update notebooks 5 (splines) and 7 (BART) (#218)

* update notebooks 5 (splines) and 7 (BART)

* add jupytext md sync

Author: aloctavodia

notebooks_updated/chp_01.ipynb

@@ -944,6 +944,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -960,6 +963,13 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.5"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

notebooks_updated/chp_01.md

@@ -0,0 +1,368 @@
+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.16.0
+  kernelspec:
+    display_name: Python 3 (ipykernel)
+    language: python
+    name: python3
+---
+
+# Code 1: Bayesian Inference
+
+
+```{admonition} This is a reference notebook for the book Bayesian Modeling and Computation in Python
+:class: tip, dropdown
+The textbook is not needed to use or run this code, though the context and explanation is missing from this notebook.
+
+If you'd like a copy it's available
+[from the CRC Press](https://www.routledge.com/Bayesian-Modeling-and-Computation-in-Python/Martin-Kumar-Lao/p/book/9780367894368)
+or from [Amazon](https://www.routledge.com/Bayesian-Modeling-and-Computation-in-Python/Martin-Kumar-Lao/p/book/9780367894368).
+``
+
+```python
+%matplotlib inline
+import arviz as az
+import matplotlib.pyplot as plt
+import numpy as np
+import pymc as pm
+from scipy import stats
+from scipy.stats import entropy
+from scipy.optimize import minimize
+```
+
+```python
+az.style.use("arviz-grayscale")
+plt.rcParams['figure.dpi'] = 300
+np.random.seed(521)
+viridish = [(0.2823529411764706, 0.11372549019607843, 0.43529411764705883, 1.0),
+            (0.1450980392156863, 0.6705882352941176, 0.5098039215686274, 1.0),
+            (0.6901960784313725, 0.8666666666666667, 0.1843137254901961, 1.0)]
+```
+
+## A DIY Sampler, Do Not Try This at Home
+
+
+### Figure 1.1
+
+```python
+grid = np.linspace(0, 1, 5000)
+prior = stats.triang.pdf(grid, 0.5)
+likelihood = stats.triang.pdf(0.2, grid)
+posterior = prior * likelihood
+log_prior = np.log(prior)
+log_likelihood = np.log(likelihood)
+log_posterior = log_prior + log_likelihood
+
+
+_, ax = plt.subplots(1, 2, figsize=(10, 4))
+ax[0].plot(grid, prior, label="prior", lw=2)
+ax[0].plot(grid, likelihood, label="likelihood", lw=2, color="C2")
+ax[0].plot(grid, posterior, label="posterior", lw=2, color="C4")
+ax[0].set_xlabel("θ")
+ax[0].legend()
+ax[0].set_yticks([])
+
+
+ax[1].plot(grid, log_prior, label="log-prior", lw=2)
+ax[1].plot(grid, log_likelihood, label="log-likelihood", lw=2, color="C2")
+ax[1].plot(grid, log_posterior, label="log-posterior", lw=2, color="C4")
+ax[1].set_xlabel("θ")
+ax[1].legend()
+ax[1].set_yticks([])
+plt.savefig("img/chp01/bayesian_triad.png")
+```
+
+### Code 1.1
+
+```python
+def post(θ, Y, α=1, β=1):
+    if 0 <= θ <= 1:
+        prior = stats.beta(α, β).pdf(θ)
+        like  = stats.bernoulli(θ).pmf(Y).prod()
+        prop = like * prior
+    else:
+        prop = -np.inf
+    return prop
+```
+
+### Code 1.2
+
+```python
+Y = stats.bernoulli(0.7).rvs(20)
+```
+
+### Code 1.3
+
+```python
+n_iters = 1000
+can_sd = 0.05
+α = β =  1
+θ = 0.5
+trace = {'θ':np.zeros(n_iters)}
+p2 = post(θ, Y, α, β)
+
+for iter in range(n_iters):
+    θ_can = stats.norm(θ, can_sd).rvs(1)
+    p1 = post(θ_can, Y, α, β)
+    pa = p1 / p2
+
+    if pa > stats.uniform(0, 1).rvs(1):
+        θ = θ_can
+        p2 = p1
+
+    trace['θ'][iter] = θ
+```
+
+### Code 1.5
+
+```python
+az.summary(trace, kind='stats', round_to=2)
+```
+
+### Code 1.4 and Figure 1.2
+
+```python
+_, axes = plt.subplots(1,2, figsize=(10, 4), constrained_layout=True, sharey=True)
+axes[1].hist(trace['θ'], color='0.5', orientation="horizontal", density=True)
+axes[1].set_xticks([])
+axes[0].plot(trace['θ'], '0.5')
+axes[0].set_ylabel('θ', rotation=0, labelpad=15)
+plt.savefig("img/chp01/traceplot.png")
+```
+
+## Say Yes to Automating Inference, Say No to Automated Model Building
+
+
+### Figure 1.3
+
+```python
+az.plot_posterior(trace)
+plt.savefig("img/chp01/plot_posterior.png")
+```
+
+### Code 1.6
+
+```python
+# Declare a model in PyMC3
+with pm.Model() as model:
+    # Specify the prior distribution of unknown parameter
+    θ = pm.Beta("θ", alpha=1, beta=1)
+
+    # Specify the likelihood distribution and condition on the observed data
+    y_obs = pm.Binomial("y_obs", n=1, p=θ, observed=Y)
+
+    # Sample from the posterior distribution
+    idata = pm.sample(1000)
+```
+
+### Code 1.7
+
+```python
+graphviz = pm.model_to_graphviz(model)
+graphviz
+```
+
+```python
+graphviz.graph_attr.update(dpi="300")
+graphviz.render("img/chp01/BetaBinomModelGraphViz", format="png")
+```
+
+## A Few Options to Quantify Your Prior Information
+
+
+### Figure 1.5
+
+```python
+pred_dists = (pm.sample_prior_predictive(1000, model).prior_predictive["y_obs"].values,
+              pm.sample_posterior_predictive(idata, model).posterior_predictive["y_obs"].values)
+```
+
+```python
+fig, axes = plt.subplots(4, 1, figsize=(9, 9))
+
+for idx, n_d, dist in zip((1, 3), ("Prior", "Posterior"), pred_dists):
+    az.plot_dist(dist.sum(-1), 
+                 hist_kwargs={"color":"0.5", "bins":range(0, 22)},
+                 ax=axes[idx])
+    axes[idx].set_title(f"{n_d} predictive distribution", fontweight='bold')
+    axes[idx].set_xlim(-1, 21)
+    axes[idx].set_ylim(0, 0.15)
+    axes[idx].set_xlabel("number of success")
+
+az.plot_dist(pm.draw(θ, 1000), plot_kwargs={"color":"0.5"},
+             fill_kwargs={'alpha':1}, ax=axes[0])
+axes[0].set_title("Prior distribution", fontweight='bold')
+axes[0].set_xlim(0, 1)
+axes[0].set_ylim(0, 4)
+axes[0].tick_params(axis='both', pad=7)
+axes[0].set_xlabel("θ")
+
+az.plot_dist(idata.posterior["θ"], plot_kwargs={"color":"0.5"},
+             fill_kwargs={'alpha':1}, ax=axes[2])
+axes[2].set_title("Posterior distribution", fontweight='bold')
+axes[2].set_xlim(0, 1)
+axes[2].set_ylim(0, 5)
+axes[2].tick_params(axis='both', pad=7)
+axes[2].set_xlabel("θ")
+
+plt.savefig("img/chp01/Bayesian_quartet_distributions.png")
+```
+
+### Figure 1.6
+
+```python
+predictions = (stats.binom(n=1, p=idata.posterior["θ"].mean()).rvs((4000, len(Y))),
+               pred_dists[1])
+
+for d, c, l in zip(predictions, ("C0", "C4"), ("posterior mean", "posterior predictive")):
+    ax = az.plot_dist(d.sum(-1),
+                      label=l,
+                      figsize=(10, 5),
+                      hist_kwargs={"alpha": 0.5, "color":c, "bins":range(0, 22)})
+    ax.set_yticks([])
+    ax.set_xlabel("number of success")
+plt.savefig("img/chp01/predictions_distributions.png")
+```
+
+### Code 1.8 and Figure 1.7
+
+```python
+_, axes = plt.subplots(2,3, figsize=(12, 6), sharey=True, sharex=True,
+                     constrained_layout=True)
+axes = np.ravel(axes)
+
+n_trials = [0, 1, 2, 3, 12, 180]
+success = [0, 1, 1, 1, 6, 59]
+data = zip(n_trials, success)
+
+beta_params = [(0.5, 0.5), (1, 1), (10, 10)]
+θ = np.linspace(0, 1, 1500)
+for idx, (N, y) in enumerate(data):
+    s_n = ('s' if (N > 1) else '')
+    for jdx, (a_prior, b_prior) in enumerate(beta_params):
+        p_theta_given_y = stats.beta.pdf(θ, a_prior + y, b_prior + N - y)
+
+        axes[idx].plot(θ, p_theta_given_y, lw=4, color=viridish[jdx])
+        axes[idx].set_yticks([])
+        axes[idx].set_ylim(0, 12)
+        axes[idx].plot(np.divide(y, N), 0, color='k', marker='o', ms=12)
+        axes[idx].set_title(f'{N:4d} trial{s_n} {y:4d} success')
+
+plt.savefig('img/chp01/beta_binomial_update.png')
+```
+
+### Figure 1.8
+
+```python
+θ = np.linspace(0, 1, 100)
+κ = (θ / (1-θ))
+y = 2
+n = 7
+
+_, axes = plt.subplots(2, 2, figsize=(10, 5),
+                     sharex='col', sharey='row', constrained_layout=False)
+
+axes[0, 0].set_title("Jeffreys' prior for Alice")
+axes[0, 0].plot(θ, θ**(-0.5) * (1-θ)**(-0.5))
+axes[1, 0].set_title("Jeffreys' posterior for Alice")
+axes[1, 0].plot(θ, θ**(y-0.5) * (1-θ)**(n-y-0.5))
+axes[1, 0].set_xlabel("θ")
+axes[0, 1].set_title("Jeffreys' prior for Bob")
+axes[0, 1].plot(κ, κ**(-0.5) * (1 + κ)**(-1))
+axes[1, 1].set_title("Jeffreys' posterior for Bob")
+axes[1, 1].plot(κ, κ**(y-0.5) * (1 + κ)**(-n-1))
+axes[1, 1].set_xlim(-0.5, 10)
+axes[1, 1].set_xlabel("κ")
+axes[1, 1].text(-4.0, 0.030, size=18, s=r'$p(\theta \mid Y) \, \frac{d\theta}{d\kappa}$')
+axes[1, 1].annotate("", xy=(-0.5, 0.025), xytext=(-4.5, 0.025),
+                  arrowprops=dict(facecolor='black', shrink=0.05))
+axes[1, 1].text(-4.0, 0.007, size=18, s= r'$p(\kappa \mid Y) \, \frac{d\kappa}{d\theta}$')
+axes[1, 1].annotate("", xy=(-4.5, 0.015), xytext=(-0.5, 0.015),
+                  arrowprops=dict(facecolor='black', shrink=0.05),
+                  annotation_clip=False)
+
+plt.subplots_adjust(wspace=0.4, hspace=0.4)
+plt.tight_layout()
+plt.savefig("img/chp01/Jeffrey_priors.png")
+```
+
+### Figure 1.9
+
+```python
+cons = [[{"type": "eq", "fun": lambda x: np.sum(x) - 1}],
+        [{"type": "eq", "fun": lambda x: np.sum(x) - 1},
+         {"type": "eq", "fun": lambda x: 1.5 - np.sum(x * np.arange(1, 7))}],
+        [{"type": "eq", "fun": lambda x: np.sum(x) - 1},
+         {"type": "eq", "fun": lambda x: np.sum(x[[2, 3]]) - 0.8}]]
+
+max_ent = []
+for i, c in enumerate(cons):
+    val = minimize(lambda x: -entropy(x), x0=[1/6]*6, bounds=[(0., 1.)] * 6,
+                   constraints=c)['x']
+    max_ent.append(entropy(val))
+    plt.plot(np.arange(1, 7), val, 'o--', color=viridish[i], lw=2.5)
+plt.xlabel("$t$")
+plt.ylabel("$p(t)$")
+
+plt.savefig("img/chp01/max_entropy.png")
+```
+
+### Code 1.10
+
+```python
+ite = 100_000
+entropies = np.zeros((3, ite))
+for idx in range(ite):
+    rnds = np.zeros(6)
+    total = 0
+    x_ = np.random.choice(np.arange(1, 7), size=6, replace=False)
+    for i in x_[:-1]:
+        rnd = np.random.uniform(0, 1-total)
+        rnds[i-1] = rnd
+        total = rnds.sum()
+    rnds[-1] = 1 - rnds[:-1].sum()
+    H = entropy(rnds)
+    entropies[0, idx] = H
+    if abs(1.5 - np.sum(rnds * x_)) < 0.01:
+        entropies[1, idx] = H
+    prob_34 = sum(rnds[np.argwhere((x_ == 3) | (x_ == 4)).ravel()])
+    if abs(0.8 - prob_34) < 0.01:
+        entropies[2, idx] = H
+```
+
+### Figure 1.10
+
+```python
+_, ax = plt.subplots(1, 3, figsize=(12,4), sharex=True, sharey=True, constrained_layout=True)
+
+for i in range(3):
+    az.plot_kde(entropies[i][np.nonzero(entropies[i])], ax=ax[i], plot_kwargs={"color":viridish[i], "lw":4})
+    ax[i].axvline(max_ent[i], 0, 1, ls="--")
+    ax[i].set_yticks([])
+    ax[i].set_xlabel("entropy")
+plt.savefig("img/chp01/max_entropy_vs_random_dist.png")
+```
+
+### Figure 1.11
+
+```python
+x = np.linspace(0, 1, 500)
+params = [(0.5, 0.5), (1, 1), (3,3), (100, 25)]
+
+labels = ["Jeffreys", "MaxEnt", "Weakly  Informative",
+          "Informative"]
+
+_, ax = plt.subplots()
+for (α, β), label, c in zip(params, labels, (0, 1, 4, 2)):
+    pdf = stats.beta.pdf(x, α, β)
+    ax.plot(x, pdf, label=f"{label}", c=f"C{c}", lw=3)
+    ax.set(yticks=[], xlabel="θ", title="Priors")
+    ax.legend()
+plt.savefig("img/chp01/prior_informativeness_spectrum.png")
+```