集成学习和代码实现

从百度云课堂上截图的基础概念，如果之前不了解的可以先看一下这篇博客：https://blog.csdn.net/weixin_30708329/article/details/97262409

 

 

 

 

 

不同的数据集训练不同的模型，根据模型进行投票得到最终预测结果

多棵决策树组成森林，每个模型训练集不同和选择的决策属性不同是RF算法随机的最主要体现

 

 

 

 

 

adaboost算法不同模型之间会有影响

 

 

 

 

 

 

 

 

 

 

多层分类器进行结果的预测

 

 

 

 bagging算法提高KNN和决策树算法精确度

 1 # 导入算法包以及数据集
 2 from sklearn import neighbors
 3 from sklearn import datasets
 4 from sklearn.ensemble import BaggingClassifier
 5 from sklearn import tree
 6 from sklearn.model_selection import train_test_split
 7 import numpy as np
 8 import matplotlib.pyplot as plt
 9 iris = datasets.load_iris()
10 x_data = iris.data[:,:2]
11 y_data = iris.target
12 def plot(model):
13     # 获取数据值所在的范围
14     x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
15     y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
16 
17     # 生成网格矩阵
18     xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
19                          np.arange(y_min, y_max, 0.02))
20 
21     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
22     z = z.reshape(xx.shape)
23     # 等高线图
24     cs = plt.contourf(xx, yy, z)
25 x_train,x_test,y_train,y_test = train_test_split(x_data, y_data)
26 knn = neighbors.KNeighborsClassifier()
27 knn.fit(x_train, y_train)
28 # 画图
29 plot(knn)
30 # 样本散点图
31 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
32 plt.show()
33 # 准确率
34 print("knn:",knn.score(x_test, y_test))
35 dtree = tree.DecisionTreeClassifier()
36 dtree.fit(x_train, y_train)
37 # 画图
38 plot(dtree)
39 # 样本散点图
40 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
41 plt.show()
42 # 准确率
43 print("dtree:",dtree.score(x_test, y_test))
44 bagging_knn = BaggingClassifier(knn, n_estimators=100)#使用bagging算法训练100组knn分类器
45 # 输入数据建立模型
46 bagging_knn.fit(x_train, y_train)
47 plot(bagging_knn)
48 # 样本散点图
49 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
50 plt.show()
51 print("bagging:",bagging_knn.score(x_test, y_test))
52 bagging_tree = BaggingClassifier(dtree, n_estimators=100)#使用bagging算法训练100组决策树分类器
53 # 输入数据建立模型
54 bagging_tree.fit(x_train, y_train)
55 plot(bagging_tree)
56 # 样本散点图
57 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
58 plt.show()
59 print("bagging_tree",bagging_tree.score(x_test, y_test))

随机森林对决策树模型优化

 1 from sklearn import tree
 2 from sklearn.model_selection import train_test_split
 3 from sklearn.ensemble import RandomForestClassifier
 4 import numpy as np
 5 import matplotlib.pyplot as plt
 6 # 载入数据
 7 data = np.genfromtxt("LR-testSet2.txt", delimiter=",")
 8 x_data = data[:,:-1]
 9 y_data = data[:,-1]
10 
11 plt.scatter(x_data[:,0],x_data[:,1],c=y_data)
12 plt.show()
13 x_train,x_test,y_train,y_test = train_test_split(x_data, y_data, test_size = 0.5)
14 def plot(model):
15     # 获取数据值所在的范围
16     x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
17     y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
18 
19     # 生成网格矩阵
20     xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
21                          np.arange(y_min, y_max, 0.02))
22 
23     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
24     z = z.reshape(xx.shape)
25     # 等高线图
26     cs = plt.contourf(xx, yy, z)
27     # 样本散点图
28     plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test)
29     plt.show()
30 dtree = tree.DecisionTreeClassifier()
31 dtree.fit(x_train, y_train)
32 plot(dtree)
33 print("dtree",dtree.score(x_test, y_test))
34 RF = RandomForestClassifier(n_estimators=50)
35 RF.fit(x_train, y_train)
36 plot(RF)
37 print("RandomForest",RF.score(x_test, y_test))

下面是用到的数据

  1 0.051267,0.69956,1
  2 -0.092742,0.68494,1
  3 -0.21371,0.69225,1
  4 -0.375,0.50219,1
  5 -0.51325,0.46564,1
  6 -0.52477,0.2098,1
  7 -0.39804,0.034357,1
  8 -0.30588,-0.19225,1
  9 0.016705,-0.40424,1
 10 0.13191,-0.51389,1
 11 0.38537,-0.56506,1
 12 0.52938,-0.5212,1
 13 0.63882,-0.24342,1
 14 0.73675,-0.18494,1
 15 0.54666,0.48757,1
 16 0.322,0.5826,1
 17 0.16647,0.53874,1
 18 -0.046659,0.81652,1
 19 -0.17339,0.69956,1
 20 -0.47869,0.63377,1
 21 -0.60541,0.59722,1
 22 -0.62846,0.33406,1
 23 -0.59389,0.005117,1
 24 -0.42108,-0.27266,1
 25 -0.11578,-0.39693,1
 26 0.20104,-0.60161,1
 27 0.46601,-0.53582,1
 28 0.67339,-0.53582,1
 29 -0.13882,0.54605,1
 30 -0.29435,0.77997,1
 31 -0.26555,0.96272,1
 32 -0.16187,0.8019,1
 33 -0.17339,0.64839,1
 34 -0.28283,0.47295,1
 35 -0.36348,0.31213,1
 36 -0.30012,0.027047,1
 37 -0.23675,-0.21418,1
 38 -0.06394,-0.18494,1
 39 0.062788,-0.16301,1
 40 0.22984,-0.41155,1
 41 0.2932,-0.2288,1
 42 0.48329,-0.18494,1
 43 0.64459,-0.14108,1
 44 0.46025,0.012427,1
 45 0.6273,0.15863,1
 46 0.57546,0.26827,1
 47 0.72523,0.44371,1
 48 0.22408,0.52412,1
 49 0.44297,0.67032,1
 50 0.322,0.69225,1
 51 0.13767,0.57529,1
 52 -0.0063364,0.39985,1
 53 -0.092742,0.55336,1
 54 -0.20795,0.35599,1
 55 -0.20795,0.17325,1
 56 -0.43836,0.21711,1
 57 -0.21947,-0.016813,1
 58 -0.13882,-0.27266,1
 59 0.18376,0.93348,0
 60 0.22408,0.77997,0
 61 0.29896,0.61915,0
 62 0.50634,0.75804,0
 63 0.61578,0.7288,0
 64 0.60426,0.59722,0
 65 0.76555,0.50219,0
 66 0.92684,0.3633,0
 67 0.82316,0.27558,0
 68 0.96141,0.085526,0
 69 0.93836,0.012427,0
 70 0.86348,-0.082602,0
 71 0.89804,-0.20687,0
 72 0.85196,-0.36769,0
 73 0.82892,-0.5212,0
 74 0.79435,-0.55775,0
 75 0.59274,-0.7405,0
 76 0.51786,-0.5943,0
 77 0.46601,-0.41886,0
 78 0.35081,-0.57968,0
 79 0.28744,-0.76974,0
 80 0.085829,-0.75512,0
 81 0.14919,-0.57968,0
 82 -0.13306,-0.4481,0
 83 -0.40956,-0.41155,0
 84 -0.39228,-0.25804,0
 85 -0.74366,-0.25804,0
 86 -0.69758,0.041667,0
 87 -0.75518,0.2902,0
 88 -0.69758,0.68494,0
 89 -0.4038,0.70687,0
 90 -0.38076,0.91886,0
 91 -0.50749,0.90424,0
 92 -0.54781,0.70687,0
 93 0.10311,0.77997,0
 94 0.057028,0.91886,0
 95 -0.10426,0.99196,0
 96 -0.081221,1.1089,0
 97 0.28744,1.087,0
 98 0.39689,0.82383,0
 99 0.63882,0.88962,0
100 0.82316,0.66301,0
101 0.67339,0.64108,0
102 1.0709,0.10015,0
103 -0.046659,-0.57968,0
104 -0.23675,-0.63816,0
105 -0.15035,-0.36769,0
106 -0.49021,-0.3019,0
107 -0.46717,-0.13377,0
108 -0.28859,-0.060673,0
109 -0.61118,-0.067982,0
110 -0.66302,-0.21418,0
111 -0.59965,-0.41886,0
112 -0.72638,-0.082602,0
113 -0.83007,0.31213,0
114 -0.72062,0.53874,0
115 -0.59389,0.49488,0
116 -0.48445,0.99927,0
117 -0.0063364,0.99927,0
118 0.63265,-0.030612,0
View Code

adaboost决策树

 1 import numpy as np
 2 import matplotlib.pyplot as plt
 3 from sklearn import tree
 4 from sklearn.ensemble import AdaBoostClassifier
 5 from sklearn.tree import DecisionTreeClassifier
 6 from sklearn.datasets import make_gaussian_quantiles
 7 from sklearn.metrics import classification_report
 8 # 生成2维正态分布，生成的数据按分位数分为两类，500个样本,2个样本特征
 9 x1, y1 = make_gaussian_quantiles(n_samples=500, n_features=2,n_classes=2)#默认mean=(0, 0)
10 # 生成2维正态分布，生成的数据按分位数分为两类，400个样本,2个样本特征均值都为3
11 x2, y2 = make_gaussian_quantiles(mean=(3, 3), n_samples=500, n_features=2, n_classes=2)
12 # 将两组数据合成一组数据
13 x_data = np.concatenate((x1, x2))
14 y_data = np.concatenate((y1, - y2 + 1))
15 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
16 plt.show()
17 # 决策树模型
18 model = tree.DecisionTreeClassifier(max_depth=3)
19 
20 # 输入数据建立模型
21 model.fit(x_data, y_data)
22 
23 # 获取数据值所在的范围
24 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
25 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
26 
27 # 生成网格矩阵
28 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
29                      np.arange(y_min, y_max, 0.02))
30 
31 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
32 z = z.reshape(xx.shape)
33 # 等高线图
34 cs = plt.contourf(xx, yy, z)
35 # 样本散点图
36 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
37 plt.show()
38 # 模型准确率
39 print("决策树：",model.score(x_data,y_data))
40 # AdaBoost模型
41 model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),n_estimators=10)#决策树深度为3，一共训练十个模型
42 # 训练模型
43 model.fit(x_data, y_data)
44 
45 # 获取数据值所在的范围
46 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
47 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
48 
49 # 生成网格矩阵
50 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
51                      np.arange(y_min, y_max, 0.02))
52 
53 # 获取预测值
54 z = model.predict(np.c_[xx.ravel(), yy.ravel()])
55 z = z.reshape(xx.shape)
56 # 等高线图
57 cs = plt.contourf(xx, yy, z)
58 # 样本散点图
59 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
60 plt.show()
61 # 模型准确率
62 print("adaboost：",model.score(x_data,y_data))#得分很高

stacking分类器算法

 1 from sklearn import datasets
 2 from sklearn import model_selection
 3 from sklearn.linear_model import LogisticRegression
 4 from sklearn.neighbors import KNeighborsClassifier
 5 from sklearn.tree import DecisionTreeClassifier
 6 from mlxtend.classifier import StackingClassifier # pip install mlxtend
 7 import numpy as np
 8 
 9 # 载入数据集
10 iris = datasets.load_iris()
11 # 只要第1,2列的特征
12 x_data, y_data = iris.data[:, 1:3], iris.target
13 
14 # 定义三个不同的分类器
15 clf1 = KNeighborsClassifier(n_neighbors=1)
16 clf2 = DecisionTreeClassifier()
17 clf3 = LogisticRegression()
18 
19 # 定义一个次级分类器
20 lr = LogisticRegression()
21 sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],
22                           meta_classifier=lr)
23 
24 for clf, label in zip([clf1, clf2, clf3, sclf],
25                       ['KNN', 'Decision Tree', 'LogisticRegression', 'StackingClassifier']):
26     scores = model_selection.cross_val_score(clf, x_data, y_data, cv=3, scoring='accuracy')
27     print("Accuracy: %0.2f [%s]" % (scores.mean(), label))

voting分类器

 1 from sklearn import datasets
 2 from sklearn import model_selection
 3 from sklearn.linear_model import LogisticRegression
 4 from sklearn.neighbors import KNeighborsClassifier
 5 from sklearn.tree import DecisionTreeClassifier
 6 from sklearn.ensemble import VotingClassifier
 7 import numpy as np
 8 
 9 # 载入数据集
10 iris = datasets.load_iris()
11 # 只要第1,2列的特征
12 x_data, y_data = iris.data[:, 1:3], iris.target
13 
14 # 定义三个不同的分类器
15 clf1 = KNeighborsClassifier(n_neighbors=1)
16 clf2 = DecisionTreeClassifier()
17 clf3 = LogisticRegression()
18 
19 sclf = VotingClassifier([('knn', clf1), ('dtree', clf2), ('lr', clf3)])
20 
21 for clf, label in zip([clf1, clf2, clf3, sclf],
22                       ['KNN', 'Decision Tree', 'LogisticRegression', 'VotingClassifier']):
23     scores = model_selection.cross_val_score(clf, x_data, y_data, cv=3, scoring='accuracy')
24     print("Accuracy: %0.2f [%s]" % (scores.mean(), label))

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

posted @ 2019-11-10 18:11  你的雷哥  阅读(2073)  评论(0)  编辑  收藏  举报