import pandas as pd 　　import math 　　import csv 　　import random 　　import numpy as np 　　from sklearn import linear_model 　　from sklearn.model_selection import cross_val_score 　　# 当每支队伍没有elo等级分时，赋予其基础elo等级分 　　base_elo = 1600 #设置基础等级分为1600 　　team_elos = {} # 队伍elo等级 　　team_stats = {} #队伍分数统计值 　　X = [] #存储输入数据 　　y = [] #存储输出数据 　　folder = 'https://download.csdn.net/download/Deng872347348/data' #存放数据的目录 　　# 根据每支队伍的Miscellaneous Opponent，Team统计数据csv文件进行初始化 　　def initialize_data(Mstat, Ostat, Tstat): 　　new_Mstat = Mstat.drop(['Rk', 'Arena'], axis=1) 　　new_Ostat = Ostat.drop(['Rk', 'G', 'MP'], axis=1) 　　new_Tstat = Tstat.drop(['Rk', 'G', 'MP'], axis=1) 　　team_stats1 = pd.merge(new_Mstat, new_Ostat, how='left', on='Team') 　　team_stats1 = pd.merge(team_stats1, new_Tstat, how='left', on='Team') 　　print(team_stats1.info()) 　　return team_stats1.set_index('Team', inplace=False, drop=True) 　　def build_dataSet(all_data): 　　print("Building data set..") 　　X = [] 　　skip = 0 　　for index, row in all_data.iterrows(): 　　Wteam = row['WTeam'] 　　Lteam = row['LTeam'] 　　#获取最初的elo或是每个队伍最初的elo值 　　team1_elo = get_elo(Wteam) 　　team2_elo = get_elo(Lteam) 　　# 给主场比赛的队伍加上100的elo值 　　if row['WLoc'] == 'H': 　　team1_elo += 100 　　else: 　　team2_elo += 100 　　# 把elo当为评价每个队伍的第一个特征值 　　team1_features = [team1_elo] 　　team2_features = [team2_elo] 　　# 添加我们从basketball reference.com获得的每个队伍的统计信息 　　for key, value in team_stats.loc[Wteam].iteritems(): 　　team1_features.append(value) 　　for key, value in team_stats.loc[Lteam].iteritems(): 　　team2_features.append(value) 　　# 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 　　# 并将对应的0/1赋给y值 　　if random.random() > 0.5: 　　X.append(team1_features + team2_features) 　　y.append(0) 　　else: 　　X.append(team2_features + team1_features) 　　y.append(1) 　　if skip == 0: 　　print('X',X) 　　skip = 1 　　# 根据这场比赛的数据更新队伍的elo值 　　new_winner_rank, new_loser_rank = calc_elo(Wteam, Lteam) 　　team_elos[Wteam] = new_winner_rank 　　team_elos[Lteam] = new_loser_rank 　　return np.nan_to_num(X), y 　　def predict_winner(team_1, team_2, model): 　　features = [] 　　# team 1，客场队伍 　　features.append(get_elo(team_1)) 　　for key, value in team_stats.loc[team_1].iteritems(): 　　features.append(value) 　　# team 2，主场队伍 　　features.append(get_elo(team_2) + 100) 　　for key, value in team_stats.loc[team_2].iteritems(): 　　features.append(value) 　　features = np.nan_to_num(features) 　　return model.predict_proba([features]) 　　def get_elo(team): 　　try: 　　return team_elos[team] 　　except: 　　# 当最初没有elo时，给每个队伍最初赋base_elo 　　team_elos[team] = base_elo 　　return team_elos[team] 　　# 计算每个球队的elo值 　　def calc_elo(win_team, lose_team): 　　winner_rank = get_elo(win_team) 　　loser_rank = get_elo(lose_team) 　　rank_diff = winner_rank - loser_rank 　　exp = (rank_diff * -1) / 400 　　odds = 1 / (1 + math.pow(10, exp)) 　　# 根据rank级别修改K值 　　if winner_rank < 2100: 　　k = 32 　　elif winner_rank >= 2100 and winner_rank < 2400: 　　k = 24 　　else: 　　k = 16 　　# 更新 rank 数值 　　new_winner_rank = round(winner_rank + (k * (1 - odds))) 　　new_loser_rank = round(loser_rank + (k * (0 - odds))) 　　return new_winner_rank, new_loser_rank 　　if __name__ == '__main__': 　　Mstat = pd.read_csv(folder + '/15-16Miscellaneous_Stat.csv') #读取Mstat表格的数据 　　Ostat = pd.read_csv(folder + '/15-16Opponent_Per_Game_Stat.csv') 　　Tstat = pd.read_csv(folder + '/15-16Team_Per_Game_Stat.csv') 　　team_stats = initialize_data(Mstat, Ostat, Tstat) 　　result_data = pd.read_csv(folder + '/2015-2016_result.csv') 　　X, y = build_dataSet(result_data) 　　# 训练网络模型 　　print("Fitting on %d game samples.." % len(X)) 　　model = linear_model.LogisticRegression() 　　model.fit(X, y) 　　# 利用10折交叉验证计算训练正确率 　　print("Doing cross-validation..") 　　print(cross_val_score(model, X, y, cv = 10, scoring='accuracy', n_jobs=-1).mean()) 　　# 利用训练好的model在16-17年的比赛中进行预测 　　print('Predicting on new schedule..') 　　schedule1617 = pd.read_csv(folder + '/16-17Schedule.csv') 　　result = []

　　for index, row in schedule1617.iterrows(): 　　team1 = row['Vteam'] 　　team2 = row['Hteam'] 　　pred = predict_winner(team1, team2, model) 　　prob = pred[0][0] 　　if prob > 0.5: 　　winner = team1 　　loser = team2 　　result.append([winner, loser, prob]) 　　else: 　　winner = team2 　　loser = team1 　　result.append([winner, loser, 1 - prob]) 　　with open('data/16-17Result.csv', 'w') as f: 　　writer = csv.writer(f) 　　writer.writerow(['win', 'lose', 'probability']) 　　writer.writerows(result) 　　print('done.') 　　Rdata = pd.read_csv(folder + '/16-17Result.csv', header=0) 　　print(Rdata)