使用迭代插补变体插补缺失值#

IterativeImputer 类非常灵活 - 它可以与各种估计器一起使用来进行循环回归，依次将每个变量视为输出。

在本例中，我们比较了一些估计器，用于使用 IterativeImputer 进行缺失特征插补。

BayesianRidge：正则化线性回归
RandomForestRegressor：随机树回归森林
make_pipeline (Nystroem, Ridge): 一个包含 2 次多项式核函数扩展和正则化线性回归的管道
KNeighborsRegressor：与其他 KNN 插补方法相当

特别值得注意的是 IterativeImputer 能够模拟 missForest 的行为，missForest 是 R 中一个流行的插补包。

请注意，KNeighborsRegressor 与 KNN 插补不同，KNN 插补通过使用考虑缺失值的距离度量来从包含缺失值的样本中学习，而不是插补它们。

目标是比较不同的估计器，以查看在使用 IterativeImputer 时，哪个估计器最适合使用 BayesianRidge 估计器对加州住房数据集进行插补，该数据集从每行中随机删除了一个值。

对于这种特定的缺失值模式，我们发现 BayesianRidge 和 RandomForestRegressor 给出了最佳结果。

需要注意的是，一些估计器，例如 HistGradientBoostingRegressor，可以原生处理缺失特征，并且通常比构建包含复杂且昂贵的缺失值插补策略的管道更推荐。

California Housing Regression with Different Imputation Methods

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[0]))
ax.set_yticklabels([" w/ ".join(label) for label in means.index.tolist()])
plt.tight_layout(pad=1)
plt.show()

脚本的总运行时间：（0 分钟 6.125 秒）