《人工智能基础及应用（Python 微课版）》程序代码 第5章 机器学习

第5章机器学习【例5-2】KNN举例importmatplotlib.pyplotaspltplt.plot([9,9.2,9.5,9.2,5.7,7,7.5],[9.0,9.2,9.2,9.2,7.1,7.4,7.5],'yx')#橙子plt.plot([7.2,7.3,7.2,7.3,7.2,7.3,7.3],[10.3,10.5,9.2,10.2,9.7,10.1,10.1],'b.')#柠檬plt.plot([7],[9],'r^')#未知物体circle1=plt.Circle((7,9),1.2,color='g')plt.gcf().gca().add_artist(circle1)#绘制未知物体周边范围plt.axis([5,11,5,11])#图表尺寸plt.ylabel('Hcm')plt.xlabel('Wcm')plt.legend(('Orange','Lemons'),loc='upperright')plt.show()【例5-3】K值选择举例【例5-4】KNN算法进行鸢尾花识别fromsklearnimportdatasets#引入数据集fromsklearn.model_selectionimporttrain_test_split#将数据分为测试集和训练集fromsklearn.preprocessingimportStandardScalerfromsklearn.neighborsimportKNeighborsClassifier#利用邻近点方式训练数据#步骤1：通过datasets加载鸢尾花数据iris=datasets.load_iris()#鸢尾花数据iris包含4个特征变量#步骤2:划分数据集x_train,x_test,y_train,y_test=train_test_split(iris.data,iris.target,random_state=5)#步骤3:特征工程：标准化transfer=StandardScaler()x_train=transfer.fit_transform(x_train)#训练集标准化x_test=transfer.transform(x_test)#测试集标准化#步骤4:KNN算法预估器estimator=KNeighborsClassifier(n_neighbors=3)estimator.fit(x_train,y_train)#步骤5:模型评估采用如下两种方法#方法1：直接比对真实值和预测值y_predict=estimator.predict(x_test)print(y_predict)print('比对真实值和预测值\n',y_test==y_predict)#方法2：计算准确率score=estimator.score(x_test,y_test)#测试集的特征值和目标值print("准确率:",score)【例5-6】决策树应用于回归问题举例#boston_tree_final.pyimportos,joblibimportnumpyasnpfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportStandardScalerfromsklearn.treeimportDecisionTreeRegressorfromsklearn.metricsimportr2_score,mean_squared_error,mean_absolute_errorfromsklearn.datasetsimportfetch_openml#----------1.数据获取（带清华镜像加速+本地缓存）----------data_home=os.path.join(os.path.expanduser("~"),"scikit_learn_data")#国内镜像站，速度快boston=fetch_openml(name="boston",version=1,data_home=data_home,as_frame=False,parser="auto")X,y=boston.data,boston.target#X:506×13y:506#----------2.数据集拆分----------X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=33)#----------3.仅对特征标准化----------scaler_X=StandardScaler()X_train=scaler_X.fit_transform(X_train)X_test=scaler_X.transform(X_test)#目标值不做标准化（房价单位保持k$）#----------4.训练----------reg=DecisionTreeRegressor(random_state=33)reg.fit(X_train,y_train)y_pred=reg.predict(X_test)#----------5.评估----------print("回归树R²：",r2_score(y_test,y_pred))print("回归树MSE：",mean_squared_error(y_test,y_pred))print("回归树MAE：",mean_absolute_error(y_test,y_pred))【例5-8】MultinomialNB应用于20newsgroups数据集#news_offline_final.pyfromsklearn.datasetsimportfetch_20newsgroupsfromsklearn.model_selectionimporttrain_test_splitfromsklearn.feature_extraction.textimportCountVectorizerfromsklearn.naive_bayesimportMultinomialNBfromsklearn.metricsimportclassification_report#强制本地读取news=fetch_20newsgroups(data_home=None,#默认即~/scikit_learn_datasubset='all',remove=('headers','footers','quotes'),download_if_missing=False#关键：不尝试联网)print('输出数据的条数:',len(news.data))#拆分/向量化/训练/评估X_train,X_test,y_train,y_test=train_test_split(news.data,news.target,test_size=0.25,random_state=33)vec=CountVectorizer(stop_words='english',max_df=0.5)X_train=vec.fit_transform(X_train)X_test=vec.transform(X_test)mnb=MultinomialNB(alpha=0.1)mnb.fit(X_train,y_train)y_pred=mnb.predict(X_test)print('准确率:{:.4f}'.format(mnb.score(X_test,y_test)))print(classification_report(y_test,y_pred,target_names=news.target_names))【例5-11】轮廓系数举例#生成数据模块fromsklearn.datasetsimportmake_blobs#k-means模块fromsklearn.clusterimportKMeans#评估指标——轮廓系数,前者为所有点的平均轮廓系数，后者返回每个点的轮廓系数fromsklearn.metricsimportsilhouette_score,silhouette_samplesimportnumpyasnpimportmatplotlib.pyplotasplt#生成数据x_true,y_true=make_blobs(n_samples=500,n_features=2,centers=4,random_state=1)#绘制出所生成的数据plt.figure(figsize=(5,5))plt.scatter(x_true[:,0],x_true[:,1],c=y_true,s=10)plt.title("Origindata")plt.show()#根据不同的n_centers进行聚类n_clusters=[xforxinrange(3,5)]foriinrange(len(n_clusters)):#实例化k-means分类器clf=KMeans(n_clusters=n_clusters[i])y_predict=clf.fit_predict(x_true)#绘制分类结果plt.figure(figsize=(5,5))plt.scatter(x_true[:,0],x_true[:,1],c=y_predict,s=10)plt.title("n_clusters={}".format(n_clusters[i]))ex=0.5step=0.01xx,yy=np.meshgrid(np.arange(x_true[:,0].min()-ex,x_true[:,0].max()+ex,step),np.arange(x_true[:,1].min()-ex,x_true[:,1].max()+ex,step))zz=clf.predict(np.c_[xx.ravel(),yy.ravel()])zz.shape=xx.shapeplt.contourf(xx,yy,zz,alpha=0.1)plt.show()#打印平均轮廓系数s=silhouette_score(x_true,y_predict)print("Whencluster={}\nThesilhouette_score={}".format(n_clusters[i],s))#利用silhouette_samples计算轮廓系数为正的点的个数n_s_bigger_than_zero=(silhouette_samples(x_true,y_predict)>0).sum()print("{}/{}\n".format(n_s_bigger_than_zero,x_true.shape[0]))【例5-12】用调整兰德指数（ARI）和标准化互信息（NMI）看聚类质量#kmeans_digits_fix.pyimportos,ssl,matplotlibmatplotlib.use('TkAgg')#1.强制后端ssl._create_default_https_context=ssl._create_unverified_contextfromsklearn.datasetsimportload_digitsfromsklearn.clusterimportKMeansfromsklearn.metricsimportadjusted_rand_score,normalized_mutual_info_scoreimportmatplotlib.pyplotaspl