[ML수업] 10주차 실습4: pipeline_stacking 예시 코드1

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>

#          Maria Telenczuk    <https://github.com/maikia>

# License: BSD 3 clause

 

1. Download the dataset

import numpy as np
from sklearn.datasets import fetch_openml
from sklearn.utils import shuffle

def load_ames_housing():
    df = fetch_openml(name="house_prices", as_frame=True, parser="pandas")
    X = df.data
    y = df.target

    features = [
        "YrSold",
        "HeatingQC",
        "Street",
        "YearRemodAdd",
        "Heating",
        "MasVnrType",
        "BsmtUnfSF",
        "Foundation",
        "MasVnrArea",
        "MSSubClass",
        "ExterQual",
        "Condition2",
        "GarageCars",
        "GarageType",
        "OverallQual",
        "TotalBsmtSF",
        "BsmtFinSF1",
        "HouseStyle",
        "MiscFeature",
        "MoSold",
    ]

    X = X.loc[:, features]
    X, y = shuffle(X, y, random_state=0)
    X = X.iloc[:600]
    y = y.iloc[:600]
    return X, np.log(y)

X, y = load_ames_housing()

 

2. Make pipeline to preprocess the data

scikit-learn의 make_column_selector 기능을 사용하여 특정 데이터 유형을 기준으로 열을 선택하는 방법을 보여줍니다. 이를 통해 데이터 전처리에서 범주형 또는 수치형 데이터를 쉽게 구분하고 처리할 수 있습니다. 

from sklearn.compose import make_column_selector

cat_selector = make_column_selector(dtype_include=object)
num_selector = make_column_selector(dtype_include=np.number)


#num_selector(X)
#cat_selector(X)

 

 

3. design the pipeline required for the tree-based models. Then, define the preprocessor used when the ending regressor
is a linear model.

from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OrdinalEncoder

cat_tree_processor = OrdinalEncoder(
    handle_unknown="use_encoded_value",
    unknown_value=-1,
    encoded_missing_value=-2,
)
num_tree_processor = SimpleImputer(strategy="mean", add_indicator=True)

tree_preprocessor = make_column_transformer(
    (num_tree_processor, num_selector), (cat_tree_processor, cat_selector)
)
tree_preprocessor

 

from sklearn.preprocessing import OneHotEncoder, StandardScaler

cat_linear_processor = OneHotEncoder(handle_unknown="ignore")
num_linear_processor = make_pipeline(
    StandardScaler(), SimpleImputer(strategy="mean", add_indicator=True)
)

linear_preprocessor = make_column_transformer(
    (num_linear_processor, num_selector), (cat_linear_processor, cat_selector)
)
linear_preprocessor

 

4. Stack of predictors on a single data set

from sklearn.linear_model import LassoCV

lasso_pipeline = make_pipeline(linear_preprocessor, LassoCV())
lasso_pipeline

from sklearn.ensemble import RandomForestRegressor

rf_pipeline = make_pipeline(tree_preprocessor, RandomForestRegressor(random_state=42))
rf_pipeline

from sklearn.ensemble import HistGradientBoostingRegressor

gbdt_pipeline = make_pipeline(
    tree_preprocessor, HistGradientBoostingRegressor(random_state=0)
)
gbdt_pipeline

from sklearn.ensemble import StackingRegressor
from sklearn.linear_model import RidgeCV

estimators = [
    ("Random Forest", rf_pipeline),
    ("Lasso", lasso_pipeline),
    ("Gradient Boosting", gbdt_pipeline),
]

stacking_regressor = StackingRegressor(estimators=estimators, final_estimator=RidgeCV())
stacking_regressor

 

5. Measure and plot the results

from sklearn.model_selection import cross_val_score

for m in estimators + [("Stacking Regressor", stacking_regressor)]:
    scores = cross_val_score(m[1], X, y, scoring="neg_mean_absolute_error")
    print(f"{m[0]}: {scores.mean()*-1:.3f}")