《Python数据分析与应用》实验四 使用scikit

1、实验四使用scikit-learn构建模型教材P196实训1-实训41、实训1#读数据import pandas as pdwine=pd.read_csv（D:桌面 实验四 datawine.csv）winequality=pd.read_csv（D:桌面 实验四 datawinequality.csv,sep=;）#数据和标签拆分开wine_data=wine.iloc:,1:wine_target=wineClassprint（wine 数据集的数据为：n,wine_data）print（wine 数据集的标签为：n,wine_target）winequality_data=winequ

2、ality.iloc:,:-1winequality_target=winequalityqualityprint（winequality 数据集的数据为：n,winequality_data）print（winequality 数据集的标签为：n,winequality_target）wine薮据集的数据为=Alcohol Malic_acid心, HueOD280/OD315_of_d iluted_wi n esProline014-231-712-431出3-921065113.201.782.141.053.401050213.162.362.671.033,171185314.37

3、1.952.50 S863,4514S6413-242-592-S72*9373517313.715 652.450641.7474017413.403.912.48-71,567517513.274.282260.591.5683517613-172.592.37S6&84617714.134.102.740,6116056617Brows x 13 columnswine数据星的标签为：_011121314117331743175317631773Name;Classj Length: 17Bjdtype:int64fixed acidity volatile acidity citric

4、 acidPHsulphatesalcohol074.3.51孔417*S32&.6B孔827.8矶了跪矶前 3269-65孔8311.2如墓3矶5E 316n5S9.S47.40.00 351e.se9.4，/. jU a 15940.600.a 3450n5B10.515955-90.559矶1/3520.7611-2159&0,510.13342n75IK15975.90.645.12 3570.7110.215980.3100.47 3390.6611.01599rows k 11 columns 1winequa lity教据集的标签为:0515253645IS 9451595615

5、9&G1575159&Name :qualityj Length: 1599jdtype: int64# 划分训练集和测试集from sklearn.model_selection import train_test_split wine_data_train, wine_data_test, wine_target_train, wine_target_test = train_test_split(wine_data, wine_target, test_size=0.1, random_state=6) winequality_data_train, winequality_data_t

6、est, winequality_target_train, winequality_target_test = train_test_split(winequality_data, winequality_target, test_size=0.1, random_state=6)#标准化数据集from sklearn.preprocessing import StandardScaler stdScale = StandardScaler().fit(wine_data_train) wine_trainScaler = stdScale.transform(wine_data_train

7、) wine_testScaler = stdScale.transform(wine_data_test) stdScale = StandardScaler().fit(winequality_data_train)winequality_trainScaler = stdScale.transform(winequality_data_train)winequality_testScaler = stdScale.transform(winequality_data_test)#PCA降维from sklearn.decomposition import PCApca = PCA(n_c

8、omponents=5).fit(wine_trainScaler)wine_trainPca = pca.transform(wine_trainScaler)wine_testPca = pca.transform(wine_testScaler)pca = PCA(n_components=5).fit(winequality_trainScaler)winequality_trainPca = pca.transform(winequality_trainScaler)winequality_testPca = pca.transform(winequality_testScaler)

9、2、实训2#根据实训1的wine数据集处理的结果，构建聚类数目为3的K-Means模型from sklearn.cluster import Kmeanskmeans = KMeans(n_clusters = 3,random_state=1).fit(wine_trainScaler) print(构建的 KMeans 模型为：n,kmeans)#对比真实标签和聚类标签求取FMIfrom sklearn.metrics import fowlkes_mallows_score score=fowlkes_mallows_score(wine_target_train,kmeans.labe

10、ls_) print(wine 数据集的 FMI:%f%(score)5=wineS据集的FMIiS, 924119#在聚类数目为210类时，确定最优聚类数目for i in range(2,11):kmeans = KMeans(n_clusters = i,random_state=123).fit(wine_trainScaler) score = fowlkes_mallows_score(wine_target_train,kmeans.labels_) print(iris 数据聚d 类 FMI 评价分值为：%f %(i,score)讣诂数握聚卫类FMI评价分值灼：如砧00跆 计诂

11、数据聚3类FMI评价分值为：&. 936567 外氐数据鞘类FMT评价分值为：&.S4636S 计诂数据聚5类斤II评倩分值为；叽74疗43 E感据聚白类评价分值为：0 .669224 2诂数据集7类FMI评价始值为：9.671255 :L坛数据聚日类FMI评价分值为：寻.6432漏数据聚9类FMI评价分值为；叽,59卷花 计也数据聚10类Fill评价分值为：0.544814#求取模型的轮廓系数，绘制轮廓系数折线图，确定最优聚类数目from sklearn.metrics import silhouette_scoreimport matplotlib.pyplot as pltsilhoue

12、ttteScore =for i in range(2,11):kmeans = KMeans(n_clusters = i,random_state=1).fit(wine)score = silhouette_score(wine,kmeans.labels_)silhouettteScore.append(score)plt.figure(figsize=(10,6)plt.plot(range(2,11),silhouettteScore,linewidth=1.5, linestyle=-)plt.show()#求取Calinski-Harabasz指数，确定最优聚类数from sk

13、learn.metrics import calinski_harabaz_scorefor i in range(2,11):kmeans = KMeans(n_clusters = i,random_state=1).fit(wine)score = calinski_harabaz_score(wine,kmeans.labels_)print(seeds 数据聚d 类 calinski_harabaz 指数为：%f%(i,score)cannot import name calin5ki_harabaz_5core from sklearnmetrics (E;anacondalib5

14、ite-package55klearnXmetric5_init_.py)出现错误，代码没有问题。换了个电脑出现结果；目曰曰曲数据聚卫类calinski_harabsz指敖为：55.425&S3 seeds数据聚3类calin3ki_harabaztB数为；561-805-171 seedsI据聚4类calinski_harab3z指数为：702-648-113 能曲m数据槃5类calinski_harabaz指数为:7S7-0L1163 seeds数据聚白类calinski_har abaz指数为 853-737946 击曰曲数据聚7类Galinski_haraLaz指数为：11S7.42.

15、1S17 seeds 数据聚8类ca 1 inski_harbazta为；1297.354659 seedsI 据聚日类 calinski_harab3z 指数为：1349-991148 能曲m数据槃IS类calinski_har3baz指数为：1B17.2&47803、实训3#读取wine数据集，区分标签和数据import pandas as pdwine = pd.read_csv (D:桌面 实验四 datawine.csv)wine_data=wine.iloc:,1:wine_target=wineClass#将wine数据集划分为训练集和测试集from sklearn.model_

16、selection import train_test_splitwine_data_train, wine_data_test, wine_target_train, wine_target_test = train_test_split(wine_data, wine_target, test_size=0.1, random_state=6)#使用离差标准化方法标准化wine数据集from sklearn.preprocessing import MinMaxScaler stdScale = MinMaxScaler().fit(wine_data_train) wine_trainS

17、caler = stdScale.transform(wine_data_train) wine_testScaler = stdScale.transform(wine_data_test) #构建SVM模型,并预测测试集结果。from sklearn.svm import SVCsvm = SVC().fit(wine_trainScaler,wine_target_train) print(建立的 SVM 模型为：n,svm)wine_target_pred = svm.predict(wine_testScaler)r四项测前10 个结果为：_n,wine_target_pred:10

18、)#打印出分类报告，评价分类模型性能from sklearn.metrics import classification_report print(使用SVM预测iris数据的分类报告为：,n, classification_report(wine_target_test,wine_target_pred)使用5VM预驰圮七数据的分类报告为；fl-scaresupportprecisionrecall11.001.001.00921.001.001.00S31.001.991.001accuracy1.09L¯o avg1.091.901.00ISweighted avg1.091.9

19、01.00IS4、实训4#根据wine_quality数据集处理的结果,构建线性回归模型。from sklearn.linear_model import LinearRegressionclf = LinearRegression().fit(winequality_trainPca,winequality_target_train)y_pred = clf.predict(winequality_testPca)print(线性回归模型预测前10个结果为：,n,y_pred:10)线性-回归模型预11前的个结果为二66.41&6791 6.23&73173 5.22673901 6.010

20、22972 5.4L15Z4B75.19534622 5-57988078 5-LS2S1258 5.42316S32#根据wine_quality数据集处理的结果，构建梯度提升回归模型from sklearn.ensemble import GradientBoostingRegressorGBR_wine = GradientBoostingRegressor().fit(winequality_trainPca,winequality_target_train)wine_target_pred = GBR_wine.predict(winequality_testPca)print(梯度

21、提升回归模型预测前10个结果为：,n,wine_target_pred:10)print(真实标签前十个预测结果为：,n,list(winequality_target_test:10)梯度握升回归模型预刑前明个结果为；&.6SS29089 6-3457347 &.025B99S4 5.56539492 5-7BB79856 5.5966SB4S5.21665472 5.71703838 5. 2&027603 5.38079&76真实标签前+个预删结果为：666566翕655#结合真实评分和预测评分,计算均方误差、中值绝对误差、可解释方差值 #根据得分,判定模型的性能优劣from sklea

22、rn.metrics import mean_absolute_errorfrom sklearn.metrics import mean_squared_errorfrom sklearn.metrics import median_absolute_errorfrom sklearn.metrics import explained_variance_scorefrom sklearn.metrics import r2_scoreprint(线性回归模型评价结果：)print(winequality数据线性回归模型的平均绝对误差为：，mean_absolute_error(winequa

23、lity_target_test,y_pred)print(winequality数据线性回归模型的均方误差为：，mean_squared_error(winequality_target_test,y_pred)print(winequality数据线性回归模型的中值绝对误差为：，median_absolute_error(winequality_target_test,y_pred)print(winequality数据线性回归模型的可解释方差值为：，explained_variance_score(winequality_target_test,y_pred) print(winequa

24、lity数据线性回归模型的R方值为：,r2_score(winequality_target_test,y_pred)print(梯度提升回归模型评价结果：)from sklearn.metrics import explained_variance_score,mean_absolute_error,mean_squared_error,median_absolute_error,r2_score print(winequality数据梯度提升回归树模型的平均绝对误差为：，mean_absolute_error(winequality_target_test,wine_target_pred)print(winequality数据梯度提升回归树模型的均方误差为：，mean_squared_error(winequality_target_test,wine_target_pred)print(winequality数据梯度提升回归树模型的中值绝对误差为：，median_absolute_error(winequality_target_test,wine_target_pred) print(winequality数据梯度提升回归树模型的可解释方差值为：，explaine