.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/impute/plot_iterative_imputer_variants_comparison.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_impute_plot_iterative_imputer_variants_comparison.py: ========================================================= Imputing missing values with variants of IterativeImputer ========================================================= .. currentmodule:: sklearn The :class:`~impute.IterativeImputer` class is very flexible - it can be used with a variety of estimators to do round-robin regression, treating every variable as an output in turn. In this example we compare some estimators for the purpose of missing feature imputation with :class:`~impute.IterativeImputer`: * :class:`~linear_model.BayesianRidge`: regularized linear regression * :class:`~ensemble.RandomForestRegressor`: Forests of randomized trees regression * :func:`~pipeline.make_pipeline` (:class:`~kernel_approximation.Nystroem`, :class:`~linear_model.Ridge`): a pipeline with the expansion of a degree 2 polynomial kernel and regularized linear regression * :class:`~neighbors.KNeighborsRegressor`: comparable to other KNN imputation approaches Of particular interest is the ability of :class:`~impute.IterativeImputer` to mimic the behavior of missForest, a popular imputation package for R. Note that :class:`~neighbors.KNeighborsRegressor` is different from KNN imputation, which learns from samples with missing values by using a distance metric that accounts for missing values, rather than imputing them. The goal is to compare different estimators to see which one is best for the :class:`~impute.IterativeImputer` when using a :class:`~linear_model.BayesianRidge` estimator on the California housing dataset with a single value randomly removed from each row. For this particular pattern of missing values we see that :class:`~linear_model.BayesianRidge` and :class:`~ensemble.RandomForestRegressor` give the best results. It should be noted that some estimators such as :class:`~ensemble.HistGradientBoostingRegressor` can natively deal with missing features and are often recommended over building pipelines with complex and costly missing values imputation strategies. .. GENERATED FROM PYTHON SOURCE LINES 46-157 .. image-sg:: /auto_examples/impute/images/sphx_glr_plot_iterative_imputer_variants_comparison_001.png :alt: California Housing Regression with Different Imputation Methods :srcset: /auto_examples/impute/images/sphx_glr_plot_iterative_imputer_variants_comparison_001.png :class: sphx-glr-single-img .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.datasets import fetch_california_housing from sklearn.ensemble import RandomForestRegressor # To use this experimental feature, we need to explicitly ask for it: from sklearn.experimental import enable_iterative_imputer # noqa from sklearn.impute import IterativeImputer, SimpleImputer from sklearn.kernel_approximation import Nystroem from sklearn.linear_model import BayesianRidge, Ridge from sklearn.model_selection import cross_val_score from sklearn.neighbors import KNeighborsRegressor from sklearn.pipeline import make_pipeline N_SPLITS = 5 rng = np.random.RandomState(0) X_full, y_full = fetch_california_housing(return_X_y=True) # ~2k samples is enough for the purpose of the example. # Remove the following two lines for a slower run with different error bars. X_full = X_full[::10] y_full = y_full[::10] n_samples, n_features = X_full.shape # Estimate the score on the entire dataset, with no missing values br_estimator = BayesianRidge() score_full_data = pd.DataFrame( cross_val_score( br_estimator, X_full, y_full, scoring="neg_mean_squared_error", cv=N_SPLITS ), columns=["Full Data"], ) # Add a single missing value to each row X_missing = X_full.copy() y_missing = y_full missing_samples = np.arange(n_samples) missing_features = rng.choice(n_features, n_samples, replace=True) X_missing[missing_samples, missing_features] = np.nan # Estimate the score after imputation (mean and median strategies) score_simple_imputer = pd.DataFrame() for strategy in ("mean", "median"): estimator = make_pipeline( SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator ) score_simple_imputer[strategy] = cross_val_score( estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS ) # Estimate the score after iterative imputation of the missing values # with different estimators estimators = [ BayesianRidge(), RandomForestRegressor( # We tuned the hyperparameters of the RandomForestRegressor to get a good # enough predictive performance for a restricted execution time. n_estimators=4, max_depth=10, bootstrap=True, max_samples=0.5, n_jobs=2, random_state=0, ), make_pipeline( Nystroem(kernel="polynomial", degree=2, random_state=0), Ridge(alpha=1e3) ), KNeighborsRegressor(n_neighbors=15), ] score_iterative_imputer = pd.DataFrame() # iterative imputer is sensible to the tolerance and # dependent on the estimator used internally. # we tuned the tolerance to keep this example run with limited computational # resources while not changing the results too much compared to keeping the # stricter default value for the tolerance parameter. tolerances = (1e-3, 1e-1, 1e-1, 1e-2) for impute_estimator, tol in zip(estimators, tolerances): estimator = make_pipeline( IterativeImputer( random_state=0, estimator=impute_estimator, max_iter=25, tol=tol ), br_estimator, ) score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score( estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS ) scores = pd.concat( [score_full_data, score_simple_imputer, score_iterative_imputer], keys=["Original", "SimpleImputer", "IterativeImputer"], axis=1, ) # plot california housing results fig, ax = plt.subplots(figsize=(13, 6)) means = -scores.mean() errors = scores.std() means.plot.barh(xerr=errors, ax=ax) ax.set_title("California Housing Regression with Different Imputation Methods") ax.set_xlabel("MSE (smaller is better)") ax.set_yticks(np.arange(means.shape[0])) ax.set_yticklabels([" w/ ".join(label) for label in means.index.tolist()]) plt.tight_layout(pad=1) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 6.778 seconds) .. _sphx_glr_download_auto_examples_impute_plot_iterative_imputer_variants_comparison.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/main?urlpath=lab/tree/notebooks/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_iterative_imputer_variants_comparison.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_iterative_imputer_variants_comparison.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_iterative_imputer_variants_comparison.zip ` .. include:: plot_iterative_imputer_variants_comparison.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_