対戦ゲームデータ分析甲子園

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LightGBMを使ったBase line

対戦ゲームデータ分析甲子園

LightGBMを使ったBase lineです。ご参考までご活用ください。

CV= 0.531795 LB= 0.534651 でした。

# ライブラリのインポート
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
import warnings
warnings.filterwarnings('ignore')

# データの読込
train = pd.read_csv("../input/train_data.csv")
test = pd.read_csv('../input/test_data.csv')

データの確認

def inspection_datas(df):
    print('######################################')
    print('①サイズ（行数、列数）の確認')
    print(df.shape)
    print('######################################')
    print('②最初の５行の表示')
    display(df.head())
    print('######################################')
    print('③各行のデータ型の確認（オブジェクト型の有無）')
    display(df.info())
    display(df.select_dtypes(include=object).columns)
    print('######################################')
    print('④各種統計値の確認（③で、Objectのものは統計されない）')
    display(df.describe())
    print('######################################')
    print('➄欠損値がある列の確認')
    null_df =df.isnull().sum()[df.columns[df.isnull().sum()!=0]]
    display(null_df)
    display(null_df.shape)
    print('######################################')
    print('⑥相関係数のヒートマップ')
    sns.heatmap(df.corr())

inspection_datas(train)

######################################
①サイズ（行数、列数）の確認
(66125, 32)
######################################
②最初の５行の表示

	id	period	game-ver	lobby-mode	lobby	mode	stage	A1-weapon	A1-rank	A1-level	...	B2-weapon	B2-rank	B2-level	B3-weapon	B3-rank	B3-level	B4-weapon	B4-rank	B4-level	y
0	1	2019-10-15T20:00:00+00:00	5.0.1	regular	standard	nawabari	sumeshi	sshooter_becchu	NaN	139	...	hokusai_becchu	NaN	26.0	herocharger_replica	NaN	68.0	sharp_neo	NaN	31.0	1
1	2	2019-12-14T04:00:00+00:00	5.0.1	regular	standard	nawabari	arowana	parashelter_sorella	NaN	198	...	squiclean_b	NaN	118.0	campingshelter	NaN	168.0	sputtery_clear	NaN	151.0	0
2	3	2019-12-25T14:00:00+00:00	5.0.1	gachi	standard	hoko	ama	nzap89	a-	114	...	nzap85	a+	163.0	prime_becchu	a-	160.0	dualsweeper_custom	a	126.0	0
3	4	2019-11-11T14:00:00+00:00	5.0.1	regular	standard	nawabari	engawa	bamboo14mk1	NaN	336	...	liter4k	NaN	189.0	promodeler_mg	NaN	194.0	hotblaster_custom	NaN	391.0	0
4	5	2019-12-14T06:00:00+00:00	5.0.1	gachi	standard	hoko	chozame	bold_7	x	299	...	sputtery_hue	x	45.0	bucketslosher_soda	x	246.0	wakaba	x	160.0	1

5 rows × 32 columns

######################################
③各行のデータ型の確認（オブジェクト型の有無）
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 66125 entries, 0 to 66124
Data columns (total 32 columns):
id            66125 non-null int64
period        66125 non-null object
game-ver      66125 non-null object
lobby-mode    66125 non-null object
lobby         66125 non-null object
mode          66125 non-null object
stage         66125 non-null object
A1-weapon     66125 non-null object
A1-rank       51681 non-null object
A1-level      66125 non-null int64
A2-weapon     66125 non-null object
A2-rank       51681 non-null object
A2-level      66125 non-null float64
A3-weapon     66125 non-null object
A3-rank       51681 non-null object
A3-level      66125 non-null float64
A4-weapon     66074 non-null object
A4-rank       51638 non-null object
A4-level      66074 non-null float64
B1-weapon     66125 non-null object
B1-rank       51681 non-null object
B1-level      66125 non-null int64
B2-weapon     66125 non-null object
B2-rank       51681 non-null object
B2-level      66125 non-null float64
B3-weapon     66124 non-null object
B3-rank       51681 non-null object
B3-level      66124 non-null float64
B4-weapon     66058 non-null object
B4-rank       51624 non-null object
B4-level      66058 non-null float64
y             66125 non-null int64
dtypes: float64(6), int64(4), object(22)
memory usage: 16.1+ MB

None

Index(['period', 'game-ver', 'lobby-mode', 'lobby', 'mode', 'stage',
       'A1-weapon', 'A1-rank', 'A2-weapon', 'A2-rank', 'A3-weapon', 'A3-rank',
       'A4-weapon', 'A4-rank', 'B1-weapon', 'B1-rank', 'B2-weapon', 'B2-rank',
       'B3-weapon', 'B3-rank', 'B4-weapon', 'B4-rank'],
      dtype='object')

######################################
④各種統計値の確認（③で、Objectのものは統計されない）

	id	A1-level	A2-level	A3-level	A4-level	B1-level	B2-level	B3-level	B4-level	y
count	66125.000000	66125.000000	66125.000000	66125.000000	66074.000000	66125.000000	66125.000000	66124.000000	66058.000000	66125.000000
mean	33063.000000	208.838926	155.351698	153.175229	156.001619	156.378722	152.764749	153.105393	157.869736	0.524703
std	19088.787612	118.986528	96.183950	95.470733	95.524571	97.109501	95.373534	94.891705	95.777647	0.499393
min	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.000000
25%	16532.000000	120.000000	82.000000	80.000000	84.000000	83.000000	80.000000	81.000000	86.000000	0.000000
50%	33063.000000	188.000000	139.000000	137.000000	140.000000	139.000000	136.000000	137.000000	142.000000	1.000000
75%	49594.000000	277.000000	208.000000	205.000000	208.000000	210.000000	205.000000	205.000000	212.000000	1.000000
max	66125.000000	585.000000	913.000000	920.000000	931.000000	920.000000	913.000000	933.000000	936.000000	1.000000

######################################
➄欠損値がある列の確認

A1-rank      14444
A2-rank      14444
A3-rank      14444
A4-weapon       51
A4-rank      14487
A4-level        51
B1-rank      14444
B2-rank      14444
B3-weapon        1
B3-rank      14444
B3-level         1
B4-weapon       67
B4-rank      14501
B4-level        67
dtype: int64

(14,)

######################################
⑥相関係数のヒートマップ

前処理

# 欠損値は、全て「－１」とする。
def fill_all_null(df):
    for col_name in df.columns[df.isnull().sum()!=0]:
        df[col_name] = df[col_name].fillna(-1)

# 訓練データ、テストデータの欠損値を補完
fill_all_null(train)
fill_all_null(test)

# ターゲットエンコーディングの関数定義
def change_to_target2(train_df,test_df,input_column_name,output_column_name):
    from sklearn.model_selection import KFold
    
    # nan埋め処理
    train[input_column_name] = train[input_column_name].fillna('-1').isnull().sum()
    test[input_column_name] = test[input_column_name].fillna('-1').isnull().sum()

    kf = KFold(n_splits=5, shuffle=True, random_state=71)
    #=========================================================#
    c=input_column_name
    # 学習データ全体で各カテゴリにおけるyの平均を計算
    data_tmp = pd.DataFrame({c: train_df[c],'target':train_df['y']})
    target_mean = data_tmp.groupby(c)['target'].mean()
    #テストデータのカテゴリを置換
    test_df[output_column_name] = test_df[c].map(target_mean)
    
    # 変換後の値を格納する配列を準備
    tmp = np.repeat(np.nan, train_df.shape[0])

    for i, (train_index, test_index) in enumerate(kf.split(train_df)): # NFOLDS回まわる
        #学習データについて、各カテゴリにおける目的変数の平均を計算
        target_mean = data_tmp.iloc[train_index].groupby(c)['target'].mean()
        #バリデーションデータについて、変換後の値を一時配列に格納
        tmp[test_index] = train_df[c].iloc[test_index].map(target_mean) 

    #変換後のデータで元の変数を置換
    train_df[output_column_name] = tmp
#========================================================#

# オブジェクトの列のリストを作成
object_col_list = train.select_dtypes(include=object).columns

# オブジェクトの列は全てターゲットエンコーディング実施
for col in object_col_list:
    change_to_target2(train,test,col,"enc_"+col)

#　変換前の列を削除
train = train.drop(object_col_list,axis=1)
test = test.drop(object_col_list,axis=1)

#　'id'の列を削除
train = train.drop('id',axis=1)
test = test.drop('id',axis=1)

データの確認

# 訓練データとテストデータの列を確認
print(train.columns)
print(test.columns)

Index(['A1-level', 'A2-level', 'A3-level', 'A4-level', 'B1-level', 'B2-level',
       'B3-level', 'B4-level', 'y', 'enc_period', 'enc_game-ver',
       'enc_lobby-mode', 'enc_lobby', 'enc_mode', 'enc_stage', 'enc_A1-weapon',
       'enc_A1-rank', 'enc_A2-weapon', 'enc_A2-rank', 'enc_A3-weapon',
       'enc_A3-rank', 'enc_A4-weapon', 'enc_A4-rank', 'enc_B1-weapon',
       'enc_B1-rank', 'enc_B2-weapon', 'enc_B2-rank', 'enc_B3-weapon',
       'enc_B3-rank', 'enc_B4-weapon', 'enc_B4-rank'],
      dtype='object')
Index(['A1-level', 'A2-level', 'A3-level', 'A4-level', 'B1-level', 'B2-level',
       'B3-level', 'B4-level', 'enc_period', 'enc_game-ver', 'enc_lobby-mode',
       'enc_lobby', 'enc_mode', 'enc_stage', 'enc_A1-weapon', 'enc_A1-rank',
       'enc_A2-weapon', 'enc_A2-rank', 'enc_A3-weapon', 'enc_A3-rank',
       'enc_A4-weapon', 'enc_A4-rank', 'enc_B1-weapon', 'enc_B1-rank',
       'enc_B2-weapon', 'enc_B2-rank', 'enc_B3-weapon', 'enc_B3-rank',
       'enc_B4-weapon', 'enc_B4-rank'],
      dtype='object')

#　訓練データに欠損がないことの確認
train.isnull().sum().sum()

#　テストデータに欠損がないことの確認
test.isnull().sum().sum()

学習の準備

# 訓練データを説明変数と目的変数に分割
target = train['y']
train_x = train.drop('y',axis=1)

# LGBMのパラメータを設定
params = {
    # 二値分類問題
    'objective': 'binary',
    # 損失関数は二値のlogloss
    #'metric': 'auc',
    'metric': 'binary_logloss',
    # 最大イテレーション回数指定
    'num_iterations' : 1000,
    # early_stopping 回数指定
    'early_stopping_rounds' : 100,
}

学習・予測の実行

# k-分割交差検証を使って学習＆予測（K=10）
FOLD_NUM = 10
kf = KFold(n_splits=FOLD_NUM,
              random_state=42)

#検証時のスコアを初期化
scores = []

#テストデータの予測値を初期化
pred_cv = np.zeros(len(test.index))

#lgbmのラウンド数を定義
num_round = 10000

for i, (tdx, vdx) in enumerate(kf.split(train_x, target)):
    print(f'Fold : {i}')
    # 訓練用データと検証用データに分割
    X_train, X_valid, y_train, y_valid = train_x.iloc[tdx], train_x.iloc[vdx], target.values[tdx], target.values[vdx]
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_valid = lgb.Dataset(X_valid, y_valid)
    
    # 学習の実行
    model = lgb.train(params, lgb_train, num_boost_round=num_round,
                      valid_names=["train", "valid"], valid_sets=[lgb_train, lgb_valid],
                      verbose_eval=100)

    # 検証データに対する予測値を求めて、勝敗（０　or　１）に変換
    va_pred = np.round(model.predict(X_valid,num_iteration=model.best_iteration))
    
    # accuracyスコアを計算
    score_ = accuracy_score(y_valid, va_pred)
    
    # フォールド毎の検証時のスコアを格納
    scores.append(score_)
    
    #テストデータに対する予測値を求める
    submission = model.predict(test,num_iteration=model.best_iteration)
    
    #テストデータに対する予測値をフォールド数で割って蓄積
    #(フォールド毎の予測値の平均値を求めることと同じ)
    pred_cv += submission/FOLD_NUM

# 最終的なテストデータに対する予測値を勝敗（０　or　１）に変換
pred_cv = np.round(pred_cv)

# 最終的なaccuracyスコアを平均値で出力
print('')
print('################################')
print('CV_score:'+ str(np.mean(scores)))

Fold : 0
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658384	valid's binary_logloss: 0.69034
Early stopping, best iteration is:
[32]	train's binary_logloss: 0.677419	valid's binary_logloss: 0.689003
Fold : 1
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658534	valid's binary_logloss: 0.690261
Early stopping, best iteration is:
[28]	train's binary_logloss: 0.678862	valid's binary_logloss: 0.688458
Fold : 2
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658397	valid's binary_logloss: 0.688168
Early stopping, best iteration is:
[30]	train's binary_logloss: 0.678108	valid's binary_logloss: 0.687281
Fold : 3
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.657908	valid's binary_logloss: 0.690272
Early stopping, best iteration is:
[23]	train's binary_logloss: 0.680255	valid's binary_logloss: 0.688652
Fold : 4
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658589	valid's binary_logloss: 0.68942
Early stopping, best iteration is:
[74]	train's binary_logloss: 0.665111	valid's binary_logloss: 0.688165
Fold : 5
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658695	valid's binary_logloss: 0.688153
Early stopping, best iteration is:
[53]	train's binary_logloss: 0.671479	valid's binary_logloss: 0.687429
Fold : 6
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658767	valid's binary_logloss: 0.689022
Early stopping, best iteration is:
[72]	train's binary_logloss: 0.665929	valid's binary_logloss: 0.688496
Fold : 7
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.65828	valid's binary_logloss: 0.690961
Early stopping, best iteration is:
[35]	train's binary_logloss: 0.676428	valid's binary_logloss: 0.689549
Fold : 8
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.657763	valid's binary_logloss: 0.691315
Early stopping, best iteration is:
[51]	train's binary_logloss: 0.671153	valid's binary_logloss: 0.690189
Fold : 9
Training until validation scores don't improve for 100 rounds
[100]	train's binary_logloss: 0.658697	valid's binary_logloss: 0.690311
Early stopping, best iteration is:
[32]	train's binary_logloss: 0.677374	valid's binary_logloss: 0.68946

################################
CV_score:0.5317956075445449

# 提出用ファイルを作成する
pd.DataFrame({"id": range(len(pred_cv)), "y": pred_cv }).to_csv("submission.csv", index=False)

# 0.5317956075445449

添付データ

LGBM_B.ipynb?X-Amz-Expires=10800&X-Amz-Date=20241118T194218Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIP7GCBGMWPMZ42PQ

対戦ゲームデータ分析甲子園

LightGBMを使ったBase line

対戦ゲームデータ分析甲子園

データの確認

前処理

データの確認

学習の準備

学習・予測の実行

添付データ

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