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.. _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 46-154



.. 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:: default


    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 5.236 seconds)


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