scikit-learn/examples/tree/plot_iris.py

"""
================================================================
Plot the decision surface of a decision tree on the iris dataset
================================================================

Plot the decision surface of a :ref:`decision tree <tree>` trained on pairs
of features of the iris dataset.

For each pair of iris features, the decision tree learns decision
boundaries made of combinations of simple thresholding rules inferred from
the training samples.
"""
print __doc__

import numpy as np
import pylab as pl

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

# Parameters
n_classes = 3
plot_colors = "bry"
plot_step = 0.02

pl.set_cmap(pl.cm.Paired)

# Load data
iris = load_iris()

for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
                                [1, 2], [1, 3], [2, 3]]):
     # 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 = DecisionTreeClassifier().fit(X, y)

    # Plot the decision boundary
    pl.subplot(2, 3, pairidx + 1)

    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))

    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    cs = pl.contourf(xx, yy, Z)

    pl.xlabel(iris.feature_names[pair[0]])
    pl.ylabel(iris.feature_names[pair[1]])
    pl.axis("tight")

    # Plot the training points
    for i, color in zip(xrange(n_classes), plot_colors):
        idx = np.where(y == i)
        pl.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i])

    pl.axis("tight")

pl.suptitle("Decision surface of a decision tree using paired features")
pl.legend()
pl.show()