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赛事介绍

实时对战游戏是人工智能研究领域的一个热点。由于游戏复杂性、部分可观察和动态实时变化战局等游戏特点使得研究变得比较困难。我们可以在选择英雄阶段预测胜负概率，也可以在比赛期间根据比赛实时数据进行建模。那么我们英雄联盟对局进行期间，能知道自己的胜率吗？

赛事任务

比赛数据使用了英雄联盟玩家的实时游戏数据，记录下用户在游戏中对局数据（如击杀数、住物理伤害）。希望参赛选手能从数据集中挖掘出数据的规律，并预测玩家在本局游戏中的输赢情况。

赛题训练集案例如下：

  • 训练集18万数据；
  • 测试集2万条数据；
import pandas as pd
import numpy as np

train = pd.read_csv('train.csv.zip')

对于数据集中每一行为一个玩家的游戏数据，数据字段如下所示：

  • id：玩家记录id
  • win：是否胜利，标签变量
  • kills：击杀次数
  • deaths：死亡次数
  • assists：助攻次数
  • largestkillingspree：最大 killing spree（游戏术语，意味大杀特杀。当你连续杀死三个对方英雄而中途没有死亡时）
  • largestmultikill：最大mult ikill（游戏术语，短时间内多重击杀）
  • longesttimespentliving：最长存活时间
  • doublekills：doublekills次数
  • triplekills：doublekills次数
  • quadrakills：quadrakills次数
  • pentakills：pentakills次数
  • totdmgdealt：总伤害
  • magicdmgdealt：魔法伤害
  • physicaldmgdealt：物理伤害
  • truedmgdealt：真实伤害
  • largestcrit：最大暴击伤害
  • totdmgtochamp：对对方玩家的伤害
  • magicdmgtochamp：对对方玩家的魔法伤害
  • physdmgtochamp：对对方玩家的物理伤害
  • truedmgtochamp：对对方玩家的真实伤害
  • totheal：治疗量
  • totunitshealed：痊愈的总单位
  • dmgtoturrets：对炮塔的伤害
  • timecc：法控时间
  • totdmgtaken：承受的伤害
  • magicdmgtaken：承受的魔法伤害
  • physdmgtaken：承受的物理伤害
  • truedmgtaken：承受的真实伤害
  • wardsplaced：侦查守卫放置次数
  • wardskilled：侦查守卫摧毁次数
  • firstblood：是否为firstblood
    测试集中label字段win为空，需要选手预测。

评审规则

  1. 数据说明

选手需要提交测试集队伍排名预测，具体的提交格式如下：

win
0
1
1
0
  1. 评估指标

本次竞赛的使用准确率进行评分，数值越高精度越高，评估代码参考：

from sklearn.metrics import accuracy_score
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
accuracy_score(y_true, y_pred)

1)加载数据

#!pip install numpy==1.19
#!pip install -U scikit-learn numpy

import sklearn

import pandas as pd
import paddle
import numpy as np
%pylab inline
import seaborn as sns

train_df_raw = pd.read_csv('data/data137276/train.csv.zip')
test_df_raw = pd.read_csv('data/data137276/test.csv.zip')

train_df = train_df_raw.drop(['id', 'timecc'], axis=1)
test_df = test_df_raw.drop(['id', 'timecc'], axis=1)

train_df_raw

train_df
winkillsdeathsassistslargestkillingspreelargestmultikilllongesttimespentlivingdoublekillstriplekillsquadrakills...tothealtotunitshealeddmgtoturretstotdmgtakenmagicdmgtakenphysdmgtakentruedmgtakenwardsplacedwardskilledfirstblood
0015201569000...84920781921785239401410
1058731880000...64243032463756071763513941000
21161601593000...2326332918749365114834263710
3012001381000...1555101213417391031876810
404112501455000...66308027891140681274910733420
..................................................................
1799951161201362000...355935751147862374123091021210
179996173451574000...2529289071101939336533552720
1799971909910000...1149446627142793661106170721
17999811415102980300...65551194319165481814110236600
179999144221559000...608115901099276813065246710

180000 rows × 30 columns

#查看标签
train_df['win']

#查看数据内容
train_df.columns

train_df.info()

2)EDA数据分析

2.1异常值处理

#缺失值
print(type(train_df.isnull()))
train_df.isnull()

#查看缺失值个数
train_df.isnull().sum()

#查看缺失值比例
train_df.isnull().mean(axis=0)

train_df['win'].value_counts().plot(kind='bar')

sns.distplot(train_df['kills'])

sns.distplot(train_df['deaths'])

sns.boxplot(y='kills', x='win', data=train_df)

plt.scatter(train_df['kills'], train_df['deaths'])
plt.xlabel('kills')
plt.ylabel('deaths')

for col in train_df.columns[1:]:
    train_df[col] /= train_df[col].max()
    test_df[col] /= test_df[col].max()

3)数据集

from sklearn.model_selection import train_test_split 
from sklearn.model_selection import KFold,cross_validate

#取出标签
x=train_df.drop(['win'], axis=1)
y=train_df.win

x
killsdeathsassistslargestkillingspreelargestmultikilllongesttimespentlivingdoublekillstriplekillsquadrakillspentakills...tothealtotunitshealeddmgtoturretstotdmgtakenmagicdmgtakenphysdmgtakentruedmgtakenwardsplacedwardskilledfirstblood
0152015690000...84920781921785239401410
1587318800000...64243032463756071763513941000
21616015930000...2326332918749365114834263710
3120013810000...1555101213417391031876810
441125014550000...66308027891140681274910733420
..................................................................
1799951612013620000...355935751147862374123091021210
179996734515740000...2529289071101939336533552720
1799979099100000...1149446627142793661106170721
17999814151029803000...65551194319165481814110236600
179999442215590000...608115901099276813065246710

180000 rows × 29 columns

y

0         0
1         0
2         1
3         0
4         0
         ..
179995    1
179996    1
179997    1
179998    1
179999    1
Name: win, Length: 180000, dtype: int64

print('特征向量形状{}'.format(x.shape))
print('标签形状{}'.format(y.shape))
print('标签类别{}'.format(np.unique(y)))
print('测试集特征形状{}'.format(test_df.shape))

特征向量形状(180000, 29)
标签形状(180000,)
标签类别[0 1]
测试集特征形状(20000, 29)

#数据集划分 /这里分出的test部分用于二次验证
Xtrain,Xtest,Ytrain,Ytest=train_test_split(x,y,test_size=0.2,random_state=1412)

#验证指验证集，而非测试集的特征向量。
print('用于训练的特征向量形状{}'.format(Xtrain.shape))
print('用于训练的标签形状{}'.format(Ytrain.shape))
print('用于验证的特征向量形状{}'.format(Xtest.shape))
print('用于验证的标签形状{}'.format(Ytest.shape))

用于训练的特征向量形状(144000, 29)
用于训练的标签形状(144000,)
用于验证的特征向量形状(36000, 29)
用于验证的标签形状(36000,)

def individual_estimators(estimators):
    train_score=[]
    cv_mean=[]
    test_score=[]

    for estimator in estimators:
        cv=KFold(n_splits=5,shuffle=True,random_state=1412)
        results=cross_validate(estimator[1],Xtrain,Ytrain
                                ,cv=cv
                                ,scoring="accuracy"
                                ,n_jobs=8
                                ,return_train_score=True
                                ,verbose=False)
        test=estimator[1].fit(Xtrain,Ytrain).score(Xtest,Ytest)
        train_score.append(results["train_score"].mean())
        cv_mean.append(results["test_score"].mean())
        test_score.append(test)
    for i in range(len(estimators)):
        print("-------------------------------------------")
        print(
            estimators[i]
            ,"\n train_score_mean:{}".format(train_score[i])
            ,"\n cv_mean:{}".format(cv_mean[i])
            ,"\n test_score:{}".format(test_score[i])
            ,"\n")

 def fusion_estimators(estimators):
   
    cv=KFold(n_splits=5,shuffle=True,random_state=1412)
    results=cross_validate(clf,Xtrain,Ytrain
                            ,cv=cv
                            ,scoring="accuracy"
                            ,n_jobs=-1
                            ,return_train_score=True
                            ,verbose=False)
    test=clf.fit(Xtrain,Ytrain).score(Xtest,Ytest)
    print("++++++++++++++++++++++++++++++++++++++++++++++")
    print(
        "\n train_score_mean:{}".format(results["train_score"].mean())
        ,"\n cv_mean:{}".format(results["test_score"].mean())
        ,"\n test_score:{}".format(test)
        )

4)模型

from sklearn.neighbors import KNeighborsClassifier as KNNC
from sklearn.tree import DecisionTreeClassifier as DTR

from sklearn.ensemble import RandomForestClassifier as RFC
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.linear_model import LogisticRegression as LogiR
from sklearn.ensemble import VotingClassifier

4.a为什么模型融合比集成算法更好？

虽然每一个弱分类器并不强，但都能代表一组其对应的假设空间。真实世界的数据分布是多远随机的复杂系统，往往其中一种并不能有一个好的近似结果。模型融合是一种简单粗暴的办法，考虑多重分布的组合。当然，模型融合的结果并不一定好，只是大部分时间是好的。

4.1弱分类器与集成

clf1=LogiR(max_iter=3000,random_state=1412,n_jobs=8)
clf2=RFC(n_estimators=100,random_state=1412,n_jobs=8)
clf3=GBC(n_estimators=100,random_state=1412)

estimators=[("Logistic Regression",clf1),("RandomForest",clf2),("GBDT",clf3)]
clf=VotingClassifier(estimators,voting="soft")

4.1.1对弱分类器分别进行评估

individual_estimators(estimators)

4.1.2对融合算法评估

logi=LogiR(max_iter=3000,n_jobs=8)
fusion_estimators(logi)

test_predict_sklearn=clf.predict(test_df)
test_predict_sklearn=clf.predict_proba(test_df)

print(test_predict_sklearn.shape)
print(test_predict_sklearn)

(20000, 2)
[[0.87535621 0.12464379]
 [0.77675525 0.22324475]
 [0.16242339 0.83757661]
 ...
 [0.94152587 0.05847413]
 [0.90214731 0.09785269]
 [0.10380786 0.89619214]]

4.2网络模型

import paddle.fluid

class MyModel(paddle.nn.Layer):
    # self代表类的实例自身
    def __init__(self):
        # 初始化父类中的一些参数
        super(MyModel, self).__init__()
        self.fc1 = paddle.nn.Linear(in_features=29, out_features=30)
        self.hidden1=paddle.fluid.BatchNorm(30)
        self.relu1=paddle.nn.ReLU()
        self.fc2 = paddle.nn.Linear(in_features=30, out_features=8)
        self.relu2=paddle.nn.LeakyReLU()
        self.fc3 = paddle.nn.Linear(in_features=8, out_features=6)
        self.relu3=paddle.nn.Sigmoid()
        self.fc4 = paddle.nn.Linear(in_features=6, out_features=4)
        self.fc5=paddle.nn.Linear(in_features=4, out_features=2)
        self.softmax = paddle.nn.Softmax()
    # 网络的前向计算
    def forward(self, inputs):
        x = self.fc1(inputs)
        #x = self.relu1(x)
        x = self.hidden1(x)
        x = self.fc2(x)
        x = self.relu2(x)
        x = self.fc3(x)
        x = self.relu3(x)
        
        x=self.fc4(x)
        x=self.fc5(x)
        #x=self.fc6(x)
        x = self.softmax(x)
        return x

model = MyModel()
model.train()
opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())

EPOCH_NUM = 10   # 设置外层循环次数
BATCH_SIZE = 100  # 设置batch大小
training_data = train_df.iloc[:-1000,].values.astype(np.float32)
val_data = train_df.iloc[-1000:, ].values.astype(np.float32)

# 定义外层循环
for epoch_id in range(EPOCH_NUM):
    # 在每轮迭代开始之前，将训练数据的顺序随机的打乱
    
    np.random.shuffle(training_data)
    
    # 将训练数据进行拆分，每个batch包含10条数据
    mini_batches = [training_data[k:k+BATCH_SIZE] for k in range(0, len(training_data), BATCH_SIZE)]
    
    # 定义内层循环
    for iter_id, mini_batch in enumerate(mini_batches):
        x_data = np.array(mini_batch[:, 1:]) # 获得当前批次训练数据
        y_label = np.array(mini_batch[:, :1]) # 获得当前批次训练标签
       
        # 将numpy数据转为飞桨动态图tensor的格式
        features = paddle.to_tensor(x_data)
        y_label = paddle.to_tensor(y_label)
        label=np.zeros([len(y_label),2])

        for i in range(len(y_label)):
            if y_label[i]==0:
                label[i,0]=1
            elif y_label[i]==1:
                label[i,1]=1
        label=paddle.to_tensor(label,dtype=float32)
        # 前向计算
        predicts = model(features)
        # 计算损失
        loss = paddle.nn.functional.softmax_with_cross_entropy(predicts, label,soft_label=True)
        avg_loss = paddle.mean(loss)
        
        # 反向传播，计算每层参数的梯度值
        avg_loss.backward()
        
        # 更新参数，根据设置好的学习率迭代一步
        opt.step()
        # 清空梯度变量，以备下一轮计算
        opt.clear_grad()

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/data_feeder.py:51: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.bool, np.float16, np.uint16, np.float32, np.float64, np.int8,

model.eval()
test_data = paddle.to_tensor(test_df.values.astype(np.float32))
test_predict_dl = model(test_data)

test_predict_dl

Tensor(shape=[20000, 2], dtype=float32, place=CPUPlace, stop_gradient=False,
       [[0.31092143, 0.68907863],
        [0.89762008, 0.10237990],
        [0.00382155, 0.99617851],
        ...,
        [0.97896796, 0.02103199],
        [0.98377025, 0.01622973],
        [0.00828540, 0.99171454]])

test_predict_sklearn

array([[0.87535621, 0.12464379],
       [0.77675525, 0.22324475],
       [0.16242339, 0.83757661],
       ...,
       [0.94152587, 0.05847413],
       [0.90214731, 0.09785269],
       [0.10380786, 0.89619214]])

#控制融合比例
test_predict_=(1/4*(np.array(test_predict_dl)))+(3/4*(test_predict_sklearn))

test_predict=np.zeros([len(test_predict_)])
for i in range(len(test_predict_)):
    if test_predict_[i,0]>test_predict_[i,1]:
        test_predict[i]=0
    elif test_predict_[i,0]<test_predict_[i,1]:
        test_predict[i]=1

test_predict

array([0., 0., 1., ..., 0., 0., 1.])

pd.DataFrame({'win':
              test_predict
             }).to_csv('submission.csv', index=None)

!zip submission.zip submission.csv

  adding: submission.csv (deflated 94%)
# 人工智能 # python
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