.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/impute/plot_missing_values.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_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: The scikit-learn developers # SPDX-License-Identifier: 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) .. GENERATED FROM PYTHON SOURCE LINES 84-89 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) .. GENERATED FROM PYTHON SOURCE LINES 104-110 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") .. GENERATED FROM PYTHON SOURCE LINES 147-153 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 .. GENERATED FROM PYTHON SOURCE LINES 252-257 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 9.595 seconds) .. _sphx_glr_download_auto_examples_impute_plot_missing_values.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/impute/plot_missing_values.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_missing_values.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_missing_values.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_missing_values.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_missing_values.zip ` .. include:: plot_missing_values.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_