plnidMgBtvdMd34VdCgiwMaV2LOVuPk2e1EeLK4gUzZ3o+MFekpprSnBYtIEBHzoSokKgo3rvsXroFZ+Pg/lmlJ696IYQozGo1JRhhYcU2rdzb0qyrAAAgAElEQVSbSu3qYTRyZXGNmTPhwQehTx+oVcriJaGh5ppdusBNN5m2aTZbOb8B/zW8fR3SzuSx0v6mw2VWF+ucPeXnn6F1a9ixw7dxCOHH5rW7kKWX3uLrMDxCkmUhhChs507IzjaJaxGrk1PpnhhXen/lI0fM6HCHDvDNNxAUVPp1GzUy9cv33GNWDbz33oBLmPu3SCA8xOJ6KUZeHrRpY0bnfSkpySx7XmQlRyGE3ZEjzHrhCvrOnOzrSDxCkmUhhChs7VrzWKRt3LH0HA6n5dC5UWzp53j4YThzBqZOhWplWOUvNBQ+/BCefhoiIor1eK7sIkKDGNAygZmbj2KzuVCLHBIC0dFmkp8vJSVBx46uvekRoirKy6NabjYDp3/p60g8QmqWhRCisKQkU4LRps35T+8/DUCnhjVKPv7sWTMp7ZlnoGXLsl9fKXj55XOfZ2aWLeH2c8Pa1mHm5mNsOZJO+/oxpR8wcCC8+64Z7Y90ofzFE/r3h5qyCI0QThW0jrME5htKGVkWQojCOnWC++83o5qFrDtwmpAgRbt61Us+PjQUpkyBf/2r/DEoZT527YJWrWBy4NzaHNAqAaVg3rbjrh0wcKApx3DQys9rXnoJHnrId9cXwt/ZW8fZJFkWQogq4JZb4M03iz297sAp2tatTnhICX8MliwxK/+Be0ooGjaEpk3hjjtg48aKn88P1KwWRsf6Mczb7mKy3K+fKX+YN8+zgTmTnm7uFgghnCuYhGsJrNKxApIsCyFEgexs0yasCKtNs+FgWsklGDabaQ13443u6w0cFmbqnmNi4OqrIS3NPef1sYGtapF04DSpWS4kodWrw7PPwoUXej4wR157DeLizOi2EMKx6tU5ERVLXngZVzatJCRZFkKIAjNmQHy8qVsuZMexDLLPWunUqIRk+aefYPNm+Oc/3Tsxr25dmDbNLFhy110BsUjHoNa10BoW7Tzh2gHPPmsWdfGFpCRo1qxYWY4QopB69Xhp6FhWXH+3ryPxCEmWhRCiwJo1EBxcbHLfugMFk/tK6IQxbpwpmbjhBvfH1a+fmfSXlWW6bFRyHevHEB8VyvztLibLWsPWrbB/v2cDcyQpqVhnFCFEcb+0vpBdgy71dRgeIcmyEEIUWLMG2reH8PDznl5/4DQ1IkNIjHfSjWHHDli4EO6+23PtxR57DH791XcdIdzIYlH0b5nAgh0nXGshl51tWrd99JHngyvs8GE4dkySZSFKoVetYufrl9P2z+m+DsUjJFkWQggwo5dr1kDXrsU2bTiYRof6Mc4XI9m0CWJjYcwYz8VnsZiP/fvh9tvNKHMl1q95TVKzzrL1aHrpO0dFQY8e3p/kt3q1eeze3bvXFaKSsZ7NI0hrOv/8ta9D8QhJloUQAkwSmpJSLFnOybOy41gGHUrqCXz11XD0qKkv9rSdO2HiRLOUdiXWt7npW7xk10nXDhg0yCSvGRkejKqItm3h1VdNO0EhhFM2e+s4HRSYy3dIsiyEEGC6Lnz6KQwbdt7T245mkG/TzpPlghri0FAPB2g3ZAg89RRMmGD6OVdSdWLCaVGrGot3pbh2wKBBpj3VokWeDayw5s3NzzoASl+E8CSdb1+UJEBXuZRkWQghwJRR3HWX6XxQyMZDpl2b09Xm7r7bTMDzphdegF69YOxY2LvXu9c+dgzWrYM9e871Vi2nvs1rsnJvCrn5LpynTx/zhmTu3Apd02Vaw8yZDlsJCiHOZ/1rZFmSZSGECFx//mlWzCti08E0akSG0CDWQf/Q3Fz45ZfyLWtdESEh8O235vNnnvH89ZKTz42gT5liJrw1a2a6f7zxhpmAVw79mtckJ8/G2n2nS985IgJmzTKt+bxh3z4YMaJSj94L4S3WOvVIC4skPzLK16F4hCTLQgihNdx0k6lPLWLjoRIm982da1Z4u+YaLwRZRJMmpi/0xx977hqpqXDvvSYxLkjOr78evv8exo83yfITT5jSkHKsctezaRxBFsXiXS62kBswwCwQ4g0yuU8Il+U1b8Ezw+9n2w13+DoUjwjMSmwhhCiL5GQ4edJ0XCikYHLf3a2aOj7uhx8gOtoki77Qp495PHMGtmxx2Mmj3H76yZSlnD4N9913blGQOnXMhEYwJSg//WRG5MtRsx0dHkKnhjVYutvFuuWsLPjf/8z37ekV/VavNiP4HTp49jpCBID8s2eZ3bwnPbsFZptFGVkWQogVK8xjkWR5e0mT+/LzTaJ46aXF+jJ73b33mglwW7a453zPPQdXXWVGr5OS4L33oEEDx/uOHGl6QIN502GzlelSvZvGs+FgGpm5+aXvHBZmFmcpGOX2pFWrTG/nsDDPX0uISi5kxgy2v30NDZbN93UoHiHJshBCrFxpEt727c97etNhM7nPYbKstUkiH3jAGxGW7MUXTceGK65wz4S0iy+GJ5+EJUtcH1ndsQPatYO33irTpXo1jcdq06xOdiHu4GBTijFnTpmuUWZWq0mWpQRDCJfYrObNbvPfv/NxJJ4hybIQQqxcCV26mNvuhWw6lE5MhJPJfSEhps65oBTClxo2NCUhBw7AJZeYOuqy+uMP+M9/zOe9esFrr5WttKJFC5NkP/OM6ZThoq6NYwkJUizb42IpxuDBptf0gQOux1ZWFgssXQqPPOK5awgRQGx59tZxwdINQwghAtN335kJa0VsOZxG27rVHU/u++Yb2L3bC8G5qE8f07lhzRq4owyTbLKzTRnFJZeYhLug60VZKWVG2oODTRmHiyJCg+jUsAbL97g4Il5QH/7nn+UI0kVKmbsMLVp47hpCBBBtH1kmODCnwkmyLIQQdeqYEoJC8q02th3NoF296sX3T0mB0aPhaz9b2nXkSNOpoqCrR0mJ79mzZiXA9u1N6cTYsbBsmWnRVl716sE//mHeSGzY4PJhvZrGs+lQGhk5eaXv3KEDNGoEBw+WP87STJxofo5CCJfY8gqS5ZCSd6ykJFkWQlRtf/4Jb75ZrPXZ7hNZ5ObbaFffQbI8Z46pWS7oEOFPrrji3IjoXXeZpZpffBEmTTIj6LNmmW0ZGXDPPRAVBQsWmBZ0FUmUCzz5pGnvtmCBy4f0/qtu+VTpOytlRvT//e8KBFmKV16Br77y3PmFCDA5LduQp4KwRgTmapeBOV4uhBCumjzZdLV49NHznt5sn9zXrp6DyX2zZkGNGtCtmzciLL8hQ8zEu2efPfdc//5mSe/4eFi7Flq3Ngmou8TGmmQ2xsmKhw50sdctL9+TwqDWtUo/wJO3ek+eND+z22/33DWECDDZbTvw0OWPcfM1g2ni62A8QEaWhRBV29Kl0LNnsYRx8+F0wkMsNK1ZZEUqrU2yPGSI/9fn3XGH6eqQmQmbNsH69TB16rntbdq4N1EuUJAon3JhpBgIDwniggY1WLHXxbrl3FyzxPgbb5QzwBIsX24ee/d2/7mFCFA6O4vljTqQ3SYw+5JLsiyEqLpSU2HrVujbt9imzYfTaF2nOsFBRX5NHjwIhw75biGS8oiKMjXZHTtC7dreueaLL5pyEBcnDPZoEsemQ2lkudpvOSsLfv+9gkE6sGwZBAVJ2zghyqD6lG9Z879R1Ni6zteheIQky0KIqmvpUvNYJFnWWrP5cLrjyX0NG5ok+5ZbvBBgJdavn5kIOWWKS7v3bBpPvk2TtP+0a+cfNsz0gc7MrECQDuzZA507m77VQgiX6DwzObfWrBk+jsQzJFkWQlRdyckmKSoyingg9QwZOfmO65XBlBlUd5BIi3MGDoRWreCLL1zavWvjWCwKVu51sd/yiBGQl+f+BUomTYJ589x7TiECnT1ZViF+XppWTpIsCyGqrvvvN6PERUYRtxwpmNznICG+7TaYNs0LwVVySpnR90WLXGrzVi0smPb1Y1yvW+7Xz7xh+fXXCgbqKJhq7j+nEAFM27sJ6bIsZFSJSLIshKjawsKKPbX5cDpBFkWrOtHnbzhyxPTg3b/fS8FVcjfeaCZEFp5UWIIeiXEkHThNbr619J1DQkybun79KhhkIW++aVZl1Np95xSiKjhrH1n290nP5STJshCialqzxrRR27Sp2KbNh9NplhBFeEiRpVsXLTKP/ft7IcAA0KIFfPstjBrl0u49msRxNt/GhoNprp3/n/+EMWMqEGARP/4Ie/d6pkOIEAEsrZNpo6kcDD4EAkmWhRBV08KFJvmNiyu2afPhNMf1yosWmc4SnTt7IcAAcdNNUMuF3slA90Tzb7HS1VIMMH2RN24sT2Tny8yElSth0KCKn0uIKia1ay/uuOZZ0i++zNeheERgjpcLIURpFi6Epk3NEs2FnMzM5Vh6ruN65UWLTP/dAL3V6BFaw6efmkVQrrmmxF1jo0JpUasaq5LLkCxfcQXk55tEtyKWLjXnGTiwYucRogpSmRnsrNkIa2IgLkkiI8tCiKrIZjPJsoPEaMvhdADaFk2WbTZISICLLvJCgAFEKRg/Ht5+26XduyXGsWbfKaw2F+uGL7vMLLziwiTCEs2ebeqgHfTcFkKUrOHnH7Lgk7sIP1LB16GfkmRZCFH1bNpkumAMGFBs0+aCZLlukWTZYjEJ1ZNPeiPCwHL55Waxj5MnS921R5NYMnLy2X40w7VzX3utefzuuwoECDRoAHfe6XedMJRSDZVS85RSW5RSm5VSD9qfj1NKzVZK7bQ/xvo6VlF1WTIzsQDRixf4OhSPkGRZCFH15OWZPr0Ok+U06teIoEZkkRZI0iGh/C67zPz8XFhxr6Bu2eVSjJYtzcqEFW3n9+CD8NFHFTuHZ+QDj2qt2wK9gPuUUm2Bp4A5WusWwBz710L4Rr69G0ZQUCk7Vk6SLAtRxSilPldKHVdKbSr0XNUaperaFWbMgMaNi23acsTJyn033ghXX+2F4AJQ585Qt65LPZEbxEZSLyaclWWpW77uOjNyfexY+eI7dAjsfWL9jdb6iNZ6rf3zDGArUB+4Epho320iMNI3EQoB6q9FSUJ8HIlnSLIsRNXzJTCiyHNVZ5RKa1OC4UBWbj57T2YVr1cGWLwYIiI8HFyAslhMKUZqqksj9N0S41idnIp2dTR/7FjYtQtq1y5ffLfeWinaASqlEoHOwAqgttb6iH3TUcDhN6+UGquUWq2UWn3ixAmvxCmqoPx8ACzBMrIshAgAWuuFQNFsseqMUm3ZAjVrwg8/FNu09Ug6WkP7om3jDh6Ew4ehZ08vBRmAPvwQ/vzTpR7G3ZvEcSw9lwOpZ1w7d61aprNJeaSlOZ3s6U+UUtWA74GHtNbphbdp867C4TsLrfV4rXU3rXW3hIQEL0QqqqIj3S8EZGRZCBHYXBqlCghz5pjRzS5dim3adMgshtG+fpFkecUK89irl6ejC1wFtYwujBb3KGvdMsDu3TByJGzeXLa4fvnFjIpdfnnZjvMipVQIJlH+Rmtd8C7vmFKqrn17XeC4r+IT4sCgEdxw06vk97vQ16F4hCTLQojzlDRKBQFwW3fOHGjWDBITi23adDidmtVCqV29yCpUy5dDaChccIF3YgxUTzzh0ghui1rViIkIKVuyXL26mUA4YULZYpo4EZo0Mf2z/ZBSSgETgK1a68L9934GCpYvHANM93ZsQhQIOpVKSmQMFhcXIKpsJFkWQkAZRqkq9W3d/HyYN89pr+RNh8zKfapoqUD37vDYYxCgS7l6TbVqZmGXlJQSd7NYFN0ax5YtWU5IMCPLX30FubmuHXPwoHnzNGaMqav2T32B0cBgpdQ6+8clwGvAUKXUTuAi+9dC+ESnj/7Ld988QfCxI6XvXAn57W8HIYRXVY1RqlWrICPDYbKck2dl5/FM2td3MLnv+uvh5Ze9EGCAGzrUlGHMnVvqrt0S49h9IouUTBcTX4C77jKJ+E8/ubZ//fomeb/7btev4WVa68Vaa6W17qi17mT/+F1rnaK1HqK1bqG1vkhrXYZ3FkK4V0j6aWrkZBKypYxlUJWEJMtCVDFKqUnAMqCVUuqgUupOqsooVdOmZqLZ4MHFNm0/moHVpotP7svIMJP7RMV1727KJf78s9RdezQx3QtX7zvl+vkvusj8G7/5pmt9sZUyK/YVWfJcCFE2Kt8KgCVUJvgJIQKA1vomrXVdrXWI1rqB1npClRmlql0b7r0X4uKKbdp02Mnkvl9/NSOQGzZ4I8LAFhwMgwaZlRBL0b5+DKHBFlbtLcN/RYvF3AG46qq/Wlk5NX063HOPeTMkhKgQZbW3jgsK9nEknhGY35UQQhSVlWWWRL70UtM6rohNh9KpHh5Mg9givZTXrDG1ym3aeCnQAHfbbbB+vVlFsYQ2U2HBQXRqWKNsdctgFo8pjdUK//yneZTe2UJUnNU+shwifZaFEKLymj/fJGpJSQ43bz6cRvv6Dib3rVljllMO0P6hXjdyJDz3nEs/z+6JsWw+nE722VJGiYuy2WDKFOcrBn77rem3/eKLZrRbCFEhe3sNAiAoQH9PSrIshKgaZs40o4gXFu8DejbfxrYjGXQoWoJhs8HatdCtm5eCrCIyMmDTplJ3654YR75Nk7T/dNnOb7PBO+/AzTeblf0KO3zYjCp37gzXXFO28wohHNpwyQ1cOuZdLB07+joUj5BkWQhRNcycCQMGQHh4sU3bjqZz1mqjY4Ma52/YtQvS06FrVy8FWUWMGQNXXlnqbl0bx2JRsLIsdctgRounTjWj19dea1rEFbj7bjh9Gj77zJ/bxQlRqUQdO4RWFizVo30dikfIbwohROBLToYdO2DECIeb1x80k/s6NigyspyQYPr2Dh3q4QCrmP79Yc8eOHCgxN2iw0NoU7d62ZNlgEaN4JtvYOtWaNny3PLmH38Mv/3mcAVHIUT5DHr/P4z/4aVSe6hXVpIsi+K0NiNqmzebJMOVFkxC+LOlS83j8OEON284cJq4qNDik/tiY2H0aJN4CfcZMMA8LlhQ6q7dE+NIOnCKs/m2sl9nxAjYvh2uvhqWLDHPNWxoknUhhNuEZ6bTIP04HJFFSURVccMN0KIFtG9vloFt1Qqef/6v2a5CVDo332xGMVu1crh5w8E0OjZwMLlvxgwzMincq2NHqFHDpWS5Z5M4cvJsf7X2K7PERPj6a3jrrfIdL4QolbLZ84MAnTArybIwfvkFMjPN57fcYm5VTp0K779v/ths2ABBgdkSxuu0hn/8w6wmJ7ynQQOzCEURWbn57DyeUbxeWWszqvz2214KsAoJCjITLV1Ilrslmp7YZeq3LITwKmW13/kJ0DxBkmVhEuIrrjAJMpiJN3/7G1x3Hdx/P8yaZRJngN274Y03pDSjIt57z/zMFy/2dSRVw/Ll5jZ8crLDzZsOpWHT0KlhkXrlgwdN/V3nzp6PsSp6/nmYPLnU3RKiw2haM6p8dctCCO+QkWUR0N56y4xyXnmlSYydKXgBfPEFPPGEmVFuK0cNYVW3bBk8/rh5c/LQQ76Opmr4+WfzERvrcPOGvyb3FRlZLujH3KmTJ6Orurp0cXmSXY8mcaxKTsVqkzfpQvijLb0Gm09kZFkEnF9+MYnbtdealc0ctNQq5sUX4ZlnYMIESZjLY+pUM8Hoiy8clgQID/jjD+jTB2JiHG5ef/A09WtEULNa2PkbkpLMv1GA9g31Cz/8cK5LRQl6No0jPSefbUfTvRCUEKKs5l49lmse+gLq1vV1KB4hyXJVdfasGUnu0gUmTnT91olSJmF+9ln4/HO47z4pyXBFwc/o7bdNWUBcnG/jqSqOHjVJ78UXO90laf9pOjWq4WBDkmk5Vq2aBwOs4saNg9dfL3W3nk3iAVixR0oxhPBHtfZuo9rZMwG70qlbkmWl1OdKqeNKqU2FnotTSs1WSu20Pzq+Byp8IzQUZs+Gn36CyMiyHasUvPACPPmkSSiysz0TY6DYtg369YN9+8zPLiHB1xFVHbNmmUcn/ZWPpuVw6PQZujZy8OtpwgRzx0V4Tt++ZoXErKwSd6tXI4KGcRGs2BuYPVyFqOyu+/B5nvv+v3DmjK9D8Qh3jSx/CRT9a/QUMEdr3QKYY/9a+IOkJDPS2bKl6RBQXq++CvPmQVSUlGM4ojV8+SX07m0mRp4u45K9ouLCwuCii+CCCxxuXrv/FABdGjtIluPjTftE4Tn9+kF+PqxcWequPRLjWbk3FZvULQvhd8LOZNH0WDLk5Pg6FI9wS7KstV4IFL0/diUw0f75RGCkO64lKmjdOuje3T3tsJSCiAjTcm74cPi//6v4Of1Naqppb/X996ZcJc3e6zUtzWxzVoLy44+mNdbtt5uEa+lSpwmb8KAbbjB3UJwsa7xm3ynCgi20rVv9/A0bNsBLL8HJk14Isgrr3ds8utAZpmfTOE5l57HzeKaHgxJClJXS9gEz6YZRZrW11gVLuRwFanvwWsIVNhvcc4+pl739dvedNzjYnPv222H6dPed15dmzDALWMTHw8CBZhLkbbfBiRNm+/jxZlv16mYBl169oEePc72qZ82Cw4fhww9Nst20qa++k6orPR1yc0vcZe3+U1zQoAahwUV+Ff75J/z731KP72mxsebN5JYtpe7aq6BuWUoxhPA7yiZ9litMa60Bh391lFJjlVKrlVKrTxQkIsIzPvsMVqww7eLcOcEsPNzUPnfrBtdff65OtLLZvNks8w2mrrhxY3jtNdNNYf16s61xY7N96FDzc7zzTvN9x8SY5LngF8Z//2v2v/dep6OawsPef9/8m6Q77qCQk2dl06E0Ojd2MLlv3TqoX1/qy71h0SL49ttSd2sYF0G9mHCW75FkWQh/owoGFgJ0ZNmT39UxpVRdrfURpVRd4LijnbTW44HxAN26dZNhHE85eRKeesqMko4a5f7zR0eb0dhBg0zP5p9/NgllZZCTAy+/bBLjm26Cr74yCXBJSX+nTiX3342Odn+comxmzjR1+dWrO9y86VAaeVbteHLfunXSX9lbajh4s+KAUopezeKZv/0ENpvGYpHWi0L4izVdB9J/4c8yslwOPwNj7J+PAQLk/nwltWeP+aP0v/95rr9vbKy5fT1ggBmVqwySk82M/JdeMomyLG0cGNLTzQIww4c73WXNPieT+3JyYOtWSZa9JTMTbr4Zpk0rddc+zWqSmnWW7ccyvBCYEMJVk256hNEv/hCwd1Ld1TpuErAMaKWUOqiUuhN4DRiqlNoJXGT/WvhKjx6wcye0a+fZ69SsacoW2rY19Z7z53v2ehWxahV07Wo6Vfz0kxlRrlnT11EJd5g3z3RZKCFZXpWcSpOaUcUXI0lONrcSJVn2jqgoMwlzxoxSd+3dzNQtL9stpRhC+JPWm1aSkJ4asIttuasbxk1a67pa6xCtdQOt9QStdYrWeojWuoXW+iKttXST9wWtTRKYm+v92yOTJ5uyjIcfNomLv2nWzMS3erUpHRGBY/Zsk4T16eNws82mWZV8ih6JDmr3W7eGjAyzJLnwPKXMv9PSpaXuWr9GBI3jI1kmdctC+JW7J7zAPVPf8nUYHhOY4+XinO++gzFjYNIk71/7uuvgH/+Ad9819ctHjpR+jDf8+ad58xAXZ34+zZv7OiLhbnfcAZ98YhbfcWDH8QzSzuTRo4mTia7BwU6PFR7Qpw9s3+5Sq77eTeNZvicFq/RbFsJvBOefpemBHb4Ow2MkWQ5kZ87A449Dx44werT3rx8cbJaz/eor04WjY0cz6cqXvvoKhg0zC6qIwNWlC9xyi9PNK/eaG10Ok+W//x3eecdTkQlHCvotL19e+q7N4snIyWfLYcddToQQ3qe0RgdoCQZIshzY3nrLLLH87ru+naE6ejSsWQMNG/q2rczXX5teyUOGwBNP+C4O4VmrVsHvv4PV6nSXFXtTqRsTToPYiPM32Gzm/8mePR4OUpynWzfo3Nmlcq2CuuUlu2XBGCH8RaAny4HZEE/A/v3wyitwzTWmLtfX2rQxSUxB0v7qq6ZjxujR3pkQMHWqKUcZONC0tYuIKPUQUUm9/76ZZHr0qMPNWmtW7U2ld7N4VNH/e3v2mHplWW3RuyIjYe1al3atFR1Oq9rRLN55knsGNPNwYEIIV1i0xqoCd/w1cL+zqi4z03TA8KdWaAWJstVqZr4XJK/r13v2uhkZcP/9pkXcL79IohzItDY16YMHO21htC8lm+MZuXR3NLkvKck8du7swSCFUzbbuYV9StCvRU1WJqeSk+f87oEQwnuWd+hLbli4r8PwGEmWA1XbtqZtW6NGvo6kuKAgE9v48WbVvC5dzEp3LkzuKZfoaJg719yaj4ryzDWEf9i2zUwkvegip7sULJfc01G98tq1plSofXtPRSicmTvX9GrfsKHUXfs1r8nZfBurk095ITAhRGleu/U5nn4zcJfTkGQ50OTkwNNPQ4qft1ayWODuu03v5/vug4kTIdXN3QXHjYP//Md83r49VKvm3vML/zN3rnkcPNjpLst2p1CzWhjNazn4/xARYTq3hIUV3yY8q0kTs5iMC5P8ejSJIyRIsXiX1C0L4XM2G4NX/kHt4wd9HYnHSM1yoHnlFbNs85AhJY6uOXM238aKvSks2nmSQ6fPkJGTT0K1MJrVimJY2zqOE4yKiI2F996D556DeDNxh3vuMbfBb7+9fO27cnPh0Ufhgw/g6qtN2UeALsEpili50txNadLE4WatNcv2pDiuVwZ49lkPByicSkyEWrXMyov33FPirlFhwXRuFMviXSeA1l4JTwjhRH4+D019k62be8C/nXchqsxkZDmQbNliEuVRo8qcKOfkWfls0R56vzqH0RNW8uXSZLYeTud09lmW7DrJf//YzkVvL2DkB0tYvPMkWru5x2lBopyVBRs3mj+WLVuayVrZ2a6fZ/Nm6N/fJMqPPGIm9vlhomyzaQ6eKsP3JVzzxRdmcQsnk0b3nMziWHouvZvGezkwUSqlTAs5F0aWwZRibD6cTmrWWQ8HJoQokb3zUMN9230ciOfIyOYxR3UAACAASURBVHKgyM83E+ZiYso8qW/jwTQemLSW5JRs+jaPZ0zvRC5skUBE6Lkk80jaGWZsPMpni/YwasIKLmpTm1ev7kBCtJtvV0dFweLFppvBSy+ZRU3+/W+zHPXAgSUfm5YGvXpBSIhZbOSaa9wbWwUdPJXNop0nWbzrJMt2p6CAVc9chMUSuO12vM5iMV1WnChYJrmg/dh5fv/dlAT9/rvp3iK8r3dvmD7dlJHFl/yGpn/LBN6evYNFO09wZSfn/+ZCCA+zJ8s2J5OqA4Eky4HirbfMss3TpkFCgsuHfbNiH8//vJma1cL4vzt7cGELx8fWjYngjn5NuKVXI75cksxbs3cw/N2FjLuxk9Njyk0puPhi87F4sVmJrWNHs+3LL80y2s2bm6Q4NdW0CJs507xRmDTJJMw1a7o3pnLIybOyfE8K87efYOGOE+w5mQVA7ephDGyVQL/mNbFqjQVJlt1i0iQzcfS995zWHC/bk0Kd6uEkxkcW37h2LSQnl5hsCw8bPtzULZfQI7tAh/oxxEaGsGC7JMtCkJZm5iwVppQpbQI4fdqUKBZmsZzLF06dgrNF7tIEB59705qaCnl5528PCTEr4Ra8XiVZFn5v1CjzH/vaa13aXWvNh/N388bM7QxslcC7N3SiRmTp9cFhwUH8bUAzBrWuxQPfJnHbF6v416VtuK1PouMa0Irq1898FDh1Co4fNysCWq2m08UFF5hfAmFhcNll7o+hDE5m5jJn6zFmbznG4l0nycmzER5ioVfTeEb1asyFLWrSvFY1z/ysqrrvvzdvGJ0kylprVuxJ4cIWCY5//klJ5k1Y9eoeDlQ41amT+XBBkEXRv2UCC3eewGbTcodGVD02m0mIlYIHHzQT5QuLizs32f/OO+GHH87f3rixGSAAuPFGmDXr/O3t25uySDB/W5ctO397r17mOftiYzaL/5U8uosky5VddrZJDurXN5PaXPTBvF28OWsHIzvV443rLiAkqGzvCFvWjub7v/fh4SnreOGXLRxNy+Gpi1t7Pgl8+GHz4UeOZ+QwY+NRft94hJXJqWgN9WtEcEO3hgxqXYteTeMJDwncXyJ+wWaDBQvg0kud7rLzeCYnM886r1dOSjIryQnfysqCXbtcWhhmQMsEpq87zObD6XRoEOOF4ITwI2+/DY8/bu7G3Hor9Ox5/vbCAwdjxxafyxQdfe7zBx6AkSPP3x5XqL3mY4/BsWPnb69d2zxGRbG5fktqhECN8n0nfk+S5cpMa9Mx4vRps8iHi7dApq0+wJuzdnBV5/q8dd0F5R6RqRYWzCejuvL8L5v5ZOEe0nPyeHlkhyoxwpN9Np+Zm4/yw9pDLNl1EpuGlrWr8Y/BLRjerg5t6kbL6LE3bdli+nQPGOB0l8U7TZuxPs0dJMspKbB3b6ldGIQXPP64WXL81KlSJ+f2b2luIS/YcVySZVH1FJRFhISYdpkltMxk+PCSz1XaXdmrry5x85h7/sewtrV5peSzVFqSLFdmb75puj28+qrLifKy3Sk8/cNG+jWvyevXdKxwYmuxKF64oh3R4cF8MG83WsMrVwVuwrzlcDrfrNjH9HWHyczNp0FsBPcNas4VF9SjRe3o0k8gPGPxYvPYv7/TXZbuPklifCQNYh3UK+fmmkTZH5aGr+p69YKPPjILzLRrV+KuNauF0aF+DPO3n+D+wS28FKAQfqJwsuxj+TZNcBnvUFcmkixXVjNnwlNPwXXXwZNPunTI0bQcHpi0lsbxkXw0qguhwe75j62U4rFhrbAoxftzdxFkUbw0sn3AjKxabZrZW44yYfFeViWfIizYwqUd6nJ994b0SIwL2DcGlYpSJslq2tTh5nyrjRV7Urm8Uz3Hx9erZxI04Xu9epnH5ctLTZYBBrZK4IN5uziVdZbYqHL0ZReisjp71gyU+UF71NGLptJhTwxc+ZavQ/EISZYrow0bTJLcvr3pK+tCUppntXHft2s5c9bK5LG9iA537ztRpRSPDG1JnlXz8YLdVAsL9k4Nswedzbfxw9qDfLxgN8kp2TSMi+CZS9pwXbcGLk2GFF70t7+ZDyc2HEojIzefvs2cdEk5dAjq1PGLPzpVXosWplZy2TIzKakUQ9rU5v25u5i/4zhXdW7ghQCF8BN5eX4xqgwwYMcKah6L8HUYHiPJcmXVtq3pJRwV5dLu783ZyZp9p3j/ps40r+WZcgGlFE+OaEVWbj6fLNxD9YgQ7hvU3CPX8qQ8q43v1xzk/bm7OHT6DB0bxPDhLV0Y3q4OQTKK7H+sVjO6UsIbsyX2emWH/ZXB9PcdOBC++soDAYoyKbhL4OLiJB3rx5AQHcafWyVZFlVMv34uDZZ5g8VmRUs3DOEXMjNNctyxoxl1cfFFsnJvKh/M28W1XRtw+QVObkO7iVKmhjkzN583Zm4nJiKEUb0ae/Sa7qK15veNR3lj5jaSU7Lp1LAGL1/VngEtnbQaE/5h6lTTIWXZMqfLXC/ZfZK2dasT5+g2/bFjcOCAyy3LhBeUYdlxi0UxuFUtft94hLP/z959h0dZZQ8c/95UEgKEmgQIvfcSQJqooCAWxAZiw7qrsqtiWfuuu7qWXdF1rYht7bg/VCyIyILSpYUSQiA0ISEFQjppM/f3x00kZQbSZt4p5/M880zyvjOTQ0ly5r7nnlNmb7TyMiE83iWXmJsHCLDb0UG+m1L67p/M12Rlmc1HF18MTz9d60S5oLiMuQvj6dgynL9ceub6v8YQEKB4/spB5Jws5fGvdtI8LJhLXZykN9SWX0/w1693EX84m95RzXj7xjjO69NOkmRvsGqVaTcWG+vw9MkSG1sOZXPjGCdv2jZvNvfSNs5zVG+BdQYT+7bjs02H2Xgwi7E9rB9IJIRbFBaarli1vMLsSoF2G/jwyrK8BfcGmZmmJczu3Wce+VzNs0t2k5J9knlXDyYi1H3vjYIDA3h11jBGdG7F3M/iWZmU4bavXRcZuUXc+1k8l7+2ltTskzx/5SC+u3s8E/tGSaLsLVavhjFjfmuMX93Gg1mU2OyMczZpctMm8+Zz6FAXBinqbNEiWLasVg8d17MNoUEB/JiYfuYHC+Er7roL+va1Ogq01hQGhVIa7qDTkI+QZNnTpaaaFeWkJPj6azj//Fo/de2+Y3yw/hA3j+1KXJdWZ35CIwsLCWTB7Dh6RTXj9x9uZsP+426PwZkym523Vx/gvBd+4tvtR7nznO6suP8cro6Llbpkb5KTAzt3mmTZiTXJxwgJDGBEl5aOH7BxI/TpU7VBv7Den/9shi7UQnhIEON6tOGHhHS01i4OTAgP4SEb/Gx2zYxrn2Pp4/+yOhSXkWTZk5WWmkT54EH49lu44IJaP/VkiY2H/m8HXds05f4LersuxjNo3iSY/9wykg6RYdz83ka2/HrCslgqbD+SzbRX1/C3b3YxvHNLfrj3bB6c0oemblx5F41k40ZzGXL0aKcPWZ18jGGdIwkPcfLve++9prRJeJbRo81Ye7u9Vg+fPCCalOyT7EzJdXFgQniIkhIIsb4zk638DaovLzRJsuzJgoPNL/EVK04/mceBF3/cw69ZhTxz+UDCQqytI2oTEcrHt51Fm2ah3PjOL5YlzAXFZfz1611c9uoaMvOKee3aYbx30wi6tLG+3kvUU3Q03H03jBzp8HRWQQm7juY6bxkH5ntr+nQXBSjq7ayzzBS/PXtq9fDz+0YRGKD4budRFwcmhIfwoJXlP//4JoO++MDqUFxGkmVPtHAhfPqp+fjKK2HEiDo9fceRHBas2s81I2M5q5uTVlluFtW8CR/fdhatmoZw/YIN/HIgy61f/8dd6Zw/7yfeXXuAWaM68eN9E5g6MEbqkr3dgAHw0ksQGenw9Lp9x9EaxvZ0kizv2GHejNpsLgxS1EvF1YJ162r18JZNQxjTvTXf70yTUgzhH0pLPWNl2a45b99GondvszoUl5HrzqeTnQ0ZGeadm5OWVI3KZoPHHoNnn4WJE2HGjDr3UCyz2Xlo0XZaR4Ty0IXWF/5X1iEyjIW/G801b63n+rc38K+ZQ5kyINqlXzM1+yRPfp3A0oR0ekVF8N9ZYxje2UntqvAuWsOWLTBwoNNfGKuTj9EsNIhBHVo4fo033oAPPjArmMKz9O5t3gTFx9f6KVMGRPPoFztJSs+jT3RzFwYnhPWOXXw5P8cfZO3n1iappTY7D9jt6EDfTSl9909WX7t2mV+gS5ZAcrI5NnWqqRkGePJJGD/e1BI35qrk0aNw/fWwfLmZRPbyy/V6/ffWHiQhNZdXZw2jRZj1l2eqi2rehM9/N5pb/7OJOz7azENT+nD72d0afYW3uMzG26sP8O/lyWg0D07pza3jukkPVl+yf79p9/bGG06n963dd4xR3VoTFOjk333dOlPCIZP7PE9AgPl5HF37N9QX9IvmsS938t32o5IsC5/31YBz+dvBXcQkHyPA4qukIcpOaPMmlsbgSpIsV3fbbWa16rzz4NZboUMHM34VzFCQl16Cv/zFrHrcfz/ccEPDL4NkZ5uBCHl58PbbcPPN9XqZlOyTzFu2h/P6tGPqQNeu2DZE64hQPr71LO77PJ5nluxm06ETPH/FIFo6GhhRR3a75rudR3nu+90czjrJ5P5RPHZRP2Jb+W5LG79VMeHtrLMcnj5yopBDxwuZPaaL4+cXFJjR8Q895Jr4RMPFxNTp4W2bhTK6W2sWb0vl3vN7SZmV8GmBxzJpcTKP5X+d7HwDs7v8Kwha+O7vWVlmAzPiNqu8hnbBAjPN69tv4U9/guuuO9UgPyLCtHL7z3/Mx7fdBj16mB359VFx6TcyEu67zwxHqGeirLXmiS93ojX8dVp/j/8lERYSyKuzhvH4xf1YsTuDSfN+4sutKfWuNbTbNUsT0rj436uZ8/FWmoYE8cEtI3nz+jjrEuUDB+D7782/a3GxNTH4svXrTTP+/o6H7axNNq0KnQ6p2LTJlD6dppOGsFhqKsyeDWvW1Pop04a05+DxQrYdyXFdXG6klHpHKZWhlNpZ6VgrpdQypdTe8nupLfNDUx6+jZe//gfBzq6cuVNUFLRyf4tad/GAv2ELlZXBnDlw440wf7451rcvtDnNzvmwMFMusXGjSYT69TMJM0BCwqmk+3RSUkwi3rGjWcUGePDBBjUXX7IzjeW7M5h7fi86tmzE5HD5clMS8uyz8MorsHix+QXWCJRS3DKuK4vnjKNjq3Du+Syei/+9mu93plFmq127qGP5xbyz+gAT5/3E7z7YTEFJGS9cNZhv/zie8c6GULiK3Q5ffgmHDpnPt22DCy80pQLR0ebNVUVpj2i4DRvM362TYSRr9h2jTUQoPdtFOH5+xcp0HafFCTeKiDA15T/+WOunTBkQQ0hgAF9uTXFhYG71HjCl2rGHgOVa657A8vLPhZ8JKC2lJDCIIE9o2RYfb/IEH+W/ZRhFRXDVVfDNN/DAA+ZWF0rB5MnmBmaz0Q03mN31U6ea4yNGQOfO0LatScwXLIDvvjM3ux2uucaca6Cck6X8ZXEC/ds356axXer/QqWlJhn+4Qd4/XVTM/jppybuypo3h7Q088ahEfRr35xFd4xh0ZYjvLIimd9/uJk2ESFMGRDNyK6t6R3VjLbNQgHILypj/7F8dqbksGrvMTYdOoHNrhkSG8m/rxnKhQOindenutKuXSYZXrsWHn8c/vpXM0BmzRrz5uLrr+GTT+DDD82Kcx3qMIUDpaWmhGLOHIentdas3XecMd1bO7/Kcu+9pnf56d4cC2s1b242cNZhZblFWDDn9WnHN9tTeeyivtb8PGhEWuuflVJdqh2eBpxT/vH7wErgT24LSngEVVaKvUmQx19J9gX+mSwXFcHll5tNfK+/Dr//fcNfUyl45x14/3347DP46itzfNYs+Ogjk3jefTe0bw/33GPGVDZSh41nl+zmWH4xC26Mq98vhoIC+Pe/zQry0aMmgX/4YejSBZ57Dp55BsLDTc12cjLk5ppE2W43/WmvugquvbZBGx4DAxRXxcUyfWgHlu/OYNGWIyzaksKH6391+py+Mc25/exuTB/agV5RFk1f0xpeew3mzjWrYO++a0p3wJQIVEyWu/JK8657yZJTibKH9Mj0SkqZUiknNa17M/LJzCtmbI/TtE4MCZER195gzBjzJtNmq/VGzMuGtuf7hDRWJx/jnN7tXBygJaK01hUNpdOAKGcPVErdDtwO0KlTJzeEJtwloKyMskAP+R0yfTpcckm9S0k9nX8my0ePQmKiKb247bbGe93Bg8141hdeMFP3tmwxdTxgkuWkJLPS3IjvAtfvP84nv/zKbeO7Mqij416zp7V9u1ldS08392+9BVOmnPqlVLkGKTwc2lX6xZOZaVZNr7/eJC5vvWUSxgYICgxgcv9oJvePpsxmJ/FoHoeyCsjMKyZAKcJCAunSuik920U0yobABnvxRVNvftFF5s1Su9P8Yo6JOfWDZOVK8//wmmvcEqbPCQoy7RWdWJt8DIAxzoaR7N1r3ijffbf5nhSea8wY82+VkACDBtXqKef2aUdkeDCfbz7iq8nyb7TWWinldLOH1no+MB8gLi5OGlD7kIDSUmxOytDcbskS6NXL6ihcxkP+lt2kYvNY164mWW7iojYnSpmvUX3luEuXRv0yRaU2Hl60g9hWYdx7fh3/k2pt4uzb17wbvOmmU6ugtRUVZepGn38eHn0Udu40JRx13MHuTFBgAAM7tmBgRyc9cj3BDTeY+3vuMW+IaqtbNzjnHJeE5Be++86sDE+a5PD02n3HiW0V5nxz5/Ll5o2OkzIO4UHGjIE+feD48Vo/JTQokOlDO/Dh+kNkFZTQyhPeWDeudKVUjNb6qFIqBsiwOiDhfiuvuo3lucFcanUgUKcrP97Iu4u56urvf4c77jD/qK5KlN3on0uTOHCsgOcuH1S3tjErVph66sxMUwbw1lt1T5QrBASY1ltLl5pa3Ouvr9/reJP9++GWW6CkxNS7zp1bt0QZQC6HNsyTT5pR8A7Y7ZoNB7IYfbrplatXmzd17hg2JBqmWzezuHHuuXV62swRnSi1aRZtOeKiwCy1GLix/OMbga8sjEVYZP0509nYZ6TVYRiSLPuIhQvNdLz8/LonNh5o08Es3l5zgOvO6sQYZ62xHHnvPbPxrKAAchqxtdKkSbBsmRkQ4cs2bzatxr78Evbtszoa/1RaajqNxMU5PJ2YlkvOydLTj3pfvRrGjWvcwULCtey165BToXd0M4Z2iuSzjYe9evy1UuoTYB3QWyl1RCl1C/AscL5Sai8wqfxz4Wcij+ynTWGu1WGY702tnXYm8gXenzXWxi+/mPZw48aZoR9e/gsyv7iM+z7fRvsWYXUbaf2vf5lyi/POM38nFS3vGsvo0eY17Xaz6a2oqHFf32pLlsCECWZz45o1DWr156mUUlOUUklKqWSllGe2o0pIMH2rhw93eHrdPnO5fnR3J8ny4cOmvd+4ca6KUDS2r74yG49T6tYObuaIWPZm5LPxoPeOM9daX6O1jtFaB2utO2qt39ZaH9daT9Ra99RaT9Ja16JnqfA1cx+axQ2rPrM6DPM7v0+fRunu5al8P1k+fBimTTOXXBctgtBQqyNqsL8sTuBwViEvzhhCRGgt38l9/LGpq73iCtPGrJkLu0esW2e6fdx226k6cW/3ySemtrtXL/Pn69PH6oganVIqEHgVuBDoB1yjlOpnbVQObN5s7p0ky+v3H6dL63BiWjhpbXjwoPmhLsmy9+jY0fSwX7WqTk+7ZHB7WoQF897aAy4KTAjrBJaVYveE1dygIFMqdeedVkfiMr6fLCclmZXkr7/2iXc9X29L5b+bj3DXuT0Y2bUO03ImTzaDUD75xPVvGMaONX2GP/zQrDD7goEDTbvBn35qtA2MHmgkkKy13q+1LgE+xfRz9Szx8ab/bvfuNU7ZKuqVna0qA4wfb7q/SNs47zF4sOm0U8dkOTwkiJkjYlmakE5K9kkXBSeEBbQmyFaGDvKQ1nE+zgPekrjYpElmQ5YPbOhLzsjjT/+3nWGdIvnjxJ61e9LBg9ChA7Ru7d7pOo8+ako97rkHhg3zzpHCaWmmR/bcuTBggKl7920dgMOVPj8CeN54u4p2fQ72HuxKzSWvqOz09crg9aVYficoyPwMqWOyDHD96M68tWo/H6w7xEMX+t4VIeGnysoAaFpyEnbvPnW8UyfT5jUnx7Qnra5LF5MPnThhFg2q69bNdBo6ftw0AaiuRw/z/ZiZeapDTWGhacP5xz+auQs+yDdXlrU2g0Zef9187gOJcl5RKb/7YDPhIYG8du3w2s2Cz8oyO8it6FAREGDG1MbGmq9f/o3tNZYsMSuPjz8uG/mqUUrdrpTapJTalOnoh6mrBQU5bcO44YD54e00Wc7IMD/sv/vORcEJlxk/3rSnPFG3+uOOLcO5oF80n/zyKwXFXvZzSAhnSksBmLZyodk/U3HbutWc/+qrqscrbnv2mPMff+z4/JHy7jFvveX4fMX330svnTo2fLjZNF3H701v4nsry1qb1cw33zSJjg8otdm586MtHDxeyIe3jCK6RS2Sf61h9mwzNOTee10eo0ORkWY1tqzMe3bJHj9uylXefhv69zct8Rp7I6TnSgFiK33esfxYFZYOOUhJMS0g58xxuMFyw4EsurQOJ6q5k++Rn382b35a1aGESXiGiy6CvDzTsrGObp/Qje8T0vh4w6/cdnY3FwQnhJsFB7Nu6LmM3rrCTI6tWBSsGAwyfrwpu6yuom3pBRc4Pl8xWOvSSx0vSlTsd5oxw5QnVoqHCy+s1x/FG3hJBlNLdru5PPvyyyZhfvJJqyNqMLtd89D/7WDV3mM8f+Wg09diVrZgganTfvFFGGXhlfTK7b0OHzYrzZ7Kbjc/YPbsgQceMHXXPnBVog42Aj2VUl0xSfJMYJa1IVWzaZOpg3dwtcRu12w8mMX5fZ1O/jU15+HhTjcHCg82bJi51eepnVoypntr5q/az/WjO9Mk2Hf7wQo/ERzMgivv5scLZvD49dfX7HHsaDBaZT17mpsz/fqZmzODBtV6oqYv8J0yDK3N6OCXXjK1M/PmeX1dot2uefTLHfzfliPcO6kXV8fVMtFMTjaryRMnmhoiT/DOO+YbMz7e6kiqOnbM/F8pKjKlI/PmmRiff97fEmW01mXAHGApkAgs1FonWBtVNdu3m+/rAQNqnNqbkU92YenpN77+9JMZwBMsm2K8UnExbNlSr6fOObcHmXnFfL7ZJ4eUCH9TXEzk8TQy2nf16WEgnsJ3kmWlzGrRCy+Y1VQvT5RLyuw88N/tfPLLYe46tzt/nFiHUoD8fLN7/N13PWcAyyWXmE2GM2aYS6lWOnrUDGe59FKz+fG++2DlSnNuyhSHiZi/0Fp/p7XupbXurrV2PCLPStu3my4YERE1Tv1y0LSaHdXVydWXrCzYscP0yhbe6amnYOTIev0MGd29NcM6RfL6imSKSm0uCE4IN0pL44V5v+eqRa9bHYlf8JBMqh7sdtNtYcaMU10KHnzQdC7w8kQ5q6CE697e8NuK8v0X9EbV5c80ZIgZmuFJJQ9t25oNBfv2wc03u7f/cmqqGcUNZtW9fXsznCU+3vSD3rnTJMnC823b5vTS3y8Hsohu3oTYVk76KxcWmjHl8m/tvSZMMGN116yp81OVUtw/uTepOUV8sO6QC4ITwo3KN/iNWf2NxYH4B+9Llh980KxSxsSYWtwlS8yldB+xIimDC//1M/GHs/nXzCHcPaln7RPlzExTa5vrAeMvHZkwAZ55Bv77X1Pu4Cp2u/llOneuqbnq0AH+9jdzrnt3M8lwyxYzxW3ePLORT3i+0lJzGzy4ximtNb8cOM6Irq2cf7907Ghq+Z2MyRZeYPRoU0JTcSWojsZ0b8OEXm15ZUUyOSdLGzc2IdypfKOrPUBKMNzB+zb4bdpkLqdOmmR2c06bZrouNBKtNfuPFbBhfxbbDmezLzOf1OyTZJ8spcyuCQpQRIYF07ZZKJ1aN6Vbm6b0jWlGv5gWxLYKq9sKcCX7MvOZ98Mevt1xlF5REbx94wgGdGhRtxd59FFTejF7tucmgPffb1rbuGpAjNamJnXDBtMrcsIEs5J9wQXmvFKeU8ct6iY42FwhsNtrnDqcdZL03GJGdmnp/PnJyebNkpdfefJrTZuaMox6JssAD07pzUUvr+bVFck8MtX3RtYLP1G+smyXemW38L5k+X//c8nLHs4qZOGmw3y7/Sj7jxUA0KppCD3bRTC6exsiw4MJDgyg1GYnu7CUjLwith3O5tvtqdjLKwpahAUzqGMLBnRoQf/2zekb05xOrcKd9kQ+ll/Mun3H+WJrCiuTMmgSHMjdE3ty57ndCQ2q4zfA5s1m1eyeezw3UQaTqHz00amEJTfXTGNriNRU0xPyscfMRoe77jKtxS69tOGvLTyPgzr8TYdMvXJcFyeb+7KyTEulv/8dHnrIldEJVzvnHDNgKS/vVBurOujfvgVXDu/IO6sPcNXwjvSMqvtrCGG535Jl70vjvJHf/y3vTMnh5eV7WZaYjsJsArlpXFfG9WhDl9bhZ1wpPlliY096HgmpuexIyWZHSg5v/byfsvIMOjBA0SEyjNYRITRrYnbg5xeVkpZTRGpOEQDRzZtwxznduWlsV9pE1GMUtdbwhz+Y1do//7nuz3e3ir/TZctg5kxTlnHuuXV/nexseO450wGlrMyM9D7rLGuGsAjXe+45U7P88cc1Tm0+dIJmoUH0cpb4rFp16qqD8G433WT6uYY5qU2vhYcv7MOyXek8/tVOPrntrHpfERTCMl27srHLQHrlpFkdiV/w22T5cFYhf/8ukSU702jeJIi7zunBrFGdaB9Ztx/AYSGBDI6NZHBsJGCafReX2dibns/utDwOHivg8IlCjueX/FYj1zQkkLO6taZ3dDPisqX44AAAIABJREFUurRkSGxLAgMa8MP6k09g3TozSKNFHUs3rNSvn6k9nzLFTFu86abaXSIvLYVXXzV1yCdOwLXXmp7Ip+spKbzf8uWnxqtWs/nQCYZ0inT+ffTTT6YV4MiRLgxQuEX37ubWAK0jQnlwSm8e/WIni7akcMXwjo0UnBBu0ro1D1/2INN7NOcuq2PxA36XLJfa7Lz50z7+/b9kApTinkk9uXlcV5o3aby+q6FBgQzo0KLuNcf1NWKE2cw2e7Z7vl5j6dDBTFS74grTpWDxYvjHP5w3Si8tNXWrAQFmQmNcnFltHDLEvXELa+zaBeedV+NwblEpSel5XDggxvlzV640m8P8rHe2z1q/3ozXvf/+er/ENSM68eXWFP7ydQKju7eu80KJEJY6dowOGYcpHjPR6kj8gvd1w2iAPel5XPbqGv75wx4m9Y3if/dP4J5JvRo1UbZEz56mv7Sn9FSui1atzIrhP/9pRkuvWmWOp6SY5HnxYlNmcfXVZkxnYaGpS161yjxeEmX/kJ1t/k84qMeP/zUbrWF4Zyeb+06cMG0Cpb+y7/jxR9MZycmVhtoICFC8cNUQbHbN/Z9vw2537+R2IRrkl194/+NHiPvmI6sj8QtemF3VndaaD9cf4pJ/ryY9t4g3rhvOq9cOI6aFl68kpKaaPtOHvLxnaECAGQxy8KApqQBTxzxtmrndey+sXQuXXQYFZvMlbdpYFq6wwK5d5t5Bsrzp0AkCFAzp5KQrTlgYfPklzPKsyd2iASZONDXoK1Y06GU6tQ7n8Yv7sXbfcV5bmdxIwQnhevZi0zqu/wrps+wOPl+GcbLExkOLtvNVfCpn92rLC1cNpm2zemyi80SPPGKSgGeesTqSxhEVderj666DsWNNDXP79qa2Wfgvm81s3nQwXXHLoRP0iW5ORKiTH2dNmpjOKMJ3jBhhOmH8+CNceWWDXmrmiFjW7z/OC8v2MLBjJBN6uaitpRCNyFZcTACgg738yriX8OmV5SMnCrn89bUs3pbK/Rf04r3ZI3wnUd68Gd5/36y6dutmdTSNr3VrU5M8fLgkygLGjzebWLt0qXLYZtfEH85mWOfT9FpfsMBMaRS+IyjIlNUsX97gl1JK8czlA+kd1Yw/frKVfZn5jRCgEK5lK19ZtgdJsuwOPpssbz50gsteXcORE4W8O3sEc87rSUBDOk54ErvdDNZo186sLgvh65yMR9+XmU9+cRlDY53UK2dnw+23w6JFLgxOWGLiRPPvm5XV4JcKDwnizeuHExSgmP3uL2TmFTdCgEK4jr3IJMuysuwePpksf7/zKLPeWk/T0CC+uHMs5/RuZ3VIjeujj0wN77PPytAN4R/69IEnnqhxOP7XbOA09co//2wS7XPOcWFwwhK//z2kp5tNwo2gc+umvD17BJl5xcx+9xdyCmUctvBcJyecw/6W7bGFN7U6FL/gc8ny+2sPcsdHW+jXvjlf3DmWHu0irA6p8V18MTz/PNx4o9WRCOF62dmwZ48ZdVxN/JFsmjUJomtrJ78wVq6U/sq+qkmTRu8ANCQ2ktevG87e9HxueGcDuUWSMAvPVBLTnhmznmXDX+ZZHYpf8JlkWWvNvGV7+PPiBCb1jeLjW8+iVdMQq8NqfHY7tGwJDzzgna3ihKir03TCiP81myGxkc5LrFauNBsDpb+yb/rPf8wbIbu90V7y3N7teO3aYew6mst1CzaQVVDSaK8tRKPZs5chR/dgby2dodzBJ7Itu13z5Ne7eHn5Xq6O68jr1w4jLCTQ6rAa34oVMHQoHDhgdSRCuE9iornv27fK4ZMlNpLS8xjc0UkJRmEh7N4tJRi+LDAQNm6ErVsb9WUn9YvijeuGk5SWx9VvriM1+2Sjvr4QDRXyzWLeWvQUsT8ttToUv+D1ybLNrnlo0XbeW3uQW8d15bkrBhEU6PV/rJoKCuDWW00CULnFmhC+bvduCA2t0QljZ2oONrtmSKyTZDk83Gz+uuce18corDFpkrlftqzRX3pi3yj+c/NI0nOKmP7aGhJScxr9awhRX/YSUyLUfuUPFkfiH7w6qyyz2Zm7MJ6Fm47wx4k9efSivijlIx0vKtPabGY5cADeftskAUL4i6FDYc4cs4pYybbDZnPfYGfJMpjyixZuGjsv3C8qCgYPdkmyDDCqW2s+v2M0AUpx9Rvr2LC//hMDhWhMuri8Y0uoj7TD9XBemyyX2uzc/Vk8X8Wn8sDk3sw9v5dvJsoAb7wBH34If/kLnH221dEI4V6zZplx6NVsPZxNh8gw573Tb74Z3nrLxcEJy51/PqxefWq6ZyPrE202i4/u3oaubaTzgPAM9pJSNKAkWXYLl0/wU0pNAf4FBAILtNbPNvQ1S2127vk0nm93HOWRqX24/ezuDY7TY2kNCxfC1Knw2GNWRyOEe5WVQV6e2dRazY4jOQyOdbJqnJNjhvbExro4QGG5adPg2DHIzXXYMaUxRLdowoIb41zy2kLUhy4t79QSIn2W3cGlK8tKqUDgVeBCoB9wjVKqX0Nes3Ki/NhFfX0/UVYKvv7a9FaW7hfC3yQkmD661YaK5BSW8mtWIQM6OEmW16wxHRImTHBDkMJS48bBu+/KpE/hV1JnzaYoMAQlybJbuDr7Ggkka633a61LgE+BafV9sTKbnXs+O5Uo3zre4jHPJSXml3lF26JVq+Bf/zI/uJcvh5QUp5PHzujDD00/5bw8iIiAyNPUZQrhqyo6YfToUeVwxWarAe2dJMs//QTBwaZtnPB9WkNSktVRCOE2+TGxjJrzH9LnPmR1KH7B1clyB+Bwpc+PlB/7jVLqdqXUJqXUpszMzNO+2ONf7eTb7Ud5dKpFiXJGBnz8Mdx1FwwbZpLYAQMgNdWcX7HC7Ly/+WazS7tjR3MZOKd8F3VZ2Zm/Rlqa6Xpx/fWQnw9FRa778wjh6RITzRWVXr2qHN6RYr6nBjpbWf75Z9N/VzbD+oc33zRTHg8dsjoSIdyi6aYNjP51OwHNZIqvO7i8ZvlMtNbzgfkAcXFxp12GvSoulh7tmnHLuK6uD8xuNysV69bBeeeZtlXLlsF115kkedQomDsXBg06tdv+T38yu/Zzckznil27TNurivPXXgt798LEiTBmjEkAOnY054uK4O67TblFcbEZOvL002Z1TAh/tXs3dO1aY6jIjpQcOkSG0dLR4CGtoX17GDHCTUEKy1WU2yxdCrffbm0sQrhBzKf/4Z9Lv+X4rDEQe5HV4fg8VyfLKUDlHTYdy4/Vy7BOLRnWqeZGnwarqA3OzIRnnzUN7jdvNhtGAF5/3bRumzrVHB88uEYbK8C0cAkNNTWWXbuaJLuycePg6FF4+eVTu/unTIElS8zzNm6Eyy6DP/8ZevZs/D+nEN4mMbHGMBKAhNRcBnRwsqKiFPzf/7k4MOFR+vQxV/EkWRb+oriYZiWFFMdvgislWXY1VyfLG4GeSqmumCR5JjDLxV/TOa3NZbqNG2HLFti509Qc33CDacsWEmIS4wEDzCrwiBGnVoDB7Mh3sCu/1v7wB3MrLjYJ+aFDpzbtKWUScV9tfydEfcydC61bVzmUW1TKgWMFXDGsg+PnlJSY72XhP5SCyZPh889NuVuQ5RdNhXApVWL6LEvrOPdw6U8UrXWZUmoOsBTTOu4drXWCK7+mgyAgPR2io6G01CTCBQXmh2mfPiYh7t/fPLZFC7OhztGqcWMKDTUbj6pvPpJEWYiqZs+ucSghxVzxcdoJY/JkaNvWtFwU/mPyZFiwADZsgLFjrY5GCJeqSJYDpBuGW7j87bfW+jvgO1d/nRrsdvjvf01JQ3AwbNtmVps++cTUMw4c6Hj1ydWJshCidlJT4fhxU4ZRaaXwt04YjpLl4mKzz+Cuu9wVpfAU559vWgwOGmR1JEK4Xvm460C5iuYWvtm49+BBM+luxgyT/N5336kWbpdcAsOHy2VaITzdxx+bxCcvr8rhhNRcops3oU2Eg8uPGzeahFkmXfqfFi1g+nRo1szqSIRwua033w3IyrK7+F6yvHGj2YC3Ywe8845ZUb7xRhnoIYS3SUyEqKga+wQSj+bSN8ZJQrR6tbmXy/D+6cgReOYZc0VCCB+W2n8YPe7/EvuNN1odil/wvQxyyBDTuSI+Hm66ScoqhPBWu3ebfQWVFJfZSM7Ip2+Mk04Yq1eb57Rp44YAhcc5cgQeeQR+/NHqSIRwqdgVSzj7wBaC5Sq5W/jOluH//c9csm3TBp57zupohBANobVZWZ4xo8rh5Ix8yuzaebI8a5YpwxD+acQIcyXi++9r/N8RwpcM+8+r9D92nKCE6TB0iNXh+DzfWFmOjze1yH/4g9WRCCEaQ2YmnDhRY2V591FTv+y0DGPWLHNFSfinwEAzPfWHH07tUxHCBwUWn6RjbiZBh3+1OhS/4P3JclYWTJtmBoG8+KLV0QghGkOzZvDdd+Z7u5LEo7mEBgXQpXXTms9JSjITMiVJ8m+TJ5tOKrt2WR2JEC4TUFYGgJIyDLfw/mR5zhzzg3HRItNLWQjh/cLC4MILzZj5ShLTcukd3YygQAc/up56CsaPd098wnOdf77pdpTg3pb+QrhTRbIsnb3cw7trlhctMn2Tn3zS1KoJIXzDypVmEtukSb8d0lqTeDSP8/tGOX7O6tVmpLwM9/FvnTpBdrZ5wyWEjwqwlSfLwdI6zh28e2V57Fh44AF4+GGrIxFCNKZnn4UHH6xyKDOvmKyCEvo4qldOTTX91aVlnABJlIXP++7G+ygJCJKVZTfx7mQ5Kgqef17eWQnhaxITzeS+SnYdNWOuHXbCWLfO3I8Z4+rIhDdITobRo02XJCF80OZxF3H2376HUaOsDsUveGeynJho6tIOHLA6EiFEY8vPh19/hX79qhxOSjOdMPpEO1hZXr/erLAMkRZKAoiJgc2bYelSqyMRwiXGLH6fkQfirQ7Db3hnzfJ995lJfRERVkcihGhsu3eb+2ory0npeUQ1DyUy3MFlx/vvh6lTIdTBCGzhf5o2hbPOguXLrY5ECJeY+ukrDGoVA2nXSnMDN/C+leVVq2DJEnj0UWjb1upohBCNLTHR3FdLlvek59Erykl/5agoOPdcFwcmvMrEibBli2kvKoSPCbSV0TX9EBQUWB2KX/CuZFlrkyTHxJiWcUII33PVVbBtG/Ts+dshm12zNz2f3o6S5b17Yd48M8hEiAoTJ5rfGStXWh2JEI1LawLsdvOxbPBzC+9KlpctMyvLjz0mu52F8FVNmpjR9UGnqsQOZxVSXGanl6N65e+/N6VZRUVuDFJ4vFGjYPp0iIy0OhIhGldFj2WQBgdu4l3J8ujR8MILcOutVkcihHCVp5+GH3+scigp3Wzuc1iGsX49dOgAsbHuiE54i+Bg04v/vPOsjsRllFJTlFJJSqlkpdRDVscj3KSk5NTHkiy7hXcly82awdy5ctlBCF9VXAxPPAE//VTl8J7yThg92znY1LtundnMJYQjGRmmw4qPUUoFAq8CFwL9gGuUUv1O/yzhE8LD+eCyO8kLbyb5kJt4V7IshPBte/eC3V6zbVx6HrGtwmgaWq2BT2amaSEpybJwZNcus/nzyy+tjsQVRgLJWuv9WusS4FNgmsUxCXdQikXnz+LOl5eZRUThcpIsCyE8x2k6YTjc3JeYCIGBMu5eONa7N7RoUeNKhY/oAByu9PmR8mNVKKVuV0ptUkptypRNsL4hJ4drvnyDHoeTrI7Eb0iyLITwHLt2gVImySlXUmZnf2aB43rls8+G3FyZ3CccCwyE8eN9NVmuFa31fK11nNY6rq20W/UNJ05w9bIPufH9Z62OxG9IsiyE8BxHjkDXrlW63Rw4VkCZXTvvsRweLptchHMTJpjynqNHrY6ksaUAlXe1diw/Jnxd+Qa/jof3WByI/5BkWQjhOd56y/RYrmRvRvnmvqhqm/u0Nq3BFi50V3TCG02YYO5//tnaOBrfRqCnUqqrUioEmAkstjgm4Q6lpQDYAwItDsR/SLIshPAs1cbYJ2fkoxR0b1stWf71V7Nx69gxNwYnvM7QobBggSnH8CFa6zJgDrAUSAQWaq0TrI1KuEVFshwYdIYHisYiybIQwjMkJcHVV8POnVUO783Ip1OrcJoEV1tF2bjR3MvmPnE6QUFwyy3Qvr3VkTQ6rfV3WuteWuvuWuunrY5HuMlvybKsLLuLvC0RQniGLVvg88/h8cerHE5Oz3fcX3njRlOrPGiQmwIUXisjA775Bi6/XCb6ifrbsgXeew+mTTPj1DMy4G9/q/m4GTNg3Dg4fBief77m+RtvhLg4U0v/8ss1z99+OwwcaBYO3nyz5vk5c3h/zJWcdyyJ8Ab/oURtSLIshPAMO3aYVcBKnTDKbHb2H8vn3D7taj5+0yYYPBhCQ90YpPBKiYlmdTkqCi66yOpohLd67TV45x3o1csky7m58PHHNR83fLhJlrOyHJ+fMMEkyxkZjs9PnWqS5ZQUx+evvpp/TLqVQ3GxPNHwP5WoBUmWhRCeYedO80uo0kSqQ1mFlNo0PRytLLdpIyUYonbi4kwbubVrJVkW9VdcDF26wJw55vMePeD4ceePHzz49OfHjj39+cmTnZ4vXbqE4EB15phFo5BkWQjhGXburJH87k03Y4odlmF89pk7ohK+oGlTGDLEjEYXor5KSjxmvPRdKz9gwhYbTJWfg+4gG/yEENYrKzMrxXFxVQ4nl7eN6149WdbaXZEJXzFmDGzYYP6vCVEfHpIs2+2afmnJxOzdeeYHi0YhybIQwnpBQfDLL/DAA1UOJ2fk0yEyjIjQahfBnnjCrBTa7W4MUni1MWOgsNBMiRSiPm67DR55xOooKLXbCbGVYZdhTG4jybIQfkQpdZVSKkEpZVdKxVU797BSKlkplaSUmmxVjJXtzch3XK+8caMZix0gP8JELV18MaSnS/cUUX9Tp8LMmVZHQalNE2S3oYMkWXYX+U0jhH/ZCVwOVBlnppTqh5kA1h+YArymlHJfE89HHoELL6xyyG7X7Mt0kCxrbVo4DRvmtvCED4iIgHYOuqoIUVt79sCBA1ZHQZnNTpC9DC0ry24jybIQfkRrnai1TnJwahrwqda6WGt9AEgGRrotsFWrIC+vyqGU7JMUldprbu47cgQyMyVZFnX31Vfw+99bHYXwVjfcAHfcYXUUlNo0hyOjyY/tYnUofkOSZSEEQAfgcKXPj5Qfcz27HeLjzVjiSpIzTCeMGpv7tmwx95Isi7pKSjJDHmREuqgPD9ngV2qzc99Fc9nyyLNWh+I3JFkWwscopX5USu10cJvWSK9/u1Jqk1JqU2ZmZsNfMDkZ8vOdJss92lZLlqOizAqP1J6KuqpoTbhpk7VxCO/kIclymc10AwoKlBTOXaTPshA+Rms9qR5PSwFiK33esfyYo9efD8wHiIuLa3gPt61bzb2DZLl10xBaNq32y+mss8xNiLoaPtxsDN24EaZMsToa4W08JFkutdt57Yu/0/34CHjrJavD8QvytkQIAbAYmKmUClVKdQV6Ar+45Su3agXTpkH//lUO78vMp3v1VWUwNcvSZ1nUR/PmZpz6xo1WRyK8kackyzY7A9L3EZF2xOpQ/IYky0L4EaXUdKXUEWA08K1SaimA1joBWAjsAr4H7tJa29wS1Pnnw5dfVvklpLUmOTO/Zr3yiRMQGwvz5rklNOGDxo+X/tyifl54AW691eooKCtvHYe0jnMbKcMQwo9orb8AvnBy7mngaTcHBLm50KJFlcPHC0rILiyt2TZu+3ZzP2CAmwIUPmf+fKsjEN7qiiusjgAwK8vBtjJKQq1f5fYXkiwLIayTkmJWit99F2bP/u3wb50w2jat+vht28z94MFuClAIIYxV733J63uLSGsVbWkcRSU2vrOXUSp9lt1GkmUhhHUquhL06lXl8L7M8k4Y1VeW4+PNYIloa39ZCS+mNUycCOecY8amC1FLZ90+g8QR09h+10NWh0J670F0HtzP6jD8hiTLQgjrrF1rapWr9UxOzsgnLDiQ9i3Cqj5+2zZZVRYNoxRkZ8OaNVZHIryJ1gSWlRIa1oRXZnlAj/dZ8v/XnSRZFkJYZ906kyg3aVLlcHJGPt3bNSUgQFV9/KOPQli1BFqIuho6FL7+2qwyK3XmxwthsxGgNXYpffBL0g1DCGGNkhJThjFmTI1T+zMLHLeNu/xyuPBCNwQnfNrQoWZkemqq1ZEIb1FcDIA92AM21dls0KePmUYp3EKSZSGENWw2eOklmDGjyuGC4jJSsk/WnNyXnGxWom3u6WgnfFjFAJyK0elCnElJCQDaA/osU1pqRrefOGF1JH5DyjCEENYIC4Pf/a7G4QPHCgBq9lhesMD0Vy4ogMBAd0QofNXgwXDJJdCsmdWRCG8RHs4/73iOQ9FdrI7kt8QdKQlxG0mWhRDW+Pln0zaua9cqhys6YdQow9ixw1x6lF8QoqEiImDxYqujEN4kNJT1A8cREuQBF+RLS829/Cx0Gw/4VxdC+KVrr4VHHqlxODkjnwAFXdqEVz2xYwcMHOim4IRfyM21OgLhLfLzGbR9DW3ysqyO5FSy7AklIX5CkmUhhPsdOABHjsDYsTVO7cvMp1OrcEKDKpVa5OTA4cOSLIvG8/rrZnJklgckP8LzHT7ME288SP998VZHAkFBZqNz585WR+I3pAxDCOF+S5ea+wsuqHFqX0ZBzWEkO3eae0mWRWPp0cPcb91qhpQIcTrl3TCUJ3TDaNMGvvvO6ij8iqwsCyHcb+lSsyrSs2eVw2U2OweOOWgbN2SIqXF2sBItRL1UdMTYutXaOIR3qNhUJ6UPfkmSZSGEe9lssGIFTJ5cYyDEkRMnKbHZaybLTZvC+PEQGenGQIVPa9MGYmJMLbwQZ1KRLDcJtTYOgF27oGNHWLLE6kj8hpRhCCHcKzAQEhNP/fKp5LdOGNXLMN5+G7p3h3POcUOAwm8MGgTbt1sdhfAG5T+vlCesLJ88CSkpUFZmdSR+Q5JlIYT7xcQ4PJycUdE2rumpg1rD/feb4SWSLIvG9LvfyQY/UTtDhnDr9c/Qt0Nns9m4unbtIDTU9IF39H8qKsqUcOTlQXZ2zfMxMWbjXm6u2dBcXfv2ZqEhJ8dsjgZpHedGUoYhhHCv++6DRYscntqXmU+biBAiwyut3qSkmF8usrlPNLbp0+GWW6yOQniDVq1Y3XkwnfclQKdONW8bN5rHLVrk+Hxiojn/wQeOzx86ZM6//rrj88ePm/P/+Adcdpn5uGlThHvIyrIQwn327zdT+Nq1c3g6OSO/Zr1yQoK5HzDAxcEJv2O3w549EB5uEhIhnDl0iPN3rCRvylgzTbS6iu4qo0c7Pt+xo7k/91zH59u2NfdTpph6+uoqpk1edpkZ5BQRYb6WcAtJloUQ7vPJJ+b+mmtqnNJak5yRz8WD21c9UZEs9+/v4uCE3ykrM3XL990HzzxjdTTCg9l/XsW/v3qe9RF3wYevOH9gjx6nEmdH+vY1N2cGDzY3Z+LizE24lZRhCCHcQ2v46CPT1cLBKt6x/BJyi8roUX1lefdusxLtaLVFiIYICTGJi2zyE2dgK++z3Psn6W/sjyRZFkK4x/btpm5v1iyHpys299UYSPLGGxDvAVOzhG+SjhiiFsqKTLJsD5FNdf5IkmUhhHvk5sKoUXDllQ5PJztrGxcQ4LR7hhANNmiQ6S4gXTHEadh+S5abWByJsIIky0II9xg/Htavd1pOsS8jn/CQQNq3qPTLKDUVbrtNVv6E61R0WZHhJOI07OXJsg71gKEkwu0kWRZCuN7GjWZl+TT2ZZpOGKryVL9t28zOcUd9R4VoDKNGwVdfmRVmIZzIvfxqEtt0xt5EVpb9kSTLQgjXKi427Y6uu+60DzNt46r1DZVOGMLVWraESy8190I4UdSmHZfdMI/NL75tdSjCAg1KlpVSVymlEpRSdqVUXLVzDyulkpVSSUqpyQ0LUwjhtd55x5RTzJnj9CH5xWUczSmqubkvIQGio6FVKxcHKfzapk3w+edWRyE8WOAvv3DJ7lUENos484OFz2noyvJO4HLg58oHlVL9gJlAf2AK8JpSKrCBX0sI4W3S0uCRR2DCBDj/fKcP25/ppBNGQoKsKgvXmz8f7rjDtDcUwoGmX3/B379/hY5LvrQ6FGGBBiXLWutErXWSg1PTgE+11sVa6wNAMjCyIV9LCOGF7r0XCgvhzTehci1yNRVt42pM7yspkVpS4Xr9+5txwhkZVkciPJQuLibIbqPtup+sDkVYwFUT/DoA6yt9fqT8mBDCXxQXm019jz4KvXuf9qHJGfkEBSg6t65WsxwfL6t9wvUqRqnv3AlRUdbGIjySLi5BK8wgG+F3zpgsK6V+BKIdnHpUa/1VQwNQSt0O3A7QycFULyGElwoNhW++Abv9jA9Nzsinc+twQoIcXOw6zYq0EI2iotQnIQEmTrQ2FuGRdHExSoOSbhh+6YzJstZ6Uj1eNwWIrfR5x/Jjjl5/PjAfIC4uTpaQhPAlSkHgmbcrJGfk0yuqWdWD778Pn34KixZBWJiLAhQCs5rcuvWp7itCVFdSAoCSlWW/5KrWcYuBmUqpUKVUV6An8IuLvpYQwosVl9k4lFVYc3Pf2rWmP7MkysLVlII1a+DFF62ORHioXXMfpzQgkABZWfZLDW0dN10pdQQYDXyrlFoKoLVOABYCu4Dvgbu01raGBiuE8D0HjxVis2t6RlVLlnftgr59rQlK+J/evSE83OoohIfKa9mW3g98Sc6jT1gdirBAQ7thfKG17qi1DtVaR2mtJ1c697TWurvWurfWeknDQxVC+KKKThg1VpYTEyVZFu6TmAgPPgiZmVZHIjxQux++5uLEnwkOdlVfBOHJZIKfEMJSezPyUKpa27jMTNPKS5Jl4S5Hj8I//gHbt1sdifBA3Ra+z59/nE/EqpVWhyIsIMmQQlZ6AAAgAElEQVSyEMJSyRn5xLYMp0lwpY2AublmkMnQodYFJvxLxRuzXbusjUN4pICiItoWZhO619FoCeHr5HqCEMJSyRn5NUswuneHlSstiUf4qehoaNHClGMIUU1ASbG5D5MNfv5IVpaFEJax2TX7jxXQs3qyLIS7KQX9+nlUsqyUukoplaCUsiul4qqde1gplayUSlJKTXb2GqJxBJSWAhDYJNTiSIQVJFkWQljmcFYhJWX2mivLl10GM2daE5TwX337Qlqa1VFUthO4HPi58kGlVD9gJtAfmAK8ppQ6c0NzUW+q1PRZDpKVZb8kybIQwjJ7nXXC2Lq1VsNMhGhUr73mUTXLWutErbWjItlpwKda62Kt9QEgGRjp3uj8y7f3PE1+SBhK2gv6JUmWhRCW2ZOeB0DPytP7Cgvh11+hTx+LohJ+KzTUW8ardwAOV/r8SPmxGpRStyulNimlNmVKW7x6O9h3GCP/tMhc9RJ+R5JlIYRl9qbn0SEyjIjQSnuN9+wx95IsC3fLy4NZs+DLL932JZVSPyqldjq4TWuM19daz9dax2mt49q2bdsYL+mXRv73bc7bv8nqMIRFpBuGEMIyex11wkgqv+rcu7f7AxL+rWlTkyhHR7ttBVFrPakeT0sBYit93rH8mHCRsxfOp09Yc0i8Qvq/+yFZWRZCWMJm1yRn5NOr+pjrmBi49lro2dOawIT/Cggwb9I8qCOGE4uBmUqpUKVUV6An8IvFMfm0wLJSOmWlQna21aEIC8jKshDCEkdOFFJcZqdnu2ZVT5x9trkJYYW+fWHtWqujAEApNR34N9AW+FYpFa+1nqy1TlBKLQR2AWXAXVprm5Wx+roAW5n5ICTE2kCEJWRlWQhhiT3pphNGz+oryydOgNYWRCQEplb+11/h5EmrI0Fr/YXWuqPWOlRrHaW1nlzp3NNa6+5a695a6yVWxunztCbQXv5eJFT6LPsjSZaFEJZw2AlDa4iNhT/9yaKohN8bONDcpHOEqFA+kASQlWU/JcmyEMISyRn5tG/RpGonjJQUKCiArl2tC0z4t+nTYds26NTJ6kiEpwgO5o1bnuB489YQFmZ1NMICUrMshLDEnvS8qqvKALt3m3vphCGE8BRKsXLkFP43YgoLY2PP/Hjhc2RlWQjhdja7Zl9mPj2dtY2THsvCSjNnwh/+YHUUwlPk5DBr4cv0PeQ50x2Fe0myLIRwu8NZhRSV2ulVfWU5KQkiIkz7OCGskpUF69ZZHYXwFNnZXLr8U2754BkoKrI6GmEBKcMQQrhdxea+XtHVkuWLL4YePbxl5LDwVX36wLvvmg2n8n9RlJQA0OnIPtOLW/gdSZaFEG73WyeM6mUYF1xgbkJYqXdvyM+Ho0ehfXuroxFWK0+WAQgOti4OYRl5iySEcLs96fl0bBlG08qdMEpLYcsWKCy0LjAh4NQG04oNp8K/lSfLtoBAudLgpyRZFkK43Z70vJr1ysnJMHw4fPGFNUEJUaFfP5g6VQZQCKM8WbYHysV4fyXJshDCrUptdvZnFtRMlvfsMfe9erk/KCEqa98evv0Wxo61OhLhCUaN4pXzZpPWuafVkQiLSLIshHCrQ8cLKLHZ6R1drV65IlnuKb+QhIcoK7M6AuEh3hw3k3fnfWZ1GMIikiwLIdwqKS0fgJ7tHKwst20LkZEWRCVENXPmQN++VkchPMG2bTy2+CXaHE+1OhJhEUmWhfAjSql/KKV2K6W2K6W+UEpFVjr3sFIqWSmVpJSa7KoY9qTnEaCgR/VOGHv3SgmG8BytWsH+/VU7IQi/pPfvZ0b890x+7W9WhyIsIsmyEP5lGTBAaz0I2AM8DKCU6gfMBPoDU4DXlFKBrghgT3oenVs3pUlwtZd/6in4619d8SWFqLvevcFuNwmz8Gv24mIAWqUcsjgSYRVJloXwI1rrH7TWFYWY64GO5R9PAz7VWhdrrQ8AycBIV8SwJz2vZn9lgHHj4LzzXPElhai7iqscFbX0wm/ZTppk2R4SYnEkwirSB0UI/3UzULFjpQMmea5wpPxYoyoqtXHweCEXDaw2zjotDTZsgHPOgRYtGvvLClF3FRtNJVn2DMePm17slQUFQZs25uNjx2puyAwJMeU0AJmZYLNVPR8aCi1bmo8zMsyVhMqaNIHISMqKigkB7CHSStBfycqyED5GKfWjUmqng9u0So95FCgDPqrH69+ulNqklNqUmZlZp+fuy8zHZtc1x1z/9BNcdhn8+mtdwxHCNSIj4d57YfBgqyPxXzt2mL0MAJdfDjExVW9Tppx67OTJNc9fccWp82PH1jx/ww2nzg8eXPP8nXcCUDR0OHbA3kSSZX8lK8tC+Bit9aTTnVdKzQYuBiZqrXX54RQgttLDOpYfc/T684H5AHFxcdrRY5xJSjNjrvtUT5YrfiF2716XlxPCtebNszoC/3bjjabn9TffwNy5cM01Vc+3bXvq44cfNqvLlVUeVf7kk5CTU/V8586nPn7uuZrTQ3v0AKCoTz9+7jKUDr37E1XPP4rwbpIsC+FHlFJTgAeBCVrryr8ZFgMfK6XmAe2BnsAvjf31k9LzCAkMoHPrplVPJCdDhw4QHt7YX1KI+rPZ4PBh6NLF6kj808mTp34mTJt2+sdeeeXpz1dPtKurvMpcTanNzuwZf2Pe1YORLvD+ScowhPAvrwDNgGVKqXil1BsAWusEYCGwC/geuEtrbXP+MvWzJy2P7u0iCA6s9qNn377fVnGE8Bj//Cd07Qq5uVZH4p9OnoSwMKujoKTM1DLX+Lkl/IasLAvhR7TWTjNSrfXTwNOu/PpJaXmM7Nqq5onkZLjoIld+aSHqrmKT3969MHy4tbH4I09Jlm123vn8L3Q+MRbefMnqcIQFJFkWQrhFblEpqTlF9I5uXvPkihVmZ7sQnqRy+zhJlt3PQ5LlUpum17FDBB6Tq1/+Sn47CSHcYk/55r7e0Q56LPfp4+ZohKiFHj1AKWkfZ5X33/eIevGSMjuhtlLKpHWc35JkWQjhFknpJlnuFVWtE0Z8PKxZYzbYNGvm4JlCWKRJE4iNNWVCwv2mT7c6AsBs8Au2lWELlaEk/kqq1YUQbpGUlkdEaBAdIqtdVv3+e5gzx5qghDiTp5+G2bOtjsL/lJbC0qVw5IjVkVBisxNiK0XJBD+/JcmyEMItdqfl0SsqAqVU1RPJyRAVJavKwjNddx1MnGh1FP4nK8sMHVm82OpIKC2zsyF2AGU9e1sdirCIJMtCCJfTWpOUlud4c19ysrSNE54rNxd+/rnmwArhWidPmnsP2OBXYrNz01VPUnDDbKtDERaRZFkI4XLpucXknCylb4yD1ePkZJncJzzXypUwYQLs3Gl1JP7Fg5LlUpvpsxwifZb9lmzwE0K43O40M9Shd/XNfSdPQmqqrCwLz1Xxf3PvXhg50tpY/El5snzCHkhxTpGloeSkZ7H69ZtpGvtXuOt3lsYirCHJshDC5XaXt43rU70MIyzMXOa2NfqwQCEaR7dupn3c3r1WR+JfypPlP361m1Xbwy0NpVVhDrNzMygstjZpF9aRZFkI4XJJaXlEN29Ci/DgmicjHPRdFsJTNGkCnTpJsuxu/frxxO//QXGHXjxzwUBLQwnLSIN/Q3gza5N2YR1JloUQLrc7LY/e0Q7qlb/7Dn76CZ56CoIdJNJCeIKePaXXsru1bMn6XiPo3jaCa0Z2sjaW/WXmXlrH+S2pVhdCuFSpzc6+jHz6ONrc9/338MYbMupaeLannoJXX7U6Cv9y6BAjt66kWZkHlD6UlJj7UJng568kWRZCuNTBYwWU2Oz0cbSyfODAqZpQITzVqFEQF2d1FP5lxQqe+uAvtC7IsToSCA830wQ7WbzCLSwjyzlCCJeq2NzXO8pBj+X9+6G3NPoXHi47G7ZuhaFDITLS6mj8Q/kGP9XUA+qEO3WCRYusjkJYSFaWhRAutTstl8AARfd2Taue0Noky926WROYELXVogWce64kyu5UniwHhFvfZ1kISZaFEC6VlJZH97ZNCQ0KrHoiK8t0GpBkWXg6KRNyv9+SZQ9YWf75Z2jTBtavtzoSYREpwxBCuFTi0TyGd25Z80Tr1nDihPRYFkLUYC8oRKsAgpt4wKa6ggI4ftzqKISFZGVZCOEyuUWlpGSfdNwJo0JgoPNzQgi/VHTzrVw961lCQzzg50NFNwxpHee3JFkWQrhMUvnmvr7VJ/cBvPsuXHedqV0WQohKimI6sLljv5rlW1aQ1nF+T5JlIYTL7D6aC+B4Zfmnn8xN6kGFENXoFSuYnLSW0CAPSFOKi829rCz7LalZFkK4TGJaHs2bBBHdvEnNk/v3Q9eu7g9KCOHxmrz9Fg/+vJ69t0+D0hgz4TM3F3Ic9F1u396Uc+XkmMdU16EDBASYPRL5+TXPx8aa+6wsU59cmVLQpQtcf710Q/FjkiwLIVwmKS2PPjHNUY5Wj/fvh0mT3B+UEMLj2U+epG3BCbpPHQV79piR4/PnwwMP1HxwairExMC8efDXv9Y8n5cHERHwt7/Biy/WPF9RCvanP8GCBVXPNWtmEvBx4xr+hxJeS5JlIYRL2O2apLQ8rhjWoebJ4mJISZGVZSGEY4WFBNrt5PXqS7N27cyxyZOhpYPOOi1amPtp0xxP2auoNZ4xA/r3d/41b7wRzjqr6rHg4LrHLnyOJMtCCJdIyT5JfnEZfWIcbO7LyoKBA6FPH/cHJoTwfOV9los7d6NZRTI8cKC5OTNsmLk5M2qUuTkzbpysIAuHJFkWQrhEYsXmvmgHm/tiYmD7djdHJITwFurkSUCjPKHPsvB7HrDNVAjhiyb1jWLVg+fSr72DlWUhhDiNbfPeIje0KUratQkPIMmyEMIlAgIUsa3CHfdJfeEFOPdc6bEshHAoJyaWMXe+R+a8V6wORQhJloUQFti6FQ4ckB7LQgiHYj59j5GHEwgNk5VlYT2pWRZCuN/Bg6Z3qRBCODDgpb/z56Ztab6sO1xzldXhCD8nK8tCCPc7dAg6d7Y6CiGEhwosLqJv5gHC4rdaHYoQkiwLIdyspMQMEZCVZSFOSyn1D6XUbqXUdqXUF0qpyErnHlZKJSulkpRSk62Ms9GVlRFgK0MBgWEOpn8K4WYNSpb99htZCFF/+fkwZQoMHWp1JEJ4umXAAK31IGAP8DCAUqofMBPoD0wBXlNKOdhJ66XKeyyDJMvCMzR0Zdk/v5GFEPXXqhV8+y1cdpnVkQjh0bTWP2ity8o/XQ90LP94GvCp1rpYa30ASAZGWhGjS0iyLDxMg5Jlv/1GFkIIIdzrZmBJ+ccdgMOVzh0pP1aDUup2pdQmpdSmzMxMF4fYSFq3ZsG/FlIYFAJhYVZHI0Sj1iz7zzeyEKL+nnoKunUDm83qSISwnFLqR6XUTge3aZUe8yhQBnxU19fXWs/XWsdprePatm3bmKG7TmAgv3buy5jHv4bbbrM6GiHO3DpOKfUjEO3g1KNa66/KH9Ogb2RgPkBcXJxMKBDC1yUnm01+gVKZJYTWetLpziulZgMXAxO1/m2KTwoQW+lhHcuP+YbUVMZ9/BoJHUdZHYkQQC2SZflGFkI0KumxLEStKKWmAA8CE7TWhZVOLQY+VkrNA9oDPYFfLAjRNQ4e5IKFr9O6+3qYNQCGD7c6IuHnGtoNo+Ib+VIH38gzlVKhSqmu+No3shCi/g4dgk6drI5CCG/wCtAMWKaUildKvQGgtU4AFgK7gO+Bu7TWvlPXVL7Bb/i+rZCWZnEwQjR8gt8rQCjmGxlgvdb691rrBKVUxTdyGb72jSyEqB+7HVJSZCCJELWgte5xmnNPA0+7MRz3Kay09hYq466F9RqULPvtN7IQon6KiuDmm2HcOKsjEUJ4qkqt42gireOE9Rq6siyEELUXHg5vvGF1FEIIT1Y5WZaVZeEBZNy1EMJ9ioulZZwQ4vRmzeL/27v3GDmr847j38fry/oCtqlRWF9o4nrtQKhjU9sYXEq4ClzUBdFErkAGlMRFGEErSBOwxCWUxiltAjQWihNoArEA1SHYQS61TQgJaW3HLoYazMWEZC82Zhu6vuBk2cvTP85ZstndWe96Z+a8M/P7SK9m5px333nOvjPvPPPOec/56rV3cuC4CeELtkhiSpZFpHhWrQo/q7a0pI5ERLJqxAh+9Mfn8sVVm+ETn0gdjYiSZREpooaG8LPq+PGpIxGRrNq4kavXP0h1laZekGxQn2URKZ76epg2DcLoOSIivf30pyz58RPMPrwPFj8FEyakjkgqnJJlESmehgaNsSySVQcOwPPP9y6fOzd8yf31r+FnP+tdP38+1NTA/v2wdWvv+jPPhBNPDMNG7tjRu/7ss2HixDAG+0svwSuv0F5VxWnbnw/DTYokpmRZRIqnvh5mz04dhYj05e23oa6ud/mjj8JVV8Grr/Zd/4MfwGWXhUS4r/rNm+H88+GFF2DJkt71W7fCggWwaRN8/vMAHKk+jpEdhzQahmSCkmURKQ53uPFGJcsiWTVzZt9nfrump587t+/66dPD7aJFfdfPiFMyXHRR3/WzZoXbyy6D008H4Inr/4G/3vp9JcuSCUqWRaQ4zOC221JHISK5jBnzYbLap3Hj+q8fP77/+okTw5LLpEkwaRLtHZ20Dauic9gwhg1XmiLpaTQMESmOgwdDn0X1QRSRfnzQ0cnBUWNpOXl66lBEACXLIlIs69bB1KmwZ0/qSEQkw1rbOll9xhWs/97G1KGIAEqWRaRY6uvD7bRpaeMQkUxrbQ+/Po0aUZU4EpFAybKIFEdDQ+iTOHp06khEJMNa2ztY/p9PsOiOm1KHIgLoAj8RKZaGhtANQ0SkH63tnXy8+ZecUN+UOhQRQGeWRaRYGhvVBUNEjqq1rZORHW24ho2TjNCZZREpjhUr4LjjUkchIhnX2t7ByI42GKNkWbJBybKIFMdnPpM6AhEpAa3t4cwyo3R9g2SDumGISOEdOhSmtD18OHUkIpJxre0d1I8/iQ9qZ6UORQRQsixSUczsbjN72cx2mtlGM5scy83MHjCzPbG+n2m4jsGOHbBwIWzbltfNikj5aW3r5NZLbqT56/+SOhQRQMmySKW5191nu/sc4Gng9lh+CVAbl2XAg3l91sbGcDtlSl43KyLl58NxlodrnGXJBiXLIhXE3Q92ezgW8Hi/DnjEgy3ABDOrydsTK1kWkQFqbe9g9ZN/z6SVX04digigC/xEKo6Z3QMsBQ4A58biKUBDt9UaY9m+vDxpYyNMmADjxuVlcyJSvlrbOzl1/y8YsfePUociAujMskjZMbPNZrarj6UOwN1XuPs0YA1wwzFsf5mZbTez7c3NzQP7o6YmTUgiIgPSNc5y1RiNhiHZoDPLImXG3S8Y4KprgA3AHUAT0H3GkKmxrK/trwZWA8ybN8/7WqeX226DlpYBhiUilaxrnOVh1dWpQxEBlCyLVBQzq3X3N+PDOuC1eH89cIOZPQ6cARxw9/x0wQCYPz9vmxKR8tY1zvKwak1KItmgZFmksqw0s1lAJ/Ar4LpYvgFYDOwBjgDX5u0Z29pg3To44wxNdy2SYR2dA/uhqNB+29bBi1NPYdHMmalDEQGULItUFHe/Ike5A8sL8qR798KnPw3f+hZ87nMFeQoRGbo5X97Iod+2pw4DgLXXfJUXl12UOgwRQMmyiBRa17BxusBPJNOWnzuDD+IYx6mdUnN86hBEPqRkWUQKS8mySEm47pyMDNXW0gLz5sGdd8JVV6WORkRDx4lIgWlCEhEZjN/8Bt56Cw4fTh2JCKBkWUQKrakJxowJk5KIiBxNa2u4HaXRMCQblCyLSGHdfDNs2gRmqSMRkVKgZFkyRn2WRaSwpkxRFwwRGTgly5IxOrMsIoX18MOwdWvqKESkVIwZA5deqouCJTOULItI4bjD9dfD2rWpIxGRUjFjBvzwh2EiI5EMULIsIoXz3nvhJ1V1wxARkRKlZFlECqepKdwqWRaRgdq0CU46CXbuTB2JCKBkWUQKae/ecDt5cto4RKR0HDoE+/drBB3JDCXLIlI4OrMsIoOl0TAkY5Qsi0jhXHklvPGGrmoXkYFTsiwZo3GWRaRwqquhtjZ1FCJSSpQsS8bozLKIFM5DD8GaNamjEJFSMn06LFkC48aljkQE0JllESmkVaugpiZ0xxARGYgLLwyLSEbozLKIFM7evbq4T0RESpqSZREpjLY2ePddJcsiMjh33QXHHx9mABXJACXLIlIY+/aFDzuNsSwig/H+++HLtsZZloxQn2URKYx33gm3OrMsUhreeguWL+9d/oUvwPnnw65dcMstvetvvx3OOgu2bQv3e/rKV2DuXHj++XC/p/vvh1mz4Jln4L77YPdujYQhmaJkWUQKY8ECOHIEqqpSRyIiA9HRAS0tvcvb2sJte/ux1be3/269/upbW0N9TQ1cfvng4xcpECXLIlI4o0enjkBEBmrmTNiyJXf9nDn91591Vv/1F1wQllzq6sIikjHqsywiIiIikoOSZRERkQwys7vN7GUz22lmG81sciw3M3vAzPbE+tNTxypSzpQsi4iIZNO97j7b3ecATwNdV89dAtTGZRnwYKL4RCqCkmUREZEMcveD3R6OBboGHq4DHvFgCzDBzGqKHqBIhdAFfiIiIhllZvcAS4EDwLmxeArQ0G21xli2r7jRiVQGnVkWERFJxMw2m9muPpY6AHdf4e7TgDXADcew/WVmtt3Mtjc3N+c7fJGKoDPLIiIiibh7P2Op/Z41wAbgDqAJmNatbmos62v7q4HVAPPmzdP80SLHQGeWRUREMsjMars9rANei/fXA0vjqBgLgQPuri4YIgWiM8siIiLZtNLMZgGdwK+A62L5BmAxsAc4AlybJjyRyqBkWUREJIPc/Yoc5Q4sL3I4IhVL3TBERERERHJQsiwiIiIikoOSZRERERGRHJQsi4iIiIjkMKRk2czuNrOXzWynmW00s8mx3MzsATPbE+tPz0+4IiIiIiLFM9Qzy/e6+2x3nwM8Ddweyy8BauOyDHhwiM8jIiIiIlJ0Q0qW3f1gt4djga7ZgeqARzzYAkwws5qhPJeIiIiISLENeZxlM7sHWAocAM6NxVOAhm6rNcYyzTAkIiIiIiXDwtjm/axgthk4qY+qFe6+rtt6twLV7n6HmT0NrHT3F2Lds8AX3X17H9tfRuiqATALeP2YWlJ4k4D/TR1EEVVSe7Pc1j909xNTB5GLmTUTZhYbrCz/z/NJ7SwvR2tnub5fj0XWXhNZiidLsUBlxzOg9+xRk+WBMrOTgQ3ufpqZfRP4sbs/FuteBz5VynPXm9l2d5+XOo5iqaT2VlJbs6JS/udqZ3mplHbmQ9b+V1mKJ0uxgOIZiKGOhlHb7WEd8Fq8vx5YGkfFWAgcKOVEWUREREQq01D7LK80s1lAJ+Gnneti+QZgMbAHOAJcO8TnEREREREpuiEly+5+RY5yB5YPZdsZtDp1AEVWSe2tpLZmRaX8z9XO8lIp7cyHrP2vshRPlmIBxXNUeeuzLCIiIiJSbjTdtYiIiIhIDhWbLJvZNDN7zsxeNbNXzOymWH6CmW0yszfj7cRY3u8U3mZ2vJk1mtk3UrTnaPLZXjM7OU5vvjtu76NpWpVbntv7j3Ebu+M6lqpdpWiw+6KUmVmVmb0Yh8/EzD5mZlvj6+oJMxuZOsahMrMJZrbWzF6L74kzy3Rf/m18ve4ys8fMrLoc92e+mdnFZvZ6/B99KXEsD5vZu2a2K2UcXXIdCxPGU21m28zspRjPXSnjiTH93jE0Kyo2WQbagZvd/VRgIbDczE4FvgQ86+61wLPxMRx9Cu+7gZ8UI/BjlM/2PkKY6vwUYAHwbnGaMCh5aa+ZnQUsAmYDpwHzgXOK2I5yMNh9UcpuAnZ3e/xV4OvuPgP4P+CzSaLKr/uBZ9z948AnCe0tq31pZlOAG4F57n4aUAUsoTz3Z96YWRWwinA8PRX4q/heT+U7wMUJn7+nXMfCVFqB89z9k8Ac4GILI5il1PMYmgkVmyy7+z53/+94/xBh50whDIH33bjad4HL4v2cU3ib2Z8AHwE2FrEJg5Kv9sY39nB33xS3ddjdjxSzLQORx/3rQDUwEhgFjAD2F60hZeAY9kVJMrOpwJ8D346PDTgPWBtXKYc2jgf+DHgIwN0/cPcWymxfRsOB0WY2HBhDmIG2rPZnASwA9rj7L9z9A+BxwmsjCXf/CfBequfvqZ9jYap43N0Px4cj4pLsQraex9AsqdhkubvYjWAusBX4SLcxod8hJMGQYwpvMxsG/DNwS1GCzYOhtBeYCbSY2ZPxp5J749mEzBpKe939v4DnCB+U+4D/cPfMfestFQPcF6XqPuDvCENpAvwB0OLu7fFx13uolH0MaAb+Nb7/v21mYymzfenuTcA/AfWE9/0BYAfltz/zLdfnhvTQ41iYMo4qM9tJ+IV4k7unjKfnMTQzKj5ZNrNxwPeBv3H3g93r4hB4R/uWdT1h5sLGAoWYV3lo73DgbMKXg/nAdOCa/EeaH0Ntr5nNAE4BphIO+ueZ2dkFCres5eG1l1lmdinwrrvvSB1LgQ0HTgcedPe5wPv06HJR6vsSIPa5riN8OZgMjCVbP+dLCevvWFhs7t7h7nMIn3ELzOy0FHFk/Rha0cmymY0gvGDXuPuTsXh/t+4VNfyuP24TMK3bn0+NZWcCN5jZLwlnIpaa2coihD9oeWpvI7Az/szWDjxF+PDMnDy193JgS+xuchj4d8I+l0EY5L4oRYuAv4jHgccJP9ffT+jO0zWefddrqpQ1Ao3dzj6tJbz/y2lfAlwAvO3uze7eBjxJ2Mfltj/zLddxVKIcx8LkYneq50j3pbDXMdTMvpcoll4qNlmO/QkfAna7+9e6Va0Hro73rwbWdSvvNYW3u1/p7ie7+0cJZ1sfcffMXdySr/YCPyd8YJwY1zsPeLXgDQe9xesAAAF9SURBVBikPLa3HjjHzIbHg9w5ZPDigyw7hn1Rctz9VnefGo8DS4AfufuVhA+fv4yrlXQbAdz9HaDBwsytAOcT3v9lsy+jemChmY2Jr9+udpbV/iyAnwO1cdSQkYT3wvrEMWVGP8fCVPGcaGYT4v3RwIXAayliyXEMvSpFLH1y94pcgD8l/FT4MrAzLosJ/QyfBd4ENgMnxPWNcJXvW8D/EK6S7rnNa4BvpG5bodtLeEO9HMu/A4xM3b5CtZdwFfw3CQnyq8DXUret1JbB7otSX4BPAU/H+9OBbcAe4N+AUanjy0P75gDb4/58CphYjvsSuIuQOOwCHiVc4Ft2+7MA/7fFwBvxWLoicSyPEfqctxF+Ffls4nj6PBYmjGc28GKMZxdwe+rXT4zrw2NoVhbN4CciIiIikkPFdsMQERERETkaJcsiIiIiIjkoWRYRERERyUHJsoiIiIhIDkqWRURERERyULIsIiIiIpKDkmURERERkRyULIuIiIiI5PD/t4hH1nOhjv4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import LinearGAM, s, f\n", + "from pygam.datasets import wage\n", + "\n", + "X, y = wage(return_X_y=True)\n", + "\n", + "## model\n", + "gam = LinearGAM(s(0) + s(1) + f(2))\n", + "gam.gridsearch(X, y)\n", + "\n", + "\n", + "## plotting\n", + "plt.figure();\n", + "fig, axs = plt.subplots(1,3);\n", + "\n", + "titles = ['year', 'age', 'education']\n", + "for i, ax in enumerate(axs):\n", + " XX = gam.generate_X_grid(term=i)\n", + " ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX))\n", + " ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX, width=.95)[1], c='r', ls='--')\n", + " if i == 0:\n", + " ax.set_ylim(-30,30)\n", + " ax.set_title(titles[i]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though our model allows coefficients, our smoothing penalty reduces us to just 19 effective degrees of freedom:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearGAM \n", + "=============================================== ==========================================================\n", + "Distribution: NormalDist Effective DoF: 19.2602\n", + "Link Function: IdentityLink Log Likelihood: -24116.7451\n", + "Number of Samples: 3000 AIC: 48274.0107\n", + " AICc: 48274.2999\n", + " GCV: 1250.3656\n", + " Scale: 1235.9245\n", + " Pseudo R-Squared: 0.2945\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [15.8489] 20 6.9 5.52e-03 ** \n", + "s(1) [15.8489] 20 8.5 1.11e-16 *** \n", + "f(2) [15.8489] 5 3.8 1.11e-16 *** \n", + "intercept 0 1 0.0 1.11e-16 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With **LinearGAMs**, we can also check the **prediction intervals**:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import LinearGAM\n", + "from pygam.datasets import mcycle\n", + "\n", + "X, y = mcycle(return_X_y=True)\n", + "\n", + "gam = LinearGAM(n_splines=25).gridsearch(X, y)\n", + "XX = gam.generate_X_grid(term=0, n=500)\n", + "\n", + "plt.plot(XX, gam.predict(XX), 'r--')\n", + "plt.plot(XX, gam.prediction_intervals(XX, width=.95), color='b', ls='--')\n", + "\n", + "plt.scatter(X, y, facecolor='gray', edgecolors='none')\n", + "plt.title('95% prediction interval');\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And simulate from the posterior:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n", + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5,1,'draw samples from the posterior of the coefficients')" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# continuing last example with the mcycle dataset\n", + "for response in gam.sample(X, y, quantity='y', n_draws=50, sample_at_X=XX):\n", + " plt.scatter(XX, response, alpha=.03, color='k')\n", + "plt.plot(XX, gam.predict(XX), 'r--')\n", + "plt.plot(XX, gam.prediction_intervals(XX, width=.95), color='b', ls='--')\n", + "plt.title('draw samples from the posterior of the coefficients')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification\n", + "\n", + "For **binary classification** problems, we can use a **logistic GAM** which models:\n", + "\n", + "$$\n", + " log\\left(\\frac{P(y=1|X)}{P(y=0|X)}\\right)=\\beta_0+f_1(X_1)+f_2(X_2, X3)+\\dots+f_M(X_N)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:04 Time: 0:00:04\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import LogisticGAM, s, f\n", + "from pygam.datasets import default\n", + "\n", + "X, y = default(return_X_y=True)\n", + "\n", + "gam = LogisticGAM(f(0) + s(1) + s(2)).gridsearch(X, y)\n", + "\n", + "fig, axs = plt.subplots(1, 3)\n", + "titles = ['student', 'balance', 'income']\n", + "\n", + "for i, ax in enumerate(axs):\n", + " XX = gam.generate_X_grid(term=i)\n", + " pdep, confi = gam.partial_dependence(term=i, width=.95)\n", + "\n", + " ax.plot(XX[:, i], pdep)\n", + " ax.plot(XX[:, i], confi, c='r', ls='--')\n", + " ax.set_title(titles[i]);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then check the accuracy:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9739" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gam.accuracy(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the **scale** of the **Binomial distribution** is known, our gridsearch minimizes the **Un-Biased Risk Estimator** (UBRE) objective:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticGAM \n", + "=============================================== ==========================================================\n", + "Distribution: BinomialDist Effective DoF: 3.8047\n", + "Link Function: LogitLink Log Likelihood: -788.877\n", + "Number of Samples: 10000 AIC: 1585.3634\n", + " AICc: 1585.369\n", + " UBRE: 2.1588\n", + " Scale: 1.0\n", + " Pseudo R-Squared: 0.4598\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "f(0) [1000.] 2 1.7 4.61e-03 ** \n", + "s(1) [1000.] 20 1.2 0.00e+00 *** \n", + "s(2) [1000.] 20 0.8 3.29e-02 * \n", + "intercept 0 1 0.0 0.00e+00 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Poisson and Histogram Smoothing\n", + "We can intuitively perform **histogram smoothing** by modeling the counts in each bin\n", + "as being distributed Poisson via **PoissonGAM**." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import PoissonGAM\n", + "from pygam.datasets import faithful\n", + "\n", + "X, y = faithful(return_X_y=True)\n", + "\n", + "gam = PoissonGAM().gridsearch(X, y)\n", + "\n", + "plt.hist(faithful(return_X_y=False)['eruptions'], bins=200, color='k');\n", + "plt.plot(X, gam.predict(X), color='r')\n", + "plt.title('Best Lambda: {0:.2f}'.format(gam.lam[0][0]));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expectiles\n", + "GAMs with a Normal distribution suffer from the limitation of an assumed constant variance.\n", + "Sometimes this is not an appropriate assumption, because we'd like the variance of our error distribution to vary. \n", + "\n", + "In this case we can resort to modeling the **expectiles** of a distribution. \n", + "\n", + "Expectiles are intuitively similar to quantiles, but model tail expectations instead of tail mass. Although they are less interpretable, expectiles are **much** faster to fit, and can also be used to non-parametrically model a distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + } + ], + "source": [ + "from pygam import ExpectileGAM\n", + "from pygam.datasets import mcycle\n", + "\n", + "X, y = mcycle(return_X_y=True)\n", + "\n", + "# lets fit the mean model first by CV\n", + "gam50 = ExpectileGAM(expectile=0.5).gridsearch(X, y)\n", + "\n", + "# and copy the smoothing to the other models\n", + "lam = gam50.lam\n", + "\n", + "# now fit a few more models\n", + "gam95 = ExpectileGAM(expectile=0.95, lam=lam).fit(X, y)\n", + "gam75 = ExpectileGAM(expectile=0.75, lam=lam).fit(X, y)\n", + "gam25 = ExpectileGAM(expectile=0.25, lam=lam).fit(X, y)\n", + "gam05 = ExpectileGAM(expectile=0.05, lam=lam).fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "XX = gam50.generate_X_grid(term=0, n=500)\n", + "\n", + "plt.scatter(X, y, c='k', alpha=0.2)\n", + "plt.plot(XX, gam95.predict(XX), label='0.95')\n", + "plt.plot(XX, gam75.predict(XX), label='0.75')\n", + "plt.plot(XX, gam50.predict(XX), label='0.50')\n", + "plt.plot(XX, gam25.predict(XX), label='0.25')\n", + "plt.plot(XX, gam05.predict(XX), label='0.05')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We fit the **mean model** by cross-validation in order to find the best smoothing parameter `lam` and then copy it over to the other models.\n", + "\n", + "This practice makes the expectiles less likely to cross. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Models\n", + "\n", + "It's also easy to build custom models by using the base **GAM** class and specifying the **distribution** and the **link function**:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:01 Time: 0:00:01\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0,0.5,'predicted volume')" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import GAM\n", + "from pygam.datasets import trees\n", + "\n", + "X, y = trees(return_X_y=True)\n", + "\n", + "gam = GAM(distribution='gamma', link='log')\n", + "gam.gridsearch(X, y)\n", + "\n", + "plt.scatter(y, gam.predict(X))\n", + "plt.xlabel('true volume')\n", + "plt.ylabel('predicted volume')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check the quality of the fit by looking at the Pseudo R-Squared:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GAM \n", + "=============================================== ==========================================================\n", + "Distribution: GammaDist Effective DoF: 25.3616\n", + "Link Function: LogLink Log Likelihood: -26.1673\n", + "Number of Samples: 31 AIC: 105.0579\n", + " AICc: 501.5549\n", + " GCV: 0.0088\n", + " Scale: 0.001\n", + " Pseudo R-Squared: 0.9993\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [0.001] 20 2.04e-08 *** \n", + "s(1) [0.001] 20 7.36e-06 *** \n", + "intercept 0 1 4.39e-13 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + } + ], + "source": [ + "gam.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Penalties / Constraints\n", + "\n", + "With GAMs we can encode **prior knowledge** and **control overfitting** by using penalties and constraints.\n", + "\n", + "**Available penalties**\n", + "- second derivative smoothing (default on numerical features)\n", + "- L2 smoothing (default on categorical features)\n", + "\n", + "**Availabe constraints**\n", + "- monotonic increasing/decreasing smoothing\n", + "- convex/concave smoothing\n", + "- periodic smoothing [soon...]\n", + "\n", + "\n", + "We can inject our intuition into our model by using **monotonic** and **concave** constraints:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import LinearGAM, s\n", + "from pygam.datasets import hepatitis\n", + "\n", + "X, y = hepatitis(return_X_y=True)\n", + "\n", + "gam1 = LinearGAM(s(0, constraints='monotonic_inc')).fit(X, y)\n", + "gam2 = LinearGAM(s(0, constraints='concave')).fit(X, y)\n", + "\n", + "fig, ax = plt.subplots(1, 2)\n", + "ax[0].plot(X, y, label='data')\n", + "ax[0].plot(X, gam1.predict(X), label='monotonic fit')\n", + "ax[0].legend()\n", + "\n", + "ax[1].plot(X, y, label='data')\n", + "ax[1].plot(X, gam2.predict(X), label='concave fit')\n", + "ax[1].legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## API\n", + "\n", + "pyGAM is intuitive, modular, and adheres to a familiar API:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n", + " fit_intercept=True, lam=[0.6, 0.6, 0.6, 0.6, 0.6, 0.6], \n", + " max_iter=100, \n", + " terms=s(0) + s(1) + s(2) + s(3) + s(4) + f(5) + intercept, \n", + " tol=0.0001, verbose=False)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pygam import LogisticGAM, s, f\n", + "from pygam.datasets import toy_classification\n", + "\n", + "X, y = toy_classification(return_X_y=True, n=5000)\n", + "\n", + "gam = LogisticGAM(s(0) + s(1) + s(2) + s(3) + s(4) + f(5))\n", + "gam.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since GAMs are additive, it is also super easy to visualize each individual **feature function**, `f_i(X_i)`. These feature functions describe the effect of each `X_i` on `y` individually while marginalizing out all other predictors:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for i, term in enumerate(gam.terms):\n", + " if term.isintercept:\n", + " continue\n", + " plt.plot(gam.partial_dependence(term=i))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Current Features\n", + "\n", + "### Models\n", + "pyGAM comes with many models out-of-the-box:\n", + "\n", + "- GAM (base class for constructing custom models)\n", + "- LinearGAM\n", + "- LogisticGAM\n", + "- GammaGAM\n", + "- PoissonGAM\n", + "- InvGaussGAM\n", + "- ExpectileGAM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Terms\n", + "- `l()` linear terms\n", + "- `s()` spline terms\n", + "- `f()` factor terms\n", + "- `te()` tensor products\n", + "- `intercept`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Distributions\n", + "\n", + "- Normal\n", + "- Binomial\n", + "- Gamma\n", + "- Poisson\n", + "- Inverse Gaussian\n", + "\n", + "### Link Functions\n", + "Link functions take the distribution mean to the linear prediction. These are the canonical link functions for the above distributions:\n", + "\n", + "- Identity\n", + "- Logit\n", + "- Inverse\n", + "- Log\n", + "- Inverse-squared\n", + "\n", + "### Callbacks\n", + "Callbacks are performed during each optimization iteration. It's also easy to write your own.\n", + "\n", + "- deviance - model deviance\n", + "- diffs - differences of coefficient norm\n", + "- accuracy - model accuracy for LogisticGAM\n", + "- coef - coefficient logging\n", + "\n", + "You can check a callback by inspecting:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plt.plot(gam.logs_['deviance'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Extrapolation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pygam import LinearGAM\n", + "from pygam.datasets import mcycle\n", + "\n", + "X, y = mcycle()\n", + "\n", + "gam = LinearGAM()\n", + "gam.gridsearch(X, y)\n", + "\n", + "XX = gam.generate_X_grid(term=0)\n", + "\n", + "m = X.min()\n", + "M = X.max()\n", + "XX = np.linspace(m - 10, M + 10, 500)\n", + "Xl = np.linspace(m - 10, m, 50)\n", + "Xr = np.linspace(M, M + 10, 50)\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(XX, gam.predict(XX), 'k')\n", + "plt.plot(Xl, gam.confidence_intervals(Xl), color='b', ls='--')\n", + "plt.plot(Xr, gam.confidence_intervals(Xr), color='b', ls='--')\n", + "_ = plt.plot(X, gam.confidence_intervals(X), color='r', ls='--')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "1. Simon N. Wood, 2006 \n", + "Generalized Additive Models: an introduction with R\n", + "\n", + "0. Hastie, Tibshirani, Friedman \n", + "The Elements of Statistical Learning \n", + "http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf \n", + "\n", + "0. James, Witten, Hastie and Tibshirani \n", + "An Introduction to Statistical Learning \n", + "http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf \n", + "\n", + "0. Paul Eilers & Brian Marx, 1996\n", + "Flexible Smoothing with B-splines and Penalties\n", + "http://www.stat.washington.edu/courses/stat527/s13/readings/EilersMarx_StatSci_1996.pdf\n", + "\n", + "0. Kim Larsen, 2015 \n", + "GAM: The Predictive Modeling Silver Bullet \n", + "http://multithreaded.stitchfix.com/assets/files/gam.pdf \n", + "\n", + "0. Deva Ramanan, 2008 \n", + "UCI Machine Learning: Notes on IRLS \n", + "http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/homework/irls_notes.pdf \n", + "\n", + "0. Paul Eilers & Brian Marx, 2015 \n", + "International Biometric Society: A Crash Course on P-splines \n", + "http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf\n", + "\n", + "0. Keiding, Niels, 1991 \n", + "Age-specific incidence and prevalence: a statistical perspective\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def ll(*args, **kwargs):\n", + " \"\"\"See Also\n", + " --------\n", + " LinearTerm : for developer details\n", + " \"\"\"\n", + " return LinearTerm(*args, **kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'See Also\\n --------\\n LinearTerm : for developer details\\n '" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ll.__doc__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py36", + "language": "python", + "name": "py36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gen_imgs.py b/gen_imgs.py index 9a75be4b..66508542 100644 --- a/gen_imgs.py +++ b/gen_imgs.py @@ -8,7 +8,7 @@ from mpl_toolkits import mplot3d from pygam import * -from pygam.datasets import hepatitis, wage, faithful, mcycle, trees, default, cake, toy_classification +from pygam.datasets import hepatitis, wage, faithful, mcycle, trees, default, cake, toy_classification, toy_interaction, chicago np.random.seed(420) fontP = FontProperties() @@ -24,7 +24,7 @@ def gen_basis_fns(): X, y = hepatitis() gam = LinearGAM(lam=.6, fit_intercept=False).fit(X, y) - XX = gam.generate_X_grid(term=0) + XX = gam.generate_X_grid(term=0, n=500) plt.figure() fig, ax = plt.subplots(2,1) @@ -59,7 +59,7 @@ def faithful_data_poisson(): plt.hist(faithful(return_X_y=False)['eruptions'], bins=200, color='k'); plt.plot(X, gam.predict(X), color='r') - plt.title('Best Lambda: {0:.2f}'.format(gam.lam)) + plt.title('Best Lambda: {0:.2f}'.format(gam.lam[0][0])) plt.savefig('imgs/pygam_poisson.png', dpi=300) def single_data_linear(): @@ -260,14 +260,13 @@ def gen_tensor_data(): fig = plt.figure(figsize=(9,6)) ax = plt.axes(projection='3d') - ax.dist = 7 + ax.dist = 7.5 ax.plot_surface(XX[0], XX[1], Z, cmap='viridis') ax.set_axis_off() fig.tight_layout() + plt.savefig('imgs/pygam_tensor.png', transparent=True, dpi=300) - plt.savefig('imgs/pygam_tensor.png', dpi=300) - -def gen_chicago_tensor(): +def chicago_tensor(): """ chicago tensor """ @@ -285,6 +284,40 @@ def gen_chicago_tensor(): plt.savefig('imgs/pygam_chicago_tensor.png', dpi=300) +def expectiles(): + """ + a bunch of expectiles + """ + + X, y = mcycle(return_X_y=True) + + # lets fit the mean model first by CV + gam50 = ExpectileGAM(expectile=0.5).gridsearch(X, y) + + # and copy the smoothing to the other models + lam = gam50.lam + + # now fit a few more models + gam95 = ExpectileGAM(expectile=0.95, lam=lam).fit(X, y) + gam75 = ExpectileGAM(expectile=0.75, lam=lam).fit(X, y) + gam25 = ExpectileGAM(expectile=0.25, lam=lam).fit(X, y) + gam05 = ExpectileGAM(expectile=0.05, lam=lam).fit(X, y) + + XX = gam50.generate_X_grid(term=0, n=500) + + fig = plt.figure() + plt.scatter(X, y, c='k', alpha=0.2) + plt.plot(XX, gam95.predict(XX), label='0.95') + plt.plot(XX, gam75.predict(XX), label='0.75') + plt.plot(XX, gam50.predict(XX), label='0.50') + plt.plot(XX, gam25.predict(XX), label='0.25') + plt.plot(XX, gam05.predict(XX), label='0.05') + plt.legend() + fig.tight_layout() + + plt.savefig('imgs/pygam_expectiles.png', dpi=300) + + if __name__ == '__main__': gen_basis_fns() faithful_data_poisson() @@ -293,5 +326,8 @@ def gen_chicago_tensor(): constraints() trees_data_custom() mcycle_data_linear() - cake_data_in_one() + # cake_data_in_one() gen_multi_data() + gen_tensor_data() + chicago_tensor() + expectiles() diff --git a/imgs/pygam_basis.png b/imgs/pygam_basis.png index 3159fd32..b1e4325b 100644 Binary files a/imgs/pygam_basis.png and b/imgs/pygam_basis.png differ diff --git a/imgs/pygam_chicago_tensor.png b/imgs/pygam_chicago_tensor.png index fba9f771..93dc028c 100644 Binary files a/imgs/pygam_chicago_tensor.png and b/imgs/pygam_chicago_tensor.png differ diff --git a/imgs/pygam_constraints.png b/imgs/pygam_constraints.png index e517ab66..8224f85a 100644 Binary files a/imgs/pygam_constraints.png and b/imgs/pygam_constraints.png differ diff --git a/imgs/pygam_custom.png b/imgs/pygam_custom.png index 13f59990..cbc17a25 100644 Binary files a/imgs/pygam_custom.png and b/imgs/pygam_custom.png differ diff --git a/imgs/pygam_default_data_logistic.png b/imgs/pygam_default_data_logistic.png index a64b29d7..70f9b4b9 100644 Binary files a/imgs/pygam_default_data_logistic.png and b/imgs/pygam_default_data_logistic.png differ diff --git a/imgs/pygam_expectiles.png b/imgs/pygam_expectiles.png new file mode 100644 index 00000000..de31262f Binary files /dev/null and b/imgs/pygam_expectiles.png differ diff --git a/imgs/pygam_mcycle_data_extrapolation.png b/imgs/pygam_mcycle_data_extrapolation.png index 3fbab13a..14543b91 100644 Binary files a/imgs/pygam_mcycle_data_extrapolation.png and b/imgs/pygam_mcycle_data_extrapolation.png differ diff --git a/imgs/pygam_mcycle_data_linear.png b/imgs/pygam_mcycle_data_linear.png index 9d08dff7..b26c324e 100644 Binary files a/imgs/pygam_mcycle_data_linear.png and b/imgs/pygam_mcycle_data_linear.png differ diff --git a/imgs/pygam_multi_deviance.png b/imgs/pygam_multi_deviance.png index d7b5230a..6c284d15 100644 Binary files a/imgs/pygam_multi_deviance.png and b/imgs/pygam_multi_deviance.png differ diff --git a/imgs/pygam_multi_pdep.png b/imgs/pygam_multi_pdep.png index b0b03d40..a199e4ab 100644 Binary files a/imgs/pygam_multi_pdep.png and b/imgs/pygam_multi_pdep.png differ diff --git a/imgs/pygam_poisson.png b/imgs/pygam_poisson.png index d3253944..3068cee7 100644 Binary files a/imgs/pygam_poisson.png and b/imgs/pygam_poisson.png differ diff --git a/imgs/pygam_tensor.png b/imgs/pygam_tensor.png index 6f0e4f44..bca583cc 100644 Binary files a/imgs/pygam_tensor.png and b/imgs/pygam_tensor.png differ diff --git a/imgs/pygam_wage_data_linear.png b/imgs/pygam_wage_data_linear.png index ccfc6a81..b2dfc9de 100644 Binary files a/imgs/pygam_wage_data_linear.png and b/imgs/pygam_wage_data_linear.png differ diff --git a/pygam/__init__.py b/pygam/__init__.py index 5b107e9b..7a5728c8 100644 --- a/pygam/__init__.py +++ b/pygam/__init__.py @@ -21,4 +21,4 @@ __all__ = ['GAM', 'LinearGAM', 'LogisticGAM', 'GammaGAM', 'PoissonGAM', 'InvGaussGAM', 'ExpectileGAM', 'l', 's', 'f', 'te', 'intercept'] -__version__ = '0.6.4' +__version__ = '0.7.0' diff --git a/pygam/pygam.py b/pygam/pygam.py index dac5d482..1e4ab1b5 100644 --- a/pygam/pygam.py +++ b/pygam/pygam.py @@ -89,35 +89,34 @@ class GAM(Core, MetaTermMixin): By default a univariate spline term will be allocated for each feature. For example: - `GAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - distribution : str or Distribution object, default: 'normal' + distribution : str or Distribution object, optional Distribution to use in the model. - link : str or Link object, default: 'identity' + link : str or Link object, optional Link function to use in the model. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. - verbose : bool, default: False - whether to show pyGAM warnings + verbose : bool, optional + whether to show pyGAM warnings. Attributes ---------- @@ -133,7 +132,7 @@ class GAM(Core, MetaTermMixin): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- @@ -314,7 +313,7 @@ def loglikelihood(self, X, y, weights=None): containing the input dataset y : array-like of shape (n,) containing target values - weights : array-like of shape (n,) + weights : array-like of shape (n,), optional containing sample weights Returns @@ -345,7 +344,7 @@ def _loglikelihood(self, y, mu, weights=None): containing target values mu : array-like of shape (n_samples,) expected value of the targets given the model and inputs - weights : array-like of shape (n,) + weights : array-like of shape (n,), optional containing sample weights Returns @@ -366,20 +365,20 @@ def _linear_predictor(self, X=None, modelmat=None, b=None, term=-1): and at least 1 of (b, feature) - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features) or None, optional containing the input dataset if None, will attempt to use modelmat - modelmat : array-like, default: None + modelmat : array-like or None, optional contains the spline basis for each feature evaluated at the input values for each feature, ie model matrix if None, will attempt to construct the model matrix from X - b : array-like, default: None + b : array-like or None, optional contains the spline coefficients if None, will use current model coefficients - feature : int, deafult: -1 + feature : int, optional feature for which to compute the linear prediction if -1, will compute for all features @@ -399,7 +398,7 @@ def predict_mu(self, X): Parameters --------- - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features), containing the input dataset Returns @@ -424,7 +423,7 @@ def predict(self, X): Parameters --------- - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features) containing the input dataset Returns @@ -442,10 +441,10 @@ def _modelmat(self, X, term=-1): Parameters --------- - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features) containing the input dataset - feature : int, default: -1 - feature index for which to compute the model matrix + term : int, optional + term index for which to compute the model matrix if -1, will create the model matrix for all features Returns @@ -582,7 +581,7 @@ def _W(self, mu, weights, y=None): expected value of the targets given the model and inputs weights : array-like of shape (n_samples,) containing sample weights - y = array-like of shape (n_samples,) or None, default None + y = array-like of shape (n_samples,) or None, optional does nothing. just for compatibility with ExpectileGAM Returns @@ -642,7 +641,7 @@ def _initial_estimate(self, y, modelmat): Notes ----- - This method implements the suggestions in + This method implements the suggestions in Wood, section 2.2.2 Geometry and IRLS convergence, pg 80 """ @@ -874,14 +873,13 @@ def fit(self, X, y, weights=None): Parameters ---------- X : array-like, shape (n_samples, m_features) - Training vectors, where n_samples is the number of samples - and m_features is the number of features. + Training vectors. y : array-like, shape (n_samples,) - Target values (integers in classification, real numbers in + Target values, + ie integers in classification, real numbers in regression) - For classification, labels must correspond to classes. - weights : array-like shape (n_samples,) or None, default: None - containing sample weights + weights : array-like shape (n_samples,) or None, optional + Sample weights. if None, defaults to array of ones Returns @@ -935,19 +933,19 @@ def deviance_residuals(self, X, y, weights=None, scaled=False): Parameters ---------- X : array-like - input data array of shape (n_saples, m_features) + Input data array of shape (n_saples, m_features) y : array-like - output data vector of shape (n_samples,) - weights : array-like shape (n_samples,) or None, default: None - containing sample weights + Output data vector of shape (n_samples,) + weights : array-like shape (n_samples,) or None, optional + Sample weights. if None, defaults to array of ones - scaled : bool, default: False - whether to scale the deviance by the (estimated) distribution scale + scaled : bool, optional + whether to scale the deviance by the (estimated) distribution scale Returns ------- deviance_residuals : np.array - with shape (n_samples,) + with shape (n_samples,) """ if not self._is_fitted: raise AttributeError('GAM has not been fitted. Call fit first.') @@ -1003,6 +1001,7 @@ def _estimate_model_statistics(self, y, modelmat, inner=None, BW=None, B : array of intermediate computations from stable optimization weights : array-like shape (n_samples,) or None, default: None containing sample weights + U1 : cropped U matrix from SVD. Returns ------- @@ -1033,8 +1032,11 @@ def _estimate_AIC(self, y, mu, weights=None): ---------- y : array-like of shape (n_samples,) output data vector - mu : array-like of shape (n_samples,) + mu : array-like of shape (n_samples,), expected value of the targets given the model and inputs + weights : array-like shape (n_samples,) or None, optional + containing sample weights + if None, defaults to array of ones Returns ------- @@ -1057,6 +1059,9 @@ def _estimate_AICc(self, y, mu, weights=None): output data vector mu : array-like of shape (n_samples,) expected value of the targets given the model and inputs + weights : array-like shape (n_samples,) or None, optional + containing sample weights + if None, defaults to array of ones Returns ------- @@ -1080,7 +1085,7 @@ def _estimate_r2(self, X=None, y=None, mu=None, weights=None): output data vector mu : array-like of shape (n_samples,) expected value of the targets given the model and inputs - weights : array-like shape (n_samples,) or None, default: None + weights : array-like shape (n_samples,) or None, optional containing sample weights if None, defaults to array of ones @@ -1249,11 +1254,11 @@ def confidence_intervals(self, X, width=.95, quantiles=None): Parameters ---------- X : array-like of shape (n_samples, m_features) - input data matrix - width : float on [0,1], default: 0.95 - quantiles : array-like of floats in (0, 1), default: None - instead of specifying the prediciton width, one can specify the - quantiles. so width=.95 is equivalent to quantiles=[.025, .975] + Input data matrix + width : float on [0,1], optional + quantiles : array-like of floats in (0, 1), optional + Instead of specifying the prediciton width, one can specify the + quantiles. So ``width=.95`` is equivalent to ``quantiles=[.025, .975]`` Returns ------- @@ -1375,22 +1380,19 @@ def generate_X_grid(self, term, n=100, meshgrid=False): so the marginal and joint distributions are likely wrong if term is >= 0, we generate n samples per feature, - which results in n^deg samples, - where deg is the degree of the interaction of the term + which results in n^deg samples, + where deg is the degree of the interaction of the term Parameters ---------- term : int, - which term to process + Which term to process. - Note: a ValueError is raised if the term requested is an intercept - since it does not make sense to process the intercept term. - - n : int, default: 100 + n : int, optional number of data points to create - meshgrid : bool, default: True - whether to return a meshgrid (useful for 3d plotting) + meshgrid : bool, optional + Whether to return a meshgrid (useful for 3d plotting) or a feature matrix (useful for inference like partial predictions) Returns @@ -1398,13 +1400,19 @@ def generate_X_grid(self, term, n=100, meshgrid=False): if meshgrid is False: np.array of shape (n, n_features) where m is the number of - (sub)terms in the requested (tensor)term. + (sub)terms in the requested (tensor)term. else: tuple of len m, where m is the number of (sub)terms in the requested (tensor)term. each element in the tuple contains a np.ndarray of size (n)^m + + Raises + ------ + ValueError : + If the term requested is an intercept + since it does not make sense to process the intercept term. """ if not self._is_fitted: raise AttributeError('GAM has not been fitted. Call fit first.') @@ -1459,10 +1467,8 @@ def partial_dependence(self, term, X=None, width=None, quantiles=None, Parameters ---------- - term : int, default: -1 - term for which to compute the partial dependence functions - - Note: a ValueError is raised if the term requested is an intercept + term : int, optional + Term for which to compute the partial dependence functions. X : array-like with input data, optional @@ -1474,32 +1480,38 @@ def partial_dependence(self, term, X=None, width=None, quantiles=None, if None, an equally spaced grid of points is generated. - width : float on (0, 1), default: None - width of the confidence interval - if None, defaults to 0.95 + width : float on (0, 1), optional + Width of the confidence interval. - quantiles : array-like of floats on (0, 1), default: None + quantiles : array-like of floats on (0, 1), optional instead of specifying the prediciton width, one can specify the - quantiles. so width=.95 is equivalent to quantiles=[.025, .975] - if None, defaults to width + quantiles. so width=.95 is equivalent to quantiles=[.025, .975]. + if None, defaults to width. meshgrid : bool, whether to return and accept meshgrids. - `meshgrid=True` helps for creating outputs that are suitable for + Useful for creating outputs that are suitable for 3D plotting. Note, for simple terms with no interactions, the output - of this function will be the same for `meshgrid=True` and - `meshgrid=False`, but the inputs will need to be different. - - see `generate_X_grid(..., meshgrid=True)` method for help - creating meshgrids. + of this function will be the same for ``meshgrid=True`` and + ``meshgrid=False``, but the inputs will need to be different. Returns ------- pdeps : np.array of shape (n_samples,) conf_intervals : list of length len(term) containing np.arrays of shape (n_samples, 2 or len(quantiles)) + + Raises + ------ + ValueError : + If the term requested is an intercept + since it does not make sense to process the intercept term. + + See Also + -------- + generate_X_grid : for help creating meshgrids. """ if not self._is_fitted: raise AttributeError('GAM has not been fitted. Call fit first.') @@ -1559,10 +1571,7 @@ def partial_dependence(self, term, X=None, width=None, quantiles=None, return out[0] def summary(self): - """ - produce a summary of the model statistics - - #TODO including term significance via F-Test + """produce a summary of the model statistics Parameters ---------- @@ -1649,93 +1658,107 @@ def gridsearch(self, X, y, weights=None, return_scores=False, keep_best=True, objective='auto', progress=True, **param_grids): """ - performs a grid search over a space of parameters for a given objective + Performs a grid search over a space of parameters for a given + objective - NOTE: - gridsearch method is lazy and will not remove useless combinations + Warnings + -------- + ``gridsearch`` is lazy and will not remove useless combinations from the search space, eg. - n_splines=np.arange(5,10), fit_splines=[True, False] + + >>> n_splines=np.arange(5,10), fit_splines=[True, False] + will result in 10 loops, of which 5 are equivalent because - even though fit_splines==False + ``fit_splines = False`` - it is not recommended to search over a grid that alternates + Also, it is not recommended to search over a grid that alternates between known scales and unknown scales, as the scores of the - cadidate models will not be comparable. + candidate models will not be comparable. Parameters ---------- - X : array + X : array-like input data of shape (n_samples, m_features) - y : array + y : array-like label data of shape (n_samples,) - weights : array-like shape (n_samples,) or None, default: None - containing sample weights - if None, defaults to array of ones + weights : array-like shape (n_samples,), optional + sample weights - return_scores : boolean, default False - whether to return the hyperpamaters - and score for each element in the grid + return_scores : boolean, optional + whether to return the hyperpamaters and score for each element + in the grid - keep_best : boolean + keep_best : boolean, optional whether to keep the best GAM as self. - default: True - objective : string, default: 'auto' - metric to optimize. must be in ['AIC', 'AICc', 'GCV', 'UBRE', 'auto'] - if 'auto', then grid search will optimize GCV for models with unknown - scale and UBRE for models with known scale. + objective : {'auto', 'AIC', 'AICc', 'GCV', 'UBRE'}, optional + Metric to optimize. + If `auto`, then grid search will optimize `GCV` for models with unknown + scale and `UBRE` for models with known scale. - progress : bool, default: True + progress : bool, optional whether to display a progress bar - **kwargs : dict, default `lam=np.logspace(-3, 3, 11)`} + **kwargs pairs of parameters and iterables of floats, or parameters and iterables of iterables of floats. - if grid is iterable of iterables of floats, - the outer iterable must have length m_features. + If no parameter are specified, ``lam=np.logspace(-3, 3, 11)`` is used. + This results in a 11 points, placed diagonally across lam space. + + If grid is iterable of iterables of floats, + the outer iterable must have length ``m_features``. the cartesian product of the subgrids in the grid will be tested. - if grid is a 2d numpy array, + If grid is a 2d numpy array, each row of the array will be tested. - the method will make a grid of all the combinations of the parameters + The method will make a grid of all the combinations of the parameters and fit a GAM to each combination. Returns ------- - if return_scores == True: - model_scores : dict - Contains each fitted model as keys and corresponding - objective scores as values + if ``return_scores=True``: + model_scores: dict containing each fitted model as keys and corresponding + objective scores as values else: - self, ie possibly the newly fitted model + self: ie possibly the newly fitted model Examples -------- - For a model with 3 terms, and where we expect 3 lam values, - our search space for lam must have 3 dimensions. + For a model with 4 terms, and where we expect 4 lam values, + our search space for lam must have 4 dimensions. + + We can search the space in 3 ways: - However we can search the space in 2 ways: - - via cartesian product by specifying the grid as a list - our grid search will consider 11 ** 3 points + 1. via cartesian product by specifying the grid as a list. + our grid search will consider ``11 ** 4`` points: >>> lam = np.logspace(-3, 3, 11) - >>> lams = [lam] * 3 + >>> lams = [lam] * 4 >>> gam.gridsearch(X, y, lam=lams) - - directly by specifying the grid as a np.ndarray - our gridsearch will consider 11 points + 2. directly by specifying the grid as a np.ndarray. + This is useful for when the dimensionality of the search space + is very large, and we would prefer to execute a randomized search: - >>> lam = np.logspace(-3, 3, 11) - >>> lams = np.array([lam] * 3) + >>> lams = np.exp(np.random.random(50, 4) * 6 - 3) >>> gam.gridsearch(X, y, lam=lams) - the latter is useful for when the dimensionality of the search space - is very large, and we would prefer to execute a randomized search. + 3. copying grids for parameters with multiple dimensions. + if we specify a 1D np.ndarray for lam, we are implicitly testing the + space where all points have the same value + + >>> gam.gridsearch(lam=np.logspace(-3, 3, 11)) + + is equivalent to: + + >>> lam = np.logspace(-3, 3, 11) + >>> lams = np.array([lam] * 4) + >>> gam.gridsearch(X, y, lam=lams) """ # check if model fitted if not self._is_fitted: @@ -1912,18 +1935,22 @@ def sample(self, X, y, quantity='y', sample_at_X=None, These samples are drawn as follows. Details are in the reference below. - 1. `n_bootstraps` many "bootstrap samples" of the response (`y`) are + 1. ``n_bootstraps`` many "bootstrap samples" of the response (``y``) are simulated by drawing random samples from the model's distribution - evaluated at the expected values (`mu`) for each sample in `X`. + evaluated at the expected values (``mu``) for each sample in ``X``. + 2. A copy of the model is fitted to each of those bootstrap samples of the response. The result is an approximation of the distribution over - the smoothing parameter `lam` given the response data `y`. + the smoothing parameter ``lam`` given the response data ``y``. + 3. Samples of the coefficients are simulated from a multivariate normal using the bootstrap samples of the coefficients and their covariance matrices. - NOTE: A `gridsearch` is done `n_bootstraps` many times, so keep - `n_bootstraps` small. Make `n_bootstraps < n_draws` to take advantage + Notes + ----- + A ``gridsearch`` is done ``n_bootstraps`` many times, so keep + ``n_bootstraps`` small. Make ``n_bootstraps < n_draws`` to take advantage of the expensive bootstrap samples of the smoothing parameters. Parameters @@ -1941,7 +1968,7 @@ def sample(self, X, y, quantity='y', sample_at_X=None, `sample_at_X`. sample_at_X : array of shape (n_samples_to_simulate, m_features) or - None, default: None + None, optional Input data at which to draw new samples. Only applies for `quantity` equal to `'y'` or to `'mu`'. @@ -1950,11 +1977,11 @@ def sample(self, X, y, quantity='y', sample_at_X=None, weights : np.array of shape (n_samples,) sample weights - n_draws : positive int, default: 100 + n_draws : positive int, optional (default=100) The number of samples to draw from the posterior distribution of the coefficients and smoothing parameters - n_bootstraps : positive int, default: 5 + n_bootstraps : positive int, optional (default=5) The number of bootstrap samples to draw from simulations of the response (from the already fitted model) to estimate the distribution of the smoothing parameters given the response data. @@ -1962,7 +1989,7 @@ def sample(self, X, y, quantity='y', sample_at_X=None, smoothing parameter is used, and the distribution over the smoothing parameters is not estimated using bootstrap sampling. - objective : string, default: 'auto' + objective : string, optional (default='auto' metric to optimize in grid search. must be in ['AIC', 'AICc', 'GCV', 'UBRE', 'auto'] if 'auto', then grid search will optimize GCV for models with @@ -2028,18 +2055,18 @@ def _sample_coef(self, X, y, weights=None, n_draws=100, n_bootstraps=1, weights : np.array of shape (n_samples,) sample weights - n_draws : positive int, default: 100 + n_draws : positive int, optional (default=100 The number of samples to draw from the posterior distribution of the coefficients and smoothing parameters - n_bootstraps : positive int, default: 1 + n_bootstraps : positive int, optional (default=1 The number of bootstrap samples to draw from simulations of the response (from the already fitted model) to estimate the distribution of the smoothing parameters given the response data. If `n_bootstraps` is 1, then only the already fitted model's smoothing parameters is used. - objective : string, default: 'auto' + objective : string, optional (default='auto' metric to optimize in grid search. must be in ['AIC', 'AICc', 'GCV', 'UBRE', 'auto'] if 'auto', then grid search will optimize GCV for models with @@ -2169,33 +2196,28 @@ class LinearGAM(GAM): By default a univariate spline term will be allocated for each feature. For example: - `LinearGAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - scale : float or None, default: None - scale of the distribution, if known a-priori. - if None, scale is estimated. - - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. - verbose : bool, default: False - whether to show pyGAM warnings + verbose : bool, optional + whether to show pyGAM warnings. Attributes ---------- @@ -2211,7 +2233,7 @@ class LinearGAM(GAM): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- @@ -2264,8 +2286,8 @@ def prediction_intervals(self, X, width=.95, quantiles=None): ---------- X : array-like of shape (n_samples, m_features) input data matrix - width : float on [0,1], default: 0.95 - quantiles : array-like of floats in [0, 1], default: None + width : float on [0,1], optional (default=0.95 + quantiles : array-like of floats in [0, 1], default: None) instead of specifying the prediciton width, one can specify the quantiles. so width=.95 is equivalent to quantiles=[.025, .975] @@ -2294,29 +2316,28 @@ class LogisticGAM(GAM): By default a univariate spline term will be allocated for each feature. For example: - `LogisticGAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. - verbose : bool, default: False - whether to show pyGAM warnings + verbose : bool, optional + whether to show pyGAM warnings. Attributes ---------- @@ -2332,7 +2353,7 @@ class LogisticGAM(GAM): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- @@ -2372,11 +2393,11 @@ def accuracy(self, X=None, y=None, mu=None): ---------- note: X or mu must be defined. defaults to mu - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features), optional (default=None) containing input data y : array-like of shape (n,) containing target data - mu : array-like of shape (n_samples,), default: None + mu : array-like of shape (n_samples,), optional (default=None expected value of the targets given the model and inputs Returns @@ -2403,7 +2424,7 @@ def predict(self, X): Parameters --------- - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features), optional (default=None) containing the input dataset Returns @@ -2419,7 +2440,7 @@ def predict_proba(self, X): Parameters --------- - X : array-like of shape (n_samples, m_features), default: None + X : array-like of shape (n_samples, m_features), optional (default=None containing the input dataset Returns @@ -2442,29 +2463,28 @@ class PoissonGAM(GAM): By default a univariate spline term will be allocated for each feature. For example: - `PoissonGAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. - verbose : bool, default: False - whether to show pyGAM warnings + verbose : bool, optional + whether to show pyGAM warnings. Attributes ---------- @@ -2480,7 +2500,7 @@ class PoissonGAM(GAM): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- @@ -2699,13 +2719,15 @@ def gridsearch(self, X, y, exposure=None, weights=None, NOTE: gridsearch method is lazy and will not remove useless combinations from the search space, eg. - n_splines=np.arange(5,10), fit_splines=[True, False] + + >>> n_splines=np.arange(5,10), fit_splines=[True, False] + will result in 10 loops, of which 5 are equivalent because even though fit_splines==False it is not recommended to search over a grid that alternates between known scales and unknown scales, as the scores of the - cadidate models will not be comparable. + candidate models will not be comparable. Parameters ---------- @@ -2789,33 +2811,28 @@ class GammaGAM(GAM): By default a univariate spline term will be allocated for each feature. For example: - `GammaGAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - scale : float or None, default: None - scale of the distribution, if known a-priori. - if None, scale is estimated. - - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. - verbose : bool, default: False - whether to show pyGAM warnings + verbose : bool, optional + whether to show pyGAM warnings. Attributes ---------- @@ -2831,7 +2848,7 @@ class GammaGAM(GAM): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- @@ -2902,33 +2919,28 @@ class InvGaussGAM(GAM): By default a univariate spline term will be allocated for each feature. For example: - `InvGaussGAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - scale : float or None, default: None - scale of the distribution, if known a-priori. - if None, scale is estimated. - - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. - verbose : bool, default: False - whether to show pyGAM warnings + verbose : bool, optional + whether to show pyGAM warnings. Attributes ---------- @@ -2944,7 +2956,7 @@ class InvGaussGAM(GAM): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- @@ -3005,34 +3017,29 @@ class ExpectileGAM(GAM): By default a univariate spline term will be allocated for each feature. For example: - `ExpectileGAM(s(0) + l(1) + f(2) + te(3, 4))` + + >>> GAM(s(0) + l(1) + f(2) + te(3, 4)) will fit a spline term on feature 0, a linear term on feature 1, a factor term on feature 2, and a tensor term on features 3 and 4. - expectile : float on (0, 1), default: 0.5 - expectile to estimate. - - callbacks : list of strings or list of CallBack objects, - default: ['deviance', 'diffs'] + callbacks : list of str or list of CallBack objects, optional Names of callback objects to call during the optimization loop. - fit_intercept : bool, default: True + fit_intercept : bool, optional Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. + Note: the intercept receives no smoothing penalty. - NOTE: the intercept receives no smoothing penalty. - - max_iter : int, default: 100 + max_iter : int, optional Maximum number of iterations allowed for the solver to converge. - scale : float or None, default: None - scale of the distribution, if known a-priori. - if None, scale is estimated. - - tol : float, default: 1e-4 + tol : float, optional Tolerance for stopping criteria. + verbose : bool, optional + whether to show pyGAM warnings. + Attributes ---------- coef_ : array, shape (n_classes, m_features) @@ -3047,7 +3054,7 @@ class ExpectileGAM(GAM): Dictionary containing the outputs of any callbacks at each optimization loop. - The logs are structured as `{callback: [...]}` + The logs are structured as ``{callback: [...]}`` References ---------- diff --git a/pygam/terms.py b/pygam/terms.py index 10af495f..239157d8 100644 --- a/pygam/terms.py +++ b/pygam/terms.py @@ -1628,22 +1628,46 @@ def build_constraints(self, coefs, constraint_lam, constraint_l2): # Minimal representations def l(*args, **kwargs): + """ + + See Also + -------- + LinearTerm : for developer details + """ return LinearTerm(*args, **kwargs) def s(*args, **kwargs): + """ + + See Also + -------- + SplineTerm : for developer details + """ return SplineTerm(*args, **kwargs) def f(*args, **kwargs): + """ + + See Also + -------- + FactorTerm : for developer details + """ return FactorTerm(*args, **kwargs) def te(*args, **kwargs): + """ + + See Also + -------- + TensorTerm : for developer details + """ return TensorTerm(*args, **kwargs) intercept = Intercept() # copy docs for minimal_, class_ in zip([l, s, f, te], [LinearTerm, SplineTerm, FactorTerm, TensorTerm]): - minimal_.__doc__ = class_.__init__.__doc__ + minimal_.__doc__ = class_.__init__.__doc__ + minimal_.__doc__ TERMS = {'term' : Term, diff --git a/requirements.txt b/requirements.txt index 357e7f09..609b7b93 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,3 +9,9 @@ pytest-cov pytest-pylint setuptools scipy>=0.17 + +# Documentation Requirements +sphinxcontrib-napoleon # Parses numpy-style docstrings +nbsphinx # Converts notebooks to reStructuredText +ipython # For syntax highlighting notebooks +sphinx_rtd_theme # The Read The Docs theme