Python版数据挖掘实验4报告：用决策树预测获胜球队

1、一、实验名称：用决策树预测获胜球队1、实验目的：用决策树预测获胜球队2、实验内容：用决策树预测获胜球队3、实验环境(设备、软件、实验数据)：设备：笔记本电脑软件：Python idle实验数据：leagues_NBA_2013_standings_expanded-standings.csv, leagues_NBA_2014_games_games.csv3、实验步骤：1.加载库，读取数据并进行处理；import pandas as pdimport osfrom sklearn.model_selection import train_test_splitfrom sklearn.mode

2、l_selection import cross_val_scoreimport numpy as npfrom collections import defaultdictfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.preprocessing import LabelEncoderfrom sklearn.preprocessing import OneHotEncoder#添加文件路径，路径最好不要出现中文dataFolder = "' G: leagues_NBA_2014_games_game

3、s.csv '"data_filename = os.path.join(dataFolder, "basketball", "leagues_NBA_2014_games_games.csv");with open(data_filename) as data_filename:# 读取数据的同时，将第一二列合并为一列dataset = pd.read_csv(data_filename, parse_dates=0,1# 我们可以通过下面这个方法对数据进行查看#dataset.iloc:5# 对数据重新命名头部dataset.col

4、umns = " Date "" Visitor Team" , "VisitorPts “'HomeTeam" , "HomePts" : OT?' “ Notes ” # 对数据进行排序并重新建立索引。当然也可以不进行排序，这个影响并不大。# sort()排序函数已经在 pandas0.20 以后被弃用，一旦使用将报错，只能使用 sort_values()/sort_index()dataset.sort_values(by="Date", ascending=True, i

5、nplace=True) dataset = dataset.reset index(drop=True)2. 创建字典，储存比赛结果，抽取数据特征；# 创建(默认)字典，用于存储球队上次比赛的结果。该语句作用为当字典内不存在查找的key时，返回括号内(factory_function )类型的默认值；int默认值0 won_last = defaultdict(bool)# 新增一列HomeWin,该列判断对应的数据是否主场胜利。将是否主场胜利作为预测结果 dataset" HomeWin" = dataset "VisitorPts " < d

6、ataset "HomePts”y_true = dataset " HomeWin" .values# 创建两个能帮助我们进行预测的特征，分别是这两支队伍上场比赛的胜负情况。赢得上场 比赛，大致可以说明该球队水平较高。dataset"HomeLastWin" = 0dataset"VisitorLastWin" = 0for index, row in dataset.iterrows():home_team = row"Home Team"visitor_team = row"Visitor

7、 Team"row"HomeLastWin" = won_lasthome_teamrow"VisitorLastWin" = won_lastvisitor_team dataset.ilocindex = row# 用当前比赛(遍历到的那一行数据所表示的比赛)的结果更新两支球队上场比赛的获胜情 况，以便下次再遍历到这两支球队时使用。won_lasthome_team = row"HomeWin"won_lastvisitor_team = not row"HomeWin"3. 从 scikit-lea

8、rn 库中导入 DecisionTreeClassfier 类，创建决策树；# 我们再次设定 random_state的值为14clf = DecisionTreeClassifier(random_state=14)X_previouswins = dataset"HomeLastWin", "VisitorLastWin".values# 交叉检验# X_train, X_test, y_train, y_test = train_test_split(X_previouswins, y_true, random_state=14)#clf.fit(

9、X_train,y_train)# 将数据导入分类器，并通过交叉检验。scores = cross_val_score(clf, X_previouswins, y_true, scoring='accuracy',cv=3)print( 初次使用决策树 Accuracy: 0:.1f%".format(np.mean(scores) * 100)输出结果:>»= RESTART: G;/Pythanm6，Liti/idl&lib/数据挖掘实验, py = 初次使用决策树肥curacy： 57.5%4. 导入新的数据集，创建一个新特征HomeT

10、eamRanksHigher并更新其值；standings_filename=os.path.join(dataFolder,"basketball","leagues_NBA_2013_standings_expanded-standings.csv"); with open (standings_filename) as standings_filename:standings = pd.read_csv(standings_filename,skiprows=0,); dataset"HomeTeamRanksHigher" =

11、 0 for index, row in dataset.iterrows():home_team = row"Home Team"visitor_team = row"Visitor Team"if home_team = "New Orleans Pelicans":home_team = "New Orleans Hornets"elif visitor_team = "New Orleans Pelicans":visitor_team = "New Orleans Horne

12、ts"# series搜索，口里面可以通过条件选择出对应行主场排名home_rank = standingsstandings"Team" =home_team"Rk".values0 #客场排名visitor_rank = standingsstandings"Team" =visitor_team"Rk".values0#判断一个队伍在主场获胜的排名和客场获胜的排名row"HomeTeamRanksHigher" = bool(home_rank > visitor_rank

13、) dataset.ilocindex = row5. 新增加HomeTeamRanksHigher#征，再次进行交叉检验；X_homehigher = dataset"HomeLastWin","VisitorLastWin","HomeTeamRanksHigher".values#建立决策树clf = DecisionTreeClassifier(random_state=14)scores = cross_val_score(clf, X_homehigher, y_true,scoring='accuracy'

14、;,cv=3) print("增力口新特征值 Accuracy: 0:.1f%".format(np.mean(scores) * 100)输出结果：»>= RESTART; G;/rython36/Lib/idlelib/数据挖掘实验, py =*«m增加新特征值配匕utmcy ： 50. 7%>» I6. 统计两支球队上场比赛的情况，创建HomeTeamWonLast!征值，并更新其值，再创建决策树并使用 cross_val_score 函数测试结果； #创建字典，保存上场比赛的获胜队伍，在数据框中建立新特征。last_matc

15、h_winner = defaultdict(int) dataset"HomeTeamWonLast" = 0 for index, row in dataset.iterrows():home_team = row"Home Team"visitor team = row"Visitor Team"teams = tuple(sorted(home_team, visitor_team) row"HomeTeamWonLast" = 1 if last_match_winnerteams = row"

16、Home Team" else 0 dataset.ilocindex = row winner = row"Home Team" if row"HomeWin" else row"Visitor Team" last_match_winnerteams = winnerX_lastwinner = dataset"HomeLastWin", "VisitorLastWin", "HomeTeamRanksHigher", "HomeTeamWonLast

17、".values clf = DecisionTreeClassifier(random_state=14) scores = cross_val_score(clf, X_lastwinner, y_true, scoring='accuracy', cv=3) print("再次增加特征 Accuracy: 0:.1f%".format(np.mean(scores) * 100)输出结果： »>= RESTART; &"71卜0#6几通/1(11虱让|/数据挖掘买蛤.了 =再次增加特征配curacy_ 60.

18、 4%>» I7.引入LabelEncoder转换器，将主场球队名称转化为整型并抽取所有比赛的主客场 球队的球队名并将其组合起来，形成一个矩阵，引入OneHotEncoder转换器，通过将整数转换为二进制来消除球队编号之间的相似情况，创建决策树并使用 cross_val_score函数测试结果；encoding = LabelEncoder()t=encoding.fit(dataset"Home Team".values)home_teams = t.transform(dataset"Home Team".values)visitor_teams = t.transform(dataset"Visitor Team".values)X_teams = np