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#get data for classification task
X_train, X_val, y_train, y_val = get_split_data(german_cred, target_name='bad_credit')

#Train and fit these models
rand_forest_cf.fit(X_train, y_train)
extra_tree_cf.fit(X_train, y_train)
bagging_meta_cf.fit(X_train, y_train)

#check their performance
print("ACC of Random Forest is : ", get_acc(rand_forest_cf.predict(X_val), y_val))

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from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold, cross_val_score


# def get_mae(pred, target):
#     return mean_absolute_error(true, pred)

def cross_validate(model, nfolds, feats, targets):
    score = -1 * (cross_val_score(model, feats, targets, cv=nfolds, scoring='neg_mean_absolute_error'))
    return np.mean(score)

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from sklearn.linear_model import LinearRegression 

lr_model = LinearRegression()
print("MAE Sccore: ", cross_validate(lr_model, 10, X_train, y_train))

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from sklearn.tree import DecisionTreeRegressor

dt_model = DecisionTreeRegressor(max_depth=6, min_samples_leaf=2)
print("MAE Sccore: ", cross_validate(dt_model, 10, X_train, y_train))

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from sklearn.neighbors import KNeighborsRegressor

knn_model = KNeighborsRegressor(n_neighbors=60)
print("MAE Sccore: ", cross_validate(knn_model, 10, X_train, y_train))

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from sklearn.ensemble import RandomForestRegressor

rf_model = RandomForestRegressor(n_estimators=100, max_depth=5)
print("MAE Sccore: ", cross_validate(rf_model, 10, X_train, y_train))

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from sklearn.ensemble import ExtraTreesRegressor

et_model = ExtraTreesRegressor(n_estimators=100, max_depth=5)
print("MAE Sccore: ", cross_validate(et_model, 10, X_train, y_train))

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from sklearn.ensemble import BaggingRegressor

bgg_model = BaggingRegressor(et_model, n_estimators=20, random_state=2)
print("MAE Sccore: ", cross_validate(bgg_model, 10, X_train, y_train))

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from sklearn.ensemble import AdaBoostRegressor

ada_model = AdaBoostRegressor(et_model, n_estimators=150)
print("MAE Sccore: ", cross_validate(ada_model, 10, X_train, y_train))

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from sklearn.ensemble import GradientBoostingRegressor

gb_model = GradientBoostingRegressor(n_estimators=100)
print("MAE Sccore: ", cross_validate(gb_model, 10, X_train, y_train))