Delete 'nba.py'

nba.py

@@ -1,170 +0,0 @@
-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
-team_elos = {}
-team_stats = {}
-X = []
-y = []
-# 存放数据的目录
-folder = r'C:\Users\lenovo\Desktop\data'
-
-# 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')
-    return team_stats1.set_index('Team', inplace=False, drop=True)
-
-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
-
-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
-
-if __name__ == '__main__':
-
-    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)
-
-    # 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())
-
-
-    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])
-
-
-    # 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('16-17Result.csv', 'w') as f:
-        writer = csv.writer(f)
-        writer.writerow(['win', 'lose', 'probability'])
-        writer.writerows(result)
-        print('done.')
-
-        pd.read_csv('16-17Result.csv', header=0)
-pd.read_csv('16-17Result.csv',header=0)