Commit d923e6b2 authored by Sylvain Marchienne's avatar Sylvain Marchienne
Browse files

Merge branch 'TP_python' into 'master'

TP Python

See merge request !1
parents 81d1a4e8 fcaa91c3
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]);
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