helpers_05_08.py

importnumpyasnp
importmatplotlib.pyplotasplt
fromsklearn.treeimportDecisionTreeClassifier
fromipywidgetsimportinteract


defvisualize_tree(estimator, X, y, boundaries=True,
xlim=None, ylim=None, ax=None):
ax=axorplt.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')
ifxlimisNone:
xlim=ax.get_xlim()
ifylimisNone:
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
defplot_boundaries(i, xlim, ylim):
ifi>=0:
tree=estimator.tree_

iftree.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)

eliftree.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]])

ifboundaries:
plot_boundaries(0, xlim, ylim)


defplot_tree_interactive(X, y):
definteractive_tree(depth=5):
clf=DecisionTreeClassifier(max_depth=depth, random_state=0)
visualize_tree(clf, X, y)

returninteract(interactive_tree, depth=[1, 5])


defrandomized_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())

deffit_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]);