plot_tree_helper.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from ipywidgets import interact


def visualize_tree(estimator, X, y, boundaries=True,
                   xlim=None, ylim=None, ax=None):
    ax = ax or plt.gca()

    # Plot the training points
    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',
               clim=(y.min(), y.max()), zorder=3)
    ax.axis('tight')
    ax.axis('off')
    if xlim is None:
        xlim = ax.get_xlim()
    if ylim is None:
        ylim = ax.get_ylim()

    # fit the estimator
    estimator.fit(X, y)
    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),
                         np.linspace(*ylim, num=200))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    n_classes = len(np.unique(y))
    Z = Z.reshape(xx.shape)
    contours = ax.contourf(xx, yy, Z, alpha=0.3,
                           levels=np.arange(n_classes + 1) - 0.5,
                           cmap='viridis', clim=(y.min(), y.max()),
                           zorder=1)

    ax.set(xlim=xlim, ylim=ylim)

    # Plot the decision boundaries
    def plot_boundaries(i, xlim, ylim):
        if i >= 0:
            tree = estimator.tree_

            if tree.feature[i] == 0:
                ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)
                plot_boundaries(tree.children_left[i],
                                [xlim[0], tree.threshold[i]], ylim)
                plot_boundaries(tree.children_right[i],
                                [tree.threshold[i], xlim[1]], ylim)

            elif tree.feature[i] == 1:
                ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)
                plot_boundaries(tree.children_left[i], xlim,
                                [ylim[0], tree.threshold[i]])
                plot_boundaries(tree.children_right[i], xlim,
                                [tree.threshold[i], ylim[1]])

    if boundaries:
        plot_boundaries(0, xlim, ylim)


def plot_tree_interactive(X, y):
    def interactive_tree(depth=5):
        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
        visualize_tree(clf, X, y)

    return interact(interactive_tree, depth=[1, 5])


def randomized_tree_interactive(X, y):
    N = int(0.75 * X.shape[0])

    xlim = (X[:, 0].min(), X[:, 0].max())
    ylim = (X[:, 1].min(), X[:, 1].max())

    def fit_randomized_tree(random_state=0):
        clf = DecisionTreeClassifier(max_depth=15)
        i = np.arange(len(y))
        rng = np.random.RandomState(random_state)
        rng.shuffle(i)
        visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,
                       xlim=xlim, ylim=ylim)

    interact(fit_randomized_tree, random_state=[0, 100]);