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118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
import numpy as np
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# Taken from http://chrisstrelioff.ws/sandbox/2015/06/25/decision_trees_in_python_again_cross_validation.html
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def get_code(tree, feature_names, target_names,
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spacer_base=" "):
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"""Produce psuedo-code for decision tree.
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Args
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----
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tree -- scikit-leant DescisionTree.
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feature_names -- list of feature names.
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target_names -- list of target (class) names.
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spacer_base -- used for spacing code (default: " ").
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Notes
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-----
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based on http://stackoverflow.com/a/30104792.
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"""
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left = tree.tree_.children_left
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right = tree.tree_.children_right
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threshold = tree.tree_.threshold
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features = [feature_names[i] for i in tree.tree_.feature]
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value = tree.tree_.value
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def recurse(left, right, threshold, features, node, depth):
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spacer = spacer_base * depth
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if (threshold[node] != -2):
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print(spacer + "if ( " + features[node] + " <= " + \
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str(threshold[node]) + " ) {")
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if left[node] != -1:
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recurse(left, right, threshold, features,
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left[node], depth+1)
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print(spacer + "}\n" + spacer +"else {")
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if right[node] != -1:
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recurse(left, right, threshold, features,
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right[node], depth+1)
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print(spacer + "}")
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else:
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target = value[node]
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for i, v in zip(np.nonzero(target)[1],
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target[np.nonzero(target)]):
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target_name = target_names[i]
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target_count = int(v)
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print(spacer + "return " + str(target_name) + \
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" ( " + str(target_count) + " examples )")
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recurse(left, right, threshold, features, 0, 0)
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# Taken from https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import load_iris
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from sklearn.tree import DecisionTreeClassifier
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def plot_tree_iris():
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"""
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Taken fromhttp://scikit-learn.org/stable/auto_examples/tree/plot_iris.html
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"""
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# Parameters
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n_classes = 3
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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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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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# 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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model = DecisionTreeClassifier(max_depth=3, random_state=1).fit(X, y)
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# Plot the decision boundary
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plt.subplot(2, 3, pairidx + 1)
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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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Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
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plt.xlabel(iris.feature_names[pair[0]])
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plt.ylabel(iris.feature_names[pair[1]])
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plt.axis("tight")
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# Plot the training points
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for i, color in zip(range(n_classes), plot_colors):
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idx = np.where(y == i)
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plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
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cmap=plt.cm.Paired)
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plt.axis("tight")
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plt.suptitle("Decision surface of a decision tree using paired features")
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plt.legend()
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plt.show()
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