Update GP Marginal and Latent notebooks to v5

.jupytext.toml

@@ -1,2 +1,2 @@
-notebook_metadata_filter = "substitutions,-jupytext.text_representation.jupytext_version"
+notebook_metadata_filter = "myst,-jupytext.text_representation.jupytext_version"
 formats = ["ipynb", ".myst.md:myst"]

.pre-commit-config.yaml

@@ -20,7 +20,6 @@ repos:
             (?x)^
             ^examples/ode_models/ODE_with_manual_gradients\.ipynb$
             |examples/samplers/DEMetropolisZ_EfficiencyComparison\.ipynb$
-            |examples/gaussian_processes/GP-Latent\.ipynb$
             |examples/gaussian_processes/GP-MaunaLoa2\.ipynb$
             |examples/samplers/MLDA_gravity_surveying\.ipynb$
             |examples/howto/sampling_callback\.ipynb$

examples/gaussian_processes/GP-Latent.myst.md

@@ -5,15 +5,18 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc-dev
+  display_name: Python 3 (ipykernel)
   language: python
-  name: pymc-dev
+  name: python3
+myst:
+  substitutions:
+    extra_dependencies: jax numpyro
 ---
 
 (gp_latent)=
 # Gaussian Processes: Latent Variable Implementation
 
-:::{post} Sept 28, 2022
+:::{post} June 6, 2023
 :tags: gaussian processes, time series
 :category: reference, intermediate
 :author: Bill Engels
@@ -80,6 +83,9 @@ with latent_gp_model:
 
 The following is an example showing how to specify a simple model with a GP prior using the {class}`gp.Latent` class.  We use a GP to generate the data so we can verify that the inference we perform is correct.  Note that the likelihood is not normal, but IID Student-T.  For a more efficient implementation when the likelihood is Gaussian, use {class}`gp.Marginal`.
 
+:::{include} ../extra_installs.md
+:::
+
 ```{code-cell} ipython3
 import arviz as az
 import matplotlib.pyplot as plt
@@ -158,7 +164,7 @@ with pm.Model() as model:
     )  # add one because student t is undefined for degrees of freedom less than one
     y_ = pm.StudentT("y", mu=f, lam=1.0 / sigma, nu=nu, observed=y)
 
-    idata = pm.sample(1000, tune=1000, chains=2, cores=1)
+    idata = pm.sample(1000, tune=1000, chains=2, cores=2, nuts_sampler="numpyro")
 ```
 
 ```{code-cell} ipython3
@@ -349,7 +355,7 @@ with pm.Model() as model:
     p = pm.Deterministic("p", pm.math.invlogit(f))
     y_ = pm.Bernoulli("y", p=p, observed=y)
 
-    idata = pm.sample(1000, chains=2, cores=1)
+    idata = pm.sample(1000, chains=2, cores=2, nuts_sampler="numpyro")
 ```
 
 ```{code-cell} ipython3
@@ -429,6 +435,7 @@ plt.legend(loc=(0.32, 0.65), frameon=True);
 * Created by [Bill Engels](https://github.com/bwengals) in 2017 ([pymc#1674](https://github.com/pymc-devs/pymc/pull/1674))
 * Reexecuted by [Colin Caroll](https://github.com/ColCarroll) in 2019 ([pymc#3397](https://github.com/pymc-devs/pymc/pull/3397))
 * Updated for V4 by Bill Engels in September 2022 ([pymc-examples#237](https://github.com/pymc-devs/pymc-examples/pull/237))
+* Updated for V5 by Chris Fonnesbeck in July 2023 ([pymc-examples#549](https://github.com/pymc-devs/pymc-examples/pull/549))
 
 +++
 

examples/gaussian_processes/GP-Marginal.myst.md

@@ -10,8 +10,15 @@ kernelspec:
   name: python3
 ---
 
+(gp_marginal)=
 # Marginal Likelihood Implementation
 
+:::{post} June 4, 2023
+:tags: gaussian processes, time series
+:category: reference, intermediate
+:author: Bill Engels, Chris Fonnesbeck
+:::
+
 The `gp.Marginal` class implements the more common case of GP regression:  the observed data are the sum of a GP and Gaussian noise.  `gp.Marginal` has a `marginal_likelihood` method, a `conditional` method, and a `predict` method.  Given a mean and covariance function, the function $f(x)$ is modeled as,
 
 $$
@@ -68,19 +75,19 @@ with pm.Model() as marginal_gp_model:
 
     # The scale of the white noise term can be provided,
     sigma = pm.HalfCauchy("sigma", beta=5)
-    y_ = gp.marginal_likelihood("y", X=X, y=y, noise=sigma)
+    y_ = gp.marginal_likelihood("y", X=X, y=y, sigma=sigma)
 
     # OR a covariance function for the noise can be given
     # noise_l = pm.Gamma("noise_l", alpha=2, beta=2)
     # cov_func_noise = pm.gp.cov.Exponential(1, noise_l) + pm.gp.cov.WhiteNoise(sigma=0.1)
-    # y_ = gp.marginal_likelihood("y", X=X, y=y, noise=cov_func_noise)
+    # y_ = gp.marginal_likelihood("y", X=X, y=y, sigma=cov_func_noise)
 ```
 
 +++
 
 ## The `.conditional` distribution
 
-The `.conditional` has an optional flag for `pred_noise`, which defaults to `False`.  When `pred_noise=False`, the `conditional` method produces the predictive distribution for the underlying function represented by the GP.  When `pred_noise=True`, the `conditional` method produces the predictive distribution for the GP plus noise.  Using the same `gp` object defined above, 
+The `.conditional` has an optional flag for `pred_noise`, which defaults to `False`.  When `pred_sigma=False`, the `conditional` method produces the predictive distribution for the underlying function represented by the GP.  When `pred_sigma=True`, the `conditional` method produces the predictive distribution for the GP plus noise.  Using the same `gp` object defined above, 
 
 ```python
 # vector of new X points we want to predict the function at
@@ -90,7 +97,7 @@ with marginal_gp_model:
     f_star = gp.conditional("f_star", Xnew=Xnew)
 
     # or to predict the GP plus noise
-    y_star = gp.conditional("y_star", Xnew=Xnew, pred_noise=True)
+    y_star = gp.conditional("y_star", Xnew=Xnew, pred_sigma=True)
 ```
 If using an additive GP model, the conditional distribution for individual components can be constructed by setting the optional argument `given`.  For more information on building additive GPs, see the main documentation page.  For an example, see the Mauna Loa CO$_2$ notebook.
 
@@ -108,7 +115,7 @@ mu, cov = gp.predict(Xnew, point=trace[-1])
 mu, var = gp.predict(Xnew, point=trace[-1],  diag=True)
 
 # With noise included
-mu, var = gp.predict(Xnew, point=trace[-1],  diag=True, pred_noise=True)
+mu, var = gp.predict(Xnew, point=trace[-1],  diag=True, pred_sigma=True)
 ```
 
 +++
@@ -120,10 +127,11 @@ mu, var = gp.predict(Xnew, point=trace[-1],  diag=True, pred_noise=True)
 jupyter:
   outputs_hidden: true
 ---
+import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import pymc3 as pm
+import pymc as pm
 import scipy as sp
 
 %matplotlib inline
@@ -137,9 +145,9 @@ n = 100  # The number of data points
 X = np.linspace(0, 10, n)[:, None]  # The inputs to the GP, they must be arranged as a column vector
 
 # Define the true covariance function and its parameters
-ℓ_true = 1.0
-η_true = 3.0
-cov_func = η_true**2 * pm.gp.cov.Matern52(1, ℓ_true)
+ell_true = 1.0
+eta_true = 3.0
+cov_func = eta_true**2 * pm.gp.cov.Matern52(1, ell_true)
 
 # A mean function that is zero everywhere
 mean_func = pm.gp.mean.Zero()
@@ -152,8 +160,8 @@ f_true = np.random.multivariate_normal(
 
 # The observed data is the latent function plus a small amount of IID Gaussian noise
 # The standard deviation of the noise is `sigma`
-σ_true = 2.0
-y = f_true + σ_true * np.random.randn(n)
+sigma_true = 2.0
+y = f_true + sigma_true * np.random.randn(n)
 
 ## Plot the data and the unobserved latent function
 fig = plt.figure(figsize=(12, 5))
@@ -167,31 +175,26 @@ plt.legend();
 
 ```{code-cell} ipython3
 with pm.Model() as model:
-    ℓ = pm.Gamma("ℓ", alpha=2, beta=1)
-    η = pm.HalfCauchy("η", beta=5)
+    ell = pm.Gamma("ell", alpha=2, beta=1)
+    eta = pm.HalfCauchy("eta", beta=5)
 
-    cov = η**2 * pm.gp.cov.Matern52(1, ℓ)
+    cov = eta**2 * pm.gp.cov.Matern52(1, ell)
     gp = pm.gp.Marginal(cov_func=cov)
 
-    σ = pm.HalfCauchy("σ", beta=5)
-    y_ = gp.marginal_likelihood("y", X=X, y=y, noise=σ)
+    sigma = pm.HalfCauchy("sigma", beta=5)
+    y_ = gp.marginal_likelihood("y", X=X, y=y, sigma=sigma)
 
-    mp = pm.find_MAP()
+    with model:
+        marginal_post = pm.sample(500, tune=2000, nuts_sampler="numpyro", chains=1)
 ```
 
 ```{code-cell} ipython3
-# collect the results into a pandas dataframe to display
-# "mp" stands for marginal posterior
-pd.DataFrame(
-    {
-        "Parameter": ["ℓ", "η", "σ"],
-        "Value at MAP": [float(mp["ℓ"]), float(mp["η"]), float(mp["σ"])],
-        "True value": [ℓ_true, η_true, σ_true],
-    }
-)
+summary = az.summary(marginal_post, var_names=["ell", "eta", "sigma"], round_to=2, kind="stats")
+summary["True value"] = [ell_true, eta_true, sigma_true]
+summary
 ```
 
-The MAP values are close to their true values.
+The estimated values are close to their true values.
 
 +++
 
@@ -205,9 +208,10 @@ X_new = np.linspace(0, 20, 600)[:, None]
 with model:
     f_pred = gp.conditional("f_pred", X_new)
 
-# To use the MAP values, you can just replace the trace with a length-1 list with `mp`
 with model:
-    pred_samples = pm.sample_posterior_predictive([mp], vars=[f_pred], samples=2000)
+    pred_samples = pm.sample_posterior_predictive(
+        marginal_post.sel(draw=slice(0, 20)), var_names=["f_pred"]
+    )
 ```
 
 ```{code-cell} ipython3
@@ -216,9 +220,10 @@ fig = plt.figure(figsize=(12, 5))
 ax = fig.gca()
 
 # plot the samples from the gp posterior with samples and shading
-from pymc3.gp.util import plot_gp_dist
+from pymc.gp.util import plot_gp_dist
 
-plot_gp_dist(ax, pred_samples["f_pred"], X_new)
+f_pred_samples = az.extract(pred_samples, group="posterior_predictive", var_names=["f_pred"])
+plot_gp_dist(ax, samples=f_pred_samples.T, x=X_new)
 
 # plot the data and the true latent function
 plt.plot(X, f_true, "dodgerblue", lw=3, label="True f")
@@ -238,19 +243,22 @@ The `conditional` method of `gp.Marginal` contains the flag `pred_noise` whose d
 ```{code-cell} ipython3
 with model:
     y_pred = gp.conditional("y_pred", X_new, pred_noise=True)
-    y_samples = pm.sample_posterior_predictive([mp], vars=[y_pred], samples=2000)
+    y_samples = pm.sample_posterior_predictive(
+        marginal_post.sel(draw=slice(0, 50)), var_names=["y_pred"]
+    )
 ```
 
 ```{code-cell} ipython3
 fig = plt.figure(figsize=(12, 5))
 ax = fig.gca()
 
 # posterior predictive distribution
-plot_gp_dist(ax, y_samples["y_pred"], X_new, plot_samples=False, palette="bone_r")
+y_pred_samples = az.extract(y_samples, group="posterior_predictive", var_names=["y_pred"])
+plot_gp_dist(ax, y_pred_samples.T, X_new, plot_samples=False, palette="bone_r")
 
 # overlay a scatter of one draw of random points from the
 #   posterior predictive distribution
-plt.plot(X_new, y_samples["y_pred"][800, :].T, "co", ms=2, label="Predicted data")
+plt.plot(X_new, y_pred_samples.sel(sample=1), "co", ms=2, label="Predicted data")
 
 # plot original data and true function
 plt.plot(X, y, "ok", ms=3, alpha=1.0, label="observed data")
@@ -268,18 +276,21 @@ Notice that the posterior predictive density is wider than the conditional distr
 
 ### Using `.predict`
 
-We can use the `.predict` method to return the mean and variance given a particular `point`.  Since we used `find_MAP` in this example, `predict` returns the same mean and covariance that the distribution of `.conditional` has.
+We can use the `.predict` method to return the mean and variance given a particular `point`. 
 
 ```{code-cell} ipython3
 # predict
-mu, var = gp.predict(X_new, point=mp, diag=True)
+with model:
+    mu, var = gp.predict(
+        X_new, point=az.extract(marginal_post.posterior.sel(draw=[0])).squeeze(), diag=True
+    )
 sd = np.sqrt(var)
 
 # draw plot
 fig = plt.figure(figsize=(12, 5))
 ax = fig.gca()
 
-# plot mean and 2σ intervals
+# plot mean and 2sigma intervals
 plt.plot(X_new, mu, "r", lw=2, label="mean and 2σ region")
 plt.plot(X_new, mu + 2 * sd, "r", lw=1)
 plt.plot(X_new, mu - 2 * sd, "r", lw=1)
@@ -291,10 +302,21 @@ plt.plot(X, f_true, "dodgerblue", lw=3, label="true f")
 
 plt.xlabel("x")
 plt.ylim([-13, 13])
-plt.title("predictive mean and 2σ interval")
+plt.title("predictive mean and 2sigma interval")
 plt.legend();
 ```
 
+## Authors
+
+* Created by [Bill Engels](https://github.com/bwengals) in 2017 ([pymc#1674](https://github.com/pymc-devs/pymc/pull/1674))
+* Reexecuted by [Colin Caroll](https://github.com/ColCarroll) in 2019 ([pymc#3397](https://github.com/pymc-devs/pymc/pull/3397))
+* Updated for V4 by Bill Engels in September 2022 ([pymc-examples#237](https://github.com/pymc-devs/pymc-examples/pull/237))
+* Updated for V5 by Chris Fonnesbeck in July 2023 ([pymc-examples#549](https://github.com/pymc-devs/pymc-examples/pull/549))
+
++++
+
+## Watermark
+
 ```{code-cell} ipython3
 %load_ext watermark
 %watermark -n -u -v -iv -w