pymc-devs · michaelosthege · Jul 3, 2021 · Jun 25, 2021 · Jun 25, 2021 · Jun 27, 2021
diff --git a/.readthedocs.yml b/.readthedocs.yml
@@ -0,0 +1,16 @@
+version: 2
+
+sphinx:
+    configuration: docs/source/conf.py
+
+python:
+   version: 3.7
+   install:
+   - requirements: requirements-dev.txt
+   - method: pip
+     path: .
+
+
+submodules:
+    include: all
+    recursive: true
diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
@@ -6,6 +6,7 @@
 + `theano.printing.pydotprint` is now hotfixed upon import (see [#4594](https://github.com/pymc-devs/pymc3/pull/4594)).
 + Fix bug in the computation of the log pseudolikelihood values (SMC-ABC). (see [#4672](https://github.com/pymc-devs/pymc3/pull/4672)).
 + Fix `LKJCorr.random` method to work with `pm.sample_prior_predictive`. (see [#4780](https://github.com/pymc-devs/pymc3/pull/4780))
++ Enable documentation generation via ReadTheDocs for upcoming v3 releases. (see [#4805](https://github.com/pymc-devs/pymc3/pull/4805)).
 
 ### New Features
 + Generalized BART, bounded distributions like Binomial and Poisson can now be used as likelihoods (see [#4675](https://github.com/pymc-devs/pymc3/pull/4675), [#4709](https://github.com/pymc-devs/pymc3/pull/4709) and

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -15,6 +15,8 @@
 import os
 import sys
 
+from pathlib import Path
+
 import pymc3
 
 # If extensions (or modules to document with autodoc) are in another directory,
@@ -330,3 +332,6 @@
 def setup(app):
     app.add_css_file("https://cdn.jsdelivr.net/npm/[email protected]/dist/semantic.min.css")
     app.add_css_file("default.css")
+    dir_root = Path(__file__).parent
+    for directory in html_static_path:
+        os.makedirs(str(dir_root / directory), exist_ok=True)
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -44,60 +44,60 @@
 
         <div class="ui container">
 
-        <h2 class="ui dividing header">Installation</h2>
-
-            <div class="row">
-                <div class="ui text container">
-                    <h3 class="ui header">Via conda-forge:</h3>
-
-.. code-block:: bash
-
-    conda install -c conda-forge pymc3
-
-
-.. raw:: html
-
-                    <h3 class="ui header">Latest (unstable):</h3>
+        <div class="ui vertical segment">
+            <h2 class="ui dividing header">Installation</h2>
+            <div class="ui three stackable cards">
 
-.. code-block:: bash
+                <a class="ui link card" href="https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(Linux)">
+                    <div class="content">
+                        <div class="header">Instructions for Linux</div>
+                    </div>
+                </a>
 
-    pip install git+https://github.com/pymc-devs/pymc3
+                <a class="ui link card" href="https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(MacOS)">
+                    <div class="content">
+                        <div class="header">Instructions for MacOS</div>
+                    </div>
+                </a>
 
-.. raw:: html
+                <a class="ui link card" href="https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(Windows)">
+                    <div class="content">
+                        <div class="header">Instructions for Windows</div>
+                    </div>
+                </a>
 
-                </div>
             </div>
         </div>
 
         <div class="ui vertical segment">
             <h2 class="ui dividing header">In-Depth Guides</h2>
             <div class="ui four stackable cards">
 
-                <a class="ui link card" href="/Probability_Distributions.html">
+                <a class="ui link card" href="Probability_Distributions.html">
                     <div class="content">
                         <div class="header">Probability Distributions</div>
                         <div class="description">PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks.
                         </div>
                     </div>
                 </a>
 
-                <a class="ui link card" href="/Gaussian_Processes.html">
+                <a class="ui link card" href="Gaussian_Processes.html">
                     <div class="content">
                         <div class="header">Gaussian Processes</div>
                         <div class="description">Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs.
                         </div>
                     </div>
                 </a>
 
-                <a class="ui link card" href="/pymc-examples/examples/variational_inference/variational_api_quickstart.html">
+                <a class="ui link card" href="pymc-examples/examples/variational_inference/variational_api_quickstart.html">
                     <div class="content">
                         <div class="header">Variational Inference</div>
                         <div class="description">Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets.
                         </div>
                     </div>
                 </a>
 
-                <a class="ui link card" href="/PyMC3_and_Theano.html">
+                <a class="ui link card" href="PyMC3_and_Theano.html">
                     <div class="content">
                         <div class="header">PyMC3 and Theano</div>
                         <div class="description">Theano is the deep-learning library PyMC3 uses to construct probability distributions and then access the gradient in order to implement cutting edge inference algorithms. More advanced models may be built by understanding this layer.

diff --git a/docs/source/pymc-examples b/docs/source/pymc-examples
diff --git a/docs/source/sphinxext/gallery_generator.py b/docs/source/sphinxext/gallery_generator.py
@@ -170,16 +170,23 @@ def build_gallery(srcdir, gallery):
         if basename.find(".rst") < 1:
             filename = os.path.join(source_dir, basename + ".ipynb")
             ex = NotebookGenerator(filename, target_dir)
+            url = Path(os.path.join(os.sep, gallery, ex.output_html))
+            # Need to chop off "/${gallery}/../" so as redirection works in multi versioned docs.
+            url = str(Path("..", *url.parts[3:]))
             data[basename] = {
                 "title": ex.pagetitle,
-                "url": os.path.join(os.sep, gallery, ex.output_html),
+                "url": url,
                 "thumb": os.path.basename(ex.png_path),
             }
+
         else:
             filename = basename.split(".")[0]
+            url = Path(os.path.join(os.sep, gallery, "../" + filename + ".html"))
+            # Need to chop off "/${gallery}/../" so as redirection works in multi versioned docs.
+            url = str(Path("..", *url.parts[3:]))
             data[basename] = {
                 "title": " ".join(filename.split("_")),
-                "url": os.path.join(os.sep, gallery, "../" + filename + ".html"),
+                "url": url,
                 "thumb": os.path.basename(default_png_path),
             }
+22 −0		.github/ISSUE_TEMPLATE/bug-report.md
+8 −0		.github/ISSUE_TEMPLATE/config.yml
+20 −0		.github/ISSUE_TEMPLATE/notebook-proposal.md
+39 −0		.github/ISSUE_TEMPLATE/tracker.md
+16 −0		.github/PULL_REQUEST_TEMPLATE.md
+1 −1		.github/workflows/pre-commit.yml
+1 −0		.gitignore
+11 −1		.pre-commit-config.yaml
+78 −0		CODE_OF_CONDUCT.md
+108 −0		CONTRIBUTING.md
+0 −2		README.md
+133 −0		README.rst
+92 −141		examples/case_studies/BEST.ipynb
+139 −96		examples/case_studies/LKJ.ipynb
+1,968 −0		examples/case_studies/bayesian_ab_testing.ipynb
+5 −5		examples/case_studies/blackbox_external_likelihood.ipynb
+5 −5		examples/case_studies/conditional-autoregressive-model.ipynb
+3 −3		examples/case_studies/disaster_model.py
+3 −3		examples/case_studies/disaster_model_theano_op.py
+333 −447		examples/case_studies/factor_analysis.ipynb
+2 −1		examples/case_studies/garch_example.py
+2 −1		examples/case_studies/gelman_schools.py
+25 −18		examples/case_studies/hierarchical_partial_pooling.ipynb
+1,145 −0		examples/case_studies/item_response_nba.ipynb
+2 −2		examples/case_studies/lightspeed_example.py
+124 −130		examples/case_studies/log-gaussian-cox-process.ipynb
+ −		examples/case_studies/mediation.png
+871 −0		examples/case_studies/mediation_analysis.ipynb
+920 −0		examples/case_studies/moderation_analysis.ipynb
+ −		examples/case_studies/moderation_figure.png
+263 −54		examples/case_studies/probabilistic_matrix_factorization.ipynb
+403 −520		examples/case_studies/putting_workflow.ipynb
+1,449 −524		examples/case_studies/rugby_analytics.ipynb
+65 −57		examples/case_studies/stochastic_volatility.ipynb
+145 −0		examples/data/AirPassengers.csv
+46,862 −0		examples/data/item_response_nba.csv
+1 −1		examples/data/mastectomy.csv
+244 −0		examples/data/muscle-percent-males-interaction.csv
+41 −34		examples/diagnostics_and_criticism/Bayes_factor.ipynb
+598 −566		examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb
+132 −171		examples/diagnostics_and_criticism/model_averaging.ipynb
+77 −77		examples/diagnostics_and_criticism/model_comparison.ipynb
+66 −61		examples/diagnostics_and_criticism/posterior_predictive.ipynb
+1,346 −182		examples/diagnostics_and_criticism/sampler-stats.ipynb
+1,775 −0		examples/gaussian_processes/GP-Heteroskedastic.ipynb
+85 −102		examples/gaussian_processes/GP-Kron.ipynb
+5 −5		examples/gaussian_processes/GP-Latent.ipynb
+4 −4		examples/gaussian_processes/GP-Marginal.ipynb
+1,082 −310		examples/gaussian_processes/GP-MeansAndCovs.ipynb
+647 −0		examples/generalized_linear_models/GLM-binomial-regression.ipynb
+448 −58		examples/generalized_linear_models/GLM-hierarchical-binominal-model.ipynb
+1,108 −108		examples/generalized_linear_models/GLM-hierarchical.ipynb
+59 −56		examples/generalized_linear_models/GLM-linear.ipynb
+81 −64		examples/generalized_linear_models/GLM-logistic.ipynb
+561 −642		examples/generalized_linear_models/GLM-model-selection.ipynb
+655 −0		examples/generalized_linear_models/GLM-out-of-sample-predictions.ipynb
+760 −196		examples/generalized_linear_models/GLM-poisson-regression.ipynb
+400 −340		examples/generalized_linear_models/GLM-robust-with-outlier-detection.ipynb
+912 −0		examples/generalized_linear_models/GLM-truncated-censored-regression.ipynb
+2,686 −395		examples/generalized_linear_models/GLM.ipynb
+426 −384		examples/getting_started.ipynb
+101 −506		examples/mixture_models/dependent_density_regression.ipynb
+1,930 −0		examples/mixture_models/dirichlet_mixture_of_multinomials.ipynb
+0 −4		examples/mixture_models/dp_mix.ipynb
+5 −5		examples/mixture_models/gaussian_mixture_model.ipynb
+7 −7		examples/ode_models/ODE_API_shapes_and_benchmarking.ipynb
+492 −176		examples/ode_models/ODE_with_manual_gradients.ipynb
+3 −3		examples/pymc3_howto/LKJ_correlation.py
+148 −159		examples/pymc3_howto/data_container.ipynb
+36 −24		examples/pymc3_howto/lasso_block_update.ipynb
+3 −2		examples/pymc3_howto/rankdata_ordered.py
+351 −58		examples/pymc3_howto/sampling_compound_step.ipynb
+27 −22		examples/pymc3_howto/sampling_conjugate_step.ipynb
+91 −83		examples/pymc3_howto/updating_priors.ipynb
+3 −7		examples/samplers/DEMetropolisZ_EfficiencyComparison.ipynb
+2 −11		examples/samplers/DEMetropolisZ_tune_drop_fraction.ipynb
+56 −56		examples/samplers/GLM-hierarchical-jax.ipynb
+711 −540		examples/samplers/MLDA_gravity_surveying.ipynb
+1 −1		examples/samplers/MLDA_introduction.ipynb
+62 −101		examples/samplers/MLDA_simple_linear_regression.ipynb
+212 −164		examples/samplers/MLDA_variance_reduction_linear_regression.ipynb
+5 −5		examples/samplers/SMC-ABC_Lotka-Volterra_example.ipynb
+1 −1		examples/samplers/SMC2_gaussians.ipynb
+6 −5		examples/samplers/samplers_mvnormal.py
+153 −207		examples/survival_analysis/bayes_param_survival_pymc3.ipynb
+290 −157		examples/survival_analysis/censored_data.ipynb
+58 −53		examples/survival_analysis/cox_model.ipynb
+684 −208		examples/survival_analysis/survival_analysis.ipynb
+10 −0		examples/table_of_contents_examples.js
+1,165 −0		examples/time_series/Air_passengers-Prophet_with_Bayesian_workflow.ipynb
+3 −2		examples/time_series/arma_example.py
+3 −2		examples/variational_inference/bayesian_neural_network_advi.ipynb
+84 −79		examples/variational_inference/empirical-approx-overview.ipynb
+1 −1		examples/variational_inference/normalizing_flows_overview.ipynb
+7 −6		examples/variational_inference/variational_api_quickstart.ipynb