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import pandas as pd
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import math
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import numpy as np
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import csv
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from sklearn import linear_model
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from sklearn.model_selection import cross_val_score
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init_elo = 1600 # 初始化elo值
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team_elos = {}
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folder = '"D:\Desktop\nba1\"' # 文件路径
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def PruneData(M_stat, O_stat, T_stat):
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#这个函数要完成的任务在于将原始读入的诸多队伍的数据经过修剪，使其变为一个以team为索引的排列的特征数据
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#丢弃与球队实力无关的统计量
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pruneM = M_stat.drop(['Rk', 'Arena'],axis = 1)
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pruneO = O_stat.drop(['Rk','G','MP'],axis = 1)
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pruneT = T_stat.drop(['Rk','G','MP'],axis = 1)
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#将多个数据通过相同的index：team合并为一个数据
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mergeMO = pd.merge(pruneM, pruneO, how = 'left', on = 'Team')
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newstat = pd.merge(mergeMO, pruneT, how = 'left', on = 'Team')
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#将team作为index的数据返回
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return newstat.set_index('Team', drop = True, append = False)
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def GetElo(team):
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# 初始化每个球队的elo等级分
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try:
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return team_elos[team]
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except:
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team_elos[team] = init_elo
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return team_elos[team]
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def CalcElo(winteam, loseteam):
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# winteam, loseteam的输入应为字符串
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# 给出当前两个队伍的elo分数
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R1 = GetElo(winteam)
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R2 = GetElo(loseteam)
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# 计算比赛后的等级分，参考elo计算公式
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E1 = 1/(1 + math.pow(10,(R2 - R1)/400))
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E2 = 1/(1 + math.pow(10,(R1 - R2)/400))
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if R1>=2400:
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K=16
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elif R1<=2100:
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K=32
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else:
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K=24
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R1new = round(R1 + K*(1 - E1))
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R2new = round(R2 + K*(0 - E2))
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return R1new, R2new
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def GenerateTrainData(stat, trainresult):
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#将输入构造为[[team1特征，team2特征]，...[]...]
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X = []
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y = []
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for index, rows in trainresult.iterrows():
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winteam = rows['WTeam']
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loseteam = rows['LTeam']
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#获取最初的elo或是每个队伍最初的elo值
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winelo = GetElo(winteam)
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loseelo = GetElo(loseteam)
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# 给主场比赛的队伍加上100的elo值
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if rows['WLoc'] == 'H':
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winelo = winelo+100
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else:
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loseelo = loseelo+100
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# 把elo当为评价每个队伍的第一个特征值
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fea_win = [winelo]
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fea_lose = [loseelo]
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# 添加我们从basketball reference.com获得的每个队伍的统计信息
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for key, value in stat.loc[winteam].iteritems():
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fea_win.append(value)
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for key, value in stat.loc[loseteam].iteritems():
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fea_lose.append(value)
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# 将两支队伍的特征值随机的分配在每场比赛数据的左右两侧
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# 并将对应的0/1赋给y值
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if np.random.random() > 0.5:
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X.append(fea_win+fea_lose)
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y.append(0)
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else:
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X.append(fea_lose+fea_win)
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y.append(1)
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# 更新team elo分数
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win_new_score, lose_new_score = CalcElo(winteam, loseteam)
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team_elos[winteam] = win_new_score
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team_elos[loseteam] = lose_new_score
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# nan_to_num(x)是使用0代替数组x中的nan元素，使用有限的数字代替inf元素
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return np.nan_to_num(X),y
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def GeneratePredictData(stat,info):
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X=[]
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#遍历所有的待预测数据，将数据变换为特征形式
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for index, rows in stat.iterrows():
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#首先将elo作为第一个特征
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team1 = rows['Vteam']
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team2 = rows['Hteam']
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elo_team1 = GetElo(team1)
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elo_team2 = GetElo(team2)
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fea1 = [elo_team1]
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fea2 = [elo_team2+100]
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#球队统计信息作为剩余特征
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for key, value in info.loc[team1].iteritems():
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fea1.append(value)
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for key, value in info.loc[team2].iteritems():
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fea2.append(value)
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#两队特征拼接
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X.append(fea1 + fea2)
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#nan_to_num的作用：1将列表变换为array，2.去除X中的非数字，保证训练器读入不出问题
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return np.nan_to_num(X)
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if __name__ == '__main__':
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# 设置导入数据表格文件的地址并读入数据
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M_stat = pd.read_csv(folder + '/20-21Miscellaneous_Stat.csv')
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O_stat = pd.read_csv(folder + '/20-21Opponent_Per_Game_Stat.csv')
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T_stat = pd.read_csv(folder + '/20-21Team_Per_Game_Stat.csv')
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team_result = pd.read_csv(folder + '/2020-2021_result.csv')
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teamstat = PruneData(M_stat, O_stat, T_stat)
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X,y = GenerateTrainData(teamstat, team_result)
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# 训练网格模型
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limodel = linear_model.LogisticRegression()
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limodel.fit(X,y)
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# 10折交叉验证
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print(cross_val_score(model, X, y, cv=10, scoring='accuracy', n_jobs=-1).mean())
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# 预测
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pre_data = pd.read_csv(folder + '/21-22Schedule.csv')
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pre_X = GeneratePredictData(pre_data, teamstat)
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pre_y = limodel.predict_proba(pre_X)
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predictlist = []
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for index, rows in pre_data.iterrows():
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reslt = [rows['Vteam'], pre_y[index][0], rows['Hteam'], pre_y[index][1]]
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predictlist.append(reslt)
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# 将预测结果输出保存为csv文件
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with open(folder+'/prediction of 2021-2022.csv', 'w',newline='') as f:
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writers = csv.writer(f)
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writers.writerow(['Visit Team', 'corresponding probability of winning', 'Home Team', 'corresponding probability of winning'])
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writers.writerows(predictlist)
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