目录
1. 下载数据集
下载后数据如下：
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：
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
| Model | Training Accuracy | Test Accuracy |
| Logistic Regression | 67.40% | 61.60% |
| SVM | 67.30% | 62.70% |
| Naive Bayes | 65.50% | 63.80% |
| Random Forest | 90.80% | 65.50% |
| XGB | 75.30% | 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_1"
-
-
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_1"
-
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)
转载：https://blog.csdn.net/keeppractice/article/details/128022027
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