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预测足球世界杯比赛

开发技术 开发技术 2022-11-29 次浏览

目录

1. 下载数据集

2. 数据预处理

3. 模型训练与选择

4. 预测

1. 下载数据集

预测足球世界杯比赛

下载后数据如下：

预测足球世界杯比赛

FIFA World Cup | Kaggle

2. 数据预处理

 reprocess_dataset() 方法是数据进行预处理。预处理过的数据如下：
 预测足球世界杯比赛

save_dataset() 方法是对预处理过的数据，进行向量化。

完整代码如下：

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
import joblib
root_path = "models"

def reprocess_dataset():
    #load data
    results = pd.read_csv('datasets/WorldCupMatches.csv', encoding='gbk')

    #Adding goal difference and establishing who is the winner
    winner = []
    for i in range (len(results['Home Team Name'])):
        if results ['Home Team Goals'][i] > results['Away Team Goals'][i]:
            winner.append(results['Home Team Name'][i])
        elif results['Home Team Goals'][i] < results ['Away Team Goals'][i]:
            winner.append(results['Away Team Name'][i])
        else:
            winner.append('Draw')
    results['winning_team'] = winner

    #adding goal difference column
    results['goal_difference'] = np.absolute(results['Home Team Goals'] - results['Away Team Goals'])

    # narrowing to team patcipating in the world cup, totally there are 32 football teams in 2022
    worldcup_teams = ['Qatar','Germany','Denmark', 'Brazil','France','Belgium', 'Serbia',
                      'Spain','Croatia', 'Switzerland', 'England','Netherlands', 'Argentina',' Iran',
                      'Korea Republic','Saudi Arabia', 'Japan', 'Uruguay','Ecuador','Canada',
                      'Senegal', 'Poland', 'Portugal','Tunisia',  'Morocco','Cameroon','USA',
                      'Mexico','Wales','Australia','Costa Rica', 'Ghana']
    df_teams_home = results[results['Home Team Name'].isin(worldcup_teams)]
    df_teams_away = results[results['Away Team Name'].isin(worldcup_teams)]
    df_teams = pd.concat((df_teams_home, df_teams_away))
    df_teams.drop_duplicates()
    df_teams.count()

    #dropping columns that wll not affect matchoutcomes

    df_teams_new =df_teams[[ 'Home Team Name','Away Team Name','winning_team']]
    print(df_teams_new.head()  )

                   #Building the model
    #the prediction label: The winning_team column will show "2" if the home team has won, "1" if it was a tie, and "0" if the away team has won.

    df_teams_new = df_teams_new.reset_index(drop=True)
    df_teams_new.loc[df_teams_new.winning_team == df_teams_new['Home Team Name'],'winning_team']=2
    df_teams_new.loc[df_teams_new.winning_team == 'Draw', 'winning_team']=1
    df_teams_new.loc[df_teams_new.winning_team == df_teams_new['Away Team Name'], 'winning_team']=0

    print(df_teams_new.count()   )
    df_teams_new.to_csv('datasets/raw_train_data.csv', encoding='gbk', index =False)

def save_dataset():
    df_teams_new = pd.read_csv('datasets/raw_train_data.csv', encoding='gbk')

    feature = df_teams_new[[ 'Home Team Name','Away Team Name']]
    vec = DictVectorizer(sparse=False)

    print(feature.to_dict(orient='records'))
    X =vec.fit_transform(feature.to_dict(orient='records'))
    X = X.astype('int')
    print("===")
    print(vec.get_feature_names())
    print(vec.feature_names_)
    y = df_teams_new[[ 'winning_team']]
    y =y.astype('int')
    print(X.shape)
    print(y.shape)
    joblib.dump(vec, root_path+"/vec.joblib")
    np.savez('datasets/train_data', x= X, y = y)

if __name__ == '__main__':
    reprocess_dataset()
    save_dataset();

3. 模型训练与选择

用不同的传统机器学习方法进行训练，训练后的模型比较

ModelTraining AccuracyTest Accuracy
Logistic Regression67.40%61.60%
SVM67.30%62.70%
Naive Bayes65.50%63.80%
Random Forest90.80%65.50%
XGB75.30%62.00%

可以看到随机森林模型在测试集上准确率最高，所以我们可以用它来做预测。

下面是完整训练代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
import matplotlib.ticker as plticker
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import svm
import sklearn as sklearn
from sklearn.feature_extraction import DictVectorizer
from sklearn.naive_bayes import  MultinomialNB
from sklearn.ensemble import RandomForestClassifier
import joblib
from sklearn.metrics import classification_report
from xgboost import XGBClassifier
from sklearn.metrics import confusion_matrix


root_path = "models"

def get_dataset():
    train_data = np.load('datasets/train_data.npz')

    return train_data

def train_by_LogisticRegression(train_data):
    X = train_data['x']
    y = train_data['y']

     # Separate train and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)

    logreg = LogisticRegression()
    logreg.fit(X_train, y_train)
    joblib.dump(logreg, root_path+'/LogisticRegression_model.joblib')

    score = logreg.score(X_train, y_train)
    score2 = logreg.score(X_test, y_test)

    print("LogisticRegression Training set accuracy: ", '%.3f'%(score))
    print("LogisticRegression Test set accuracy: ", '%.3f'%(score2))

def train_by_svm(train_data):
    X = train_data['x']
    y = train_data['y']

    # Separate train and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)

    model = svm.SVC(kernel='linear', verbose=True, probability=True)
    model.fit(X_train, y_train)
    joblib.dump(model, root_path+'/svm_model.joblib')

    score = model.score(X_train, y_train)
    score2 = model.score(X_test, y_test)

    print("SVM Training set accuracy: ", '%.3f' % (score))
    print("SVM Test set accuracy: ", '%.3f' % (score2))

def train_by_naive_bayes(train_data):
    X = train_data['x']
    y = train_data['y']

    # Separate train and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)

    model = MultinomialNB()
    model.fit(X_train, y_train)
    joblib.dump(model, root_path+'/naive_bayes_model.joblib')

    score = model.score(X_train, y_train)
    score2 = model.score(X_test, y_test)

    print("naive_bayes Training set accuracy: ", '%.3f' % (score))
    print("naive_bayes Test set accuracy: ", '%.3f' % (score2))

def train_by_random_forest(train_data):
    X = train_data['x']
    y = train_data['y']

    # Separate train and test sets
    X_train = X
    y_train = y
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)

    model = RandomForestClassifier(criterion='gini', max_features='sqrt')
    model.fit(X_train, y_train)
    joblib.dump(model, root_path+'/random_forest_model.joblib')

    score = model.score(X_train, y_train)
    score2 = model.score(X_test, y_test)

    print("random forest Training set accuracy: ", '%.3f' % (score))
    print("random forest Test set accuracy: ", '%.3f' % (score2))


def train_by_xgb(train_data):
    X = train_data['x']
    y = train_data['y']

    # Separate train and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)

    model = XGBClassifier(use_label_encoder=False)
    model.fit(X_train, y_train)
    joblib.dump(model, root_path+'/xgb_model.joblib')

    score = model.score(X_train, y_train)

    score2 = model.score(X_test, y_test)

    print("xgb Training set accuracy: ", '%.3f' % (score))
    print("xgb Test set accuracy: ", '%.3f' % (score2))

    y_pred = model.predict(X_test)

    report = classification_report(y_test, y_pred, output_dict=True)
    # show_confusion_matrix(y_test, y_pred)
    print(report)

def show_confusion_matrix(y_true, y_pred, pic_name = "confusion_matrix"):
    confusion = confusion_matrix(y_true=y_true, y_pred=y_pred)
    print(confusion)

    sns.heatmap(confusion, annot=True, cmap= 'Blues', xticklabels=['0','1','2'], yticklabels=['0','1','2'], fmt = '.20g')
    plt.xlabel('Predicted class')
    plt.ylabel('Actual Class')
    plt.title(pic_name)
    # plt.savefig('pic/' + pic_name)
    plt.show()

if __name__ == '__main__':
    train_data = get_dataset()
    train_by_LogisticRegression(train_data)
    train_by_svm(train_data)
    train_by_naive_bayes(train_data)
    train_by_random_forest(train_data)
    train_by_xgb(train_data)

4. 预测

执行下面预测代码，结果是Ecuador胜于Qatar, 英国队胜于伊朗队。

[2]
[[0.05       0.22033333 0.72966667]]
Probability of  Ecuador  winning: 0.730
Probability of Draw: 0.220
Probability of  Qatar  winning: 0.050
[2]
[[0.02342857 0.21770455 0.75886688]]
Probability of  England  winning: 0.759
Probability of Draw: 0.218
Probability of   Iran  winning: 0.023

完整代码

import joblib

worldcup_teams = ['Qatar','Germany','Denmark', 'Brazil','France','Belgium', 'Serbia',
                  'Spain','Croatia', 'Switzerland', 'England','Netherlands', 'Argentina',' Iran',
                  'Korea Republic','Saudi Arabia', 'Japan', 'Uruguay','Ecuador','Canada',
                  'Senegal', 'Poland', 'Portugal','Tunisia',  'Morocco','Cameroon','USA',
                  'Mexico','Wales','Australia','Costa Rica', 'Ghana']
root_path = "models"
def verify_team_name(team_name):

    for worldcup_team in worldcup_teams:
        if team_name==worldcup_team:
            return True
    return False


def predict(model_dir =root_path+'/LogisticRegression_model.joblib', team_a='France', team_b = 'Mexico'):

    if not verify_team_name(team_a):
        print(team_a, ' is not correct')
        return
    if not verify_team_name(team_b) :
        print(team_b, ' is not correct')
        return

    logreg = joblib.load(model_dir)

    input_x = [{'Home Team Name': team_a, 'Away Team Name': team_b}]

    vec = joblib.load(root_path+"/vec.joblib")
    input_x = vec.transform(input_x)

    result = logreg.predict(input_x)
    print(result)
    result1 = logreg.predict_proba(input_x)



    print(result1)
    print('Probability of ',team_a , ' winning:', '%.3f'%result1[0][2])
    print('Probability of Draw:', '%.3f' % result1[0][1])
    print('Probability of ', team_b, ' winning:', '%.3f' % result1[0][0])

if __name__ == '__main__':
    team_a = 'Ecuador'
    team_b = 'Qatar'
    predict('models/random_forest_model.joblib', team_a, team_b)
    team_a = 'England'
    team_b = ' Iran'


    predict('models/random_forest_model.joblib', team_a, team_b)

5. 个人总结

特征少的可怜，如果可以加一些球员的信息和状态的特征会更好，数据也相对太少，如果可以把欧洲杯，亚洲杯，非洲杯和美洲杯的每届数据加入进来就好了。数据有点旧（1930年后所有数据），这也是没办法，因为没有其它数据了。

如果预测的是比分，是不是也可以用分类做呢？比如最多可以踢入4个球每个队，当然像西班牙队可以踢进去7个球太罕见了，我们可以忽略不计，哈哈。那就是25个类别。效果会不会好呢？有待检验。

6. 2018后新的数据

YearHome Team NameAway Team Namewinning_team
2018FranceCroatia2
2018BelgiumEngland2
2018CroatiaEngland2
2018FranceBelgium2
2018RussiaCroatia1
2018SwedenEngland0
2018BrazilBelgium0
2018UruguayFrance0
2018ColombiaEngland1
2018SwedenSwitzerland2
2018BelgiumJapan2
2018BrazilMexico2
2018CroatiaDenmark1
2018SpainRussia1
2018UruguayPortugal2
2018FranceArgentina2
2018EnglandBelgium0
2018ParaguayTunisia0
2018SenegalColombia0
2018JapanPoland0
2018SwitzerlandCosta Rica1
2018SerbiaBrazil0
2018MexicoSweden0
2018Korea RepublicGermany2
2018IcelandCroatia0
2018NigeriaArgentina0
2018DenmarkFrance1
2018AustraliaPeru0
2018IR IranPortugal1
2018SpainMorocco1
2018Saudi ArabiaEgypt2
2018UruguayRussia2
2018PolandColombia0
2018JapanSenegal1
2018EnglandPanama2
2018GermanySweden2
2018Korea RepublicMexico0
2018BelgiumTunisia2
2018SerbiaSwitzerland0
2018NigeriaIceland2
2018BrazilCosta Rica2
2018ArgentinaCroatia0
2018FrancePeru2
2018DenmarkAustralia1
2018IR IranSpain0
2018UruguaySaudi Arabia2
2018PortugalMorocco2
2018RussiaEgypt2
2018PolandSenegal0
2018ColombiaJapan0
2018TunisiaEngland0
2018BelgiumPanama2
2018SwedenKorea Republic2
2018BrazilSwitzerland1
2018GermanyMexico0
2018Costa RicaSerbia0
2018CroatiaNigeria2
2018PeruDenmark0
2018ArgentinaIceland1
2018FranceAustralia2
2018PortugalSpain1
2018MoroccoIR Iran0
2018EgyptUruguay0
2018RussiaSaudi Arabia2
2022QatarEcuador0
2022EnglandIran2
2022SenegalNetherlands0
2022USAWales1
2022ArgentinaSaudi Arabia0
2022DenmarkTunisia1
2022MexicoPoland1
2022FranceAustralia2
2022MoroccoCroatia1
2022GermanyJapan0
2022SpainCosta Rica2
2022BelgiumCanada2
2022SwitzerlandCameroon2
2022UruguayKorea Republic1
2022PortugalGhana2
2022BrazilSerbia2
2022WalesIran0
2022QatarSenegal0
2022NetherlandsEcuador1
2022EnglandUSA1
2022TunisiaAustralia0
2022PolandSaudi Arabia2
2022FranceDenmark2
2022ArgentinaMexico2
2022JapanCosta Rica0
2022BelgiumMorocco0
2022CroatiaCanada2
2022SpainGermany1

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