基于决策树的鸢尾花图像分类

生成决策树决策区域图

#构建决策树
from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import matplotlib
from distutils.version import LooseVersion
from sklearn.tree import DecisionTreeClassifier

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0],
                    y=X[y == cl, 1],
                    alpha=0.8,
                    color=colors[idx],
                    marker=markers[idx],
                    label=cl,
                    edgecolor='black')

    if test_idx:
        # plot all examples
        X_test, y_test = X[test_idx, :], y[test_idx]

        if LooseVersion(matplotlib.__version__) < LooseVersion('0.3.4'):
            plt.scatter(X_test[:, 0],
                        X_test[:, 1],
                        c='',
                        edgecolor='black',
                        alpha=1.0,
                        linewidth=1,
                        marker='o',
                        s=100,
                        label='test set')
        else:
            plt.scatter(X_test[:, 0],
                        X_test[:, 1],
                        c='none',
                        edgecolor='black',
                        alpha=1.0,
                        linewidth=1,
                        marker='o',
                        s=100,
                        label='test set')

iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)

tree_model = DecisionTreeClassifier(criterion='gini',max_depth=4,random_state=1)
tree_model.fit(X_train, y_train)
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X_combined,y_combined,classifier=tree_model,test_idx=range(105,150))

plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()

保存决策树图像（先安装graphviz，pydotplus，pyparsing）

from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from pydotplus import graph_from_dot_data
from sklearn.tree import export_graphviz

tree_model = DecisionTreeClassifier(criterion='gini',
				max_depth=4,
				random_state=1)
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)

tree_model.fit(X_train, y_train)
X_combined = np.vstack((X_train, X_test))
dot_data = export_graphviz(tree_model,
              filled=True,
              rounded=True,
              class_names=['Setosa',
                      'Versicolor',
                      'Virginica'],
              feature_names=['petal length',
                     'petal width'],
             out_file=None)
graph = graph_from_dot_data(dot_data)
graph.write_png('tree.png')