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自動車の走行距離予測

SIGNATEの練習問題をやったのでログを残す。

import optuna
import pandas as pd
from sklearn.model_selection import cross_validate
import xgboost as xgb

def objective(trial):
    # ハイパーパラメータ
    cv = trial.suggest_int("cv", 3, 5)
    eta = trial.suggest_uniform("eta", 0.01, 0.2)
    learning_rate = trial.suggest_uniform(" learning_rate", 0.1, 1.0)
    max_depth = trial.suggest_int("max_depth", 2, 100)
    subsample = trial.suggest_uniform("subsample", 0.5, 1.0)
    colsample_bytree = trial.suggest_uniform("colsample_bytree", 0.5, 1.0)

    # 学習
    model = xgb.XGBRegressor(
        eta=eta,
        learning_rate=learning_rate,
        max_depth=max_depth,
        subsample=subsample,
        colsample_bytree=colsample_bytree)

    # 交差検証
    scores = cross_validate(model, x, y, cv=cv)

    return scores['test_score'].mean()

# データロード
train = pd.read_csv('./train.tsv', delimiter='\t')
train = train.drop({'id', 'car name'}, axis=1)
train = train.replace('?', np.nan)
train['horsepower'] = train['horsepower'].astype(float)

test = pd.read_csv('./test.tsv', delimiter='\t')
test_id = test['id']
test = test.drop({'id', 'car name'}, axis=1)
test = test.replace('?', np.nan)
test['horsepower'] = test['horsepower'].astype(float)

# 学習
x = train.drop('mpg', axis=1)
y = train['mpg']
study = optuna.create_study()
study.optimize(objective, n_trials=10000)
print(study.best_params)
print(study.best_value)
# 予測
model = xgb.XGBRegressor(**study.best_params)
model.fit(x, y)y_pred = model.predict(test)
df_pred = pd.DataFrame({'mpg': y_pred})
df = pd.concat([test_id, test, df_pred], axis=1)
df.loc[:,['id','mpg']].to_csv('submit.csv', header=False,  index=False)
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