Note

Click here to download the full example code or to run this example in your browser via Binder

Imputing missing values with variants of IterativeImputer

The sklearn.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 sklearn.impute.IterativeImputer:

Of particular interest is the ability of sklearn.impute.IterativeImputer to mimic the behavior of missForest, a popular imputation package for R. In this example, we have chosen to use sklearn.ensemble.ExtraTreesRegressor instead of sklearn.ensemble.RandomForestRegressor (as in missForest) due to its increased speed.

Note that sklearn.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 sklearn.impute.IterativeImputer when using a sklearn.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 sklearn.ensemble.ExtraTreesRegressor and sklearn.linear_model.BayesianRidge give the best results.

../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_001.png 
print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# To use this experimental feature, we need to explicitly ask for it:
from sklearn.experimental import enable_iterative_imputer  # noqa
from sklearn.datasets import fetch_california_housing
from sklearn.impute import SimpleImputer
from sklearn.impute import IterativeImputer
from sklearn.linear_model import BayesianRidge
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_val_score

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(),
    DecisionTreeRegressor(max_features='sqrt', random_state=0),
    ExtraTreesRegressor(n_estimators=10, random_state=0),
    KNeighborsRegressor(n_neighbors=15)
]
score_iterative_imputer = pd.DataFrame()
for impute_estimator in estimators:
    estimator = make_pipeline(
        IterativeImputer(random_state=0, estimator=impute_estimator),
        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 boston 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()

Total running time of the script: ( 0 minutes 17.209 seconds)

Estimated memory usage: 60 MB

Gallery generated by Sphinx-Gallery