bagging_trees.ipynb

Kernel: Unknown Kernel

# Baggging decision trees in 2d
# Based on https://github.com/ageron/handson-ml2/blob/master/06_decision_trees.ipynb


import numpy as np
import matplotlib.pyplot as plt
import os

try:
    from sklearn.metrics import accuracy_score
except ModuleNotFoundError:
    %pip install -qq scikit-learn
    from sklearn.metrics import accuracy_score


from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier

from matplotlib.colors import ListedColormap

from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons

X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)


def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True):
    x1s = np.linspace(axes[0], axes[1], 100)
    x2s = np.linspace(axes[2], axes[3], 100)
    x1, x2 = np.meshgrid(x1s, x2s)
    X_new = np.c_[x1.ravel(), x2.ravel()]
    y_pred = clf.predict(X_new).reshape(x1.shape)
    custom_cmap = ListedColormap(["#fafab0", "#9898ff", "#a0faa0"])
    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
    if contour:
        custom_cmap2 = ListedColormap(["#7d7d58", "#4c4c7f", "#507d50"])
        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
    plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], "yo", alpha=alpha)
    plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], "bs", alpha=alpha)
    plt.axis(axes)
    plt.xlabel(r"$x_1$", fontsize=18)
    plt.ylabel(r"$x_2$", fontsize=18, rotation=0)


tree_clf = DecisionTreeClassifier(random_state=42)
tree_clf.fit(X_train, y_train)
y_pred_tree = tree_clf.predict(X_test)
dtree_acc = accuracy_score(y_test, y_pred_tree)


plt.figure()
plot_decision_boundary(tree_clf, X, y)
plt.title("Decision Tree, test accuracy={:0.2f}".format(dtree_acc))
plt.savefig("figures/dtree_bag_size0.pdf", dpi=300)

bag_sizes = [10, 50, 100]
for bag_size in bag_sizes:
    bag_clf = BaggingClassifier(
        DecisionTreeClassifier(random_state=42), n_estimators=bag_size, max_samples=100, bootstrap=True, random_state=42
    )
    bag_clf.fit(X_train, y_train)
    y_pred = bag_clf.predict(X_test)
    bag_acc = accuracy_score(y_test, y_pred)

    plt.figure()
    plot_decision_boundary(bag_clf, X, y)
    plt.title("Bag of {} decision trees, test accuracy={:0.2f}".format(bag_size, bag_acc))
    plt.savefig("figures/dtree_bag_size{}.pdf".format(bag_size), dpi=300)