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.

"""
print(__doc__)

# 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)
    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, the dataset has many missing values. To impute them, we will exchange
# categorical missing values with the new category 'missing' while the
# numerical missing values with the 'mean' of the column. We will also encode
# the categories with either :class:`sklearn.preprocessing.OneHotEncoder
# <sklearn.preprocessing.OneHotEncoder>` or
# :class:`sklearn.preprocessing.OrdinalEncoder
# <sklearn.preprocessing.OrdinalEncoder>` depending for which type of model we
# will use them (linear or non-linear model). To falicitate this preprocessing
# we will make two pipelines.
# You can skip this section if your data is ready to use and does
# not need preprocessing


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


cat_cols = X.columns[X.dtypes == 'O']
num_cols = X.columns[X.dtypes == 'float64']

categories = [
    X[column].unique() for column in X[cat_cols]]

for cat in categories:
    cat[cat == None] = 'missing'  # noqa

cat_proc_nlin = make_pipeline(
    SimpleImputer(missing_values=None, strategy='constant',
                  fill_value='missing'),
    OrdinalEncoder(categories=categories)
    )

num_proc_nlin = make_pipeline(SimpleImputer(strategy='mean'))

cat_proc_lin = make_pipeline(
    SimpleImputer(missing_values=None,
                  strategy='constant',
                  fill_value='missing'),
    OneHotEncoder(categories=categories)
)

num_proc_lin = make_pipeline(
    SimpleImputer(strategy='mean'),
    StandardScaler()
)

# transformation to use for non-linear estimators
processor_nlin = make_column_transformer(
    (cat_proc_nlin, cat_cols),
    (num_proc_nlin, num_cols),
    remainder='passthrough')

# transformation to use for linear estimators
processor_lin = make_column_transformer(
    (cat_proc_lin, cat_cols),
    (num_proc_lin, num_cols),
    remainder='passthrough')


###############################################################################
# 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 RidgeCV()
# does not need preprocessing of the data as it will be fed with the already
# preprocessed output from the 3 learners.


from sklearn.experimental import enable_hist_gradient_boosting  # noqa
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import StackingRegressor
from sklearn.linear_model import LassoCV
from sklearn.linear_model import RidgeCV


lasso_pipeline = make_pipeline(processor_lin,
                               LassoCV())

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

gradient_pipeline = make_pipeline(
    processor_nlin,
    HistGradientBoostingRegressor(random_state=0))

estimators = [('Random Forest', rf_pipeline),
              ('Lasso', lasso_pipeline),
              ('Gradient Boosting', gradient_pipeline)]

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


###############################################################################
# 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=-1, verbose=0)
    elapsed_time = time.time() - start_time

    y_pred = cross_val_predict(est, X, y, n_jobs=-1, 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.