ai-机器学习算法实现之随机森林分类.zip

#RandomForestClassifier import math import matplotlib as mpl import warnings import numpy as np from sklearn.model_selection import cross_val_score from sklearn.datasets import make_blobs from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import ExtraTreesClassifier from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split #忽略一些版本不兼容等警告 warnings.filterwarnings("ignore") #源数据产生具体看https://blog.csdn.net/ichuzhen/article/details/51768934 n_features=2 #每个样本有几个属性或特征 x,y = make_blobs(n_samples=300, n_features=n_features, centers=6) x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.7) #核心代码 #传统决策树、随机森林算法、极端随机树关于区别:https://blog.csdn.net/hanss2/article/details/53525503 #关于其中参数的说明请看http://www.jb51.net/article/131172.htm clf1 = DecisionTreeClassifier(max_depth=None, min_samples_split=2,random_state=0) clf2 = RandomForestClassifier(n_estimators=10,max_features=math.sqrt(n_features), max_depth=None,min_samples_split=2, bootstrap=True) clf3 = ExtraTreesClassifier(n_estimators=10,max_features=math.sqrt(n_features), max_depth=None,min_samples_split=2, bootstrap=False) ''' #交叉验证 scores1 = cross_val_score(clf1, x_train, y_train) scores2 = cross_val_score(clf2, x_train, y_train) scores3 = cross_val_score(clf3, x_train, y_train) print('DecisionTreeClassifier交叉验证准确率为:'+str(scores1.mean())) print('RandomForestClassifier交叉验证准确率为:'+str(scores2.mean())) print('ExtraTreesClassifier交叉验证准确率为:'+str(scores3.mean())) ''' clf1.fit(x_train, y_train) clf2.fit(x_train, y_train) clf3.fit(x_train, y_train) #区域预测 x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围 x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围 x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]# 生成网格采样点行列均为200点 area_smaple_point = np.stack((x1.flat, x2.flat), axis=1) # 将区域划分为一系列测试点去用学习的模型预测，进而根据预测结果画区域 area1_predict = clf1.predict(area_smaple_point) # 所有区域点进行预测 area1_predict = area1_predict.reshape(x1.shape) # 转化为和x1一样的数组因为用plt.pcolormesh(x1, x2, area_flag, cmap=classifier_area_color) # 时x1和x2组成的是200*200矩阵，area_flag要与它对应 area2_predict = clf2.predict(area_smaple_point) area2_predict = area2_predict.reshape(x1.shape) area3_predict = clf3.predict(area_smaple_point) area3_predict = area3_predict.reshape(x1.shape) mpl.rcParams['font.sans-serif'] = [u'SimHei'] #用来正常显示中文标签 mpl.rcParams['axes.unicode_minus'] = False #用来正常显示负号 classifier_area_color = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF']) #区域颜色 cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) #样本所属类别颜色 #绘图 #第一个子图 plt.subplot(2,2,1) plt.pcolormesh(x1, x2, area1_predict, cmap=classifier_area_color) plt.scatter(x_train[:,0], x_train[:,1], c=y_train,marker='o', s=50, cmap=cm_dark) plt.scatter(x_test[:,0],x_test[:,1], c=y_test,marker='x', s=50, cmap=cm_dark) plt.xlabel('data_x', fontsize=8) plt.ylabel('data_y', fontsize=8) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'DecisionTreeClassifier:传统决策树', fontsize=8) plt.text(x1_max-9, x2_max-2, u'$o---train ; x---test$') #第二个子图 plt.subplot(2,2,2) plt.pcolormesh(x1, x2, area2_predict, cmap=classifier_area_color) plt.scatter(x_train[:,0], x_train[:,1], c=y_train,marker='o', s=50, cmap=cm_dark) plt.scatter(x_test[:,0],x_test[:,1], c=y_test,marker='x', s=50, cmap=cm_dark) plt.xlabel('data_x', fontsize=8) plt.ylabel('data_y', fontsize=8) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'RandomForestClassifier:随机森林算法', fontsize=8) plt.text(x1_max-9,x2_max-2, u'$o---train ; x---test$') #第三个子图 plt.subplot(2,2,3) plt.pcolormesh(x1, x2, area3_predict, cmap=classifier_area_color) plt.scatter(x_train[:,0], x_train[:,1], c=y_train,marker='o', s=50, cmap=cm_dark) plt.scatter(x_test[:,0],x_test[:,1], c=y_test,marker='x', s=50, cmap=cm_dark) plt.xlabel('data_x', fontsize=8) plt.ylabel('data_y', fontsize=8) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'ExtraTreesClassifier:极端随机树', fontsize=8) plt.text(x1_max-9, x2_max-2, u'$o---train ; x---test$') #第四个子图 plt.subplot(2,2,4) y=[] scores1 = cross_val_score(clf1, x_train, y_train) y.append(scores1.mean()) scores2 = cross_val_score(clf2, x_train, y_train) y.append(scores2.mean()) scores3 = cross_val_score(clf3, x_train, y_train) y.append(scores3.mean()) x=[0,1,2] plt.bar(x,y,0.4,color="green") plt.xlabel("0--DecisionTreeClassifier;1--RandomForestClassifier;2--ExtraTreesClassifie", fontsize=8) plt.ylabel("平均准确率", fontsize=8) plt.ylim(0.9, 0.99) plt.title("交叉验证",fontsize=8) for a, b in zip(x, y): plt.text(a, b, b, ha='center', va='bottom', fontsize=10) plt.show()