update GLM out of sample prediction notebook to v4

myst_nbs/generalized_linear_models/GLM-out-of-sample-predictions.myst.md

@@ -6,87 +6,70 @@ jupytext:
     format_version: 0.13
     jupytext_version: 1.13.7
 kernelspec:
-  display_name: 'Python 3.7.6 64-bit (''website_projects'': conda)'
-  metadata:
-    interpreter:
-      hash: fbddea5140024843998ae64bf59a7579a9660d103062604797ea5984366c686c
+  display_name: Python 3.9.12 ('pymc-dev-py39')
+  language: python
   name: python3
 ---
 
-# GLM in PyMC3: Out-Of-Sample Predictions
+(GLM-out-of-sample-predictions)=
+# Out-Of-Sample Predictions
 
-In this notebook I explore the [glm](https://docs.pymc.io/api/glm.html) module of [PyMC3](https://docs.pymc.io/). I am particularly interested in the model definition using [patsy](https://patsy.readthedocs.io/en/latest/) formulas, as it makes the model evaluation loop faster (easier to include features and/or interactions). There are many good resources on this subject, but most of them evaluate the model in-sample. For many applications we require doing predictions on out-of-sample data. This experiment was motivated by the discussion of the thread ["Out of sample" predictions with the GLM sub-module](https://discourse.pymc.io/t/out-of-sample-predictions-with-the-glm-sub-module/773) on the (great!) forum [discourse.pymc.io/](https://discourse.pymc.io/), thank you all for your input!
-
-**Resources**
-
-
-- [PyMC3 Docs: Example Notebooks](https://docs.pymc.io/nb_examples/index.html)
-
-    - In particular check [GLM: Logistic Regression](https://docs.pymc.io/notebooks/GLM-logistic.html)
-
-- [Bambi](https://bambinos.github.io/bambi/), a more complete implementation of the GLM submodule which also allows for mixed-effects models.
-
-- [Bayesian Analysis with Python (Second edition) - Chapter 4](https://github.com/aloctavodia/BAP/blob/master/code/Chp4/04_Generalizing_linear_models.ipynb)
-- [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)
+:::{post} June, 2022
+:tags: generalized linear model, logistic regression, out of sample predictions, patsy
+:category: beginner
+:::
 
 +++
 
-## Prepare Notebook
+For many applications we require doing predictions on out-of-sample data. This experiment was motivated by the discussion of the thread ["Out of sample" predictions with the GLM sub-module](https://discourse.pymc.io/t/out-of-sample-predictions-with-the-glm-sub-module/773) on the (great!) forum [discourse.pymc.io/](https://discourse.pymc.io/), thank you all for your input! But note that this GLM sub-module was deprecated in favour of [`bambi`](https://github.com/bambinos/bambi). But this notebook implements a 'raw' PyMC model.
 
 ```{code-cell} ipython3
+import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import seaborn as sns
-
-sns.set_style(style="darkgrid", rc={"axes.facecolor": ".9", "grid.color": ".8"})
-sns.set_palette(palette="deep")
-sns_c = sns.color_palette(palette="deep")
-
-import arviz as az
 import patsy
-import pymc3 as pm
+import pymc as pm
+import seaborn as sns
 
-from pymc3 import glm
+from scipy.special import expit as inverse_logit
+from sklearn.metrics import RocCurveDisplay, accuracy_score, auc, roc_curve
+from sklearn.model_selection import train_test_split
+```
 
-plt.rcParams["figure.figsize"] = [7, 6]
-plt.rcParams["figure.dpi"] = 100
+```{code-cell} ipython3
+RANDOM_SEED = 8927
+rng = np.random.default_rng(RANDOM_SEED)
+az.style.use("arviz-darkgrid")
 ```
 
 ## Generate Sample Data
 
 We want to fit a logistic regression model where there is a multiplicative interaction between two numerical features.
 
 ```{code-cell} ipython3
-SEED = 42
-np.random.seed(SEED)
-
-# Number of data points.
+# Number of data points
 n = 250
-# Create features.
-x1 = np.random.normal(loc=0.0, scale=2.0, size=n)
-x2 = np.random.normal(loc=0.0, scale=2.0, size=n)
-epsilon = np.random.normal(loc=0.0, scale=0.5, size=n)
-# Define target variable.
+# Create features
+x1 = rng.normal(loc=0.0, scale=2.0, size=n)
+x2 = rng.normal(loc=0.0, scale=2.0, size=n)
+# Define target variable
 intercept = -0.5
 beta_x1 = 1
 beta_x2 = -1
 beta_interaction = 2
 z = intercept + beta_x1 * x1 + beta_x2 * x2 + beta_interaction * x1 * x2
-p = 1 / (1 + np.exp(-z))
-y = np.random.binomial(n=1, p=p, size=n)
-
+p = inverse_logit(z)
+# note binimial with n=1 is equal to a bernoulli
+y = rng.binomial(n=1, p=p, size=n)
 df = pd.DataFrame(dict(x1=x1, x2=x2, y=y))
-
 df.head()
 ```
 
 Let us do some exploration of the data:
 
 ```{code-cell} ipython3
-sns.pairplot(
-    data=df, kind="scatter", height=2, plot_kws={"color": sns_c[1]}, diag_kws={"color": sns_c[2]}
-);
+sns.pairplot(data=df, kind="scatter");
 ```
 
 - $x_1$ and $x_2$ are not correlated.
@@ -95,91 +78,85 @@ sns.pairplot(
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
-sns_c_div = sns.diverging_palette(240, 10, n=2)
-sns.scatterplot(x="x1", y="x2", data=df, hue="y", palette=[sns_c_div[0], sns_c_div[-1]])
-ax.legend(title="y", loc="center left", bbox_to_anchor=(1, 0.5))
+sns.scatterplot(x="x1", y="x2", data=df, hue="y")
+ax.legend(title="y")
 ax.set(title="Sample Data", xlim=(-9, 9), ylim=(-9, 9));
 ```
 
 ## Prepare Data for Modeling
 
-I wanted to use the *`classmethod`* `from_formula` (see [documentation](https://docs.pymc.io/api/glm.html)), but I was not able to generate out-of-sample predictions with this approach (if you find a way please let me know!). As a workaround, I created the features from a formula using [patsy](https://patsy.readthedocs.io/en/latest/) directly and then use *`class`* `pymc3.glm.linear.GLM` (this was motivated by going into the [source code](https://github.com/pymc-devs/pymc3/blob/master/pymc3/glm/linear.py)).
-
 ```{code-cell} ipython3
-# Define model formula.
-formula = "y ~ x1 * x2"
-# Create features.
-y, x = patsy.dmatrices(formula_like=formula, data=df)
+y, x = patsy.dmatrices("y ~ x1 * x2", data=df)
 y = np.asarray(y).flatten()
 labels = x.design_info.column_names
 x = np.asarray(x)
 ```
 
-As pointed out on the [thread](https://discourse.pymc.io/t/out-of-sample-predictions-with-the-glm-sub-module/773) (thank you @Nicky!), we need to keep the labels of the features in the design matrix.
-
-```{code-cell} ipython3
-print(f"labels = {labels}")
-```
-
 Now we do a train-test split.
 
 ```{code-cell} ipython3
-from sklearn.model_selection import train_test_split
-
-x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=SEED)
+x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7)
 ```
 
 ## Define and Fit the Model
 
-We now specify the model in PyMC3.
+We now specify the model in PyMC.
 
 ```{code-cell} ipython3
-with pm.Model() as model:
-    # Set data container.
-    data = pm.Data("data", x_train)
-    # Define GLM family.
-    family = pm.glm.families.Binomial()
-    # Set priors.
-    priors = {
-        "Intercept": pm.Normal.dist(mu=0, sd=10),
-        "x1": pm.Normal.dist(mu=0, sd=10),
-        "x2": pm.Normal.dist(mu=0, sd=10),
-        "x1:x2": pm.Normal.dist(mu=0, sd=10),
-    }
-    # Specify model.
-    glm.GLM(y=y_train, x=data, family=family, intercept=False, labels=labels, priors=priors)
-    # Configure sampler.
-    trace = pm.sample(5000, chains=5, tune=1000, target_accept=0.87, random_seed=SEED)
+COORDS = {"coeffs": labels}
+
+with pm.Model(coords=COORDS) as model:
+    # data containers
+    X = pm.MutableData("X", x_train)
+    y = pm.MutableData("y", y_train)
+    # priors
+    b = pm.Normal("b", mu=0, sigma=1, dims="coeffs")
+    # linear model
+    mu = pm.math.dot(X, b)
+    # link function
+    p = pm.Deterministic("p", pm.math.invlogit(mu))
+    # likelihood
+    pm.Bernoulli("obs", p=p, observed=y)
+
+pm.model_to_graphviz(model)
 ```
 
 ```{code-cell} ipython3
-# Plot chains.
-az.plot_trace(data=trace);
+with model:
+    idata = pm.sample()
 ```
 
 ```{code-cell} ipython3
-az.summary(trace)
+az.plot_trace(idata, var_names="b", compact=False);
 ```
 
 The chains look good.
 
-+++
+```{code-cell} ipython3
+az.summary(idata, var_names="b")
+```
+
+And we do a good job of recovering the true parameters for this simulated dataset.
+
+```{code-cell} ipython3
+az.plot_posterior(
+    idata, var_names=["b"], ref_val=[intercept, beta_x1, beta_x2, beta_interaction], figsize=(15, 4)
+);
+```
 
 ## Generate Out-Of-Sample Predictions
 
 Now we generate predictions on the test set.
 
 ```{code-cell} ipython3
-# Update data reference.
-pm.set_data({"data": x_test}, model=model)
-# Generate posterior samples.
-ppc_test = pm.sample_posterior_predictive(trace, model=model, samples=1000)
+with model:
+    pm.set_data({"X": x_test, "y": y_test})
+    idata.extend(pm.sample_posterior_predictive(idata))
 ```
 
 ```{code-cell} ipython3
-# Compute the point prediction by taking the mean
-# and defining the category via a threshold.
-p_test_pred = ppc_test["y"].mean(axis=0)
+# Compute the point prediction by taking the mean and defining the category via a threshold.
+p_test_pred = idata.posterior_predictive["obs"].mean(dim=["chain", "draw"])
 y_test_pred = (p_test_pred >= 0.5).astype("int")
 ```
 
@@ -188,24 +165,20 @@ y_test_pred = (p_test_pred >= 0.5).astype("int")
 First let us compute the accuracy on the test set.
 
 ```{code-cell} ipython3
-from sklearn.metrics import accuracy_score
-
 print(f"accuracy = {accuracy_score(y_true=y_test, y_pred=y_test_pred): 0.3f}")
 ```
 
 Next, we plot the [roc curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and compute the [auc](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve).
 
 ```{code-cell} ipython3
-from sklearn.metrics import RocCurveDisplay, auc, roc_curve
-
 fpr, tpr, thresholds = roc_curve(
     y_true=y_test, y_score=p_test_pred, pos_label=1, drop_intermediate=False
 )
 roc_auc = auc(fpr, tpr)
 
 fig, ax = plt.subplots()
 roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc)
-roc_display = roc_display.plot(ax=ax, marker="o", color=sns_c[4], markersize=4)
+roc_display = roc_display.plot(ax=ax, marker="o", markersize=4)
 ax.set(title="ROC");
 ```
 
@@ -238,60 +211,92 @@ Observe that this curve is a hyperbola centered at the singularity point $x_1 =
 Let us now plot the model decision boundary using a grid:
 
 ```{code-cell} ipython3
-# Construct grid.
-x1_grid = np.linspace(start=-9, stop=9, num=300)
-x2_grid = x1_grid
-
-x1_mesh, x2_mesh = np.meshgrid(x1_grid, x2_grid)
-
-x_grid = np.stack(arrays=[x1_mesh.flatten(), x2_mesh.flatten()], axis=1)
-
-# Create features on the grid.
-x_grid_ext = patsy.dmatrix(formula_like="x1 * x2", data=dict(x1=x_grid[:, 0], x2=x_grid[:, 1]))
-
-x_grid_ext = np.asarray(x_grid_ext)
+def make_grid():
+    x1_grid = np.linspace(start=-9, stop=9, num=300)
+    x2_grid = x1_grid
+    x1_mesh, x2_mesh = np.meshgrid(x1_grid, x2_grid)
+    x_grid = np.stack(arrays=[x1_mesh.flatten(), x2_mesh.flatten()], axis=1)
+    return x1_grid, x2_grid, x_grid
+
+
+x1_grid, x2_grid, x_grid = make_grid()
+
+with model:
+    # Create features on the grid.
+    x_grid_ext = patsy.dmatrix(formula_like="x1 * x2", data=dict(x1=x_grid[:, 0], x2=x_grid[:, 1]))
+    x_grid_ext = np.asarray(x_grid_ext)
+    # set the observed variables
+    pm.set_data({"X": x_grid_ext})
+    # calculate pushforward values of `p`
+    ppc_grid = pm.sample_posterior_predictive(idata, var_names=["p"])
+```
 
-# Generate model predictions on the grid.
-pm.set_data({"data": x_grid_ext}, model=model)
-ppc_grid = pm.sample_posterior_predictive(trace, model=model, samples=1000)
+```{code-cell} ipython3
+# grid of predictions
+grid_df = pd.DataFrame(x_grid, columns=["x1", "x2"])
+grid_df["p"] = ppc_grid.posterior_predictive.p.mean(dim=["chain", "draw"])
+p_grid = grid_df.pivot(index="x2", columns="x1", values="p").to_numpy()
 ```
 
 Now we compute the model decision boundary on the grid for visualization purposes.
 
 ```{code-cell} ipython3
-numerator = -(trace["Intercept"].mean(axis=0) + trace["x1"].mean(axis=0) * x1_grid)
-denominator = trace["x2"].mean(axis=0) + trace["x1:x2"].mean(axis=0) * x1_grid
-bd_grid = numerator / denominator
-
-grid_df = pd.DataFrame(x_grid, columns=["x1", "x2"])
-grid_df["p"] = ppc_grid["y"].mean(axis=0)
-grid_df.sort_values("p", inplace=True)
-
-p_grid = grid_df.pivot(index="x2", columns="x1", values="p").to_numpy()
+def calc_decision_boundary(idata, x1_grid):
+    # posterior mean of coefficients
+    intercept = idata.posterior["b"].sel(coeffs="Intercept").mean().data
+    b1 = idata.posterior["b"].sel(coeffs="x1").mean().data
+    b2 = idata.posterior["b"].sel(coeffs="x2").mean().data
+    b1b2 = idata.posterior["b"].sel(coeffs="x1:x2").mean().data
+    # decision boundary equation
+    return -(intercept + b1 * x1_grid) / (b2 + b1b2 * x1_grid)
 ```
 
 We finally get the plot and the predictions on the test set:
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
-cmap = sns.diverging_palette(240, 10, n=50, as_cmap=True)
+
+# data
 sns.scatterplot(
     x=x_test[:, 1].flatten(),
     y=x_test[:, 2].flatten(),
     hue=y_test,
-    palette=[sns_c_div[0], sns_c_div[-1]],
     ax=ax,
 )
-sns.lineplot(x=x1_grid, y=bd_grid, color="black", ax=ax)
-ax.contourf(x1_grid, x2_grid, p_grid, cmap=cmap, alpha=0.3)
+
+# decision boundary
+ax.plot(x1_grid, calc_decision_boundary(idata, x1_grid), color="black", linestyle=":")
+
+# grid of predictions
+ax.contourf(x1_grid, x2_grid, p_grid, alpha=0.3)
+
 ax.legend(title="y", loc="center left", bbox_to_anchor=(1, 0.5))
-ax.lines[0].set_linestyle("dotted")
 ax.set(title="Model Decision Boundary", xlim=(-9, 9), ylim=(-9, 9), xlabel="x1", ylabel="x2");
 ```
 
-**Remark:** Note that we have computed the model decision boundary by using the mean of the posterior samples. However, we can generate a better (and more informative!) plot if we use the complete distribution (similarly for other metrics like accuracy and auc). One way of doing this is by storing and computing it inside the model definition as a `Deterministic` variable as in [Bayesian Analysis with Python (Second edition) - Chapter 4](https://github.com/aloctavodia/BAP/blob/master/code/Chp4/04_Generalizing_linear_models.ipynb).
+Note that we have computed the model decision boundary by using the mean of the posterior samples. However, we can generate a better (and more informative!) plot if we use the complete distribution (similarly for other metrics like accuracy and AUC).
+
++++
+
+## References
+
+- [Bambi](https://bambinos.github.io/bambi/), a more complete implementation of the GLM submodule which also allows for mixed-effects models.
+- [Bayesian Analysis with Python (Second edition) - Chapter 4](https://github.com/aloctavodia/BAP/blob/master/code/Chp4/04_Generalizing_linear_models.ipynb)
+- [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)
+
++++
+
+## Authors
+- Created by [Juan Orduz](https://github.com/juanitorduz).
+- Updated by [Benjamin T. Vincent](https://github.com/drbenvincent) to PyMC v4 in June 2022
+
++++
+
+## Watermark
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w
+%watermark -n -u -v -iv -w -p aesara,aeppl
 ```
+
+:::{include} ../page_footer.md :::