Use networkx instead of causalgraphicalmodels

.github/workflows/ci.yml

@@ -53,4 +53,3 @@ jobs:
     - name: Test with nbval
       run: |
         find notebooks -maxdepth 1 -name "[01][456789]*.ipynb" | sort -n | xargs pytest -vx --nbval-lax --durations=0
-

.github/workflows/main.yml

@@ -0,0 +1,26 @@
+on:
+  push:
+    branches: [ master ]
+
+jobs:
+  nikola_build:
+
+    name: 'Deploy Nikola to GitHub Pages'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.9]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Build and Deploy Nikola
+      run: |
+        python -m pip install --upgrade pip
+        pip install "Nikola[extras]"
+        git fetch origin gh-pages
+        cd site
+        nikola github_deploy

.gitignore

@@ -7,4 +7,3 @@ output
 .doit.db*
 site/pages/*.ipynb
 site/listings/*.py
-

README.md

@@ -13,10 +13,10 @@ I am a fan of the book [*Statistical Rethinking*](https://xcelab.net/rm/statisti
 
 ## Installation
 
-The following tools are used for some analysis and visualizations: [arviz](https://arviz-devs.github.io/arviz/) for [posteriors](https://en.wikipedia.org/wiki/Posterior_probability), [causalgraphicalmodels](https://github.com/ijmbarr/causalgraphicalmodels) and [daft](https://docs.daft-pgm.org/en/latest/) for [causal graphs](https://en.wikipedia.org/wiki/Causal_graph), and (optional) [ete3](http://etetoolkit.org/) for [phylogenetic trees](https://en.wikipedia.org/wiki/Phylogenetic_tree).
+The following tools are used for some analysis and visualizations: [arviz](https://arviz-devs.github.io/arviz/) for [posteriors](https://en.wikipedia.org/wiki/Posterior_probability), [networkx](https://networkx.org/) and [daft](https://docs.daft-pgm.org/en/latest/) for [causal graphs](https://en.wikipedia.org/wiki/Causal_graph), and (optional) [ete3](http://etetoolkit.org/) for [phylogenetic trees](https://en.wikipedia.org/wiki/Phylogenetic_tree).
 
 ```sh
-pip install numpyro arviz causalgraphicalmodels daft
+pip install numpyro arviz daft networkx
 ```
 
 ## Excercises

notebooks/00_preface.ipynb

@@ -13,7 +13,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q numpyro arviz causalgraphicalmodels daft"
+    "!pip install -q numpyro arviz"
    ]
   },
   {
@@ -231,14 +231,14 @@
    },
    "source": [
     "```sh\n",
-    "pip install numpyro arviz causalgraphicalmodels daft\n",
+    "pip install numpyro arviz daft networkx\n",
     "```"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -252,7 +252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.11.6"
   },
   "varInspector": {
    "cols": {

notebooks/01_the_golem_of_prague.ipynb

@@ -17,7 +17,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -31,7 +31,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.11.6"
   },
   "varInspector": {
    "cols": {

notebooks/02_small_worlds_and_large_worlds.ipynb

@@ -21,7 +21,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q numpyro arviz causalgraphicalmodels daft"
+    "!pip install -q numpyro arviz"
    ]
   },
   {
@@ -224,7 +224,7 @@
     "params = svi_result.params\n",
     "\n",
     "# display summary of quadratic approximation\n",
-    "samples = guide.sample_posterior(random.PRNGKey(1), params, (1000,))\n",
+    "samples = guide.sample_posterior(random.PRNGKey(1), params, sample_shape=(1000,))\n",
     "numpyro.diagnostics.print_summary(samples, prob=0.89, group_by_chain=False)"
    ]
   },
@@ -323,7 +323,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -337,7 +337,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/03_sampling_the_imaginary.ipynb

@@ -21,7 +21,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q numpyro arviz causalgraphicalmodels daft"
+    "!pip install -q numpyro arviz"
    ]
   },
   {
@@ -816,7 +816,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.11.6"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

notebooks/04_geocentric_models.ipynb

@@ -13,7 +13,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q numpyro arviz causalgraphicalmodels daft"
+    "!pip install -q numpyro arviz"
    ]
   },
   {
@@ -904,7 +904,7 @@
     }
    ],
    "source": [
-    "samples = m4_1.sample_posterior(random.PRNGKey(1), p4_1, (1000,))\n",
+    "samples = m4_1.sample_posterior(random.PRNGKey(1), p4_1, sample_shape=(1000,))\n",
     "print_summary(samples, 0.89, False)"
    ]
   },
@@ -978,7 +978,7 @@
     "svi = SVI(model, m4_2, optim.Adam(1), Trace_ELBO(), height=d2.height.values)\n",
     "svi_result = svi.run(random.PRNGKey(0), 2000)\n",
     "p4_2 = svi_result.params\n",
-    "samples = m4_2.sample_posterior(random.PRNGKey(1), p4_2, (1000,))\n",
+    "samples = m4_2.sample_posterior(random.PRNGKey(1), p4_2, sample_shape=(1000,))\n",
     "print_summary(samples, 0.89, False)"
    ]
   },
@@ -1007,7 +1007,7 @@
     }
    ],
    "source": [
-    "samples = m4_1.sample_posterior(random.PRNGKey(1), p4_1, (1000,))\n",
+    "samples = m4_1.sample_posterior(random.PRNGKey(1), p4_1, sample_shape=(1000,))\n",
     "vcov = jnp.cov(jnp.stack(list(samples.values()), axis=0))\n",
     "vcov"
    ]
@@ -1064,7 +1064,7 @@
     }
    ],
    "source": [
-    "post = m4_1.sample_posterior(random.PRNGKey(1), p4_1, (int(1e4),))\n",
+    "post = m4_1.sample_posterior(random.PRNGKey(1), p4_1, sample_shape=(int(1e4),))\n",
     "{latent: list(post[latent][:6]) for latent in post}"
    ]
   },
@@ -1369,7 +1369,7 @@
     }
    ],
    "source": [
-    "samples = m4_3.sample_posterior(random.PRNGKey(1), p4_3, (1000,))\n",
+    "samples = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(1000,))\n",
     "samples.pop(\"mu\")\n",
     "print_summary(samples, 0.89, False)"
    ]
@@ -1429,7 +1429,7 @@
    ],
    "source": [
     "az.plot_pair(d2[[\"weight\", \"height\"]].to_dict(orient=\"list\"))\n",
-    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, (1000,))\n",
+    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(1000,))\n",
     "a_map = jnp.mean(post[\"a\"])\n",
     "b_map = jnp.mean(post[\"b\"])\n",
     "x = jnp.linspace(d2.weight.min(), d2.weight.max(), 101)\n",
@@ -1464,7 +1464,7 @@
     }
    ],
    "source": [
-    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, (1000,))\n",
+    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(1000,))\n",
     "{latent: list(post[latent].reshape(-1)[:5]) for latent in post}"
    ]
   },
@@ -1539,7 +1539,7 @@
    ],
    "source": [
     "# extract 20 samples from the posterior\n",
-    "post = mN.sample_posterior(random.PRNGKey(1), pN, (20,))\n",
+    "post = mN.sample_posterior(random.PRNGKey(1), pN, sample_shape=(20,))\n",
     "\n",
     "# display raw data and sample size\n",
     "ax = az.plot_pair(dN[[\"weight\", \"height\"]].to_dict(orient=\"list\"))\n",
@@ -1568,7 +1568,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, (1000,))\n",
+    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(1000,))\n",
+    "post.pop(\"mu\")\n",
     "mu_at_50 = post[\"a\"] + post[\"b\"] * (50 - xbar)"
    ]
   },
@@ -1797,7 +1798,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, (1000,))\n",
+    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(1000,))\n",
     "mu_link = lambda weight: post[\"a\"] + post[\"b\"] * (weight - xbar)\n",
     "weight_seq = jnp.arange(start=25, stop=71, step=1)\n",
     "mu = vmap(mu_link)(weight_seq).T\n",
@@ -1829,6 +1830,7 @@
     }
    ],
    "source": [
+    "post.pop(\"mu\")\n",
     "sim_height = Predictive(m4_3.model, post, return_sites=[\"height\"])(\n",
     "    random.PRNGKey(2), weight_seq, None\n",
     ")[\"height\"]\n",
@@ -1905,6 +1907,7 @@
    "outputs": [],
    "source": [
     "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(int(1e4),))\n",
+    "post.pop(\"mu\")\n",
     "sim_height = Predictive(m4_3.model, post, return_sites=[\"height\"])(\n",
     "    random.PRNGKey(2), weight_seq, None\n",
     ")[\"height\"]\n",
@@ -1924,7 +1927,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, (1000,))\n",
+    "post = m4_3.sample_posterior(random.PRNGKey(1), p4_3, sample_shape=(1000,))\n",
     "weight_seq = jnp.arange(25, 71)\n",
     "sim_height = vmap(\n",
     "    lambda i, weight: dist.Normal(\n",
@@ -2126,7 +2129,7 @@
     }
    ],
    "source": [
-    "samples = m4_5.sample_posterior(random.PRNGKey(1), p4_5, (1000,))\n",
+    "samples = m4_5.sample_posterior(random.PRNGKey(1), p4_5, sample_shape=(1000,))\n",
     "print_summary({k: v for k, v in samples.items() if k != \"mu\"}, 0.89, False)"
    ]
   },
@@ -2145,7 +2148,8 @@
    "source": [
     "weight_seq = jnp.linspace(start=-2.2, stop=2, num=30)\n",
     "pred_dat = {\"weight_s\": weight_seq, \"weight_s2\": weight_seq**2}\n",
-    "post = m4_5.sample_posterior(random.PRNGKey(1), p4_5, (1000,))\n",
+    "post = m4_5.sample_posterior(random.PRNGKey(1), p4_5, sample_shape=(1000,))\n",
+    "post.pop(\"mu\")\n",
     "predictive = Predictive(m4_5.model, post)\n",
     "mu = predictive(random.PRNGKey(2), **pred_dat)[\"mu\"]\n",
     "mu_mean = jnp.mean(mu, 0)\n",
@@ -2479,7 +2483,7 @@
     }
    ],
    "source": [
-    "post = m4_7.sample_posterior(random.PRNGKey(1), p4_7, (1000,))\n",
+    "post = m4_7.sample_posterior(random.PRNGKey(1), p4_7, sample_shape=(1000,))\n",
     "w = jnp.mean(post[\"w\"], 0)\n",
     "plt.subplot(\n",
     "    xlim=(d2.year.min(), d2.year.max()),\n",
@@ -2578,7 +2582,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.11.6"
   },
   "toc": {
    "base_numbering": 1,