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.. only:: html

    .. note::
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.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_impute_plot_missing_values.py:


====================================================
Imputing missing values before building an estimator
====================================================

Missing values can be replaced by the mean, the median or the most frequent
value using the basic :class:`~sklearn.impute.SimpleImputer`.

In this example we will investigate different imputation techniques:

- imputation by the constant value 0
- imputation by the mean value of each feature combined with a missing-ness
  indicator auxiliary variable
- k nearest neighbor imputation
- iterative imputation

We will use two datasets: Diabetes dataset which consists of 10 feature
variables collected from diabetes patients with an aim to predict disease
progression and California Housing dataset for which the target is the median
house value for California districts.

As neither of these datasets have missing values, we will remove some
values to create new versions with artificially missing data. The performance
of
:class:`~sklearn.ensemble.RandomForestRegressor` on the full original dataset
is then compared the performance on the altered datasets with the artificially
missing values imputed using different techniques.

.. GENERATED FROM PYTHON SOURCE LINES 30-34

.. code-block:: Python


    # Authors: Maria Telenczuk  <https://github.com/maikia>
    # License: BSD 3 clause








.. GENERATED FROM PYTHON SOURCE LINES 35-44

Download the data and make missing values sets
###############################################

 First we download the two datasets. Diabetes dataset is shipped with
 scikit-learn. It has 442 entries, each with 10 features. California Housing
 dataset is much larger with 20640 entries and 8 features. It needs to be
 downloaded. We will only use the first 400 entries for the sake of speeding
 up the calculations but feel free to use the whole dataset.


.. GENERATED FROM PYTHON SOURCE LINES 44-83

.. code-block:: Python


    import numpy as np

    from sklearn.datasets import fetch_california_housing, load_diabetes

    rng = np.random.RandomState(42)

    X_diabetes, y_diabetes = load_diabetes(return_X_y=True)
    X_california, y_california = fetch_california_housing(return_X_y=True)
    X_california = X_california[:300]
    y_california = y_california[:300]
    X_diabetes = X_diabetes[:300]
    y_diabetes = y_diabetes[:300]


    def add_missing_values(X_full, y_full):
        n_samples, n_features = X_full.shape

        # Add missing values in 75% of the lines
        missing_rate = 0.75
        n_missing_samples = int(n_samples * missing_rate)

        missing_samples = np.zeros(n_samples, dtype=bool)
        missing_samples[:n_missing_samples] = True

        rng.shuffle(missing_samples)
        missing_features = rng.randint(0, n_features, n_missing_samples)
        X_missing = X_full.copy()
        X_missing[missing_samples, missing_features] = np.nan
        y_missing = y_full.copy()

        return X_missing, y_missing


    X_miss_california, y_miss_california = add_missing_values(X_california, y_california)

    X_miss_diabetes, y_miss_diabetes = add_missing_values(X_diabetes, y_diabetes)









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Impute the missing data and score
#################################
Now we will write a function which will score the results on the differently
imputed data. Let's look at each imputer separately:


.. GENERATED FROM PYTHON SOURCE LINES 89-103

.. code-block:: Python


    rng = np.random.RandomState(0)

    from sklearn.ensemble import RandomForestRegressor

    # To use the experimental IterativeImputer, we need to explicitly ask for it:
    from sklearn.experimental import enable_iterative_imputer  # noqa
    from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
    from sklearn.model_selection import cross_val_score
    from sklearn.pipeline import make_pipeline

    N_SPLITS = 4
    regressor = RandomForestRegressor(random_state=0)








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Missing information
-------------------
In addition to imputing the missing values, the imputers have an
`add_indicator` parameter that marks the values that were missing, which
might carry some information.


.. GENERATED FROM PYTHON SOURCE LINES 110-127

.. code-block:: Python



    def get_scores_for_imputer(imputer, X_missing, y_missing):
        estimator = make_pipeline(imputer, regressor)
        impute_scores = cross_val_score(
            estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS
        )
        return impute_scores


    x_labels = []

    mses_california = np.zeros(5)
    stds_california = np.zeros(5)
    mses_diabetes = np.zeros(5)
    stds_diabetes = np.zeros(5)








.. GENERATED FROM PYTHON SOURCE LINES 128-132

Estimate the score
------------------
First, we want to estimate the score on the original data:


.. GENERATED FROM PYTHON SOURCE LINES 132-146

.. code-block:: Python



    def get_full_score(X_full, y_full):
        full_scores = cross_val_score(
            regressor, X_full, y_full, scoring="neg_mean_squared_error", cv=N_SPLITS
        )
        return full_scores.mean(), full_scores.std()


    mses_california[0], stds_california[0] = get_full_score(X_california, y_california)
    mses_diabetes[0], stds_diabetes[0] = get_full_score(X_diabetes, y_diabetes)
    x_labels.append("Full data")









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Replace missing values by 0
---------------------------

Now we will estimate the score on the data where the missing values are
replaced by 0:


.. GENERATED FROM PYTHON SOURCE LINES 153-172

.. code-block:: Python



    def get_impute_zero_score(X_missing, y_missing):
        imputer = SimpleImputer(
            missing_values=np.nan, add_indicator=True, strategy="constant", fill_value=0
        )
        zero_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
        return zero_impute_scores.mean(), zero_impute_scores.std()


    mses_california[1], stds_california[1] = get_impute_zero_score(
        X_miss_california, y_miss_california
    )
    mses_diabetes[1], stds_diabetes[1] = get_impute_zero_score(
        X_miss_diabetes, y_miss_diabetes
    )
    x_labels.append("Zero imputation")









.. GENERATED FROM PYTHON SOURCE LINES 173-178

kNN-imputation of the missing values
------------------------------------

:class:`~sklearn.impute.KNNImputer` imputes missing values using the weighted
or unweighted mean of the desired number of nearest neighbors.

.. GENERATED FROM PYTHON SOURCE LINES 178-195

.. code-block:: Python



    def get_impute_knn_score(X_missing, y_missing):
        imputer = KNNImputer(missing_values=np.nan, add_indicator=True)
        knn_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
        return knn_impute_scores.mean(), knn_impute_scores.std()


    mses_california[2], stds_california[2] = get_impute_knn_score(
        X_miss_california, y_miss_california
    )
    mses_diabetes[2], stds_diabetes[2] = get_impute_knn_score(
        X_miss_diabetes, y_miss_diabetes
    )
    x_labels.append("KNN Imputation")









.. GENERATED FROM PYTHON SOURCE LINES 196-199

Impute missing values with mean
-------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 199-214

.. code-block:: Python



    def get_impute_mean(X_missing, y_missing):
        imputer = SimpleImputer(missing_values=np.nan, strategy="mean", add_indicator=True)
        mean_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
        return mean_impute_scores.mean(), mean_impute_scores.std()


    mses_california[3], stds_california[3] = get_impute_mean(
        X_miss_california, y_miss_california
    )
    mses_diabetes[3], stds_diabetes[3] = get_impute_mean(X_miss_diabetes, y_miss_diabetes)
    x_labels.append("Mean Imputation")









.. GENERATED FROM PYTHON SOURCE LINES 215-225

Iterative imputation of the missing values
------------------------------------------

Another option is the :class:`~sklearn.impute.IterativeImputer`. This uses
round-robin linear regression, modeling each feature with missing values as a
function of other features, in turn.
The version implemented assumes Gaussian (output) variables. If your features
are obviously non-normal, consider transforming them to look more normal
to potentially improve performance.


.. GENERATED FROM PYTHON SOURCE LINES 225-251

.. code-block:: Python



    def get_impute_iterative(X_missing, y_missing):
        imputer = IterativeImputer(
            missing_values=np.nan,
            add_indicator=True,
            random_state=0,
            n_nearest_features=3,
            max_iter=1,
            sample_posterior=True,
        )
        iterative_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
        return iterative_impute_scores.mean(), iterative_impute_scores.std()


    mses_california[4], stds_california[4] = get_impute_iterative(
        X_miss_california, y_miss_california
    )
    mses_diabetes[4], stds_diabetes[4] = get_impute_iterative(
        X_miss_diabetes, y_miss_diabetes
    )
    x_labels.append("Iterative Imputation")

    mses_diabetes = mses_diabetes * -1
    mses_california = mses_california * -1








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Plot the results
################

Finally we are going to visualize the score:


.. GENERATED FROM PYTHON SOURCE LINES 257-305

.. code-block:: Python


    import matplotlib.pyplot as plt

    n_bars = len(mses_diabetes)
    xval = np.arange(n_bars)

    colors = ["r", "g", "b", "orange", "black"]

    # plot diabetes results
    plt.figure(figsize=(12, 6))
    ax1 = plt.subplot(121)
    for j in xval:
        ax1.barh(
            j,
            mses_diabetes[j],
            xerr=stds_diabetes[j],
            color=colors[j],
            alpha=0.6,
            align="center",
        )

    ax1.set_title("Imputation Techniques with Diabetes Data")
    ax1.set_xlim(left=np.min(mses_diabetes) * 0.9, right=np.max(mses_diabetes) * 1.1)
    ax1.set_yticks(xval)
    ax1.set_xlabel("MSE")
    ax1.invert_yaxis()
    ax1.set_yticklabels(x_labels)

    # plot california dataset results
    ax2 = plt.subplot(122)
    for j in xval:
        ax2.barh(
            j,
            mses_california[j],
            xerr=stds_california[j],
            color=colors[j],
            alpha=0.6,
            align="center",
        )

    ax2.set_title("Imputation Techniques with California Data")
    ax2.set_yticks(xval)
    ax2.set_xlabel("MSE")
    ax2.invert_yaxis()
    ax2.set_yticklabels([""] * n_bars)

    plt.show()




.. image-sg:: /auto_examples/impute/images/sphx_glr_plot_missing_values_001.png
   :alt: Imputation Techniques with Diabetes Data, Imputation Techniques with California Data
   :srcset: /auto_examples/impute/images/sphx_glr_plot_missing_values_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 306-309

You can also try different techniques. For instance, the median is a more
robust estimator for data with high magnitude variables which could dominate
results (otherwise known as a 'long tail').


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 11.186 seconds)


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