使用不同变体的 IterativeImputer 填充缺失值#

IterativeImputer 类非常灵活——它可以与各种估计器结合使用，通过循环回归的方式，轮流将每个变量作为输出进行预测。

在本示例中，我们比较了用于 IterativeImputer 缺失特征填充任务的一些估计器：

BayesianRidge：正则化线性回归
RandomForestRegressor：随机森林回归
make_pipeline (Nystroem, Ridge)：包含 2 次多项式核展开和正则化线性回归的流水线
KNeighborsRegressor：可与其他 KNN 填充方法相媲美

特别值得关注的是 IterativeImputer 模拟 missForest（R 语言中一个流行的填充包）行为的能力。

请注意，KNeighborsRegressor 与 KNN 填充不同。KNN 填充通过使用能够处理缺失值的距离度量从含有缺失值的样本中学习，而不是通过对缺失值进行填充。

我们的目标是比较不同的估计器，以确定当使用加州住房数据集（其中每行随机删除一个值）时，哪种估计器最适合 IterativeImputer，并将其与 BayesianRidge 估计器进行对比。

对于这种特定的缺失值模式，我们发现 BayesianRidge 和 RandomForestRegressor 能够提供最好的结果。

需要注意的是，一些估计器（例如 HistGradientBoostingRegressor）可以原生处理缺失特征，通常建议优先使用此类估计器，而不是构建复杂且昂贵的缺失值填充策略流水线。

California Housing Regression with Different Imputation Methods

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

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: F401
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
from sklearn.preprocessing import RobustScaler

N_SPLITS = 5

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


def compute_score_for(X, y, imputer=None):
    # We scale data before imputation and training a target estimator,
    # because our target estimator and some of the imputers assume
    # that the features have similar scales.
    if imputer is None:
        estimator = make_pipeline(RobustScaler(), BayesianRidge())
    else:
        estimator = make_pipeline(RobustScaler(), imputer, BayesianRidge())
    return cross_val_score(
        estimator, X, y, scoring="neg_mean_squared_error", cv=N_SPLITS
    )


# Estimate the score on the entire dataset, with no missing values
score_full_data = pd.DataFrame(
    compute_score_for(X_full, y_full),
    columns=["Full Data"],
)

# Add a single missing value to each row
rng = np.random.RandomState(0)
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"):
    score_simple_imputer[strategy] = compute_score_for(
        X_missing, y_missing, SimpleImputer(strategy=strategy)
    )

# Estimate the score after iterative imputation of the missing values
# with different estimators
named_estimators = [
    ("Bayesian Ridge", BayesianRidge()),
    (
        "Random Forest",
        RandomForestRegressor(
            # We tuned the hyperparameters of the RandomForestRegressor to get a good
            # enough predictive performance for a restricted execution time.
            n_estimators=5,
            max_depth=10,
            bootstrap=True,
            max_samples=0.5,
            n_jobs=2,
            random_state=0,
        ),
    ),
    (
        "Nystroem + Ridge",
        make_pipeline(
            Nystroem(kernel="polynomial", degree=2, random_state=0), Ridge(alpha=1e4)
        ),
    ),
    (
        "k-NN",
        KNeighborsRegressor(n_neighbors=10),
    ),
]
score_iterative_imputer = pd.DataFrame()
# Iterative imputer is sensitive 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 (name, impute_estimator), tol in zip(named_estimators, tolerances):
    score_iterative_imputer[name] = compute_score_for(
        X_missing,
        y_missing,
        IterativeImputer(
            random_state=0, estimator=impute_estimator, max_iter=40, tol=tol
        ),
    )

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()

脚本总运行时间：（0 分 6.865 秒）