数据集来源：1. 2013-14 NBA Schedule and Results
参考书籍：《Python数据挖掘入门与实践》
1.加载数据集：
使用pandas加载数据集，有1319行数据， 8个特征， 查看前5项数据集，并查找是否有重复数据
#coding=gbk #使用决策树来预测获胜球队 import time start = time.clock() #加载数据集 import pandas as pd file_name = r'D:\datasets\NBA_2014_games.csv' data = pd.read_csv(file_name) print(data.head()) #读取前5项数据集 # Date Unnamed: 1 Visitor/Neutral PTS Home/Neutral \..... # 0 Tue Oct 29 2013 Box Score Orlando Magic 87 Indiana Pacers # 1 Tue Oct 29 2013 Box Score Los Angeles Clippers 103 Los Angeles Lakers # 2 Tue Oct 29 2013 Box Score Chicago Bulls 95 Miami Heat # 3 Wed Oct 30 2013 Box Score Brooklyn Nets 94 Cleveland Cavaliers # 4 Wed Oct 30 2013 Box Score Atlanta Hawks 109 Dallas Mavericks print(data.shape) # (1319, 8) print(data[data.duplicated()]) # Empty DataFrame 没有重复元素
数据集清洗：1.第一列数据日期是字符串格式，改为日期格式； 2.修改表头。
#修复表头数据参数 data = pd.read_csv(file_name, parse_dates= ['Date']) #skiprows 忽略的行数 data.columns = ['Date','Score Type', 'Visitor Team', 'VisitorPts', 'Home Team', 'HomePts', 'OT?', 'Notes'] print(data.head()) #重命名表头 # Date Score Type Visitor Team VisitorPts \。。。。 # 0 2013-10-29 Box Score Orlando Magic 87 # 1 2013-10-29 Box Score Los Angeles Clippers 103 # 2 2013-10-29 Box Score Chicago Bulls 95 # 3 2013-10-30 Box Score Brooklyn Nets 94 # 4 2013-10-30 Box Score Atlanta Hawks 109 print('-----') # print(data.ix[1] ) #打印出第2行的数据提取新特征：通过现有的数据抽取特征， 首先确定类别，篮球只有胜负之分， 不像足球还有 平，局， 以1 代表球队取胜，0为失败。
#提取新特征 #找出获胜的球队 data['HomeWin'] = data['VisitorPts'] < data['HomePts'] y_true = data['HomeWin'].values print(y_true[:5]) #[ True True True True True] 是 numpy 数组 # print(data.head()) #创建2个新特征， 分别是这两只球队的上一场比赛的胜负情况 #创建字典，存放上次比赛结果 from collections import defaultdict won_last = defaultdict(int) data['HomeLastWin'] = None data['VisitorLastWin'] = None #此两行代码原书上没有，应该增加这2列，否则下面的循环不能创建这2列 for index, row in data.iterrows(): home_team = row['Home Team'] visitor_team = row['Visitor Team'] #循环获得球队名称 row['HomeLastWin'] = won_last[home_team] row['VisitorLastWin'] = won_last[visitor_team] data.ix[index] = row #更新行数 won_last[home_team] = row['HomeWin'] #判断上一场是否获胜 won_last[visitor_team] =not row['HomeWin'] print('----') # print(data.ix[20:25]) # Home Team HomePts OT? Notes HomeWin HomeLastWin VisitorLastWin # 20 Boston Celtics 98 NaN NaN False False False # 21 Brooklyn Nets 101 NaN NaN True False False # 22 Charlotte Bobcats 90 NaN NaN True False True # 23 Denver Nuggets 98 NaN NaN False False False # 24 Houston Rockets 113 NaN NaN True True True # 25 Los Angeles Lakers 85 NaN NaN False False True 一些练习测试代码：defaultdict 和 iterrows（）的使用方法
won_last['jj'] = 12 dd = won_last['Indiana Pacers'] #defaultdict的作用是在于，当字典里的key不存在但被查找时，返回的不是keyError而是一个默认值 print(dd) # 0 print(won_last) # defaultdict(<class 'int'>, {'Indiana Pacers': 0, 'jj': 12}) 返回的是defaultdict类型 dataset = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]]) print(dataset) for index, row in dataset.iterrows(): print(index) # 0, 1, 2 打印出行号 print(row) #打印出第 1， 2， 3 行的全部元素 2.使用决策树
这里直接使用决策树， 没有刻意地去调参数，可能是作者为了对比不同特征的优劣吧。
从数据集中构建有效的特征， （Feature Engineering 特征工程）是数据挖掘的难点所在， 好的特征直接关系到结果的正确率， -------甚至比选择合适的算法更重要。
#使用决策树 from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(random_state =14) #设置随机种子，使结果复现，。。。 但是还是不同。 X_previousWins = data[['HomeLastWin', 'VisitorLastWin']].values #使用新创建的2个特征作为输入 from sklearn.model_selection import cross_val_score # 使 用交叉验证模型平均得分 import numpy as np scores = cross_val_score(clf, X_previousWins, y_true, scoring='accuracy') mean_score = np.mean(scores) *100 print('the accuracy is %0.2f'%mean_score+'%') #准确率为 the accuracy is 57.47% 使用另一数据集：13年NBA 排名情况
#读取2013年球队排名情况 file_name2 = r'D:\datasets\NBA_2013_stangdings.csv' standings = pd.read_csv(file_name2) # print(standings.head()) # Rk Team Overall Home Road E W A C \.... # 0 1 Miami Heat 66-16 37-4 29-12 41-11 25-5 14-4 12-6 # 1 2 Oklahoma City Thunder 60-22 34-7 26-15 21-9 39-13 7-3 8-2 # 2 3 San Antonio Spurs 58-24 35-6 23-18 25-5 33-19 8-2 9-1 # 3 4 Denver Nuggets 57-25 38-3 19-22 19-11 38-14 5-5 10-0 # 4 5 Los Angeles Clippers 56-26 32-9 24-17 21-9 35-17 7-3 8-2 # print(standings.shape) # (30, 24) 有30只球队 创建一个新特征值， 主场球队是否比对手排名高。然后使用创建的3个特征去 fit 模型
#创建一个新特征值， 主场球队是否比对手排名高 data['HomeTeamRanksHigher'] = 0 for index, row in data.iterrows(): home_team = row['Home Team'] visitor_team = row['Visitor Team'] if home_team =='New Orleans Pelicans': #更换了名字的球队 home_team ='New Orleans Hornets' elif visitor_team == 'New Orleans Pelicans': visitor_team='New Orleans Hornets' #比较排名， 更新特征值 home_rank = standings[standings['Team']== home_team]['Rk'].values[0] visitor_rank = standings[standings['Team']== visitor_team]['Rk'].values[0] row['HomeTeamRanksHigher'] = int(home_rank > visitor_rank) data.ix[index] = row X_homehigher = data[['HomeLastWin', 'VisitorLastWin', 'HomeTeamRanksHigher']].values # clf1 = DecisionTreeClassifier(random_state=14) # scores = cross_val_score(clf1, X_homehigher, y_true, scoring='accuracy') # mean_score1 = np.mean(scores) *100 # print('the new accuracy is %.2f'%mean_score1 + '%') #the new accuracy is 59.67%再创建新特征， 对比比赛的2队上一场2队比赛的结果
#再创建新特征， 对比比赛的2队上一场2队比赛的结果 last_match_winner = defaultdict(int) data['HomeTeamWonLast'] = 0 for index, row in data.iterrows(): home_team = row['Home Team'] visitor_team = row['Visitor Team'] teams = tuple(sorted([home_team, visitor_team])) row['HomeTeamWonLast'] = 1 if last_match_winner[teams] == row['Home Team'] else 0 data.ix[index] = row winner = row['Home Team'] if row['HomeWin'] else row['Visitor Team'] last_match_winner[teams] = winner X_lastwinner = data[['HomeTeamWonLast', 'HomeTeamRanksHigher']] # clf2 = DecisionTreeClassifier(random_state=14) # scores = cross_val_score(clf2, X_lastwinner, y_true, scoring='accuracy') # mean_score2 = np.mean(scores) *100 # print('the accuracy is %.2f'%mean_score2 + '%') # the accuracy is 57.85% 观察决策树在训练数据量很大的情况下， 能否得到有效的模型，使用球队，并对其编码
#使用LabelEncoder 转换器把字符串类型的队名转换成整型 from sklearn.preprocessing import LabelEncoder encoding = LabelEncoder() encoding.fit(data['Home Team'].values) #将主队名称转换成整型 home_teams = encoding.transform(data['Home Team'].values) visitor_teams = encoding.transform(data['Visitor Team'].values) X_teams = np.vstack([home_teams, visitor_teams]).T from sklearn.preprocessing import OneHotEncoder onehot = OneHotEncoder() X_teams_expanded = onehot.fit_transform(X_teams).todense() clf3 = DecisionTreeClassifier(random_state=14) # scores = cross_val_score(clf3, X_teams_expanded, y_true, scoring='accuracy') # mean_score3 = np.mean(scores) *100 # print('the accuracy is %.2f'%mean_score3+'%') # the accuracy is 59.52%3.使用随机森林
print('----rf-----') #使用随机森林进行预测 from sklearn.ensemble import RandomForestClassifier # rf = RandomForestClassifier(random_state = 14, n_jobs =-1) #最好调下决策树的参数 # rf_scores = cross_val_score(rf, X_teams, y_true, scoring='accuracy') # mean_rf_score = np.mean(rf_scores) *100 # print('the randforestclassifier accuracy is %.2f'%mean_rf_score+'%') #the randforestclassifier accuracy is 58.38% #多使用几个特征 print('使用多个参数') X_all = np.hstack([X_homehigher, X_teams]) # rf_clf2 = RandomForestClassifier(random_state = 14, n_jobs=-1) # rf_scores2 = cross_val_score(rf_clf2, X_all, y_true, scoring='accuracy') # mean_rf_score2 = np.mean(rf_scores2) *100 # print('the accuracy is %.2f'%mean_rf_score2+'%') # the accuracy is 57.62%使用网格搜索查找最佳的模型， 并查看使用的参数。
#调参数， 使用网格搜索 from sklearn.model_selection import GridSearchCV param_grid = { 'max_features':[2,3,'auto'], 'n_estimators': [100,110,120 ], 'criterion': ['gini', 'entropy'], "min_samples_leaf": [2, 4, 6] } clf = RandomForestClassifier(random_state=14, n_jobs=-1) grid = GridSearchCV(clf, param_grid) grid.fit(X_all, y_true) score = grid.best_score_ *100 print('the accuracy is %.2f'%score +'%') #the accuracy is 62.02% something= str(grid.best_estimator_) print(something) #输出网格搜索找到的最佳模型 print(grid.best_params_) #输出返回最好的参数 # the accuracy is 62.02% # RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy', # max_depth=None, max_features=3, max_leaf_nodes=None, # min_impurity_decrease=0.0, min_impurity_split=None, # min_samples_leaf=2, min_samples_split=2, # min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1, # oob_score=False, random_state=14, verbose=0, warm_start=False) # {'n_estimators': 100, 'criterion': 'entropy', 'max_features': 3, 'min_samples_leaf': 2} # 所花费的时间 ： 117.93s end = time.clock() time = end - start print('所花费的时间 ： %.2f'%time + 's') 