76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
"""
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=================================================
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Plot multi-class DecisionTree on the iris dataset
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=================================================
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Plot decision surface of multi-class :ref:`decision tree <tree>` on iris
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dataset on pairwise selection of features.
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For each pair of iris features, the decision tree learn decision
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boundaries made of combination of simple thresholding rules on the train
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observations.
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"""
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print __doc__
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import numpy as np
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import pylab as pl
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from sklearn import datasets
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from sklearn.tree import DecisionTreeClassifier
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# import some data to play with
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iris = datasets.load_iris()
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for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
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[1, 2], [1, 3], [2, 3]]):
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X = iris.data[:, pair] # we only take the two corresponding features
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y = iris.target
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K=3
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colors = "bry"
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# shuffle
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idx = np.arange(X.shape[0])
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np.random.seed(13)
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np.random.shuffle(idx)
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X = X[idx]
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y = y[idx]
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# standardize
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mean = X.mean(axis=0)
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std = X.std(axis=0)
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X = (X - mean) / std
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h = .02 # step size in the mesh
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clf = DecisionTreeClassifier().fit(X, y)
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# create a mesh to plot in
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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pl.subplot(2, 3, pairidx + 1)
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pl.set_cmap(pl.cm.Paired)
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# Plot the decision boundary. For that, we will asign a color to each
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# point in the mesh [x_min, m_max]x[y_min, y_max].
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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pl.set_cmap(pl.cm.Paired)
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cs = pl.contourf(xx, yy, Z)
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pl.xlabel(iris.feature_names[pair[0]])
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pl.ylabel(iris.feature_names[pair[1]])
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pl.axis('tight')
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# Plot also the training points
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for i, color in zip(xrange(K), colors):
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idx = np.where(y == i)
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pl.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i])
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pl.axis('tight')
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pl.suptitle("Decision surface of multi-class decision tree using paired features")
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pl.legend()
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pl.show()
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