from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
wine_data = load_wine()
X_wine = wine_data.data
y_wine = wine_data.target
X_train_wine, X_test_wine, y_train_wine, y_test_wine = train_test_split(X_wine, y_wine, test_size=0.20, random_state=42)

from autogluon.tabular import TabularPredictor
train_data_wine = pd.DataFrame(X_train_wine, columns=wine_data.feature_names)
train_data_wine['target'] = y_train_wine
predictor_wine = TabularPredictor(label='target').fit(train_data=train_data_wine, time_limit=120, presets='best_quality')

print(predictor_wine.fit_summary())
performance_wine = predictor_wine.evaluate(pd.DataFrame(X_test_wine, columns=wine_data.feature_names, index=y_test_wine))
predictor_wine.leaderboard(silent=True).plot(kind="bar",x="model",y="score_val")

from sklearn.datasets import load_diabetes
diabetes_data = load_diabetes()
X_diabetes = diabetes_data.data
y_diabetes = diabetes_data.target
X_train_diabetes, X_test_diabetes, y_train_diabetes, y_test_diabetes = train_test_split(X_diabetes, y_diabetes, test_size=0.20, random_state=42)

train_data_diabetes = pd.DataFrame(X_train_diabetes, columns=diabetes_data.feature_names)
train_data_diabetes['target'] = y_train_diabetes
predictor_diabetes = TabularPredictor(label='target',problem_type="regression",eval_metric='r2').fit(train_data=train_data_diabetes, time_limit=120, presets='best_quality')

print(predictor_diabetes.fit_summary())
performance_diabetes = predictor_diabetes.evaluate(pd.DataFrame(X_test_diabetes, columns=diabetes_data.feature_names, index=y_test_diabetes))
predictor_diabetes.leaderboard(silent=True).plot(kind="bar",x="model",y="score_val")