.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ensemble/plot_stack_predictors.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ensemble_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. .. GENERATED FROM PYTHON SOURCE LINES 18-22 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 23-39 Download the dataset #################### We will use the `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 .. GENERATED FROM PYTHON SOURCE LINES 39-84 .. code-block:: Python 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.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() .. GENERATED FROM PYTHON SOURCE LINES 85-91 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. .. GENERATED FROM PYTHON SOURCE LINES 91-98 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none ['HeatingQC', 'Street', 'Heating', 'MasVnrType', 'Foundation', 'ExterQual', 'Condition2', 'GarageType', 'HouseStyle', 'MiscFeature'] .. GENERATED FROM PYTHON SOURCE LINES 99-101 .. code-block:: Python num_selector(X) .. rst-class:: sphx-glr-script-out .. code-block:: none ['YrSold', 'YearRemodAdd', 'BsmtUnfSF', 'MasVnrArea', 'MSSubClass', 'GarageCars', 'OverallQual', 'TotalBsmtSF', 'BsmtFinSF1', 'MoSold'] .. GENERATED FROM PYTHON SOURCE LINES 102-111 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. .. GENERATED FROM PYTHON SOURCE LINES 111-129 .. code-block:: Python 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 .. raw:: html 
ColumnTransformer(transformers=[('simpleimputer',
                                     SimpleImputer(add_indicator=True),
                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                    ('ordinalencoder',
                                     OrdinalEncoder(encoded_missing_value=-2,
                                                    handle_unknown='use_encoded_value',
                                                    unknown_value=-1),
                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e6070>)])
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.. GENERATED FROM PYTHON SOURCE LINES 130-132 Then, we will now define the preprocessor used when the ending regressor is a linear model. .. GENERATED FROM PYTHON SOURCE LINES 132-145 .. code-block:: Python 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 .. raw:: html 
ColumnTransformer(transformers=[('pipeline',
                                     Pipeline(steps=[('standardscaler',
                                                      StandardScaler()),
                                                     ('simpleimputer',
                                                      SimpleImputer(add_indicator=True))]),
                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                    ('onehotencoder',
                                     OneHotEncoder(handle_unknown='ignore'),
                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e6070>)])
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.. GENERATED FROM PYTHON SOURCE LINES 146-164 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. .. GENERATED FROM PYTHON SOURCE LINES 164-170 .. code-block:: Python from sklearn.linear_model import LassoCV lasso_pipeline = make_pipeline(linear_preprocessor, LassoCV()) lasso_pipeline .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('pipeline',
                                                      Pipeline(steps=[('standardscaler',
                                                                       StandardScaler()),
                                                                      ('simpleimputer',
                                                                       SimpleImputer(add_indicator=True))]),
                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                                     ('onehotencoder',
                                                      OneHotEncoder(handle_unknown='ignore'),
                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e6070>)])),
                    ('lassocv', LassoCV())])
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.. GENERATED FROM PYTHON SOURCE LINES 171-176 .. code-block:: Python from sklearn.ensemble import RandomForestRegressor rf_pipeline = make_pipeline(tree_preprocessor, RandomForestRegressor(random_state=42)) rf_pipeline .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('simpleimputer',
                                                      SimpleImputer(add_indicator=True),
                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                                     ('ordinalencoder',
                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                     handle_unknown='use_encoded_value',
                                                                     unknown_value=-1),
                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e6070>)])),
                    ('randomforestregressor',
                     RandomForestRegressor(random_state=42))])
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.. GENERATED FROM PYTHON SOURCE LINES 177-184 .. code-block:: Python from sklearn.ensemble import HistGradientBoostingRegressor gbdt_pipeline = make_pipeline( tree_preprocessor, HistGradientBoostingRegressor(random_state=0) ) gbdt_pipeline .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('simpleimputer',
                                                      SimpleImputer(add_indicator=True),
                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                                     ('ordinalencoder',
                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                     handle_unknown='use_encoded_value',
                                                                     unknown_value=-1),
                                                      <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e6070>)])),
                    ('histgradientboostingregressor',
                     HistGradientBoostingRegressor(random_state=0))])
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.. GENERATED FROM PYTHON SOURCE LINES 185-197 .. code-block:: Python 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 .. raw:: html 
StackingRegressor(estimators=[('Random Forest',
                                   Pipeline(steps=[('columntransformer',
                                                    ColumnTransformer(transformers=[('simpleimputer',
                                                                                     SimpleImputer(add_indicator=True),
                                                                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                                                                    ('ordinalencoder',
                                                                                     OrdinalEncoder(encoded_missing_value=-2,
                                                                                                    handle_unknown='use_encoded_value',
                                                                                                    unknown_v...
                                                                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e65b0>),
                                                                                    ('ordinalencoder',
                                                                                     OrdinalEncoder(encoded_missing_value=-2,
                                                                                                    handle_unknown='use_encoded_value',
                                                                                                    unknown_value=-1),
                                                                                     <sklearn.compose._column_transformer.make_column_selector object at 0x7fdec35e6070>)])),
                                                   ('histgradientboostingregressor',
                                                    HistGradientBoostingRegressor(random_state=0))]))],
                      final_estimator=RidgeCV())
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.. GENERATED FROM PYTHON SOURCE LINES 198-204 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. .. GENERATED FROM PYTHON SOURCE LINES 204-255 .. code-block:: Python import time import matplotlib.pyplot as plt from sklearn.metrics import PredictionErrorDisplay from sklearn.model_selection import cross_val_predict, cross_validate 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)] ): scorers = {"R2": "r2", "MAE": "neg_mean_absolute_error"} start_time = time.time() scores = cross_validate( est, X, y, scoring=list(scorers.values()), n_jobs=-1, verbose=0 ) elapsed_time = time.time() - start_time y_pred = cross_val_predict(est, X, y, n_jobs=-1, verbose=0) scores = { key: ( f"{np.abs(np.mean(scores[f'test_{value}'])):.2f} +- " f"{np.std(scores[f'test_{value}']):.2f}" ) for key, value in scorers.items() } display = PredictionErrorDisplay.from_predictions( y_true=y, y_pred=y_pred, kind="actual_vs_predicted", ax=ax, scatter_kwargs={"alpha": 0.2, "color": "tab:blue"}, line_kwargs={"color": "tab:red"}, ) ax.set_title(f"{name}\nEvaluation in {elapsed_time:.2f} seconds") for name, score in scores.items(): ax.plot([], [], " ", label=f"{name}: {score}") ax.legend(loc="upper left") plt.suptitle("Single predictors versus stacked predictors") plt.tight_layout() plt.subplots_adjust(top=0.9) plt.show() .. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png :alt: Single predictors versus stacked predictors, Random Forest Evaluation in 1.23 seconds, Lasso Evaluation in 0.35 seconds, Gradient Boosting Evaluation in 0.50 seconds, Stacking Regressor Evaluation in 10.28 seconds :srcset: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 256-259 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. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 24.967 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_stack_predictors.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_stack_predictors.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/ensemble/plot_stack_predictors.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_stack_predictors.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_stack_predictors.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_stack_predictors.zip ` .. include:: plot_stack_predictors.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_