機械学習コンペで使うスニペットたち

メモです。自分がコピペでぺたぺたする用なので随時更新です。

HyperoptでLightGBMモデルをチューニングする

from hyperopt import fmin, hp, tpe

def objective(params):
    params['num_leaves'] = int(params['num_leaves'])
    params['max_depth'] = int(params['max_depth'])
    params['min_data_in_leaf'] = int(params['min_data_in_leaf'])
    
    model = LGBMRegressor(**params, random_state=0, n_jobs=-1)
    
    # calc score
    return score

space = {
    'num_leaves': hp.quniform('num_leaves', 50, 200, 10),
    'max_depth': hp.quniform('max_depth', 3, 10, 1),
    'min_data_in_leaf': hp.quniform('min_data_in_leaf',  5, 25, 2),
    'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1.0),
    'learning_rate': hp.uniform('learning_rate', 0.03, 0.2),
    'subsample': hp.uniform('subsample', 0.5, 1.0)
}

best = fmin(
    objective,
    space=space,
    algo=tpe.suggest,
    max_evals=200)