数据集来源：1.  2013-14 NBA Schedule and Results

                        2.2013年 NBA 赛季排名情况

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

决策树原理参考：

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

编码可以参考

#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

随机森林是一种集成学习的算法

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')

 

# 决策树 # 随机森林 # 数据挖掘 # 机器学习
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