下面的demo是使用决策树算法的一个例子，使用的数据链接如下：
https://files.cnblogs.com/files/henuliulei/决策树.zip
from sklearn.feature_extraction import DictVectorizer from sklearn import tree from sklearn import preprocessing import csv # 读入数据 Dtree = open(r'AllElectronics.csv', 'r') reader = csv.reader(Dtree) # 获取第一行数据 headers = reader.__next__() print(headers) # 定义两个列表 featureList = [] labelList = [] # for row in reader: # 把label存入list labelList.append(row[-1])#类别标签 rowDict = {} for i in range(1, len(row)-1): #建立一个数据字典 rowDict[headers[i]] = row[i] # 把数据字典存入list featureList.append(rowDict) print(featureList) # 把数据转换成01表示 vec = DictVectorizer() x_data = vec.fit_transform(featureList).toarray() print("x_data: " + str(x_data)) # 打印属性名称 print(vec.get_feature_names()) # 打印标签 print("labelList: " + str(labelList)) # 把标签转换成01表示 lb = preprocessing.LabelBinarizer() y_data = lb.fit_transform(labelList) print("y_data: " + str(y_data)) # 创建决策树模型 model = tree.DecisionTreeClassifier(criterion='entropy')##建立决策树模型,基于信息熵 # 输入数据建立模型 model.fit(x_data, y_data) # 测试 x_test = x_data[0] print("x_test: " + str(x_test)) predict = model.predict(x_test.reshape(1,-1)) print("predict: " + str(predict)) # 导出决策树 # pip install graphviz # http://www.graphviz.org/ import graphviz dot_data = tree.export_graphviz(model, out_file = None, feature_names = vec.get_feature_names(), class_names = lb.classes_, filled = True, rounded = True, special_characters = True) graph = graphviz.Source(dot_data) graph.render('computer')#生成一个决策树图表文件
下面的demo是用决策树进行线性二分类
1 import matplotlib.pyplot as plt 2 import numpy as np 3 from sklearn.metrics import classification_report 4 from sklearn import tree 5 # 载入数据 6 data = np.genfromtxt("LR-testSet.csv", delimiter=",") 7 x_data = data[:,:-1] 8 y_data = data[:,-1] 9 10 plt.scatter(x_data[:,0],x_data[:,1],c=y_data) 11 plt.show() 12 # 创建决策树模型 13 model = tree.DecisionTreeClassifier() 14 # 输入数据建立模型 15 model.fit(x_data, y_data) 16 # 导出决策树 17 import graphviz # http://www.graphviz.org/ 18 19 dot_data = tree.export_graphviz(model, 20 out_file = None, 21 feature_names = ['x','y'], 22 class_names = ['label0','label1'], 23 filled = True, 24 rounded = True, 25 special_characters = True) 26 graph = graphviz.Source(dot_data) 27 # 获取数据值所在的范围 28 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1 29 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1 30 31 # 生成网格矩阵 32 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), 33 np.arange(y_min, y_max, 0.02)) 34 35 z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据 36 z = z.reshape(xx.shape) 37 # 等高线图 38 cs = plt.contourf(xx, yy, z) 39 # 样本散点图 40 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data) 41 plt.show() 42 predictions = model.predict(x_data) 43 print(classification_report(predictions,y_data))
下面这个demo是用决策树进行非线性二分类
1 import matplotlib.pyplot as plt 2 import numpy as np 3 from sklearn.metrics import classification_report 4 from sklearn import tree 5 from sklearn.model_selection import train_test_split 6 7 # 载入数据 8 data = np.genfromtxt("LR-testSet2.txt", delimiter=",") 9 x_data = data[:, :-1] 10 y_data = data[:, -1] 11 12 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data) 13 plt.show() 14 #分割数据 15 x_train,x_test,y_train,y_test = train_test_split(x_data, y_data) 16 17 # 创建决策树模型 18 # max_depth，树的深度 19 # min_samples_split 内部节点再划分所需最小样本数 20 model = tree.DecisionTreeClassifier(max_depth=7,min_samples_split=4) 21 # 输入数据建立模型 22 model.fit(x_train, y_train) 23 # 导出决策树 24 import graphviz # http://www.graphviz.org/ 25 26 dot_data = tree.export_graphviz(model, 27 out_file = None, 28 feature_names = ['x','y'], 29 class_names = ['label0','label1'], 30 filled = True, 31 rounded = True, 32 special_characters = True) 33 graph = graphviz.Source(dot_data) 34 # 获取数据值所在的范围 35 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1 36 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1 37 38 # 生成网格矩阵 39 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), 40 np.arange(y_min, y_max, 0.02)) 41 42 z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据 43 z = z.reshape(xx.shape) 44 # 等高线图 45 cs = plt.contourf(xx, yy, z) 46 # 样本散点图 47 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data) 48 plt.show() 49 predictions = model.predict(x_train) 50 print(classification_report(predictions,y_train)) 51 predictions = model.predict(x_test) 52 print(classification_report(predictions,y_test))
1 from sklearn import tree 2 import numpy as np 3 # 载入数据 4 data = np.genfromtxt("cart.csv", delimiter=",") 5 x_data = data[1:,1:-1] 6 y_data = data[1:,-1] 7 # 创建决策树模型 8 model = tree.DecisionTreeClassifier() 9 # 输入数据建立模型 10 model.fit(x_data, y_data) 11 # 导出决策树 12 import graphviz # http://www.graphviz.org/ 13 14 dot_data = tree.export_graphviz(model, 15 out_file = None, 16 feature_names = ['house_yes','house_no','single','married','divorced','income'], 17 class_names = ['no','yes'], 18 filled = True, 19 rounded = True, 20 special_characters = True) 21 graph = graphviz.Source(dot_data) 22 graph.render('cart') 23 print(graph)