add new data section (pymc-devs#771)

Author: AlexAndorra

examples/howto/spline.ipynb

@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: Python 3 (ipykernel)
+  display_name: pymc-examples
   language: python
-  name: python3
+  name: pymc-examples
 ---
 
 (spline)=
@@ -43,14 +43,15 @@ import numpy as np
 import pandas as pd
 import pymc as pm
 
-from patsy import dmatrix
+from patsy import build_design_matrices, dmatrix
 ```
 
 ```{code-cell} ipython3
 %matplotlib inline
 %config InlineBackend.figure_format = "retina"
 
-RANDOM_SEED = 8927
+seed = sum(map(ord, "splines"))
+rng = np.random.default_rng(seed)
 az.style.use("arviz-darkgrid")
 ```
 
@@ -84,7 +85,12 @@ If we visualize the data, it is clear that there a lot of annual variation, but
 
 ```{code-cell} ipython3
 blossom_data.plot.scatter(
-    "year", "doy", color="cornflowerblue", s=10, title="Cherry Blossom Data", ylabel="Days in bloom"
+    "year",
+    "doy",
+    color="cornflowerblue",
+    s=10,
+    title="Cherry Blossom Data",
+    ylabel="Days in bloom",
 );
 ```
 
@@ -106,18 +112,23 @@ The spline will have 15 *knots*, splitting the year into 16 sections (including
 
 ```{code-cell} ipython3
 num_knots = 15
-knot_list = np.quantile(blossom_data.year, np.linspace(0, 1, num_knots))
+knot_list = np.percentile(blossom_data.year, np.linspace(0, 100, num_knots + 2))[1:-1]
 knot_list
 ```
 
 Below is a plot of the locations of the knots over the data.
 
 ```{code-cell} ipython3
 blossom_data.plot.scatter(
-    "year", "doy", color="cornflowerblue", s=10, title="Cherry Blossom Data", ylabel="Day of Year"
+    "year",
+    "doy",
+    color="cornflowerblue",
+    s=10,
+    title="Cherry Blossom Data",
+    ylabel="Day of Year",
 )
 for knot in knot_list:
-    plt.gca().axvline(knot, color="grey", alpha=0.4);
+    plt.gca().axvline(knot, color="grey", alpha=0.4)
 ```
 
 We can use `patsy` to create the matrix $B$ that will be the b-spline basis for the regression.
@@ -128,7 +139,7 @@ The degree is set to 3 to create a cubic b-spline.
 
 B = dmatrix(
     "bs(year, knots=knots, degree=3, include_intercept=True) - 1",
-    {"year": blossom_data.year.values, "knots": knot_list[1:-1]},
+    {"year": blossom_data.year.values, "knots": knot_list},
 )
 B
 ```
@@ -160,9 +171,14 @@ COORDS = {"splines": np.arange(B.shape[1])}
 with pm.Model(coords=COORDS) as spline_model:
     a = pm.Normal("a", 100, 5)
     w = pm.Normal("w", mu=0, sigma=3, size=B.shape[1], dims="splines")
-    mu = pm.Deterministic("mu", a + pm.math.dot(np.asarray(B, order="F"), w.T))
+
+    mu = pm.Deterministic(
+        "mu",
+        a + pm.math.dot(np.asarray(B, order="F"), w.T),
+    )
     sigma = pm.Exponential("sigma", 1)
-    D = pm.Normal("D", mu=mu, sigma=sigma, observed=blossom_data.doy, dims="obs")
+
+    D = pm.Normal("D", mu=mu, sigma=sigma, observed=blossom_data.doy)
 ```
 
 ```{code-cell} ipython3
@@ -172,7 +188,15 @@ pm.model_to_graphviz(spline_model)
 ```{code-cell} ipython3
 with spline_model:
     idata = pm.sample_prior_predictive()
-    idata.extend(pm.sample(draws=1000, tune=1000, random_seed=RANDOM_SEED, chains=4))
+    idata.extend(
+        pm.sample(
+            nuts_sampler="nutpie",
+            draws=1000,
+            tune=1000,
+            random_seed=rng,
+            chains=4,
+        )
+    )
     pm.sample_posterior_predictive(idata, extend_inferencedata=True)
 ```
 
@@ -230,7 +254,7 @@ spline_df_merged.plot("year", "value", c="black", lw=2, ax=plt.gca())
 plt.legend(title="Spline Index", loc="lower center", fontsize=8, ncol=6)
 
 for knot in knot_list:
-    plt.gca().axvline(knot, color="grey", alpha=0.4);
+    plt.gca().axvline(knot, color="grey", alpha=0.4)
 ```
 
 ### Model predictions
@@ -267,6 +291,150 @@ plt.fill_between(
 );
 ```
 
+## Predicting on new data
+
+Now imagine we got a new data set, with the same range of years as the original data set, and we want to get predictions for this new data set with our fitted model. We can do this with the classic PyMC workflow of `Data` containers and `set_data` method.
+
+Before we get there though, let's note that we didn't say the new data set contains *new* years, i.e out-of-sample years. And that's on purpose, because splines can't extrapolate beyond the range of the data set used to fit the model -- hence their limitation for time series analysis. On data ranges previously seen though, that's no problem. 
+
+That precision out of the way, let's redefine our model, this time adding `Data` containers.
+
+```{code-cell} ipython3
+COORDS = {"obs": blossom_data.index}
+```
+
+```{code-cell} ipython3
+with pm.Model(coords=COORDS) as spline_model:
+    year_data = pm.Data("year", blossom_data.year)
+    doy = pm.Data("doy", blossom_data.doy)
+
+    # intercept
+    a = pm.Normal("a", 100, 5)
+
+    # Create spline bases & coefficients
+    ## Store knots & design matrix for prediction
+    spline_model.knots = np.percentile(year_data.eval(), np.linspace(0, 100, num_knots + 2))[1:-1]
+    spline_model.dm = dmatrix(
+        "bs(x, knots=spline_model.knots, degree=3, include_intercept=False) - 1",
+        {"x": year_data.eval()},
+    )
+    spline_model.add_coords({"spline": np.arange(spline_model.dm.shape[1])})
+    splines_basis = pm.Data("splines_basis", np.asarray(spline_model.dm), dims=("obs", "spline"))
+    w = pm.Normal("w", mu=0, sigma=3, dims="spline")
+
+    mu = pm.Deterministic(
+        "mu",
+        a + pm.math.dot(splines_basis, w),
+    )
+    sigma = pm.Exponential("sigma", 1)
+
+    D = pm.Normal("D", mu=mu, sigma=sigma, observed=doy)
+```
+
+```{code-cell} ipython3
+pm.model_to_graphviz(spline_model)
+```
+
+```{code-cell} ipython3
+with spline_model:
+    idata = pm.sample(
+        nuts_sampler="nutpie",
+        random_seed=rng,
+    )
+    idata.extend(pm.sample_posterior_predictive(idata, random_seed=rng))
+```
+
+Now we can swap out the data and update the design matrix with the new data:
+
+```{code-cell} ipython3
+new_blossom_data = (
+    blossom_data.sample(50, random_state=rng).sort_values("year").reset_index(drop=True)
+)
+
+# update design matrix with new data
+year_data_new = new_blossom_data.year.to_numpy()
+dm_new = build_design_matrices([spline_model.dm.design_info], {"x": year_data_new})[0]
+```
+
+Use `set_data` to update the data in the model:
+
+```{code-cell} ipython3
+with spline_model:
+    pm.set_data(
+        new_data={
+            "year": year_data_new,
+            "doy": new_blossom_data.doy.to_numpy(),
+            "splines_basis": np.asarray(dm_new),
+        },
+        coords={
+            "obs": new_blossom_data.index,
+        },
+    )
+```
+
+And all that's left is to sample from the posterior predictive distribution:
+
+```{code-cell} ipython3
+with spline_model:
+    preds = pm.sample_posterior_predictive(idata, var_names=["mu"])
+```
+
+Plot the predictions, to check if everything went well:
+
+```{code-cell} ipython3
+_, axes = plt.subplots(1, 2, figsize=(16, 5), sharex=True, sharey=True)
+
+blossom_data.plot.scatter(
+    "year",
+    "doy",
+    color="cornflowerblue",
+    s=10,
+    title="Posterior predictions",
+    ylabel="Days in bloom",
+    ax=axes[0],
+)
+axes[0].vlines(
+    spline_model.knots,
+    blossom_data.doy.min(),
+    blossom_data.doy.max(),
+    color="grey",
+    alpha=0.4,
+)
+axes[0].plot(
+    blossom_data.year,
+    idata.posterior["mu"].mean(("chain", "draw")),
+    color="firebrick",
+)
+az.plot_hdi(blossom_data.year, idata.posterior["mu"], color="firebrick", ax=axes[0])
+
+new_blossom_data.plot.scatter(
+    "year",
+    "doy",
+    color="cornflowerblue",
+    s=10,
+    title="Predictions on new data",
+    ylabel="Days in bloom",
+    ax=axes[1],
+)
+axes[1].vlines(
+    spline_model.knots,
+    blossom_data.doy.min(),
+    blossom_data.doy.max(),
+    color="grey",
+    alpha=0.4,
+)
+axes[1].plot(
+    new_blossom_data.year,
+    preds.posterior_predictive.mu.mean(("chain", "draw")),
+    color="firebrick",
+)
+az.plot_hdi(new_blossom_data.year, preds.posterior_predictive.mu, color="firebrick", ax=axes[1]);
+```
+
+And... voilà! Granted, this example is not the most realistic one, but we trust you to adapt it to your wildest dreams ;)
+
++++
+
 ## References
 
 :::{bibliography}
@@ -280,6 +448,7 @@ plt.fill_between(
 - Created by Joshua Cook
 - Updated by Tyler James Burch
 - Updated by Chris Fonnesbeck
+- Predictions on new data added by Alex Andorra
 
 +++