Bayesian Regression

# Install the PRML package from the repo 
!pip install git+https://github.com/pantelis-classes/PRML.git#egg=prml

Collecting prml
  Cloning https://github.com/pantelis-classes/PRML.git to /tmp/pip-install-9z3vs784/prml_ef4e2960120e4a02a2023803d96fb66a
  Running command git clone --filter=blob:none --quiet https://github.com/pantelis-classes/PRML.git /tmp/pip-install-9z3vs784/prml_ef4e2960120e4a02a2023803d96fb66a
  Resolved https://github.com/pantelis-classes/PRML.git to commit 14cf88538d2704fd18b391ae6309fb38c65a6412
  Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy in /workspaces/artificial_intelligence/.venv/lib/python3.10/site-packages (from prml) (1.24.2)
Requirement already satisfied: scipy in /workspaces/artificial_intelligence/.venv/lib/python3.10/site-packages (from prml) (1.10.1)

# Import seaborn
import seaborn as sns

# Apply the default theme
sns.set_theme()

import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
%matplotlib inline

from prml.preprocess import GaussianFeature, PolynomialFeature, SigmoidalFeature
from prml.linear import (
    BayesianRegression,
    EmpiricalBayesRegression,
    LinearRegression,
    RidgeRegression
)

np.random.seed(1234)

def create_toy_data(func, sample_size, std, domain=[0, 1]):
    x = np.linspace(domain[0], domain[1], sample_size)
    np.random.shuffle(x)
    t = func(x) + np.random.normal(scale=std, size=x.shape)
    return x, t

x = np.linspace(-1, 1, 100)
X_polynomial = PolynomialFeature(11).transform(x[:, None])
X_gaussian = GaussianFeature(np.linspace(-1, 1, 11), 0.1).transform(x)
X_sigmoidal = SigmoidalFeature(np.linspace(-1, 1, 11), 10).transform(x)

def sinusoidal(x):
    return np.sin(2 * np.pi * x)

x_train, y_train = create_toy_data(sinusoidal, 10, 0.25)
x_test = np.linspace(0, 1, 100)
y_test = sinusoidal(x_test)

The Bias-Variance Decomposition

feature = PolynomialFeature(24)
# feature = GaussianFeature(np.linspace(0, 1, 24), 0.1)
# feature = SigmoidalFeature(np.linspace(0, 1, 24), 10)

for a in [1e2, 1., 1e-9]:
    y_list = []
    plt.figure(figsize=(20, 5))
    plt.subplot(1, 2, 1)
    for i in range(100):
        x_train, y_train = create_toy_data(sinusoidal, 25, 0.25)
        X_train = feature.transform(x_train)
        X_test = feature.transform(x_test)
        model = BayesianRegression(alpha=a, beta=1.)
        model.fit(X_train, y_train)
        y = model.predict(X_test)
        y_list.append(y)
        if i < 20:
            plt.plot(x_test, y, c="orange")
    plt.ylim(-1.5, 1.5)
    
    plt.subplot(1, 2, 2)
    plt.plot(x_test, y_test)
    plt.plot(x_test, np.asarray(y_list).mean(axis=0))
    plt.ylim(-1.5, 1.5)
    plt.show()

Online Bayesian Linear Regression

Parameter distribution

def linear(x):
    return -0.3 + 0.5 * x


x_train, y_train = create_toy_data(linear, 20, 0.1, [-1, 1])
x = np.linspace(-1, 1, 100)
w0, w1 = np.meshgrid(
    np.linspace(-1, 1, 100),
    np.linspace(-1, 1, 100))
w = np.array([w0, w1]).transpose(1, 2, 0)

feature = PolynomialFeature(degree=1)
X_train = feature.transform(x_train)
X = feature.transform(x)
model = BayesianRegression(alpha=1., beta=100.)

for begin, end in [[0, 0], [0, 1], [1, 2], [2, 3], [3, 20]]:
    model.fit(X_train[begin: end], y_train[begin: end])
    plt.subplot(1, 2, 1)
    plt.scatter(-0.3, 0.5, s=200, marker="x")
    plt.contour(w0, w1, multivariate_normal.pdf(w, mean=model.w_mean, cov=model.w_cov))
    plt.gca().set_aspect('equal')
    plt.xlabel("$w_0$")
    plt.ylabel("$w_1$")
    plt.title("prior/posterior")

    plt.subplot(1, 2, 2)
    plt.scatter(x_train[:end], y_train[:end], s=100, facecolor="none", edgecolor="steelblue", lw=1)
    plt.plot(x, model.predict(X, sample_size=6), c="orange")
    plt.xlim(-1, 1)
    plt.ylim(-1, 1)
    plt.gca().set_aspect('equal', adjustable='box')
    plt.show()

Predictive distribution

x_train, y_train = create_toy_data(sinusoidal, 25, 0.25)
x_test = np.linspace(0, 1, 100)
y_test = sinusoidal(x_test)

feature = GaussianFeature(np.linspace(0, 1, 9), 0.1)
X_train = feature.transform(x_train)
X_test = feature.transform(x_test)

model = BayesianRegression(alpha=1e-3, beta=2.)

for begin, end in [[0, 1], [1, 2], [2, 4], [4, 8], [8, 25]]:
    model.fit(X_train[begin: end], y_train[begin: end])
    y, y_std = model.predict(X_test, return_std=True)
    plt.scatter(x_train[:end], y_train[:end], s=100, facecolor="none", edgecolor="steelblue", lw=2)
    plt.plot(x_test, y_test)
    plt.plot(x_test, y)
    plt.fill_between(x_test, y - y_std, y + y_std, color="orange", alpha=0.5)
    plt.xlim(0, 1)
    plt.ylim(-2, 2)
    plt.show()

The Evidence Approximation

def cubic(x):
    return x * (x - 5) * (x + 5)

x_train, y_train = create_toy_data(cubic, 30, 10, [-5, 5])
x_test = np.linspace(-5, 5, 100)
evidences = []
models = []
for i in range(8):
    feature = PolynomialFeature(degree=i)
    X_train = feature.transform(x_train)
    model = EmpiricalBayesRegression(alpha=100., beta=100.)
    model.fit(X_train, y_train, max_iter=100)
    evidences.append(model.log_evidence(X_train, y_train))
    models.append(model)

degree = np.nanargmax(evidences)
regression = models[degree]

X_test = PolynomialFeature(degree=int(degree)).transform(x_test)
y, y_std = regression.predict(X_test, return_std=True)

plt.scatter(x_train, y_train, s=50, facecolor="none", edgecolor="steelblue", label="observation")
plt.plot(x_test, cubic(x_test), label="x(x-5)(x+5)")
plt.plot(x_test, y, label="prediction")
plt.fill_between(x_test, y - y_std, y + y_std, alpha=0.5, label="std", color="orange")
plt.legend()
plt.show()

plt.plot(evidences)
plt.title("Model evidence")
plt.xlabel("degree")
plt.ylabel("log evidence")
plt.show()