EXAMPLE: merge plot_adaboost_iris into plot_forest_iris
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Gilles Louppe
Noel Dawe
parent
81aaedeae6
commit
562b60ce8a
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@ -1,99 +0,0 @@
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"""
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========================================================================
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Plot the decision surfaces of boosted decision trees on the iris dataset
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========================================================================
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Plot the decision surfaces of boosted decision trees trained on pairs of
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features of the iris dataset.
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This plot compares the decision surfaces learned by a decision tree classifier
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(first column), by a boosted decision tree classifier (second column).
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In the first row, the classifiers are built using the sepal width and the sepal
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length features only, on the second row using the petal length and sepal length
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only, and on the third row using the petal width and the petal length only.
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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 clone
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from sklearn.datasets import load_iris
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.tree import DecisionTreeClassifier
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# Parameters
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n_classes = 3
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n_estimators = 30
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plot_colors = "bry"
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plot_step = 0.02
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# Load data
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iris = load_iris()
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plot_idx = 1
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for pair in ([0, 1], [0, 2], [2, 3]):
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for model in (DecisionTreeClassifier(),
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AdaBoostClassifier(n_estimators=n_estimators)):
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# We only take the two corresponding features
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X = iris.data[:, pair]
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y = iris.target
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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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# Train
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clf = clone(model)
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clf = model.fit(X, y)
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# Plot the decision boundary
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pl.subplot(3, 2, plot_idx)
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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, plot_step),
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np.arange(y_min, y_max, plot_step))
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if isinstance(model, DecisionTreeClassifier):
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Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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cs = pl.contourf(xx, yy, Z,
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cmap=pl.cm.Paired)
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else:
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norm = sum(model.boost_weights_)
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for weight, tree in zip(model.boost_weights_, model.estimators_):
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Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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cs = pl.contourf(xx, yy, Z, alpha=weight / norm,
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cmap=pl.cm.Paired)
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#pl.xlabel("%s / %s" % (iris.feature_names[pair[0]],
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# model.__class__.__name__))
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#pl.ylabel(iris.feature_names[pair[1]])
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pl.axis("tight")
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# Plot the training points
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for i, c in zip(xrange(n_classes), plot_colors):
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idx = np.where(y == i)
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pl.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],
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cmap=pl.cm.Paired)
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pl.axis("tight")
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plot_idx += 1
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pl.set_cmap(pl.cm.Paired)
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pl.suptitle("Decision surfaces of a decision tree and of "
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"a boosted decision tree.")
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pl.show()
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@ -7,8 +7,8 @@ Plot the decision surfaces of forests of randomized trees trained on pairs of
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features of the iris dataset.
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This plot compares the decision surfaces learned by a decision tree classifier
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(first column), by a random forest classifier (second column) and by an extra-
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trees classifier (third column).
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(first column), by a random forest classifier (second column), by an extra-
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trees classifier (third column) and by an AdaBoost classifier (fourth column).
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In the first row, the classifiers are built using the sepal width and the sepal
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length features only, on the second row using the petal length and sepal length
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@ -21,7 +21,7 @@ import pylab as pl
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from sklearn import clone
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from sklearn.datasets import load_iris
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier
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from sklearn.tree import DecisionTreeClassifier
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# Parameters
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@ -38,7 +38,8 @@ plot_idx = 1
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for pair in ([0, 1], [0, 2], [2, 3]):
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for model in (DecisionTreeClassifier(),
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RandomForestClassifier(n_estimators=n_estimators),
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ExtraTreesClassifier(n_estimators=n_estimators)):
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ExtraTreesClassifier(n_estimators=n_estimators),
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AdaBoostClassifier(n_estimators=n_estimators)):
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# We only take the two corresponding features
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X = iris.data[:, pair]
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y = iris.target
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@ -60,7 +61,7 @@ for pair in ([0, 1], [0, 2], [2, 3]):
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clf = model.fit(X, y)
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# Plot the decision boundary
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pl.subplot(3, 3, plot_idx)
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pl.subplot(3, 4, plot_idx)
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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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@ -89,6 +90,5 @@ for pair in ([0, 1], [0, 2], [2, 3]):
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plot_idx += 1
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pl.suptitle("Decision surfaces of a decision tree, of a random forest, and of "
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"an extra-trees classifier")
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pl.suptitle("Decision surfaces of DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier and AdaBoostClassifier")
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pl.show()
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