pymc-devs · twiecki · Sep 8, 2023 · Aug 23, 2023 · Sep 1, 2023 · Sep 1, 2023
diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
@@ -15,5 +15,5 @@ jobs:
     - uses: actions/setup-python@v2
     - uses: actions/setup-node@v2
       with:
-        node-version: '15'
+        node-version: '18'
     - uses: pre-commit/action@v2.0.0
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,18 +1,18 @@
 repos:
 - repo: https://github.com/psf/black
-  rev: 22.3.0
+  rev: 23.7.0
   hooks:
     - id: black-jupyter
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.1.0
+  rev: 1.7.0
   hooks:
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]
     - id: nbqa-pyupgrade
       additional_dependencies: [pyupgrade==2.7.4]
       args: [--py37-plus]
 - repo: https://github.com/MarcoGorelli/madforhooks
-  rev: 0.3.0
+  rev: 0.4.1
   hooks:
     - id: check-execution-order
       args: [--strict]
@@ -32,7 +32,7 @@ repos:
             |examples/samplers/MLDA_variance_reduction_linear_regression\.ipynb$
 
 - repo: https://github.com/FlamingTempura/bibtex-tidy
-  rev: v1.8.5
+  rev: v1.11.0
   hooks:
     - id: bibtex-tidy
       files: examples/references.bib
@@ -96,7 +96,7 @@ repos:
       language: pygrep
       types_or: [markdown, rst, jupyter]
 - repo: https://github.com/mwouts/jupytext
-  rev: v1.13.7
+  rev: v1.15.1
   hooks:
   - id: jupytext
     files: ^examples/.+\.ipynb$

diff --git a/examples/case_studies/GEV.myst.md b/examples/case_studies/GEV.myst.md
@@ -10,8 +10,6 @@ kernelspec:
   name: pymc4-dev
 ---
 
-+++ {"tags": []}
-
 # Generalized Extreme Value Distribution
 
 :::{post} Sept 27, 2022
@@ -20,7 +18,7 @@ kernelspec:
 :author: Colin Caprani 
 :::
 
-+++ {"tags": []}
++++
 
 ## Introduction
 
@@ -94,8 +92,6 @@ And now set up the model using priors estimated from a quick review of the histo
 - $\xi$: we are agnostic to the tail behaviour so centre this at zero, but limit to physically reasonable bounds of $\pm 0.6$, and keep it somewhat tight near zero.
 
 ```{code-cell} ipython3
-:tags: []
-
 # Optionally centre the data, depending on fitting and divergences
 # cdata = (data - data.mean())/data.std()
 

diff --git a/examples/case_studies/Missing_Data_Imputation.ipynb b/examples/case_studies/Missing_Data_Imputation.ipynb
@@ -1934,7 +1934,6 @@
     "\n",
     "\n",
     "def make_model(priors, normal_pred_assumption=True):\n",
-    "\n",
     "    coords = {\n",
     "        \"alpha_dim\": [\"lmx_imputed\", \"climate_imputed\", \"empower_imputed\"],\n",
     "        \"beta_dim\": [\n",
@@ -8628,7 +8627,6 @@
     "\n",
     "\n",
     "with pm.Model(coords=coords) as hierarchical_model:\n",
-    "\n",
     "    # Priors\n",
     "    company_beta_lmx = pm.Normal(\"company_beta_lmx\", 0, 1)\n",
     "    company_beta_male = pm.Normal(\"company_beta_male\", 0, 1)\n",

diff --git a/examples/case_studies/Missing_Data_Imputation.myst.md b/examples/case_studies/Missing_Data_Imputation.myst.md
@@ -426,7 +426,6 @@ priors = {
 
 
 def make_model(priors, normal_pred_assumption=True):
-
     coords = {
         "alpha_dim": ["lmx_imputed", "climate_imputed", "empower_imputed"],
         "beta_dim": [
@@ -707,7 +706,6 @@ coords = {"team": teams, "employee": np.arange(len(df_employee))}
 
 
 with pm.Model(coords=coords) as hierarchical_model:
-
     # Priors
     company_beta_lmx = pm.Normal("company_beta_lmx", 0, 1)
     company_beta_male = pm.Normal("company_beta_male", 0, 1)

diff --git a/examples/case_studies/bart_heteroscedasticity.myst.md b/examples/case_studies/bart_heteroscedasticity.myst.md
@@ -24,8 +24,6 @@ kernelspec:
 In this notebook we show how to use BART to model heteroscedasticity as described in Section 4.1 of [`pymc-bart`](https://github.com/pymc-devs/pymc-bart)'s paper {cite:p}`quiroga2022bart`. We use the `marketing` data set provided by the R package `datarium` {cite:p}`kassambara2019datarium`. The idea is to model a marketing channel contribution to sales as a function of budget.
 
 ```{code-cell} ipython3
-:tags: []
-
 import os
 
 import arviz as az
@@ -37,8 +35,6 @@ import pymc_bart as pmb
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 %config InlineBackend.figure_format = "retina"
 az.style.use("arviz-darkgrid")
 plt.rcParams["figure.figsize"] = [10, 6]
@@ -157,8 +153,6 @@ The fit looks good! In fact, we see that the mean and variance increase as a fun
 ## Watermark
 
 ```{code-cell} ipython3
-:tags: []
-
 %load_ext watermark
 %watermark -n -u -v -iv -w -p pytensor
 ```

diff --git a/examples/case_studies/binning.myst.md b/examples/case_studies/binning.myst.md
@@ -106,8 +106,6 @@ Hypothetically we could have used base python, or numpy, to describe the generat
 The approach was illustrated with a Gaussian distribution, and below we show a number of worked examples using Gaussian distributions. However, the approach is general, and at the end of the notebook we provide a demonstration that the approach does indeed extend to non-Gaussian distributions.
 
 ```{code-cell} ipython3
-:tags: []
-
 import warnings
 
 import arviz as az
@@ -219,8 +217,6 @@ We will start by investigating what happens when we use only one set of bins to
 ### Model specification
 
 ```{code-cell} ipython3
-:tags: []
-
 with pm.Model() as model1:
     sigma = pm.HalfNormal("sigma")
     mu = pm.Normal("mu")
@@ -235,8 +231,6 @@ pm.model_to_graphviz(model1)
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 with model1:
     trace1 = pm.sample()
 ```
@@ -248,8 +242,6 @@ Given the posterior values,
 we should be able to generate observations that look close to what we observed.
 
 ```{code-cell} ipython3
-:tags: []
-
 with model1:
     ppc = pm.sample_posterior_predictive(trace1)
 ```
@@ -294,22 +286,16 @@ The more important question is whether we have recovered the parameters of the d
 Recall that we used `mu = -2` and `sigma = 2` to generate the data.
 
 ```{code-cell} ipython3
-:tags: []
-
 az.plot_posterior(trace1, var_names=["mu", "sigma"], ref_val=[true_mu, true_sigma]);
 ```
 
 Pretty good! And we can access the posterior mean estimates (stored as [xarray](http://xarray.pydata.org/en/stable/index.html) types) as below. The MCMC samples arrive back in a 2D matrix with one dimension for the MCMC chain (`chain`), and one for the sample number (`draw`). We can calculate the overall posterior average with `.mean(dim=["draw", "chain"])`.
 
 ```{code-cell} ipython3
-:tags: []
-
 trace1.posterior["mu"].mean(dim=["draw", "chain"]).values
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 trace1.posterior["sigma"].mean(dim=["draw", "chain"]).values
 ```
 
@@ -324,8 +310,6 @@ Above, we used one set of binned data. Let's see what happens when we swap out f
 As with the above, here's the model specification.
 
 ```{code-cell} ipython3
-:tags: []
-
 with pm.Model() as model2:
     sigma = pm.HalfNormal("sigma")
     mu = pm.Normal("mu")
@@ -336,15 +320,11 @@ with pm.Model() as model2:
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 with model2:
     trace2 = pm.sample()
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 az.plot_trace(trace2);
 ```
 
@@ -353,23 +333,17 @@ az.plot_trace(trace2);
 Let's run a PPC check to ensure we are generating data that are similar to what we observed.
 
 ```{code-cell} ipython3
-:tags: []
-
 with model2:
     ppc = pm.sample_posterior_predictive(trace2)
 ```
 
 We calculate the mean bin posterior predictive bin counts, averaged over samples.
 
 ```{code-cell} ipython3
-:tags: []
-
 ppc.posterior_predictive.counts2.mean(dim=["chain", "draw"]).values
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 c2.values
 ```
 
@@ -399,14 +373,10 @@ az.plot_posterior(trace2, var_names=["mu", "sigma"], ref_val=[true_mu, true_sigm
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 trace2.posterior["mu"].mean(dim=["draw", "chain"]).values
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 trace2.posterior["sigma"].mean(dim=["draw", "chain"]).values
 ```
 
@@ -419,8 +389,6 @@ Now we need to see what happens if we add in both ways of binning.
 ### Model Specification
 
 ```{code-cell} ipython3
-:tags: []
-
 with pm.Model() as model3:
     sigma = pm.HalfNormal("sigma")
     mu = pm.Normal("mu")
@@ -442,8 +410,6 @@ pm.model_to_graphviz(model3)
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 with model3:
     trace3 = pm.sample()
 ```
@@ -488,20 +454,14 @@ ax[1].set_title("Seven bin discretization of N(-2, 2)")
 ### Recovering parameters
 
 ```{code-cell} ipython3
-:tags: []
-
 trace3.posterior["mu"].mean(dim=["draw", "chain"]).values
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 trace3.posterior["sigma"].mean(dim=["draw", "chain"]).values
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 az.plot_posterior(trace3, var_names=["mu", "sigma"], ref_val=[true_mu, true_sigma]);
 ```
 

diff --git a/examples/case_studies/blackbox_external_likelihood.ipynb b/examples/case_studies/blackbox_external_likelihood.ipynb
@@ -499,7 +499,6 @@
    "source": [
     "# define a theano Op for our likelihood function\n",
     "class LogLikeWithGrad(tt.Op):\n",
-    "\n",
     "    itypes = [tt.dvector]  # expects a vector of parameter values when called\n",
     "    otypes = [tt.dscalar]  # outputs a single scalar value (the log likelihood)\n",
     "\n",

diff --git a/examples/case_studies/blackbox_external_likelihood.myst.md b/examples/case_studies/blackbox_external_likelihood.myst.md
@@ -423,7 +423,6 @@ It's not quite so simple! The `grad()` method itself requires that its inputs ar
 ```{code-cell} ipython3
 # define a theano Op for our likelihood function
 class LogLikeWithGrad(tt.Op):
-
     itypes = [tt.dvector]  # expects a vector of parameter values when called
     otypes = [tt.dscalar]  # outputs a single scalar value (the log likelihood)
 

diff --git a/examples/case_studies/blackbox_external_likelihood_numpy.ipynb b/examples/case_studies/blackbox_external_likelihood_numpy.ipynb
@@ -438,7 +438,6 @@
    "source": [
     "# define a pytensor Op for our likelihood function\n",
     "class LogLikeWithGrad(pt.Op):\n",
-    "\n",
     "    itypes = [pt.dvector]  # expects a vector of parameter values when called\n",
     "    otypes = [pt.dscalar]  # outputs a single scalar value (the log likelihood)\n",
     "\n",

diff --git a/examples/case_studies/blackbox_external_likelihood_numpy.myst.md b/examples/case_studies/blackbox_external_likelihood_numpy.myst.md
@@ -246,7 +246,6 @@ It's not quite so simple! The `grad()` method itself requires that its inputs ar
 ```{code-cell} ipython3
 # define a pytensor Op for our likelihood function
 class LogLikeWithGrad(pt.Op):
-
     itypes = [pt.dvector]  # expects a vector of parameter values when called
     otypes = [pt.dscalar]  # outputs a single scalar value (the log likelihood)
 

diff --git a/examples/case_studies/conditional_autoregressive_priors.myst.md b/examples/case_studies/conditional_autoregressive_priors.myst.md
@@ -13,8 +13,6 @@ myst:
     extra_dependencies: geopandas libpysal
 ---
 
-+++ {"tags": []}
-
 (conditional_autoregressive_priors)=
 # Conditional Autoregressive (CAR) Models for Spatial Data
 
@@ -82,8 +80,6 @@ except FileNotFoundError:
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
 df_scot_cancer.head()
 ```
 

diff --git a/examples/case_studies/disaster_model.py b/examples/case_studies/disaster_model.py
@@ -137,7 +137,6 @@
 year = arange(1851, 1962)
 
 with pm.Model() as model:
-
     switchpoint = pm.DiscreteUniform("switchpoint", lower=year.min(), upper=year.max())
     early_mean = pm.Exponential("early_mean", lam=1.0)
     late_mean = pm.Exponential("late_mean", lam=1.0)

diff --git a/examples/case_studies/disaster_model_theano_op.py b/examples/case_studies/disaster_model_theano_op.py
@@ -142,7 +142,6 @@ def rate_(switchpoint, early_mean, late_mean):
 
 
 with pm.Model() as model:
-
     # Prior for distribution of switchpoint location
     switchpoint = pm.DiscreteUniform("switchpoint", lower=0, upper=years)
     # Priors for pre- and post-switch mean number of disasters

diff --git a/examples/case_studies/gelman_bioassay.py b/examples/case_studies/gelman_bioassay.py
@@ -8,7 +8,6 @@
 dose = array([-0.86, -0.3, -0.05, 0.73])
 
 with pm.Model() as model:
-
     # Logit-linear model parameters
     alpha = pm.Normal("alpha", 0, sigma=100.0)
     beta = pm.Normal("beta", 0, sigma=1.0)

diff --git a/examples/case_studies/gelman_schools.py b/examples/case_studies/gelman_schools.py
@@ -31,7 +31,6 @@
 sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])
 
 with Model() as schools:
-
     eta = Normal("eta", 0, 1, shape=J)
     mu = Normal("mu", 0, sigma=1e6)
     tau = HalfCauchy("tau", 25)

diff --git a/examples/case_studies/hierarchical_partial_pooling.ipynb b/examples/case_studies/hierarchical_partial_pooling.ipynb
@@ -155,7 +155,6 @@
     "coords = {\"player_names\": player_names.tolist()}\n",
     "\n",
     "with pm.Model(coords=coords) as baseball_model:\n",
-    "\n",
     "    phi = pm.Uniform(\"phi\", lower=0.0, upper=1.0)\n",
     "\n",
     "    kappa_log = pm.Exponential(\"kappa_log\", lam=1.5)\n",
@@ -186,7 +185,6 @@
    "outputs": [],
    "source": [
     "with baseball_model:\n",
-    "\n",
     "    theta_new = pm.Beta(\"theta_new\", alpha=phi * kappa, beta=(1.0 - phi) * kappa)\n",
     "    y_new = pm.Binomial(\"y_new\", n=4, p=theta_new, observed=0)"
    ]