plot_stack_predictors.py

"""
=================================
Combine predictors using stacking
=================================

.. currentmodule:: sklearn

Stacking refers to a method to blend estimators. In this strategy, some
estimators are individually fitted on some training data while a final
estimator is trained using the stacked predictions of these base estimators.

In this example, we illustrate the use case in which different regressors are
stacked together and a final linear penalized regressor is used to output the
prediction. We compare the performance of each individual regressor with the
stacking strategy. Stacking slightly improves the overall performance.

"""

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
#          Maria Telenczuk    <https://github.com/maikia>
# License: BSD 3 clause

# %%
# Download the dataset
##############################################################################
#
# We will use `Ames Housing`_ dataset which was first compiled by Dean De Cock
# and became better known after it was used in Kaggle challenge. It is a set
# of 1460 residential homes in Ames, Iowa, each described by 80 features. We
# will use it to predict the final logarithmic price of the houses. In this
# example we will use only 20 most interesting features chosen using
# GradientBoostingRegressor() and limit number of entries (here we won't go
# into the details on how to select the most interesting features).
#
# The Ames housing dataset is not shipped with scikit-learn and therefore we
# will fetch it from `OpenML`_.
#
# .. _`Ames Housing`: http://jse.amstat.org/v19n3/decock.pdf
# .. _`OpenML`: https://www.openml.org/d/42165

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[features]
    X, y = shuffle(X, y, random_state=0)

    X = X[:600]
    y = y[:600]
    return X, np.log(y)


X, y = load_ames_housing()


# %%
# Make pipeline to preprocess the data
##############################################################################
#
# Before we can use Ames dataset we still need to do some preprocessing.
# First, we will select the categorical and numerical columns of the dataset to
# construct the first step of the pipeline.

from sklearn.compose import make_column_selector

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

# %%
num_selector(X)

# %%
# Then, we will need to design preprocessing pipelines which depends on the
# ending regressor. If the ending regressor is a linear model, one needs to
# one-hot encode the categories. If the ending regressor is a tree-based model
# an ordinal encoder will be sufficient. Besides, numerical values need to be
# standardized for a linear model while the raw numerical data can be treated
# as is by a tree-based model. However, both models need an imputer to
# handle missing values.
#
# We will first design the pipeline required for the tree-based models.

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

# %%
# Then, we will now define the preprocessor used when the ending regressor
# is a linear model.

from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import 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

# %%
# Stack of predictors on a single data set
##############################################################################
#
# It is sometimes tedious to find the model which will best perform on a given
# dataset. Stacking provide an alternative by combining the outputs of several
# learners, without the need to choose a model specifically. The performance of
# stacking is usually close to the best model and sometimes it can outperform
# the prediction performance of each individual model.
#
# Here, we combine 3 learners (linear and non-linear) and use a ridge regressor
# to combine their outputs together.
#
# .. note::
#    Although we will make new pipelines with the processors which we wrote in
#    the previous section for the 3 learners, the final estimator
#    :class:`~sklearn.linear_model.RidgeCV()` does not need preprocessing of
#    the data as it will be fed with the already preprocessed output from the 3
#    learners.

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

# %%
# Measure and plot the results
##############################################################################
#
# Now we can use Ames Housing dataset to make the predictions. We check the
# performance of each individual predictor as well as of the stack of the
# regressors.
#
# The function ``plot_regression_results`` is used to plot the predicted and
# true targets.


import time
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_validate, cross_val_predict


def plot_regression_results(ax, y_true, y_pred, title, scores, elapsed_time):
    """Scatter plot of the predicted vs true targets."""
    ax.plot(
        [y_true.min(), y_true.max()], [y_true.min(), y_true.max()], "--r", linewidth=2
    )
    ax.scatter(y_true, y_pred, alpha=0.2)

    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()
    ax.spines["left"].set_position(("outward", 10))
    ax.spines["bottom"].set_position(("outward", 10))
    ax.set_xlim([y_true.min(), y_true.max()])
    ax.set_ylim([y_true.min(), y_true.max()])
    ax.set_xlabel("Measured")
    ax.set_ylabel("Predicted")
    extra = plt.Rectangle(
        (0, 0), 0, 0, fc="w", fill=False, edgecolor="none", linewidth=0
    )
    ax.legend([extra], [scores], loc="upper left")
    title = title + "\n Evaluation in {:.2f} seconds".format(elapsed_time)
    ax.set_title(title)


fig, axs = plt.subplots(2, 2, figsize=(9, 7))
axs = np.ravel(axs)

for ax, (name, est) in zip(
    axs, estimators + [("Stacking Regressor", stacking_regressor)]
):
    start_time = time.time()
    score = cross_validate(
        est, X, y, scoring=["r2", "neg_mean_absolute_error"], n_jobs=2, verbose=0
    )
    elapsed_time = time.time() - start_time

    y_pred = cross_val_predict(est, X, y, n_jobs=2, verbose=0)

    plot_regression_results(
        ax,
        y,
        y_pred,
        name,
        (r"$R^2={:.2f} \pm {:.2f}$" + "\n" + r"$MAE={:.2f} \pm {:.2f}$").format(
            np.mean(score["test_r2"]),
            np.std(score["test_r2"]),
            -np.mean(score["test_neg_mean_absolute_error"]),
            np.std(score["test_neg_mean_absolute_error"]),
        ),
        elapsed_time,
    )

plt.suptitle("Single predictors versus stacked predictors")
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.show()

# %%
# The stacked regressor will combine the strengths of the different regressors.
# However, we also see that training the stacked regressor is much more
# computationally expensive.