95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
"""
|
|
====================================================================
|
|
Plot the decision surfaces of ensembles of trees on the iris dataset
|
|
====================================================================
|
|
|
|
Plot the decision surfaces of forests of randomized trees trained on pairs of
|
|
features of the iris dataset.
|
|
|
|
This plot compares the decision surfaces learned by a decision tree classifier
|
|
(first column), by a random forest classifier (second column) and by an extra-
|
|
trees classifier (third column).
|
|
|
|
In the first row, the classifiers are built using the sepal width and the sepal
|
|
length features only, on the second row using the petal length and sepal length
|
|
only, and on the third row using the petal width and the petal length only.
|
|
"""
|
|
print __doc__
|
|
|
|
import numpy as np
|
|
import pylab as pl
|
|
|
|
from sklearn import clone
|
|
from sklearn.datasets import load_iris
|
|
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
|
|
from sklearn.tree import DecisionTreeClassifier
|
|
|
|
# Parameters
|
|
n_classes = 3
|
|
n_estimators = 30
|
|
plot_colors = "bry"
|
|
plot_step = 0.02
|
|
|
|
# Load data
|
|
iris = load_iris()
|
|
|
|
plot_idx = 1
|
|
|
|
for pair in ([0, 1], [0, 2], [2, 3]):
|
|
for model in (DecisionTreeClassifier(),
|
|
RandomForestClassifier(n_estimators=n_estimators),
|
|
ExtraTreesClassifier(n_estimators=n_estimators)):
|
|
# We only take the two corresponding features
|
|
X = iris.data[:, pair]
|
|
y = iris.target
|
|
|
|
# Shuffle
|
|
idx = np.arange(X.shape[0])
|
|
np.random.seed(13)
|
|
np.random.shuffle(idx)
|
|
X = X[idx]
|
|
y = y[idx]
|
|
|
|
# Standardize
|
|
mean = X.mean(axis=0)
|
|
std = X.std(axis=0)
|
|
X = (X - mean) / std
|
|
|
|
# Train
|
|
clf = clone(model)
|
|
clf = model.fit(X, y)
|
|
|
|
# Plot the decision boundary
|
|
pl.subplot(3, 3, plot_idx)
|
|
|
|
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
|
|
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
|
|
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
|
|
np.arange(y_min, y_max, plot_step))
|
|
|
|
if isinstance(model, DecisionTreeClassifier):
|
|
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
|
|
Z = Z.reshape(xx.shape)
|
|
cs = pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
|
|
else:
|
|
for tree in model.estimators_:
|
|
Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
|
|
Z = Z.reshape(xx.shape)
|
|
cs = pl.contourf(xx, yy, Z, alpha=0.1, cmap=pl.cm.Paired)
|
|
|
|
pl.axis("tight")
|
|
|
|
# Plot the training points
|
|
for i, c in zip(xrange(n_classes), plot_colors):
|
|
idx = np.where(y == i)
|
|
pl.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],
|
|
cmap=pl.cm.Paired)
|
|
|
|
pl.axis("tight")
|
|
|
|
plot_idx += 1
|
|
|
|
pl.suptitle("Decision surfaces of a decision tree, of a random forest, and of "
|
|
"an extra-trees classifier")
|
|
pl.show()
|