Python大数据分析LOL游戏胜率
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Python大数据分析LOL游戏胜率
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基于數(shù)據(jù)的LOL游戲勝利預(yù)測
目錄:
文章目錄
- 基于數(shù)據(jù)的LOL游戲勝利預(yù)測
- 目錄:
- 1.背景
- a.LOL簡介:
- 游戲規(guī)則簡介:
- b.對其進行數(shù)據(jù)分析的可行性和目的:
- 2.數(shù)據(jù)簡介和展示:
- 簡介:
- 數(shù)據(jù)展示:
- 數(shù)據(jù)結(jié)構(gòu):
- 數(shù)據(jù)名詞詳解:
- 數(shù)據(jù)清洗處理:
- 1.檢查數(shù)據(jù)是否規(guī)范和有缺失:
- **刪除有空值的行:**
- 查看數(shù)據(jù)是否規(guī)范:
- 2.對數(shù)據(jù)進行處理:
- **預(yù)處理:**
- **熱力圖分析處理:**
- 數(shù)據(jù)分析和建模（Logistic Regression）:
- 1.數(shù)據(jù)分析處理:
- 1.初步處理后數(shù)據(jù):
- 2.一般分析處理:
- 2.數(shù)據(jù)建模:
- 處理后數(shù)據(jù):
- 邏輯回歸簡介:
- 分析:
- 標(biāo)準(zhǔn)化數(shù)據(jù):
- 數(shù)據(jù)切片:
- 模型訓(xùn)練分析:
- 數(shù)據(jù)測試:
- 結(jié)果分析:
- 源碼：
- 數(shù)據(jù)下載地址：
1.背景
a.LOL簡介:
英雄聯(lián)盟(LOL)是一個MOBA（多人在線戰(zhàn)斗競技場），其中2支隊伍（藍色和紅色）對峙。有3條車道，一個叢林和5個角色。目標(biāo)是擊倒敵方水晶以贏得比賽.
- 名詞解釋:
- Warding totem:(視野眼) 玩家可以放置在地圖上以顯示附近區(qū)域的物品。對于地圖/目標(biāo)控制非常有用。
- Minions: (小兵)屬于兩個團隊的NPC。當(dāng)被玩家殺死時，他們給予金幣。
- Jungle minions: 叢林NPC。當(dāng)被玩家殺死時，他們會給予金幣和增益。
- Elite monsters: 具有很高的血量和傷害的怪獸，在被團隊殺死時會給予巨額獎勵（金幣/ XP /屬性）。
- Dragons: 精英怪獸，被殺死后會給予團隊加成。被團隊殺死的第四條龍給予了巨大的屬性加值。第五龍（長者龍）為球隊提供了巨大的優(yōu)勢
- Herald: 精英怪物，被玩家殺死后會給予屬性加成。它有助于推開車道并破壞建筑物
- Towers:(防御塔) 您必須摧毀的結(jié)構(gòu)才能到達敵方水晶。他們給金幣。
- Level:等級,從1開始,最大為18。
游戲規(guī)則簡介:
玩家—>殺死小兵和野怪—>獲得金幣和buff–>摧毀敵方防御塔------>摧毀敵方水晶(獲得勝利)
? | | |
? | | 擊殺敵人
? 放置視野眼,獲得視野優(yōu)勢 | |
? |__—>購買裝備,提升實力
?
b.對其進行數(shù)據(jù)分析的可行性和目的:
隨著網(wǎng)絡(luò)游戲在年輕人中的盛行,電子競技也變得越來越流行.LOL(英雄聯(lián)盟)作為電子競技的代表游戲之一,受到越來越多年輕人的關(guān)注和喜愛
-
可行性:在每一次比賽的過程中,最終的勝利受到許許多多因素的影響,不可否認,玩家的操作技術(shù)和意識是決定比賽輸贏的關(guān)鍵因素,但玩家的所有自身實力都會反映在游戲中的數(shù)據(jù)里面,而LOL又是一款團隊競技游戲,因此,一局比賽進行到后期時的數(shù)據(jù)能夠比較好的反映出操作者的水平和團隊間的配合.此時的數(shù)據(jù)就可以比較準(zhǔn)確的用來對比賽的輸贏進行預(yù)測.
-
目的:通過對LOL數(shù)據(jù)的分析,不僅可以用來對比賽的輸贏進行預(yù)測,同時,也可以發(fā)現(xiàn)對一局比賽輸贏影響較大的因素,從而對現(xiàn)實生活中的比賽具有指導(dǎo)意義.
2.數(shù)據(jù)簡介和展示:
簡介:
-
數(shù)據(jù)來源:網(wǎng)絡(luò)
-
**數(shù)據(jù)集簡介:**此數(shù)據(jù)集包含前10分鐘大約統(tǒng)計 從高ELO（鉆石I到大師）的10k次排位游戲。玩家的水平大致相同。游戲開始10分鐘后，每支隊伍收集了19項數(shù)據(jù)（總共38項）。其中包括殺戮，死亡，金錢，經(jīng)驗，等級……
數(shù)據(jù)展示:
data = pd.read_csv('high_diamond_ranked_10min.csv', index_col=0) print(data.head()) gameId blueWins blueWardsPlaced blueWardsDestroyed blueFirstBlood \ 0 4519157822 0 28 2 1 1 4523371949 0 12 1 0 2 4521474530 0 15 0 0 3 4524384067 0 43 1 0 4 4436033771 0 75 4 0 blueKills blueDeaths blueAssists blueEliteMonsters blueDragons \ 0 9 6 11 0 0 1 5 5 5 0 0 2 7 11 4 1 1 3 4 5 5 1 0 4 6 6 6 0 0 blueHeralds blueTowersDestroyed blueTotalGold blueAvgLevel \ 0 0 0 17210 6.6 1 0 0 14712 6.6 2 0 0 16113 6.4 3 1 0 15157 7.0 4 0 0 16400 7.0 blueTotalExperience blueTotalMinionsKilled blueTotalJungleMinionsKilled \ 0 17039 195 36 1 16265 174 43 2 16221 186 46 3 17954 201 55 4 18543 210 57 blueGoldDiff blueExperienceDiff blueCSPerMin blueGoldPerMin \ 0 643 -8 19.5 1721.0 1 -2908 -1173 17.4 1471.2 2 -1172 -1033 18.6 1611.3 3 -1321 -7 20.1 1515.7 4 -1004 230 21.0 1640.0 redWardsPlaced redWardsDestroyed redFirstBlood redKills redDeaths \ 0 15 6 0 6 9 1 12 1 1 5 5 2 15 3 1 11 7 3 15 2 1 5 4 4 17 2 1 6 6 redAssists redEliteMonsters redDragons redHeralds redTowersDestroyed \ 0 8 0 0 0 0 1 2 2 1 1 1 2 14 0 0 0 0 3 10 0 0 0 0 4 7 1 1 0 0 redTotalGold redAvgLevel redTotalExperience redTotalMinionsKilled \ 0 16567 6.8 17047 197 1 17620 6.8 17438 240 2 17285 6.8 17254 203 3 16478 7.0 17961 235 4 17404 7.0 18313 225 redTotalJungleMinionsKilled redGoldDiff redExperienceDiff redCSPerMin \ 0 55 -643 8 19.7 1 52 2908 1173 24.0 2 28 1172 1033 20.3 3 47 1321 7 23.5 4 67 1004 -230 22.5 redGoldPerMin 0 1656.7 1 1762.0 2 1728.5 3 1647.8 4 1740.4數(shù)據(jù)結(jié)構(gòu):
print(data.shape) 數(shù)據(jù)形狀: (9879, 40) #集合共包含9879個元數(shù)據(jù),每個數(shù)據(jù)有40列 print(data.describe) 數(shù)據(jù)概覽: blueWins blueWardsPlaced blueWardsDestroyed blueFirstBlood \ count 9879.000000 9879.000000 9879.000000 9879.000000 mean 0.499038 22.288288 2.824881 0.504808 std 0.500024 18.019177 2.174998 0.500002 min 0.000000 5.000000 0.000000 0.000000 25% 0.000000 14.000000 1.000000 0.000000 50% 0.000000 16.000000 3.000000 1.000000 75% 1.000000 20.000000 4.000000 1.000000 max 1.000000 250.000000 27.000000 1.000000 blueKills blueDeaths blueAssists blueEliteMonsters blueDragons \ count 9879.000000 9879.000000 9879.000000 9879.000000 9879.000000 mean 6.183925 6.137666 6.645106 0.549954 0.361980 std 3.011028 2.933818 4.064520 0.625527 0.480597 min 0.000000 0.000000 0.000000 0.000000 0.000000 25% 4.000000 4.000000 4.000000 0.000000 0.000000 50% 6.000000 6.000000 6.000000 0.000000 0.000000 75% 8.000000 8.000000 9.000000 1.000000 1.000000 max 22.000000 22.000000 29.000000 2.000000 1.000000 blueHeralds blueTowersDestroyed blueTotalGold blueAvgLevel \ count 9879.000000 9879.000000 9879.000000 9879.000000 mean 0.187974 0.051422 16503.455512 6.916004 std 0.390712 0.244369 1535.446636 0.305146 min 0.000000 0.000000 10730.000000 4.600000 25% 0.000000 0.000000 15415.500000 6.800000 50% 0.000000 0.000000 16398.000000 7.000000 75% 0.000000 0.000000 17459.000000 7.200000 max 1.000000 4.000000 23701.000000 8.000000 blueTotalExperience blueTotalMinionsKilled \ count 9879.000000 9879.000000 mean 17928.110133 216.699565 std 1200.523764 21.858437 min 10098.000000 90.000000 25% 17168.000000 202.000000 50% 17951.000000 218.000000 75% 18724.000000 232.000000 max 22224.000000 283.000000 blueTotalJungleMinionsKilled blueGoldDiff blueExperienceDiff \ count 9879.000000 9879.000000 9879.000000 mean 50.509667 14.414111 -33.620306 std 9.898282 2453.349179 1920.370438 min 0.000000 -10830.000000 -9333.000000 25% 44.000000 -1585.500000 -1290.500000 50% 50.000000 14.000000 -28.000000 75% 56.000000 1596.000000 1212.000000 max 92.000000 11467.000000 8348.000000 blueCSPerMin blueGoldPerMin redWardsPlaced redWardsDestroyed \ count 9879.000000 9879.000000 9879.000000 9879.000000 mean 21.669956 1650.345551 22.367952 2.723150 std 2.185844 153.544664 18.457427 2.138356 min 9.000000 1073.000000 6.000000 0.000000 25% 20.200000 1541.550000 14.000000 1.000000 50% 21.800000 1639.800000 16.000000 2.000000 75% 23.200000 1745.900000 20.000000 4.000000 max 28.300000 2370.100000 276.000000 24.000000 redFirstBlood redKills redDeaths redAssists redEliteMonsters \ count 9879.000000 9879.000000 9879.000000 9879.000000 9879.000000 mean 0.495192 6.137666 6.183925 6.662112 0.573135 std 0.500002 2.933818 3.011028 4.060612 0.626482 min 0.000000 0.000000 0.000000 0.000000 0.000000 25% 0.000000 4.000000 4.000000 4.000000 0.000000 50% 0.000000 6.000000 6.000000 6.000000 0.000000 75% 1.000000 8.000000 8.000000 9.000000 1.000000 max 1.000000 22.000000 22.000000 28.000000 2.000000 redDragons redHeralds redTowersDestroyed redTotalGold \ count 9879.000000 9879.000000 9879.000000 9879.000000 mean 0.413098 0.160036 0.043021 16489.041401 std 0.492415 0.366658 0.216900 1490.888406 min 0.000000 0.000000 0.000000 11212.000000 25% 0.000000 0.000000 0.000000 15427.500000 50% 0.000000 0.000000 0.000000 16378.000000 75% 1.000000 0.000000 0.000000 17418.500000 max 1.000000 1.000000 2.000000 22732.000000 redAvgLevel redTotalExperience redTotalMinionsKilled \ count 9879.000000 9879.000000 9879.000000 mean 6.925316 17961.730438 217.349226 std 0.305311 1198.583912 21.911668 min 4.800000 10465.000000 107.000000 25% 6.800000 17209.500000 203.000000 50% 7.000000 17974.000000 218.000000 75% 7.200000 18764.500000 233.000000 max 8.200000 22269.000000 289.000000 redTotalJungleMinionsKilled redGoldDiff redExperienceDiff \ count 9879.000000 9879.000000 9879.000000 mean 51.313088 -14.414111 33.620306 std 10.027885 2453.349179 1920.370438 min 4.000000 -11467.000000 -8348.000000 25% 44.000000 -1596.000000 -1212.000000 50% 51.000000 -14.000000 28.000000 75% 57.000000 1585.500000 1290.500000 max 92.000000 10830.000000 9333.000000 redCSPerMin redGoldPerMin count 9879.000000 9879.000000 mean 21.734923 1648.904140 std 2.191167 149.088841 min 10.700000 1121.200000 25% 20.300000 1542.750000 50% 21.800000 1637.800000 75% 23.300000 1741.850000 max 28.900000 2273.200000數(shù)據(jù)名詞詳解:
pd.set_option('display.width', 10) #設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行 print("數(shù)據(jù)列名:",data.columns) Index(['gameId', #每局游戲的唯一ID。#--------------------------------------------------------------------'blueWins', #藍方是否獲得勝利 1:勝利 0:失敗 *****因變量****#--------------------------------------------------------------------19項'blueWardsPlaced', #藍色團隊在地圖上放置的視野眼數(shù)量'blueWardsDestroyed', #藍隊摧毀的敵方視野眼數(shù)量'blueFirstBlood', #藍方是否獲得一血(游戲的第一殺) 1:獲得 0:未獲得'blueKills', #藍隊殺死的敵人數(shù)量'blueDeaths', #死亡人數(shù)（藍隊）'blueAssists', #擊殺助攻數(shù)（藍隊）'blueEliteMonsters', #藍隊殺死的精銳怪物數(shù)量（龍與先驅(qū)隊）'blueDragons', #藍隊殺死的龍數(shù)量'blueHeralds', #藍隊殺死的精英怪物數(shù)量'blueTowersDestroyed', #藍隊摧毀防御塔數(shù)量'blueTotalGold', #藍隊總的金幣數(shù)量'blueAvgLevel', #藍隊平均等級'blueTotalExperience', #藍隊總的經(jīng)驗'blueTotalMinionsKilled', #藍隊殺死的小兵總數(shù)'blueTotalJungleMinionsKilled', #藍隊殺死的野怪總數(shù)'blueGoldDiff', #藍隊金幣與紅隊差值'blueExperienceDiff', #藍隊經(jīng)驗差值'blueCSPerMin', #藍隊每分鐘摧毀視野眼數(shù)量'blueGoldPerMin', #藍隊每分鐘獲得金幣數(shù)量#紅方與藍方相同--------------------------------------------------------19項'redWardsPlaced', 'redWardsDestroyed','redFirstBlood','redKills','redDeaths','redAssists','redEliteMonsters','redDragons','redHeralds','redTowersDestroyed','redTotalGold','redAvgLevel','redTotalExperience','redTotalMinionsKilled','redTotalJungleMinionsKilled','redGoldDiff','redExperienceDiff','redCSPerMin','redGoldPerMin'],數(shù)據(jù)清洗處理:
1.檢查數(shù)據(jù)是否規(guī)范和有缺失:
刪除有空值的行:
data.dropna(axis=0, how='any', inplace=True)查看數(shù)據(jù)是否規(guī)范:
print("數(shù)據(jù)概覽:",data.info()) <class 'pandas.core.frame.DataFrame'> Int64Index: 9879 entries, 0 to 9878 Data columns (total 40 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 gameId 9879 non-null int64 1 blueWins 9879 non-null int64 2 blueWardsPlaced 9879 non-null int64 3 blueWardsDestroyed 9879 non-null int64 4 blueFirstBlood 9879 non-null int64 5 blueKills 9879 non-null int64 6 blueDeaths 9879 non-null int64 7 blueAssists 9879 non-null int64 8 blueEliteMonsters 9879 non-null int64 9 blueDragons 9879 non-null int64 10 blueHeralds 9879 non-null int64 11 blueTowersDestroyed 9879 non-null int64 12 blueTotalGold 9879 non-null int64 13 blueAvgLevel 9879 non-null float6414 blueTotalExperience 9879 non-null int64 15 blueTotalMinionsKilled 9879 non-null int64 16 blueTotalJungleMinionsKilled 9879 non-null int64 17 blueGoldDiff 9879 non-null int64 18 blueExperienceDiff 9879 non-null int64 19 blueCSPerMin 9879 non-null float6420 blueGoldPerMin 9879 non-null float6421 redWardsPlaced 9879 non-null int64 22 redWardsDestroyed 9879 non-null int64 23 redFirstBlood 9879 non-null int64 24 redKills 9879 non-null int64 25 redDeaths 9879 non-null int64 26 redAssists 9879 non-null int64 27 redEliteMonsters 9879 non-null int64 28 redDragons 9879 non-null int64 29 redHeralds 9879 non-null int64 30 redTowersDestroyed 9879 non-null int64 31 redTotalGold 9879 non-null int64 32 redAvgLevel 9879 non-null float6433 redTotalExperience 9879 non-null int64 34 redTotalMinionsKilled 9879 non-null int64 35 redTotalJungleMinionsKilled 9879 non-null int64 36 redGoldDiff 9879 non-null int64 37 redExperienceDiff 9879 non-null int64 38 redCSPerMin 9879 non-null float6439 redGoldPerMin 9879 non-null float64 dtypes: float64(6), int64(34)2.對數(shù)據(jù)進行處理:
預(yù)處理:
由于gameId與游戲勝利無關(guān),因此刪去
data=data.drop(['gameId'], axis=1)熱力圖分析處理:
在相關(guān)性矩陣的熱力圖中可以發(fā)現(xiàn)存在高度相關(guān)的變量，這些變量解釋了相同的事物。因此，如果它們顯示的數(shù)據(jù)與另一列相同，則它們對分類沒有幫助。例如在列 RedKills(紅色團隊擊殺的次數(shù))和BlueDeaths(藍隊被擊殺的人數(shù))中。紅隊的擊殺人數(shù)就是藍隊的死亡人數(shù)。因此，正確的做法是刪除一個列。注:(有時在游戲redkills和bluedeaths不一定相等,因為玩家可能會被野怪和防御塔殺死,但我們的數(shù)據(jù)來自于高段位玩家,這種情況可以忽略不記)
plt.figure(figsize=(20,15)) sns.heatmap(round(data.corr(),1), cmap="coolwarm", annot=True, linewidths=.5) plt.savefig('熱力圖相關(guān)性分析.jpg', bbox_inches='tight') # data.corr():計算列與列之間的相關(guān)系數(shù)，返回相關(guān)系數(shù)矩陣 # sns.heatmap():利用seaborn繪制變量之間相關(guān)性的熱力圖將數(shù)據(jù)中相關(guān)性較高的數(shù)據(jù)刪去,降低分析難度
#定義一個函數(shù) 作用:找出相關(guān)系數(shù)矩陣中相關(guān)性大的一組數(shù)據(jù),同時返回其中一列數(shù)據(jù) def remove_redundancy(r):to_remove = []for i in range(len(r.columns)):for j in range(i):if (abs(r.iloc[i,j]) >= 1 and (r.columns[j] not in to_remove)):print("相關(guān)性:",r.iloc[i,j], r.columns[j], r.columns[i])to_remove.append(r.columns[i])return to_removeclean_data = data.drop(remove_redundancy(data.corr()), axis=1) #刪去相關(guān)性較高項這幾組數(shù)據(jù)的本質(zhì)是一樣的,故刪去
相關(guān)性: 1.000000000000002 blueTotalMinionsKilled blueCSPerMin 相關(guān)性: 1.0000000000000013 blueTotalGold blueGoldPerMin 相關(guān)性: -1.0 blueFirstBlood redFirstBlood 相關(guān)性: 1.0 blueDeaths redKills 相關(guān)性: 1.0 blueKills redDeaths 相關(guān)性: -1.0 blueGoldDiff redGoldDiff 相關(guān)性: -1.0 blueExperienceDiff redExperienceDiff 相關(guān)性: 1.0000000000000042 redTotalMinionsKilled redCSPerMin 相關(guān)性: 1.0000000000000049 redTotalGold redGoldPerMin數(shù)據(jù)分析和建模（Logistic Regression）:
1.數(shù)據(jù)分析處理:
1.初步處理后數(shù)據(jù):
print("初步處理后的數(shù)據(jù):",clean_data.columns)blueEliteMonsters redEliteMonsters 初步處理后的數(shù)據(jù): Index(['blueWins','blueWardsPlaced','blueWardsDestroyed','blueFirstBlood','blueKills','blueDeaths','blueAssists','blueEliteMonsters','blueDragons','blueHeralds','blueTowersDestroyed','blueTotalGold','blueAvgLevel','blueTotalExperience','blueTotalMinionsKilled','blueTotalJungleMinionsKilled','blueGoldDiff','blueExperienceDiff',#---------------------------------------------------------- 'redWardsPlaced','redWardsDestroyed','redAssists','redEliteMonsters','redDragons','redHeralds','redTowersDestroyed','redTotalGold','redAvgLevel','redTotalExperience','redTotalMinionsKilled','redTotalJungleMinionsKilled'],dtype='object')2.一般分析處理:
- 由于在游戲中擊殺野怪和小兵都是獲得經(jīng)驗和金幣,因此將野怪和小兵的擊殺數(shù)合并起來
-
由熱力圖分析可知,等級和經(jīng)驗的相關(guān)性較高,故進行分析:
#等級和經(jīng)驗分析: plt.figure(figsize=(12,12)) plt.subplot(121) sns.scatterplot(x='blueAvgLevel', y='blueTotalExperience', hue='blueWins', data=clean_data) plt.title('blue') plt.xlabel('blueAvgLevel') plt.ylabel('blueTotalExperience') plt.grid(True) plt.subplot(122) sns.scatterplot(x='redAvgLevel', y='redTotalExperience', hue='blueWins', data=clean_data) plt.title('red') plt.xlabel('redAvgLevel') plt.ylabel('redTotalExperience') plt.grid(True) plt.savefig('等級和經(jīng)驗分析.jpg', bbox_inches='tight')
可看出等級和經(jīng)驗呈線性關(guān)系,并且具有很強的相關(guān)性(見熱力圖),同時由于等級的差異不明顯,故刪去等級
#刪去等級列 clean_data=clean_data.drop(['blueAvgLevel'], axis=1) clean_data=clean_data.drop(['redAvgLevel'], axis=1)-
數(shù)據(jù)可視化分析:
sns.set(font_scale=1.5) plt.figure(figsize=(20,20)) sns.set_style("whitegrid")# 擊殺和被擊殺數(shù)繪制散點圖 plt.subplot(321) sns.scatterplot(x='blueKills', y='blueDeaths', hue='blueWins', data=clean_data) plt.title('blueKills&&blueDeaths') plt.xlabel('blueKills') plt.ylabel('blueDeaths') plt.grid(True)# 助攻數(shù)繪制散點圖 plt.subplot(322) sns.scatterplot(x='blueAssists', y='redAssists', hue='blueWins', data=clean_data) plt.title('Assists') plt.xlabel('blueAssists') plt.ylabel('redAssists') plt.tight_layout(pad=1.5) plt.grid(True)#雙方金幣數(shù)繪制散點圖 plt.subplot(323) sns.scatterplot(x='blueTotalGold', y='redTotalGold', hue='blueWins', data=clean_data) plt.title('TotalGold') plt.xlabel('blueTotalGold') plt.ylabel('redTotalGold') plt.tight_layout(pad=1.5) plt.grid(True)#雙方經(jīng)驗繪制散點圖 plt.subplot(324) sns.scatterplot(x='blueTotalExperience', y='redTotalExperience', hue='blueWins', data=clean_data) plt.title('Experience') plt.xlabel('blueTotalExperience') plt.ylabel('redTotalExperience') plt.tight_layout(pad=1.5) plt.grid(True)# 雙方插眼數(shù)量繪制散點圖 plt.subplot(325) sns.scatterplot(x='blueWardsPlaced', y='redWardsPlaced', hue='blueWins', data=clean_data) plt.title('WardsPlaced') plt.xlabel('blueWardsPlaced') plt.ylabel('redWardsPlaced') plt.tight_layout(pad=1.5) plt.grid(True)# 擊殺的小兵和野怪總數(shù)繪制散點圖 plt.subplot(326) sns.scatterplot(x='blueMinionsTotales', y='redMinionsTotales', hue='blueWins', data=clean_data) plt.title('MinionsTotales') plt.xlabel('Equipo Azul') plt.ylabel('Equipo Rojo') plt.tight_layout(pad=1.5) plt.grid(True) plt.savefig('數(shù)據(jù)分析.jpg', bbox_inches='tight')
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- 由于在游戲中blueWardsPlaced 和redWardsPlaced, blueWardsDestroyed和redWardsDestroyed, blueEliteMonsters和redEliteMonsters等數(shù)據(jù)的值不大,并且比賽的勝利多與其之間的差值有關(guān),同時數(shù)據(jù)與比賽雙方息息相關(guān),因此,將兩個值用他們之間的差值來展示(減小數(shù)據(jù)量).
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blueFirstBlood,blueDragonsDiff ,EliteMonstersDiff分析:
#一血,龍與精英怪物分析 sns.catplot(x="blueWins", y="blueGoldDiff", hue="blueFirstBlood", data=clean_data) plt.savefig('一血.jpg', bbox_inches='tight') sns.catplot(x="blueWins", y="blueGoldDiff", hue="blueDragonsDiff", data=clean_data) plt.savefig('龍.jpg', bbox_inches='tight') sns.catplot(x="blueWins", y="blueGoldDiff", hue="EliteMonstersDiff", data=clean_data) plt.savefig('精英怪物.jpg', bbox_inches='tight')一血:
擊殺龍的差值:
擊殺精英怪物的差值:
2.數(shù)據(jù)建模:
處理后數(shù)據(jù):
最終數(shù)據(jù): Index(['blueWins', #藍方是否獲得勝利 1:勝利 0:失敗 *****因變量****'blueFirstBlood', #藍方是否獲得一血(游戲的第一殺) 1:獲得 0:未獲得'blueKills', #藍隊殺死的敵人數(shù)量'blueDeaths', #死亡人數(shù)（藍隊）'blueTotalGold', #藍隊總的金幣數(shù)量'blueTotalExperience', #藍隊總的經(jīng)驗'blueGoldDiff', #藍隊與紅隊金幣差值'blueExperienceDiff', #藍隊與紅隊經(jīng)驗差值'blueMinionsTotales', #藍隊殺死的野怪和小兵總數(shù)量'redMinionsTotales', #紅隊殺死的野怪和小兵總數(shù)量'WardsPlacedDiff', #兩隊在地圖上放置的視野眼數(shù)量差異'WardsDestroyedDiff', #兩隊在地圖上摧毀的視野眼數(shù)量差異'AssistsDiff', #兩隊助攻差異'blueHeraldsDiff', #兩隊殺死的精英怪物數(shù)量差異'blueDragonsDiff', #兩隊殺死的龍數(shù)量差異'blueTowersDestroyedDiff', #兩隊摧毀防御塔數(shù)量差異'EliteMonstersDiff'], #兩隊殺死的精銳怪物數(shù)量（龍與先驅(qū)隊）差異dtype='object') blueWins blueFirstBlood blueKills blueDeaths blueTotalGold \ 0 0 1 9 6 17210 1 0 0 5 5 14712 2 0 0 7 11 16113 3 0 0 4 5 15157 4 0 0 6 6 16400 ... ... ... ... ... 9874 1 1 7 4 17765 9875 1 0 6 4 16238 9876 0 0 6 7 15903 9877 0 1 2 3 14459 9878 1 1 6 6 16266 blueTotalExperience blueGoldDiff blueExperienceDiff \ 0 17039 643 -8 1 16265 -2908 -1173 2 16221 -1172 -1033 3 17954 -1321 -7 4 18543 -1004 230 ... ... ... 9874 18967 2519 2469 9875 19255 782 888 9876 18032 -2416 -1877 9877 17229 -839 -1085 9878 17321 927 -58 blueMinionsTotales redMinionsTotales WardsPlacedDiff \ 0 231 252 13 1 217 292 0 2 232 231 0 3 256 282 28 4 267 292 58 ... ... ... 9874 280 263 -29 9875 281 262 42 9876 255 321 9 9877 272 287 -52 9878 251 247 9 WardsDestroyedDiff AssistsDiff blueHeraldsDiff blueDragonsDiff \ 0 -4 3 0 0 1 0 3 -1 -1 2 -3 -10 0 1 3 -1 -5 1 0 4 2 -1 0 -1 ... ... ... ... 9874 -1 -2 0 1 9875 -21 5 0 1 9876 1 -6 0 -1 9877 0 2 0 1 9878 -2 1 0 -1 blueTowersDestroyedDiff EliteMonstersDiff 0 0 0 1 -1 -2 2 0 1 3 0 1 4 0 -1 ... ... 9874 0 1 9875 0 1 9876 0 -1 9877 0 1 9878 0 -1邏輯回歸簡介:
logistic回歸又稱logistic回歸分析，是一種廣義的線性回歸分析模型，常用于數(shù)據(jù)挖掘，疾病自動診斷，經(jīng)濟預(yù)測等領(lǐng)域。例如，探討引發(fā)疾病的危險因素，并根據(jù)危險因素預(yù)測疾病發(fā)生的概率等。以胃癌病情分析為例，選擇兩組人群，一組是胃癌組，一組是非胃癌組，兩組人群必定具有不同的體征與生活方式等。因此因變量就為是否胃癌，值為“是”或“否”，自變量就可以包括很多了，如年齡、性別、飲食習(xí)慣、幽門螺桿菌感染等。自變量既可以是連續(xù)的，也可以是分類的。然后通過logistic回歸分析，可以得到自變量的權(quán)重，從而可以大致了解到底哪些因素是胃癌的危險因素。同時根據(jù)該權(quán)值可以根據(jù)危險因素預(yù)測一個人患癌癥的可能性。
分析:
在對LOL游戲勝利預(yù)測的分析中,有非常多的自變量,而應(yīng)變量只有blueWins,即游戲是否取得勝利這一個應(yīng)變量,值為“是”或“否”,因此,宜采用Logistic Regression模型進行分析.
標(biāo)準(zhǔn)化數(shù)據(jù):
簡介:數(shù)據(jù)的標(biāo)準(zhǔn)化（normalization）是將數(shù)據(jù)按比例縮放，使之落入一個小的特定區(qū)間。在某些比較和評價的指標(biāo)處理中經(jīng)常會用到，去除數(shù)據(jù)的單位限制，將其轉(zhuǎn)化為無量綱的純數(shù)值，便于不同單位或量級的指標(biāo)能夠進行比較和加權(quán)。其中最典型的就是數(shù)據(jù)的歸一化處理，即將數(shù)據(jù)統(tǒng)一映射到[0,1]區(qū)間上。數(shù)據(jù)集的標(biāo)準(zhǔn)化對于眾多機器學(xué)習(xí)評估器來說是必須的；如果各獨立特征不進行標(biāo)準(zhǔn)化，結(jié)果標(biāo)準(zhǔn)正態(tài)分布數(shù)據(jù)差距很大：比如使用均值為0、方差為1的高斯分布.
標(biāo)準(zhǔn)化的流程簡單來說可以表達為:將數(shù)據(jù)按其屬性(按列進行)減去其均值，然后除以其方差。最后得到的結(jié)果是，對每個屬性/每列來說所有數(shù)據(jù)都聚集在0附近，方差值為1
# 創(chuàng)建自定義縮放器類(標(biāo)準(zhǔn)化) class CustomScaler(BaseEstimator, TransformerMixin):# 聲明一些基本內(nèi)容和信息def __init__(self, columns, copy=True, with_mean=True, with_std=True):# scaler是Standard Scaler對象self.scaler = StandardScaler(copy, with_mean, with_std)self.columns = columnsself.mean_ = Noneself.var_ = None# 基于StandardScale的擬合方法def fit(self, X, y=None):self.scaler.fit(X[self.columns], y)self.mean_ = np.mean(X[self.columns])self.var_ = np.var(X[self.columns])return self# 進行實際縮放的變換方法def transform(self, X, y=None, copy=None):# 記錄列的初始順序init_col_order = X.columns# 縮放創(chuàng)建類實例時選擇的所有功能X_scaled = pd.DataFrame(self.scaler.transform(X[self.columns]), columns=self.columns)# 聲明一個包含所有未縮放信息的變量X_not_scaled = X.loc[:, ~X.columns.isin(self.columns)]# 返回包含所有已縮放要素和所有未縮放要素的數(shù)據(jù)框return pd.concat([X_not_scaled, X_scaled], axis=1)[init_col_order]# 數(shù)據(jù)縮放要忽略的列 columns_to_omit = ['blueFirstBlood'] # 忽略一血,因為它是分類變量# 根據(jù)要縮放的列創(chuàng)建列表 columns_to_scale = [x for x in unscaled_inputs.columns.values if x not in columns_to_omit] blue_scaler = CustomScaler(columns_to_scale) blue_scaler.fit(unscaled_inputs) scaled_inputs = blue_scaler.transform(unscaled_inputs) pd.set_option('display.width', 80) # 設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行 print("標(biāo)準(zhǔn)化處理后的數(shù)據(jù):", scaled_inputs) 標(biāo)準(zhǔn)化處理后的數(shù)據(jù):blueFirstBlood blueKills blueDeaths blueTotalGold \ 0 1 0.935301 -0.046926 0.460179 1 0 -0.393216 -0.387796 -1.166792 2 0 0.271042 1.657424 -0.254307 3 0 -0.725346 -0.387796 -0.876959 4 0 -0.061087 -0.046926 -0.067382 ... ... ... ... 9874 1 0.271042 -0.728666 0.821656 9875 0 -0.061087 -0.728666 -0.172894 9876 0 -0.061087 0.293944 -0.391082 9877 1 -1.389604 -1.069536 -1.331573 9878 1 -0.061087 -0.046926 -0.154657 blueTotalExperience blueGoldDiff blueExperienceDiff \ 0 -0.740639 0.256228 0.013342 1 -1.385391 -1.191254 -0.593342 2 -1.422043 -0.483614 -0.520436 3 0.021567 -0.544350 0.013863 4 0.512211 -0.415133 0.137283 ... ... ... 9874 0.865408 1.020936 1.303263 9875 1.105315 0.312888 0.479942 9876 0.086541 -0.990702 -0.959957 9877 -0.582367 -0.347874 -0.547516 9878 -0.505730 0.371994 -0.012696 blueMinionsTotales redMinionsTotales WardsPlacedDiff \ 0 -1.419968 -0.651842 0.503853 1 -1.968987 0.912988 0.003069 2 -1.380753 -1.473378 0.003069 3 -0.439577 0.521780 1.081682 4 -0.008205 0.912988 2.237338 ... ... ... 9874 0.501598 -0.221514 -1.114066 9875 0.540814 -0.260635 1.620988 9876 -0.478793 2.047489 0.349766 9877 0.187873 0.717384 -2.000069 9878 -0.635655 -0.847446 0.349766 WardsDestroyedDiff AssistsDiff blueHeraldsDiff blueDragonsDiff \ 0 -1.436801 0.523196 -0.047412 0.058162 1 -0.035635 0.523196 -1.744448 -1.079624 2 -1.086510 -1.731206 -0.047412 1.195948 3 -0.385927 -0.864129 1.649624 0.058162 4 0.664947 -0.170466 -0.047412 -1.079624 ... ... ... ... 9874 -0.385927 -0.343882 -0.047412 1.195948 9875 -7.391756 0.870027 -0.047412 1.195948 9876 0.314656 -1.037544 -0.047412 -1.079624 9877 -0.035635 0.349780 -0.047412 1.195948 9878 -0.736218 0.176365 -0.047412 -1.079624 blueTowersDestroyedDiff EliteMonstersDiff 0 -0.025866 0.021707 1 -3.104510 -1.851163 2 -0.025866 0.958142 3 -0.025866 0.958142 4 -0.025866 -0.914728 ... ... 9874 -0.025866 0.958142 9875 -0.025866 0.958142 9876 -0.025866 -0.914728 9877 -0.025866 0.958142 9878 -0.025866 -0.914728FutureWarning警告不影響代碼運行,可忽略
數(shù)據(jù)切片:
#數(shù)據(jù)切片 x_train, x_test, y_train, y_test = train_test_split(scaled_inputs, target, train_size=0.8, random_state=2) print("訓(xùn)練數(shù)據(jù):",x_train.shape,y_train.shape,"測試數(shù)據(jù):",x_test.shape,y_test.shape) 訓(xùn)練數(shù)據(jù): (7903, 16) (7903, 1) 測試數(shù)據(jù) (1976, 16) (1976, 1)模型訓(xùn)練分析:
#模型訓(xùn)練 reg = LogisticRegression() reg.fit(x_train, y_train) #創(chuàng)建一個匯總表以可視化變量以及各自的系數(shù)和幾率 variables = unscaled_inputs.columns.values summary_table = pd.DataFrame(columns=['Variables'], data = variables) summary_table['Coef'] = np.transpose(reg.coef_) # add the intercept at index 0 summary_table.index = summary_table.index + 1 summary_table.loc[0] = ['Intercept', reg.intercept_[0]] # calculate the Odds Ratio and add to the table summary_table['Odds Ratio'] = np.exp(summary_table.Coef) summary_table.sort_values(by=['Odds Ratio'], ascending=False)可視化變量:
模型變量評價: Variables Coef Odds Ratio 6 blueGoldDiff 1.211278 3.357772 7 blueExperienceDiff 0.473859 1.606180 14 blueDragonsDiff 0.181283 1.198754 9 redMinionsTotales 0.156352 1.169237 16 EliteMonstersDiff 0.143389 1.154178 3 blueDeaths 0.071080 1.073667 4 blueTotalGold 0.064979 1.067136 1 blueFirstBlood 0.062815 1.064830 11 WardsDestroyedDiff 0.031517 1.032019 10 WardsPlacedDiff 0.002818 1.002822 5 blueTotalExperience -0.002409 0.997594 0 Intercept -0.029890 0.970552 13 blueHeraldsDiff -0.045495 0.955524 8 blueMinionsTotales -0.079767 0.923332 12 AssistsDiff -0.092587 0.911570 15 blueTowersDestroyedDiff -0.110353 0.895518 2 blueKills -0.114022 0.892239數(shù)據(jù)測試:
# 模型測試 print("訓(xùn)練數(shù)據(jù)評分:", reg.score(x_train, y_train)) print("訓(xùn)練數(shù)據(jù)評分:", reg.score(x_test, y_test)) #將測試結(jié)果寫入到原始數(shù)據(jù)集中 predicted_prob = reg.predict_proba(x_test) data['predicted'] = reg.predict_proba(scaled_inputs)[:, 1] print("經(jīng)過預(yù)測后的包含預(yù)測結(jié)果的完整數(shù)據(jù)集:", data) #原始數(shù)據(jù)和勝率分析對比 col_n = ['blueWins','predicted'] a = pd.DataFrame(data,columns = col_n) print("原始數(shù)據(jù)和勝率分析對比:", a) 訓(xùn)練數(shù)據(jù)評分: 0.7327597115019613 訓(xùn)練數(shù)據(jù)評分: 0.7358299595141701可見兩個數(shù)據(jù)的模型評分都非常相似,說明模型擬合得還不錯
結(jié)果分析:
在LOL等MOBA(多人在線戰(zhàn)斗競技場)中,一局游戲的勝利受到非常多因素的影響,在這類團隊競技游戲中,游戲的勝利與否非常考驗玩家的操作,意識和相互之間的配合,由于游戲的參與者是人,因此會存在許多未知因素并會受到許多不確定因素的影響,如玩家的心情,狀態(tài)甚至是網(wǎng)絡(luò)情況等.
因此,對此類游戲的勝利預(yù)測不可能達到100%的準(zhǔn)確度,可見以上模型的擬合結(jié)果還過得去
結(jié)論及規(guī)律分析:
由以上的分析可看出,一血,龍,小兵野怪等都對經(jīng)濟有一定的貢獻率.在一局比賽的第10分鐘時,影響雙方勝率最大的因素是blueGoldDiff(經(jīng)濟差),并且當(dāng)雙方的經(jīng)濟差增加一個標(biāo)準(zhǔn)化單位的時候,勝率增加235%,同時,雙方的經(jīng)驗差blueExperienceDiff對數(shù)據(jù)也有很大影響,當(dāng)雙方經(jīng)驗差增加一個標(biāo)準(zhǔn)化單位的時候,勝率增加60.6%
在比賽中擊殺一條龍的時候,可以為你的勝率增加20%左右,同時擊殺EliteMonstersDiff(精英怪物)也可以為比賽帶來比較大的助力.但在分析結(jié)果中也出現(xiàn)了一些反常現(xiàn)象,在一局比賽中,如果對方擊殺的野怪和小兵數(shù)redMinionsTotales越高,我方擊殺的野怪和小兵數(shù)blueMinionsTotales越少,則我方的勝率越高,我猜測可能是由于勝率高的一方在前期更喜歡進行團戰(zhàn)取得經(jīng)濟優(yōu)勢的原因,在比賽中blueKills擊殺數(shù)反而與勝率呈反比,我猜測可能是由于這是一個推塔游戲,而擊殺數(shù)比較高的一些隊伍可能更關(guān)注于擊殺而忽略了推塔
因此,在游戲時要注意與對方拉開經(jīng)濟差,經(jīng)濟差越大,則勝率越高.
原始數(shù)據(jù)集及勝率預(yù)測結(jié)果:
經(jīng)過預(yù)測后的包含預(yù)測結(jié)果的完整數(shù)據(jù)集: blueWins blueWardsPlaced blueWardsDestroyed blueFirstBlood \ 0 0 28 2 1 1 0 12 1 0 2 0 15 0 0 3 0 43 1 0 4 0 75 4 0 ... ... ... ... 9874 1 17 2 1 9875 1 54 0 0 9876 0 23 1 0 9877 0 14 4 1 9878 1 18 0 1 blueKills blueDeaths blueAssists blueEliteMonsters blueDragons \ 0 9 6 11 0 0 1 5 5 5 0 0 2 7 11 4 1 1 3 4 5 5 1 0 4 6 6 6 0 0 ... ... ... ... ... 9874 7 4 5 1 1 9875 6 4 8 1 1 9876 6 7 5 0 0 9877 2 3 3 1 1 9878 6 6 5 0 0 blueHeralds blueTowersDestroyed blueTotalGold blueAvgLevel \ 0 0 0 17210 6.6 1 0 0 14712 6.6 2 0 0 16113 6.4 3 1 0 15157 7.0 4 0 0 16400 7.0 ... ... ... ... 9874 0 0 17765 7.2 9875 0 0 16238 7.2 9876 0 0 15903 7.0 9877 0 0 14459 6.6 9878 0 0 16266 7.0 blueTotalExperience blueTotalMinionsKilled \ 0 17039 195 1 16265 174 2 16221 186 3 17954 201 4 18543 210 ... ... 9874 18967 211 9875 19255 233 9876 18032 210 9877 17229 224 9878 17321 207 blueTotalJungleMinionsKilled blueGoldDiff blueExperienceDiff \ 0 36 643 -8 1 43 -2908 -1173 2 46 -1172 -1033 3 55 -1321 -7 4 57 -1004 230 ... ... ... 9874 69 2519 2469 9875 48 782 888 9876 45 -2416 -1877 9877 48 -839 -1085 9878 44 927 -58 blueCSPerMin blueGoldPerMin redWardsPlaced redWardsDestroyed \ 0 19.5 1721.0 15 6 1 17.4 1471.2 12 1 2 18.6 1611.3 15 3 3 20.1 1515.7 15 2 4 21.0 1640.0 17 2 ... ... ... ... 9874 21.1 1776.5 46 3 9875 23.3 1623.8 12 21 9876 21.0 1590.3 14 0 9877 22.4 1445.9 66 4 9878 20.7 1626.6 9 2 redFirstBlood redKills redDeaths redAssists redEliteMonsters \ 0 0 6 9 8 0 1 1 5 5 2 2 2 1 11 7 14 0 3 1 5 4 10 0 4 1 6 6 7 1 ... ... ... ... ... 9874 0 4 7 7 0 9875 1 4 6 3 0 9876 1 7 6 11 1 9877 0 3 2 1 0 9878 0 6 6 4 1 redDragons redHeralds redTowersDestroyed redTotalGold redAvgLevel \ 0 0 0 0 16567 6.8 1 1 1 1 17620 6.8 2 0 0 0 17285 6.8 3 0 0 0 16478 7.0 4 1 0 0 17404 7.0 ... ... ... ... ... 9874 0 0 0 15246 6.8 9875 0 0 0 15456 7.0 9876 1 0 0 18319 7.4 9877 0 0 0 15298 7.2 9878 1 0 0 15339 6.8 redTotalExperience redTotalMinionsKilled redTotalJungleMinionsKilled \ 0 17047 197 55 1 17438 240 52 2 17254 203 28 3 17961 235 47 4 18313 225 67 ... ... ... 9874 16498 229 34 9875 18367 206 56 9876 19909 261 60 9877 18314 247 40 9878 17379 201 46 redGoldDiff redExperienceDiff redCSPerMin redGoldPerMin predicted 0 -643 8 19.7 1656.7 0.549459 1 2908 1173 24.0 1762.0 0.170271 2 1172 1033 20.3 1728.5 0.387228 3 1321 7 23.5 1647.8 0.393685 4 1004 -230 22.5 1740.4 0.356486 ... ... ... ... ... 9874 -2519 -2469 22.9 1524.6 0.892957 9875 -782 -888 20.6 1545.6 0.610317 9876 2416 1877 26.1 1831.9 0.178771 9877 839 1085 24.7 1529.8 0.433183 9878 -927 58 20.1 1533.9 0.511128原始數(shù)據(jù)和勝率分析對比:
blueWins predicted 0 0 0.549459 1 0 0.170271 2 0 0.387228 3 0 0.393685 4 0 0.356486... ... 9874 1 0.892957 9875 1 0.610317 9876 0 0.178771 9877 0 0.433183 9878 1 0.5111281762.0 0.170271
2 1172 1033 20.3 1728.5 0.387228
3 1321 7 23.5 1647.8 0.393685
4 1004 -230 22.5 1740.4 0.356486
… … … … …
9874 -2519 -2469 22.9 1524.6 0.892957
9875 -782 -888 20.6 1545.6 0.610317
9876 2416 1877 26.1 1831.9 0.178771
9877 839 1085 24.7 1529.8 0.433183
9878 -927 58 20.1 1533.9 0.511128
源碼：
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.preprocessing import StandardScaler from sklearn.base import BaseEstimator, TransformerMixinplt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = Falsepd.set_option('display.max_columns', None) # 顯示所有列 pd.set_option('mode.chained_assignment', None) # 關(guān)閉警告 # pd.set_option('display.width', 100) #設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行data = pd.read_csv('high_diamond_ranked_10min.csv')print("前五行數(shù)據(jù):", data.head()) print("數(shù)據(jù)形狀:", data.shape) pd.set_option('display.width', 10) # 設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行 print("數(shù)據(jù)列名:", data.columns) pd.set_option('display.width', 80) # 設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行 print("數(shù)據(jù)概覽:", data.describe()) print("數(shù)據(jù)概覽:", data.info())data.dropna(axis=0, how='any', inplace=True) # 刪除有空值的行 data = data.drop(['gameId'], axis=1) # 刪除gameIdplt.figure(figsize=(18, 15)) sns.heatmap(round(data.corr(), 1), cmap="coolwarm", annot=True, linewidths=.5) # 相關(guān)性(-1,1) plt.savefig('熱力圖相關(guān)性分析.jpg', bbox_inches='tight')# data.corr():計算列與列之間的相關(guān)系數(shù)，返回相關(guān)系數(shù)矩陣 # sns.heatmap():利用seaborn繪制個變量之間相關(guān)性的熱力圖# 定義一個函數(shù) 作用:找出相關(guān)系數(shù)矩陣中相關(guān)性大的一組數(shù)據(jù),同時返回其中一列數(shù)據(jù) def remove_redundancy(r):to_remove = []for i in range(len(r.columns)):for j in range(i):if (abs(r.iloc[i, j]) >= 1 and (r.columns[j] not in to_remove)):print("相關(guān)性:", r.iloc[i, j], r.columns[j], r.columns[i])to_remove.append(r.columns[i])return to_removeclean_data = data.drop(remove_redundancy(data.corr()), axis=1) # 刪去相關(guān)性較高項pd.set_option('display.width', 10) # 設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行 print("初步處理后的數(shù)據(jù):", clean_data.columns)# 將擊殺野怪和小兵數(shù)合并: clean_data['blueMinionsTotales'] = clean_data['blueTotalMinionsKilled'] + clean_data['blueTotalJungleMinionsKilled'] clean_data['redMinionsTotales'] = clean_data['redTotalMinionsKilled'] + clean_data['redTotalJungleMinionsKilled'] clean_data = clean_data.drop(['blueTotalMinionsKilled'], axis=1) clean_data = clean_data.drop(['blueTotalJungleMinionsKilled'], axis=1) clean_data = clean_data.drop(['redTotalMinionsKilled'], axis=1) clean_data = clean_data.drop(['redTotalJungleMinionsKilled'], axis=1)# 等級和經(jīng)驗分析: plt.figure(figsize=(12, 12)) plt.subplot(121) sns.scatterplot(x='blueAvgLevel', y='blueTotalExperience', hue='blueWins', data=clean_data) plt.title('blue') plt.xlabel('blueAvgLevel') plt.ylabel('blueTotalExperience') plt.grid(True) plt.subplot(122) sns.scatterplot(x='redAvgLevel', y='redTotalExperience', hue='blueWins', data=clean_data) plt.title('red') plt.xlabel('redAvgLevel') plt.ylabel('redTotalExperience') plt.grid(True) plt.savefig('等級和經(jīng)驗分析.jpg', bbox_inches='tight')# 刪去等級列 clean_data = clean_data.drop(['blueAvgLevel'], axis=1) clean_data = clean_data.drop(['redAvgLevel'], axis=1)sns.set(font_scale=1.5) plt.figure(figsize=(20, 20)) sns.set_style("whitegrid")# 擊殺和被擊殺數(shù)繪制散點圖 plt.subplot(321) sns.scatterplot(x='blueKills', y='blueDeaths', hue='blueWins', data=clean_data) plt.title('blueKills&&blueDeaths') plt.xlabel('blueKills') plt.ylabel('blueDeaths') plt.grid(True)# 助攻數(shù)繪制散點圖 plt.subplot(322) sns.scatterplot(x='blueAssists', y='redAssists', hue='blueWins', data=clean_data) plt.title('Assists') plt.xlabel('blueAssists') plt.ylabel('redAssists') plt.tight_layout(pad=1.5) plt.grid(True)# 雙方金幣數(shù)繪制散點圖 plt.subplot(323) sns.scatterplot(x='blueTotalGold', y='redTotalGold', hue='blueWins', data=clean_data) plt.title('TotalGold') plt.xlabel('blueTotalGold') plt.ylabel('redTotalGold') plt.tight_layout(pad=1.5) plt.grid(True)# 雙方經(jīng)驗繪制散點圖 plt.subplot(324) sns.scatterplot(x='blueTotalExperience', y='redTotalExperience', hue='blueWins', data=clean_data) plt.title('Experience') plt.xlabel('blueTotalExperience') plt.ylabel('redTotalExperience') plt.tight_layout(pad=1.5) plt.grid(True)# 雙方插眼數(shù)量繪制散點圖 plt.subplot(325) sns.scatterplot(x='blueWardsPlaced', y='redWardsPlaced', hue='blueWins', data=clean_data) plt.title('WardsPlaced') plt.xlabel('blueWardsPlaced') plt.ylabel('redWardsPlaced') plt.tight_layout(pad=1.5) plt.grid(True)# 擊殺的小兵和野怪總數(shù)繪制散點圖 plt.subplot(326) sns.scatterplot(x='blueMinionsTotales', y='redMinionsTotales', hue='blueWins', data=clean_data) plt.title('MinionsTotales') plt.xlabel('blueMinionsTotales') plt.ylabel('redMinionsTotales') plt.tight_layout(pad=1.5) plt.grid(True) plt.savefig('數(shù)據(jù)分析.jpg', bbox_inches='tight')# 將一些數(shù)據(jù)轉(zhuǎn)換為它們的差值: clean_data['WardsPlacedDiff'] = clean_data['blueWardsPlaced'] - clean_data['redWardsPlaced'] clean_data['WardsDestroyedDiff'] = clean_data['blueWardsDestroyed'] - clean_data['redWardsDestroyed'] clean_data['AssistsDiff'] = clean_data['blueAssists'] - clean_data['redAssists'] clean_data['blueHeraldsDiff'] = clean_data['blueHeralds'] - clean_data['redHeralds'] clean_data['blueDragonsDiff'] = clean_data['blueDragons'] - clean_data['redDragons'] clean_data['blueTowersDestroyedDiff'] = clean_data['blueTowersDestroyed'] - clean_data['redTowersDestroyed'] clean_data['EliteMonstersDiff'] = clean_data['blueEliteMonsters'] - clean_data['redEliteMonsters'] clean_data = clean_data.drop(['blueWardsPlaced'], axis=1) clean_data = clean_data.drop(['redWardsPlaced'], axis=1) clean_data = clean_data.drop(['blueWardsDestroyed'], axis=1) clean_data = clean_data.drop(['redWardsDestroyed'], axis=1) clean_data = clean_data.drop(['blueAssists'], axis=1) clean_data = clean_data.drop(['redAssists'], axis=1) clean_data = clean_data.drop(['blueHeralds'], axis=1) clean_data = clean_data.drop(['redHeralds'], axis=1) clean_data = clean_data.drop(['blueTowersDestroyed'], axis=1) clean_data = clean_data.drop(['redTowersDestroyed'], axis=1) clean_data = clean_data.drop(['blueDragons'], axis=1) clean_data = clean_data.drop(['redDragons'], axis=1) clean_data = clean_data.drop(['blueEliteMonsters'], axis=1) clean_data = clean_data.drop(['redEliteMonsters'], axis=1) clean_data = clean_data.drop(['redTotalGold'], axis=1) # 紅隊金幣數(shù)可由藍隊金幣數(shù)與差值一起得到,故刪去 clean_data = clean_data.drop(['redTotalExperience'], axis=1) # 紅隊經(jīng)驗可由藍隊經(jīng)驗與差值一起得到,故刪去# 一血,龍與精英怪物分析 sns.catplot(x="blueWins", y="blueGoldDiff", hue="blueFirstBlood", data=clean_data) plt.savefig('一血.jpg', bbox_inches='tight') sns.catplot(x="blueWins", y="blueGoldDiff", hue="blueDragonsDiff", data=clean_data) plt.savefig('龍.jpg', bbox_inches='tight') sns.catplot(x="blueWins", y="blueGoldDiff", hue="EliteMonstersDiff", data=clean_data) plt.savefig('精英怪物.jpg', bbox_inches='tight')print("最終數(shù)據(jù):", clean_data.columns)# 數(shù)據(jù)標(biāo)準(zhǔn)化處理 標(biāo)準(zhǔn)化非分類數(shù)據(jù) unscaled_inputs = clean_data.filter(['blueFirstBlood','blueKills','blueDeaths','blueTotalGold','blueTotalExperience','blueGoldDiff','blueExperienceDiff','blueMinionsTotales','redMinionsTotales','WardsPlacedDiff','WardsDestroyedDiff','AssistsDiff','blueHeraldsDiff','blueDragonsDiff','blueTowersDestroyedDiff','EliteMonstersDiff'], axis=1) target = clean_data.filter(['blueWins'])# 創(chuàng)建自定義縮放器類 class CustomScaler(BaseEstimator, TransformerMixin):# 聲明一些基本內(nèi)容和信息def __init__(self, columns, copy=True, with_mean=True, with_std=True):# scaler是Standard Scaler對象self.scaler = StandardScaler(copy, with_mean, with_std)self.columns = columnsself.mean_ = Noneself.var_ = None# 基于StandardScale的擬合方法def fit(self, X, y=None):self.scaler.fit(X[self.columns], y)self.mean_ = np.mean(X[self.columns])self.var_ = np.var(X[self.columns])return self# 進行實際縮放的變換方法def transform(self, X, y=None, copy=None):# 記錄列的初始順序init_col_order = X.columns# 縮放創(chuàng)建類實例時選擇的所有功能X_scaled = pd.DataFrame(self.scaler.transform(X[self.columns]), columns=self.columns)# 聲明一個包含所有未縮放信息的變量X_not_scaled = X.loc[:, ~X.columns.isin(self.columns)]# 返回包含所有已縮放要素和所有未縮放要素的數(shù)據(jù)框return pd.concat([X_not_scaled, X_scaled], axis=1)[init_col_order]# 數(shù)據(jù)縮放要忽略的列 columns_to_omit = ['blueFirstBlood', 'blueDragonsDiff'] # 忽略一血,因為它是分類變量# 根據(jù)要縮放的列創(chuàng)建列表 columns_to_scale = [x for x in unscaled_inputs.columns.values if x not in columns_to_omit] blue_scaler = CustomScaler(columns_to_scale) blue_scaler.fit(unscaled_inputs) scaled_inputs = blue_scaler.transform(unscaled_inputs) pd.set_option('display.width', 80) # 設(shè)置Console每一行展示的最大寬度，屏幕一行顯示滿之后才會進行換行 print("標(biāo)準(zhǔn)化處理后的數(shù)據(jù):", scaled_inputs)# 數(shù)據(jù)切片 x_train, x_test, y_train, y_test = train_test_split(scaled_inputs, target, train_size=0.8, random_state=2) print("訓(xùn)練數(shù)據(jù):", x_train.shape, y_train.shape, "測試數(shù)據(jù):", x_test.shape, y_test.shape)# 模型訓(xùn)練 reg = LogisticRegression() reg.fit(x_train, y_train) # 創(chuàng)建一個匯總表以可視化變量以及各自的系數(shù)和幾率 variables = unscaled_inputs.columns.values intercept = reg.intercept_ # 截距 summary_table = pd.DataFrame(columns=['Variables'], data=variables) summary_table['Coef'] = np.transpose(reg.coef_) summary_table.index = summary_table.index + 1 summary_table.loc[0] = ['Intercept', reg.intercept_[0]] summary_table['Odds Ratio'] = np.exp(summary_table.Coef) summary_table.sort_values(by=['Odds Ratio'], ascending=False) print("模型變量評價:", summary_table.sort_values(by=['Odds Ratio'], ascending=False))# 模型測試 print("訓(xùn)練數(shù)據(jù)評分:", reg.score(x_train, y_train)) print("訓(xùn)練數(shù)據(jù)評分:", reg.score(x_test, y_test)) # 將測試結(jié)果寫入到原始數(shù)據(jù)集中 predicted_prob = reg.predict_proba(x_test) data['predicted'] = reg.predict_proba(scaled_inputs)[:, 1] print("經(jīng)過預(yù)測后的包含預(yù)測結(jié)果的完整數(shù)據(jù)集:", data)# 原始數(shù)據(jù)和勝率分析對比 col_n = ['blueWins', 'predicted'] a = pd.DataFrame(data, columns=col_n) print("原始數(shù)據(jù)和勝率分析對比:", a)數(shù)據(jù)下載地址：
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提取碼：h3qa
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