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@dev_fukuro

LightGBMの俺用テンプレート

Last updated at Posted at 2019-10-11

LightGBMの俺用テンプレート

LightGBMモデルを学習する際の、テンプレ的なコードを自分用も兼ねてまとめました。

対象

・LightGBMについては知っている方
・LightGBMでoptuna使いたい方
・書き方はなんとなくわかるけど毎回１から書くのが面倒な方

お品書き

  1. シンプルなモデル
  2. early_stopping版
  3. optunaでパラメータ探索 ＋ early_stopping版

評価方法

trainとtestの分割によって精度が大きく変わる場合を考慮して、
kfoldの平均値を最終的な精度としています。

データ

ここでは下記のようにダミーデータを作っています。

# データセット作成  

import pandas as pd
from sklearn.datasets import make_classification


data = make_classification(n_samples=10000, n_features=100, n_classes=2)

X = pd.DataFrame(data[0])
y = pd.DataFrame(data[1])

評価に使う関数


from sklearn import metrics

def get_evaluate(y_test, predict):
                 
    fpr, tpr, thr_arr = metrics.roc_curve(y_test, predict)
    
    auc = metrics.auc(fpr, tpr)
    precision = metrics.precision_score(y_test, predict)
    recall = metrics.recall_score(y_test, predict)      
                 
    return auc, precision, recall

1. シンプルなモデル


import numpy as np
import pandas as pd

from sklearn import metrics
from sklearn.model_selection import StratifiedKFold

import lightgbm as lgb

# kfoldの分割数
k = 5
skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=0)

lgbm_params = {'objective': 'binary'}

auc_list = []
precision_list = []
recall_list = []

# kfoldで分割
for train_index, test_index in skf.split(X, y):

    X_train = X.iloc[train_index]
    y_train = y.iloc[train_index]
    X_test = X.iloc[test_index]
    y_test = y.iloc[test_index]

    # データセットを生成する
    lgb_train = lgb.Dataset(X_train, y_train)
    
    # 学習
    model = lgb.train(lgbm_params, lgb_train)
    
    predict_proba = model.predict(X_test, num_iteration=model.best_iteration)
    # predict_probaが0.5以上なら1とする
    predict = [0 if i < 0.5 else 1 for i in predict_proba]

    auc, precision, recall = get_evaluate(y_test, predict)
    
    print('AUC:{}, precision:{}, recall:{}'.format(auc, precision, recall))
    
    auc_list.append(auc)
    precision_list.append(precision)
    recall_list.append(recall)

# kfoldの平均値を取得
print('Kfold平均 AUC:{}, precision:{}, recall:{}'.format(np.mean(auc_list), 
                                                         np.mean(precision_list), 
                                                         np.mean(recall_list)))

2. early_stopping版

import numpy as np
import pandas as pd

from sklearn import metrics
from sklearn.model_selection import train_test_split, StratifiedKFold

import lightgbm as lgb

from tqdm import tqdm_notebook as tqdm

# kfoldの分割数
k = 5
skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=0)

lgbm_params = {'objective': 'binary'}

auc_list = []
precision_list = []
recall_list = []

# kfoldで学習
for train_index, test_index in skf.split(X, y):

    X_train = X.iloc[train_index]
    y_train = y.iloc[train_index]
    X_test = X.iloc[test_index]
    y_test = y.iloc[test_index]

    # ここではvalidをモデル評価、evalをフォールドアウト検証に使う・・・分割の大きさはデータセットと相談する
    X_eval, X_valid, y_eval, y_valid = train_test_split(X_test, y_test, random_state=90, 
                                                        shuffle=True, stratify=y_test, test_size=0.3)

    # データセットを生成する
    lgb_train = lgb.Dataset(X_train, y_train)
    
    # モデル評価用
    lgb_valid = lgb.Dataset(X_valid, y_valid, reference=lgb_train)

    model = lgb.train(lgbm_params, 
                      lgb_train,
                      valid_sets=lgb_valid,
                      num_boost_round=100000,
                      early_stopping_rounds=10)
    
    predict_proba = model.predict(X_test, num_iteration=model.best_iteration)
    predict = [0 if i < 0.5 else 1 for i in predict_proba]

    auc, precision, recall = get_evaluate(y_test, predict)
    
    print('AUC:{}, precision:{}, recall:{}'.format(auc, precision, recall))
    
    auc_list.append(auc)
    precision_list.append(precision)
    recall_list.append(recall)

# kfoldの平均値を取得
print('Kfold平均 AUC:{}, precision:{}, recall:{}'.format(np.mean(auc_list), 
                                                         np.mean(precision_list), 
                                                         np.mean(recall_list)))

3. optunaでパラメータ探索 ＋ early_stopping版

インポート

# optuna

import numpy as np
import pandas as pd

from sklearn import metrics
from sklearn.model_selection import train_test_split, StratifiedKFold

import optuna
import lightgbm as lgb

パラメータ探索処理

これは公式サンプルそのままだったはず・・・
他にも探索したい場合は、同じようにparamsに渡してやる。


def objective(trial):

    train_x, test_x, train_y, test_y = train_test_split(X_train, y_train, test_size=0.25, 
                                                        shuffle=True, stratify=y_train)
    dtrain = lgb.Dataset(train_x, label=train_y)

    param = {
        'objective': 'binary',
        'metric': 'binary_logloss',
        'verbosity': -1,
        'boosting_type': trial.suggest_categorical('boosting', ['gbdt', 'dart', 'goss']),
        'num_leaves': trial.suggest_int('num_leaves', 10, 1000),
        'learning_rate': trial.suggest_loguniform('learning_rate', 1e-8, 1.0)
    }

    if param['boosting_type'] == 'dart':
        param['drop_rate'] = trial.suggest_loguniform('drop_rate', 1e-8, 1.0)
        param['skip_drop'] = trial.suggest_loguniform('skip_drop', 1e-8, 1.0)
    if param['boosting_type'] == 'goss':
        param['top_rate'] = trial.suggest_uniform('top_rate', 0.0, 1.0)
        param['other_rate'] = trial.suggest_uniform('other_rate', 0.0, 1.0 - param['top_rate'])

    gbm = lgb.train(param, dtrain)
    preds = gbm.predict(test_x)
    pred_labels = np.rint(preds)
    
    accuracy = metrics.accuracy_score(test_y, pred_labels)
    
    return accuracy

学習

探索したパラメータを使って学習


# kfoldの分割数
k = 5
skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=0)

lgbm_params = {'objective': 'binary'}

auc_list = []
precision_list = []
recall_list = []

for train_index, test_index in skf.split(X, y):

    X_train = X.iloc[train_index]
    y_train = y.iloc[train_index]
    X_test = X.iloc[test_index]
    y_test = y.iloc[test_index]

    study = optuna.create_study(direction='maximize')
    study.optimize(objective, n_trials=100)

    print('Number of finished trials: {}'.format(len(study.trials)))

    print('Best trial:')
    trial = study.best_trial

    print('  Value: {}'.format(trial.value))

    print('  Params: ')
    for key, value in trial.params.items():
        print('    {}: {}'.format(key, value))    

    # optunaでサーチしたパラメータ
    trial.params['objective'] = 'binary'
    lgbm_params = trial.params

    # ここではvalidをモデル評価、evalをフォールドアウト検証に使う・・・分割の大きさはデータセットと相談する
    X_eval, X_valid, y_eval, y_valid = train_test_split(X_test, y_test, random_state=90, 
                                                        shuffle=True, stratify=y_test, test_size=0.3)

    # データセットを生成する
    lgb_train = lgb.Dataset(X_train, y_train)
    
    # モデル評価用
    lgb_valid = lgb.Dataset(X_valid, y_valid, reference=lgb_train)

    model = lgb.train(lgbm_params, 
                      lgb_train,
                      valid_sets=lgb_valid,
                      num_boost_round=100000,
                      early_stopping_rounds=10)

    predict_proba = model.predict(X_test, num_iteration=model.best_iteration)
    predict = [0 if i < 0.5 else 1 for i in predict_proba]

    auc, precision, recall = get_evaluate(y_test, predict)
    
    print('AUC:{}, precision:{}, recall:{}'.format(auc, precision, recall))
    
    auc_list.append(auc)
    precision_list.append(precision)
    recall_list.append(recall)

# kfoldの平均値を取得
print('Kfold平均 AUC:{}, precision:{}, recall:{}'.format(np.mean(auc_list), 
                                                         np.mean(precision_list), 
                                                         np.mean(recall_list)))

参考

LightGBMを使ってみる CUBE SUGAR CONTAINER
https://blog.amedama.jp/entry/2018/05/01/081842

LightGBM 公式ドキュメント
https://lightgbm.readthedocs.io/en/latest/

optuna
https://github.com/pfnet/optuna

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