Imputing missing values with variants of IterativeImputer¶
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
BayesianRidge: regularized linear regression
RandomForestRegressor: Forests of randomized trees regression
KNeighborsRegressor: comparable to other KNN imputation approaches
Of particular interest is the ability of
IterativeImputer to mimic the behavior of missForest, a
popular imputation package for R.
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
IterativeImputer when using a
BayesianRidge estimator on the California housing
dataset with a single value randomly removed from each row.
It should be noted that some estimators such as
HistGradientBoostingRegressor can natively deal with
missing features and are often recommended over building pipelines with
complex and costly missing values imputation strategies.
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)) 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 5.769 seconds)