【Python】模拟预测2022卡塔尔世界杯结果
一、前言在逛github的时候看到一个大佬发布了模拟2022世界杯的程序，在我试验了以后发现最终模拟的结果是巴西会夺冠。
这应该符合很多人的想法猜测吧，哈哈哈哈。
声明：本贴仅供学习交流使用，如存在侵权行为请告知我立即删帖。
github原作者【QuarterTime】 --反正我是在他那里找到的，至于他是不是原作者我也就不清楚了。
如有违规，请吧主删帖，不要留情。
https://static.52pojie.cn/static/image/hrline/1.gif
二、代码测试截图
https://static.52pojie.cn/static/image/hrline/1.gif
三、程序代码
本程序使用的配套数据我已经打包好放在了最后面，代码由两部分构成，先上第一部分保存数据的代码
# -*- coding:utf-8 -*-
# Wan Jiongming @Copyright 2022
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
with open("country_name.txt", "r", encoding="utf-8") as f:
info = f.readlines()
info = list(map(lambda x:x.strip(), info))
English_name = info[:32]
Chinese_name = info
country_name = {}
for each in zip(English_name, Chinese_name):
country_name] = each
def cv2AddChineseText(img, text, position, textColor=(0, 255, 0), textSize=30):
if (isinstance(img, np.ndarray)):# 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# 创建一个可以在给定图像上绘图的对象
draw = ImageDraw.Draw(img)
# 字体的格式
fontStyle = ImageFont.truetype(
"simsun.ttc", textSize, encoding="utf-8")
# 绘制文本
draw.text(position, text, textColor, font=fontStyle)
# 转换回OpenCV格式
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
def save_res(a, b, winner, prob, save_name):
img = np.zeros((520, 850, 3), np.uint8)
a_img_path = "./world_cup/" + country_name + ".png"
a_img = cv2.imdecode(np.fromfile(a_img_path, dtype = np.uint8), -1)
a_img_h, a_img_w, _ = a_img.shape
b_img_path = "./world_cup/" + country_name + ".png"
b_img = cv2.imdecode(np.fromfile(b_img_path, dtype = np.uint8), -1)
b_img_h, b_img_w, _ = b_img.shape
winner_img_path = "./world_cup/" + country_name + ".png"
winner_img = cv2.imdecode(np.fromfile(winner_img_path, dtype = np.uint8), -1)
winner_img_h, winner_img_w, _ = winner_img.shape
img = a_img[:, :, :3]
img = b_img[:,:,:3]
img = cv2AddChineseText(img, country_name + "(" + a + ")", (10, 20 + a_img_h),(255, 255, 255), 30)
img = cv2AddChineseText(img, country_name + "(" + b + ")", (10, 20 + 100 + a_img_h + b_img_h), (255, 255, 255), 30)
point1 = (10 + a_img_w, 10 + (a_img_h) // 2)
point2 = (10 + a_img_w + 100, 10 + (a_img_h) // 2)
cv2.line(img, point1, point2, (255, 255, 255), 10)
point3 = (10 + a_img_w, 10 + 100 + a_img_h + (b_img_h) // 2)
point4 = (10 + b_img_w + 100, 10 + 100 + a_img_h + (b_img_h) // 2)
cv2.line(img, point3, point4, (255, 255, 255), 10)
cv2.line(img, point2, point4, (255, 255, 255), 10)
point5 = (10 + a_img_w + 100, 10 + 50 + a_img_h)
point6 = (10 + a_img_w + 300, 10 + 50 + a_img_h)
cv2.line(img, point5, point6, (255, 255, 255), 10)
img = cv2AddChineseText(img, "胜率：{}".format(prob), (10 + a_img_w + 100 + 20, 10 + 50 + a_img_h - 40),(255, 255, 255), 30)
img = winner_img[:,:,:3]
img = cv2AddChineseText(img, country_name + "(" + winner + ")", (10 + a_img_w + 300, 10 + 50 + a_img_h + winner_img_h // 2 + 10), (255, 255, 255), 30)
cv2.imwrite(save_name, img)
def save_res_draw(a, b, winner, prob, save_name):
img = np.zeros((520, 850, 3), np.uint8)
a_img_path = "./world_cup/" + country_name + ".png"
a_img = cv2.imdecode(np.fromfile(a_img_path, dtype = np.uint8), -1)
a_img_h, a_img_w, _ = a_img.shape
b_img_path = "./world_cup/" + country_name + ".png"
b_img = cv2.imdecode(np.fromfile(b_img_path, dtype = np.uint8), -1)
b_img_h, b_img_w, _ = b_img.shape
winner_img_path = "./world_cup/" + country_name + ".png"
winner_img = cv2.imdecode(np.fromfile(winner_img_path, dtype = np.uint8), -1)
winner_img_h, winner_img_w, _ = winner_img.shape
img = a_img[:, :, :3]
img = b_img[:,:,:3]
img = cv2AddChineseText(img, country_name + "(" + a + ")", (10, 20 + a_img_h),(255, 255, 255), 30)
img = cv2AddChineseText(img, country_name + "(" + b + ")", (10, 20 + 100 + a_img_h + b_img_h), (255, 255, 255), 30)
point1 = (10 + a_img_w, 10 + (a_img_h) // 2)
point2 = (10 + a_img_w + 100, 10 + (a_img_h) // 2)
cv2.line(img, point1, point2, (255, 255, 255), 10)
point3 = (10 + a_img_w, 10 + 100 + a_img_h + (b_img_h) // 2)
point4 = (10 + b_img_w + 100, 10 + 100 + a_img_h + (b_img_h) // 2)
cv2.line(img, point3, point4, (255, 255, 255), 10)
cv2.line(img, point2, point4, (255, 255, 255), 10)
point5 = (10 + a_img_w + 100, 10 + 50 + a_img_h)
point6 = (10 + a_img_w + 300, 10 + 50 + a_img_h)
cv2.line(img, point5, point6, (255, 255, 255), 10)
img = cv2AddChineseText(img, "胜率：{}".format(prob), (10 + a_img_w + 100 + 20, 10 + 50 + a_img_h - 40),(255, 255, 255), 30)
# img = winner_img[:,:,:3]
# img = cv2AddChineseText(img, country_name + "(" + winner + ")", (10 + a_img_w + 300, 10 + 50 + a_img_h + winner_img_h // 2 + 10), (255, 255, 255), 30)
cv2.imwrite(save_name, img)
if __name__ == "__main__":
save_res_draw("Switzerland", "Cameroon", "Switzerland", 0.62, "tmp.png")
这里是第二部分代码，程序运行的主代码
# -*- coding:utf-8 -*-
# Wan Jiongming @Copyright 2022
import numpy as np
import pandas as pd
from operator import itemgetter
from save_res import save_res, save_res_draw
import time
df = pd.read_csv("./kaggle/results.csv")
df["date"] = pd.to_datetime(df["date"])
df.dropna(inplace=True)
df = df[(df["date"] >= "2018-8-1")].reset_index(drop=True)
rank = pd.read_csv("./kaggle/fifa_ranking-2022-10-06.csv")
rank["rank_date"] = pd.to_datetime(rank["rank_date"])
rank = rank[(rank["rank_date"] >= "2018-8-1")].reset_index(drop=True)
rank["country_full"] = rank["country_full"].str.replace("IR Iran", "Iran").str.replace("Korea Republic", "South Korea").str.replace("USA", "United States")
rank = rank.set_index(['rank_date']).groupby(['country_full'], group_keys=False).resample('D').first().fillna(method='ffill').reset_index()
df_wc_ranked = df.merge(rank[["country_full", "total_points", "previous_points", "rank", "rank_change", "rank_date"]], left_on=["date", "home_team"], right_on=["rank_date", "country_full"]).drop(["rank_date", "country_full"], axis=1)
df_wc_ranked = df_wc_ranked.merge(rank[["country_full", "total_points", "previous_points", "rank", "rank_change", "rank_date"]], left_on=["date", "away_team"], right_on=["rank_date", "country_full"], suffixes=("_home", "_away")).drop(["rank_date", "country_full"], axis=1)
df = df_wc_ranked
# print(df[(df_wc_ranked.home_team == "Brazil") | (df.away_team == "Brazil")].tail(10))
def result_finder(home, away):
if home > away:
return pd.Series()
if home < away:
return pd.Series()
else:
return pd.Series()
results = df.apply(lambda x: result_finder(x["home_score"], x["away_score"]), axis=1)
df[["result", "home_team_points", "away_team_points"]] = results
# print(df[(df_wc_ranked.home_team == "Brazil") | (df.away_team == "Brazil")].tail(10))
import seaborn as sns
import matplotlib.pyplot as plt
# plt.figure(figsize=(15, 10))
# sns.heatmap(df[["total_points_home", "rank_home", "total_points_away", "rank_away"]].corr())
# plt.show()
df["rank_dif"] = df["rank_home"] - df["rank_away"]
df["sg"] = df["home_score"] - df["away_score"]
df["points_home_by_rank"] = df["home_team_points"]/df["rank_away"]
df["points_away_by_rank"] = df["away_team_points"]/df["rank_home"]
home_team = df[["date", "home_team", "home_score", "away_score", "rank_home", "rank_away","rank_change_home", "total_points_home", "result", "rank_dif", "points_home_by_rank", "home_team_points"]]
away_team = df[["date", "away_team", "away_score", "home_score", "rank_away", "rank_home","rank_change_away", "total_points_away", "result", "rank_dif", "points_away_by_rank", "away_team_points"]]
home_team.columns =
away_team.columns =
team_stats = home_team.append(away_team)#.sort_values("date")
team_stats_raw = team_stats.copy()
stats_val = []
for index, row in team_stats.iterrows():
team = row["team"]
date = row["date"]
past_games = team_stats.loc[(team_stats["team"] == team) & (team_stats["date"] < date)].sort_values(by=['date'], ascending=False)
last5 = past_games.head(5)
goals = past_games["score"].mean()
goals_l5 = last5["score"].mean()
goals_suf = past_games["suf_score"].mean()
goals_suf_l5 = last5["suf_score"].mean()
rank = past_games["rank_suf"].mean()
rank_l5 = last5["rank_suf"].mean()
if len(last5) > 0:
points = past_games["total_points"].values - past_games["total_points"].values[-1]#qtd de pontos ganhos
points_l5 = last5["total_points"].values - last5["total_points"].values[-1]
else:
points = 0
points_l5 = 0
gp = past_games["team_points"].mean()
gp_l5 = last5["team_points"].mean()
gp_rank = past_games["points_by_rank"].mean()
gp_rank_l5 = last5["points_by_rank"].mean()
stats_val.append()
stats_cols = ["goals_mean", "goals_mean_l5", "goals_suf_mean", "goals_suf_mean_l5", "rank_mean", "rank_mean_l5", "points_mean", "points_mean_l5", "game_points_mean", "game_points_mean_l5", "game_points_rank_mean", "game_points_rank_mean_l5"]
stats_df = pd.DataFrame(stats_val, columns=stats_cols)
full_df = pd.concat(, axis=1, ignore_index=False)
home_team_stats = full_df.iloc[:int(full_df.shape/2),:]
away_team_stats = full_df.iloc/2):,:]
home_team_stats = home_team_stats]
away_team_stats = away_team_stats]
home_team_stats.columns = ['home_'+str(col) for col in home_team_stats.columns]
away_team_stats.columns = ['away_'+str(col) for col in away_team_stats.columns]
match_stats = pd.concat(, axis=1, ignore_index=False)
full_df = pd.concat(, axis=1, ignore_index=False)
def find_friendly(x):
if x == "Friendly":
return 1
else: return 0
full_df["is_friendly"] = full_df["tournament"].apply(lambda x: find_friendly(x))
full_df = pd.get_dummies(full_df, columns=["is_friendly"])
base_df = full_df[["date", "home_team", "away_team", "rank_home", "rank_away","home_score", "away_score","result", "rank_dif", "rank_change_home", "rank_change_away", 'home_goals_mean',
'home_goals_mean_l5', 'home_goals_suf_mean', 'home_goals_suf_mean_l5',
'home_rank_mean', 'home_rank_mean_l5', 'home_points_mean',
'home_points_mean_l5', 'away_goals_mean', 'away_goals_mean_l5',
'away_goals_suf_mean', 'away_goals_suf_mean_l5', 'away_rank_mean',
'away_rank_mean_l5', 'away_points_mean', 'away_points_mean_l5','home_game_points_mean', 'home_game_points_mean_l5',
'home_game_points_rank_mean', 'home_game_points_rank_mean_l5','away_game_points_mean',
'away_game_points_mean_l5', 'away_game_points_rank_mean',
'away_game_points_rank_mean_l5',
'is_friendly_0', 'is_friendly_1']]
base_df_no_fg = base_df.dropna()
df = base_df_no_fg
def no_draw(x):
if x == 2:
return 1
else:
return x
df["target"] = df["result"].apply(lambda x: no_draw(x))
def create_db(df):
columns = ["home_team", "away_team", "target", "rank_dif", "home_goals_mean", "home_rank_mean", "away_goals_mean", "away_rank_mean", "home_rank_mean_l5", "away_rank_mean_l5", "home_goals_suf_mean", "away_goals_suf_mean", "home_goals_mean_l5", "away_goals_mean_l5", "home_goals_suf_mean_l5", "away_goals_suf_mean_l5", "home_game_points_rank_mean", "home_game_points_rank_mean_l5", "away_game_points_rank_mean", "away_game_points_rank_mean_l5","is_friendly_0", "is_friendly_1"]
base = df.loc[:, columns]
base.loc[:, "goals_dif"] = base["home_goals_mean"] - base["away_goals_mean"]
base.loc[:, "goals_dif_l5"] = base["home_goals_mean_l5"] - base["away_goals_mean_l5"]
base.loc[:, "goals_suf_dif"] = base["home_goals_suf_mean"] - base["away_goals_suf_mean"]
base.loc[:, "goals_suf_dif_l5"] = base["home_goals_suf_mean_l5"] - base["away_goals_suf_mean_l5"]
base.loc[:, "goals_per_ranking_dif"] = (base["home_goals_mean"] / base["home_rank_mean"]) - (base["away_goals_mean"] / base["away_rank_mean"])
base.loc[:, "dif_rank_agst"] = base["home_rank_mean"] - base["away_rank_mean"]
base.loc[:, "dif_rank_agst_l5"] = base["home_rank_mean_l5"] - base["away_rank_mean_l5"]
base.loc[:, "dif_points_rank"] = base["home_game_points_rank_mean"] - base["away_game_points_rank_mean"]
base.loc[:, "dif_points_rank_l5"] = base["home_game_points_rank_mean_l5"] - base["away_game_points_rank_mean_l5"]
model_df = base[["home_team", "away_team", "target", "rank_dif", "goals_dif", "goals_dif_l5", "goals_suf_dif", "goals_suf_dif_l5", "goals_per_ranking_dif", "dif_rank_agst", "dif_rank_agst_l5", "dif_points_rank", "dif_points_rank_l5", "is_friendly_0", "is_friendly_1"]]
return model_df
model_db = create_db(df)
X = model_db.iloc[:, 3:]
y = model_db[["target"]]
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state=1)
gb = GradientBoostingClassifier(random_state=5)
params = {"learning_rate": ,
"min_samples_split": ,
"min_samples_leaf": ,
"max_depth":,
"max_features":["sqrt"],
"n_estimators":
}
gb_cv = GridSearchCV(gb, params, cv = 3, n_jobs = -1, verbose = False)
gb_cv.fit(X_train.values, np.ravel(y_train))
gb = gb_cv.best_estimator_
params_rf = {"max_depth": ,
"min_samples_split": ,
"max_leaf_nodes": ,
"min_samples_leaf": ,
"n_estimators": ,
"max_features": ["sqrt"],
}
rf = RandomForestClassifier(random_state=1)
rf_cv = GridSearchCV(rf, params_rf, cv = 3, n_jobs = -1, verbose = False)
rf_cv.fit(X_train.values, np.ravel(y_train))
rf = rf_cv.best_estimator_
# from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score
# def analyze(model):
# fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test.values)[:,1]) #test AUC
# plt.figure(figsize=(15,10))
# plt.plot(, , 'k--')
# plt.plot(fpr, tpr, label="test")
# fpr_train, tpr_train, _ = roc_curve(y_train, model.predict_proba(X_train.values)[:,1]) #train AUC
# plt.plot(fpr_train, tpr_train, label="train")
# auc_test = roc_auc_score(y_test, model.predict_proba(X_test.values)[:,1])
# auc_train = roc_auc_score(y_train, model.predict_proba(X_train.values)[:,1])
# plt.legend()
# plt.title('AUC score is %.2f on test and %.2f on training'%(auc_test, auc_train))
# plt.show()
# plt.figure(figsize=(15, 10))
# cm = confusion_matrix(y_test, model.predict(X_test.values))
# sns.heatmap(cm, annot=True, fmt="d")
# analyze(gb)
with open("country_name.txt", "r", encoding="utf-8") as f:
info = f.readlines()
info = list(map(lambda x:x.strip(), info))
English_name = info[:32]
Chinese_name = info
country_name = {}
for each in zip(English_name, Chinese_name):
country_name] = each
table = {'A': [['Qatar', 0, []],
['Ecuador', 0, []],
['Senegal', 0, []],
['Netherlands', 0, []]],
'B': [['England', 0, []],
['Iran', 0, []],
['United States', 0, []],
['Wales', 0, []]],
'C': [['Argentina', 0, []],
['Saudi Arabia', 0, []],
['Mexico', 0, []],
['Poland', 0, []]],
'D': [['France', 0, []],
['Australia', 0, []],
['Denmark', 0, []],
['Tunisia', 0, []]],
'E': [['Spain', 0, []],
['Costa Rica', 0, []],
['Germany', 0, []],
['Japan', 0, []]],
'F': [['Belgium', 0, []],
['Canada', 0, []],
['Morocco', 0, []],
['Croatia', 0, []]],
'G': [['Brazil', 0, []],
['Serbia', 0, []],
['Switzerland', 0, []],
['Cameroon', 0, []]],
'H': [['Portugal', 0, []],
['Ghana', 0, []],
['Uruguay', 0, []],
['South Korea', 0, []]]}
groups = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
group_count = 7
matches = [('A', 'Qatar', 'Ecuador'),
('A', 'Senegal', 'Netherlands'),
('A', 'Qatar', 'Senegal'),
('A', 'Netherlands', 'Ecuador'),
('A', 'Ecuador', 'Senegal'),
('A', 'Netherlands', 'Qatar'),
('B', 'England', 'Iran'),
('B', 'United States', 'Wales'),
('B', 'Wales', 'Iran'),
('B', 'England', 'United States'),
('B', 'Wales', 'England'),
('B', 'Iran', 'United States'),
('C', 'Argentina', 'Saudi Arabia'),
('C', 'Mexico', 'Poland'),
('C', 'Poland', 'Saudi Arabia'),
('C', 'Argentina', 'Mexico'),
('C', 'Poland', 'Argentina'),
('C', 'Saudi Arabia', 'Mexico'),
('D', 'Denmark', 'Tunisia'),
('D', 'France', 'Australia'),
('D', 'Tunisia', 'Australia'),
('D', 'France', 'Denmark'),
('D', 'Australia', 'Denmark'),
('D', 'Tunisia', 'France'),
('E', 'Germany', 'Japan'),
('E', 'Spain', 'Costa Rica'),
('E', 'Japan', 'Costa Rica'),
('E', 'Spain', 'Germany'),
('E', 'Japan', 'Spain'),
('E', 'Costa Rica', 'Germany'),
('F', 'Morocco', 'Croatia'),
('F', 'Belgium', 'Canada'),
('F', 'Belgium', 'Morocco'),
('F', 'Croatia', 'Canada'),
('F', 'Croatia', 'Belgium'),
('F', 'Canada', 'Morocco'),
('G', 'Switzerland', 'Cameroon'),
('G', 'Brazil', 'Serbia'),
('G', 'Cameroon', 'Serbia'),
('G', 'Brazil', 'Switzerland'),
('G', 'Serbia', 'Switzerland'),
('G', 'Cameroon', 'Brazil'),
('H', 'Uruguay', 'South Korea'),
('H', 'Portugal', 'Ghana'),
('H', 'South Korea', 'Ghana'),
('H', 'Portugal', 'Uruguay'),
('H', 'Ghana', 'Uruguay'),
('H', 'South Korea', 'Portugal')]
def find_stats(team_1):
#team_1 = "Qatar"
past_games = team_stats_raw[(team_stats_raw["team"] == team_1)].sort_values("date")
last5 = team_stats_raw[(team_stats_raw["team"] == team_1)].sort_values("date").tail(5)
team_1_rank = past_games["rank"].values[-1]
team_1_goals = past_games.score.mean()
team_1_goals_l5 = last5.score.mean()
team_1_goals_suf = past_games.suf_score.mean()
team_1_goals_suf_l5 = last5.suf_score.mean()
team_1_rank_suf = past_games.rank_suf.mean()
team_1_rank_suf_l5 = last5.rank_suf.mean()
team_1_gp_rank = past_games.points_by_rank.mean()
team_1_gp_rank_l5 = last5.points_by_rank.mean()
return
def find_features(team_1, team_2):
rank_dif = team_1 - team_2
goals_dif = team_1 - team_2
goals_dif_l5 = team_1 - team_2
goals_suf_dif = team_1 - team_2
goals_suf_dif_l5 = team_1 - team_2
goals_per_ranking_dif = (team_1/team_1) - (team_2/team_2)
dif_rank_agst = team_1 - team_2
dif_rank_agst_l5 = team_1 - team_2
dif_gp_rank = team_1 - team_2
dif_gp_rank_l5 = team_1 - team_2
return
advanced_group = []
last_group = ""
for k in table.keys():
for t in table:
t = 0
t = []
for idx, teams in enumerate(matches):
draw = False
team_1 = find_stats(teams)
team_2 = find_stats(teams)
features_g1 = find_features(team_1, team_2)
features_g2 = find_features(team_2, team_1)
probs_g1 = gb.predict_proba()
probs_g2 = gb.predict_proba()
team_1_prob_g1 = probs_g1
team_1_prob_g2 = probs_g2
team_2_prob_g1 = probs_g1
team_2_prob_g2 = probs_g2
team_1_prob = (probs_g1 + probs_g2)/2
team_2_prob = (probs_g2 + probs_g1)/2
if ((team_1_prob_g1 > team_2_prob_g1) & (team_2_prob_g2 > team_1_prob_g2)) | ((team_1_prob_g1 < team_2_prob_g1) & (team_2_prob_g2 < team_1_prob_g2)):
draw=True
for i in table]:
if i == teams or i == teams:
i += 1
elif team_1_prob > team_2_prob:
winner = teams
winner_proba = team_1_prob
for i in table]:
if i == teams:
i += 3
elif team_2_prob > team_1_prob:
winner = teams
winner_proba = team_2_prob
for i in table]:
if i == teams:
i += 3
for i in table]: #adding criterio de desempate (probs por jogo)
if i == teams:
i.append(team_1_prob)
if i == teams:
i.append(team_2_prob)
if last_group != teams:
if last_group != "":
print("\n")
print("小组 %s 排名: "%(last_group))
for i in table: #adding crieterio de desempate
i = np.mean(i)
final_points = table
final_table = sorted(final_points, key=itemgetter(1, 2), reverse = True)
advanced_group.append(, final_table])
for i in final_table:
print("%s -------- %d"%(country_name] + "(" + i + ")", i))
print("\n")
print("-"*10+" 开始分析小组 %s "%(teams)+"-"*10)
if draw == False:
print("小组 %s - %s vs. %s: %s 获胜，胜率： %.2f"%(teams, country_name] + "(" + teams + ")", country_name] + "(" + teams + ")", country_name + "(" + winner + ")", winner_proba))
save_res(teams, teams, winner, winner_proba, "{}-{}.png".format(teams, idx))
else:
print("小组 %s - %s vs. %s: Draw"%(teams, teams, teams))
save_res_draw(teams, teams, teams, 0.5, "{}-{}.png".format(teams, idx))
last_group =teams
print("\n")
print("小组 %s 排名: "%(last_group))
for i in table: #adding crieterio de desempate
i = np.mean(i)
final_points = table
final_table = sorted(final_points, key=itemgetter(1, 2), reverse = True)
advanced_group.append(, final_table])
for i in final_table:
print("%s -------- %d"%(country_name] + "(" + i + ")", i))
advanced = advanced_group
playoffs = {"16 强": [], "四分之一决赛": [], "半决赛": [], "决赛": []}
for p in playoffs.keys():
playoffs = []
actual_round = ""
next_rounds = []
for p in playoffs.keys():
if p == "16 强":
control = []
for a in range(0, len(advanced*2), 1):
if a < len(advanced):
if a % 2 == 0:
control.append((advanced*2))
else:
control.append((advanced*2))
else:
if a % 2 == 0:
control.append((advanced*2))
else:
control.append((advanced*2))
playoffs = [, control] for c in range(0, len(control)-1, 1) if c%2 == 0]
for i in range(0, len(playoffs), 1):
game = playoffs
home = game
away = game
team_1 = find_stats(home)
team_2 = find_stats(away)
features_g1 = find_features(team_1, team_2)
features_g2 = find_features(team_2, team_1)
probs_g1 = gb.predict_proba()
probs_g2 = gb.predict_proba()
team_1_prob = (probs_g1 + probs_g2)/2
team_2_prob = (probs_g2 + probs_g1)/2
if actual_round != p:
print("-"*10)
print("开始模拟 %s"%(p))
print("-"*10)
print("\n")
if team_1_prob < team_2_prob:
print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name + "(" + home + ")", country_name + "(" + away + ")", country_name + "(" + away + ")", team_2_prob))
save_res(home, away, away, team_2_prob, "%s.png" % str(time.time()).replace(".", "_"))
next_rounds.append(away)
else:
print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name + "(" + home + ")", country_name + "(" + away + ")", country_name + "(" + home + ")", team_1_prob))
save_res(home, away, home, team_1_prob, "%s.png" % str(time.time()).replace(".", "_"))
next_rounds.append(home)
game.append()
playoffs = game
actual_round = p
else:
playoffs = [, next_rounds] for c in range(0, len(next_rounds)-1, 1) if c%2 == 0]
next_rounds = []
for i in range(0, len(playoffs)):
game = playoffs
home = game
away = game
team_1 = find_stats(home)
team_2 = find_stats(away)
features_g1 = find_features(team_1, team_2)
features_g2 = find_features(team_2, team_1)
probs_g1 = gb.predict_proba()
probs_g2 = gb.predict_proba()
team_1_prob = (probs_g1 + probs_g2)/2
team_2_prob = (probs_g2 + probs_g1)/2
if actual_round != p:
print("-"*10)
print("开始模拟 %s"%(p))
print("-"*10)
print("\n")
if team_1_prob < team_2_prob:
print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name + "(" + home + ")", country_name + "(" + away + ")", country_name + "(" + away + ")", team_2_prob))
save_res(home, away, away, team_2_prob, "%s.png" % str(time.time()).replace(".", "_"))
next_rounds.append(away)
else:
print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name + "(" + home + ")", country_name + "(" + away + ")", country_name + "(" + home + ")", team_1_prob))
save_res(home, away, home, team_1_prob, "%s.png" % str(time.time()).replace(".", "_"))
next_rounds.append(home)
game.append()
playoffs = game
actual_round = p
https://static.52pojie.cn/static/image/hrline/1.gif
四、成品链接
蓝奏云下载链接：
https://wwm.lanzout.com/iyqmF0gw8uif
密码:52pj C:\Python\Python310\python.exe C:/Users/Administrator/Desktop/Predicting-FIFA-2022-World-Cup-main/predict.py
C:\Users\Administrator\Desktop\Predicting-FIFA-2022-World-Cup-main\predict.py:56: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
team_stats = home_team.append(away_team)#.sort_values("date")
C:\Users\Administrator\Desktop\Predicting-FIFA-2022-World-Cup-main\predict.py:141: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df["target"] = df["result"].apply(lambda x: no_draw(x))
一堆好家伙。。。感觉你也是不知哪搞来的 Traceback (most recent call last):
File "C:\Users\Administrator\Desktop\Predicting-FIFA-2022-World-Cup-main\predict.py", line 7, in <module>
from save_res import save_res, save_res_draw
File "C:\Users\Administrator\Desktop\Predicting-FIFA-2022-World-Cup-main\save_res.py", line 4, in <module>
import cv2
ModuleNotFoundError: No module named 'cv2'
没有cv2 我只想知道结果 拿走了。 拿走了，谢谢分享。 可靠不可靠，看看 巴西！巴西！哈哈！ 哈哈 会玩 C罗点球进去以后，葡萄牙整体移速降低30%，球员全属性降低了30%，越看越假了 莱奥进球之后也开始梦游 都没时间好好看看，真是遗憾了