"""
=========================================================
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:`~tree.DecisionTreeRegressor`: non-linear regression
* :class:`~ensemble.ExtraTreesRegressor`: similar to missForest in R
* :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. In this example, we have chosen to use
:class:`~ensemble.ExtraTreesRegressor` instead of
:class:`~ensemble.RandomForestRegressor` (as in missForest) due to its
increased speed.
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:`~ensemble.ExtraTreesRegressor` and
:class:`~linear_model.BayesianRidge` give the best results.
"""
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 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()