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
base_elo = 1600
team_elos = {}
team_stats = {}
X = []
y = []
#初始化数据，从T，O，M表格中读取数据，取出一些无关数据并将这三个表格通过team树形列进行连接：
#根据每个队伍的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')
return team_stats1.set_index('Team',inplace=False,drop=True)
def get_elo(team):
try:
return team_elos[team]
except:
team_elos[team] = base_elo
return team_elos[team]
def calc_elo(win_team,lose_team):
winner_rank = get_elo(win_team)
loser_rank = get_elo(lose_team)
#根据Logistic Distribution计算 PK 双方（A和B）对各自的胜率期望值计算公式
rank_diff = winner_rank - loser_rank
exp = (rank_diff *-1)/400
odds = 1/(1+math.pow(10,exp))
#根据rank界别修改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
#基于统计好的数据，给每只队伍的eloscore计算结果，建立对应15-16年数据集，我们认为主场作战的队伍更有优势，因此会给主场队伍加上100分
def build_dataSet(all_data):
print("Building data set..")
X = []
skip = 0
for index,row in all_data.iterrows():水原三星赛事分析预测
Wteam = row['WTeam']
Lteam = row['LTeam']
#获取最初的elo或者每个队伍最初的elo值
team1_elo = get_elo(Wteam)
team2_elo = get_elo(Lteam)
#给主场比赛队伍加上100的elo值
if row['WLoc'] == 'H':
team1_elo += 100
else:
team2_elo += 100
#把elo当成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
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
#最终利用训练好的模型在 16~17 年的常规赛数据中进行预测
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])
#最终在 main 函数中调用这些数据处理函数，使用 sklearn 的Logistic Regression方法建立回归模型
if __name__=='__main__':
folder = 'data'
Mstat = pd.read_csv(folder + '/15-16Miscellaneous_Stat.csv')
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)
print("Doing cross-validation..")
cross_val_score(model,X,y,cv = 10,scoring='accuracy',n_jobs=-1).mean()
print(model)
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('16-17Result.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(['win', 'lose', 'probability'])
writer.writerows(result)
print('done.')
