在构建估计器之前估算缺失值#

可以使用基本的SimpleImputer用均值、中位数或最频繁的值替换缺失值。

在这个例子中，我们将研究不同的估算技术

用常数 0 估算
用每个特征的平均值估算，并结合缺失值指示器辅助变量
k 近邻估算
迭代估算

我们将使用两个数据集：糖尿病数据集，包含从糖尿病患者收集的 10 个特征变量，旨在预测疾病进展；加州住房数据集，其目标是加州地区的房屋中位数价值。

由于这两个数据集都没有缺失值，我们将删除一些值以创建具有人工缺失数据的新版本。RandomForestRegressor在完整原始数据集上的性能将与在使用不同技术估算人工缺失值后的更改数据集上的性能进行比较。

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

下载数据并创建缺失值集#

首先，我们下载两个数据集。糖尿病数据集随 scikit-learn 一起提供。它有 442 个条目，每个条目有 10 个特征。加州住房数据集要大得多，有 20640 个条目和 8 个特征。它需要下载。为了加快计算速度，我们将只使用前 400 个条目，但您可以随意使用整个数据集。

import numpy as np

from sklearn.datasets import fetch_california_housing, load_diabetes

rng = np.random.RandomState(42)

X_diabetes, y_diabetes = load_diabetes(return_X_y=True)
X_california, y_california = fetch_california_housing(return_X_y=True)
X_california = X_california[:300]
y_california = y_california[:300]
X_diabetes = X_diabetes[:300]
y_diabetes = y_diabetes[:300]


def add_missing_values(X_full, y_full):
    n_samples, n_features = X_full.shape

    # Add missing values in 75% of the lines
    missing_rate = 0.75
    n_missing_samples = int(n_samples * missing_rate)

    missing_samples = np.zeros(n_samples, dtype=bool)
    missing_samples[:n_missing_samples] = True

    rng.shuffle(missing_samples)
    missing_features = rng.randint(0, n_features, n_missing_samples)
    X_missing = X_full.copy()
    X_missing[missing_samples, missing_features] = np.nan
    y_missing = y_full.copy()

    return X_missing, y_missing


X_miss_california, y_miss_california = add_missing_values(X_california, y_california)

X_miss_diabetes, y_miss_diabetes = add_missing_values(X_diabetes, y_diabetes)

估算缺失数据并评分#

现在，我们将编写一个函数来对不同估算的数据进行评分。让我们分别看看每个估算器

rng = np.random.RandomState(0)

from sklearn.ensemble import RandomForestRegressor

# To use the experimental IterativeImputer, we need to explicitly ask for it:
from sklearn.experimental import enable_iterative_imputer  # noqa
from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline

N_SPLITS = 4
regressor = RandomForestRegressor(random_state=0)

缺失信息#

除了估算缺失值外，估算器还有一个add_indicator参数，用于标记缺失的值，这些值可能包含一些信息。

def get_scores_for_imputer(imputer, X_missing, y_missing):
    estimator = make_pipeline(imputer, regressor)
    impute_scores = cross_val_score(
        estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS
    )
    return impute_scores


x_labels = []

mses_california = np.zeros(5)
stds_california = np.zeros(5)
mses_diabetes = np.zeros(5)
stds_diabetes = np.zeros(5)

估算分数#

首先，我们想估算原始数据的评分

def get_full_score(X_full, y_full):
    full_scores = cross_val_score(
        regressor, X_full, y_full, scoring="neg_mean_squared_error", cv=N_SPLITS
    )
    return full_scores.mean(), full_scores.std()


mses_california[0], stds_california[0] = get_full_score(X_california, y_california)
mses_diabetes[0], stds_diabetes[0] = get_full_score(X_diabetes, y_diabetes)
x_labels.append("Full data")

用 0 替换缺失值#

现在，我们将估算用 0 替换缺失值的数据的评分

def get_impute_zero_score(X_missing, y_missing):
    imputer = SimpleImputer(
        missing_values=np.nan, add_indicator=True, strategy="constant", fill_value=0
    )
    zero_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
    return zero_impute_scores.mean(), zero_impute_scores.std()


mses_california[1], stds_california[1] = get_impute_zero_score(
    X_miss_california, y_miss_california
)
mses_diabetes[1], stds_diabetes[1] = get_impute_zero_score(
    X_miss_diabetes, y_miss_diabetes
)
x_labels.append("Zero imputation")

缺失值的 kNN 估算#

KNNImputer 使用所需数量的最近邻的加权或未加权平均值来估算缺失值。

def get_impute_knn_score(X_missing, y_missing):
    imputer = KNNImputer(missing_values=np.nan, add_indicator=True)
    knn_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
    return knn_impute_scores.mean(), knn_impute_scores.std()


mses_california[2], stds_california[2] = get_impute_knn_score(
    X_miss_california, y_miss_california
)
mses_diabetes[2], stds_diabetes[2] = get_impute_knn_score(
    X_miss_diabetes, y_miss_diabetes
)
x_labels.append("KNN Imputation")

用均值估算缺失值#

def get_impute_mean(X_missing, y_missing):
    imputer = SimpleImputer(missing_values=np.nan, strategy="mean", add_indicator=True)
    mean_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
    return mean_impute_scores.mean(), mean_impute_scores.std()


mses_california[3], stds_california[3] = get_impute_mean(
    X_miss_california, y_miss_california
)
mses_diabetes[3], stds_diabetes[3] = get_impute_mean(X_miss_diabetes, y_miss_diabetes)
x_labels.append("Mean Imputation")

缺失值的迭代估算#

另一个选择是IterativeImputer。这使用循环回归，依次将每个具有缺失值的特征建模为其他特征的函数。实现的版本假设高斯（输出）变量。如果您的特征显然是非正态的，请考虑将它们转换为看起来更正态的形式，以潜在地提高性能。

def get_impute_iterative(X_missing, y_missing):
    imputer = IterativeImputer(
        missing_values=np.nan,
        add_indicator=True,
        random_state=0,
        n_nearest_features=3,
        max_iter=1,
        sample_posterior=True,
    )
    iterative_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)
    return iterative_impute_scores.mean(), iterative_impute_scores.std()


mses_california[4], stds_california[4] = get_impute_iterative(
    X_miss_california, y_miss_california
)
mses_diabetes[4], stds_diabetes[4] = get_impute_iterative(
    X_miss_diabetes, y_miss_diabetes
)
x_labels.append("Iterative Imputation")

mses_diabetes = mses_diabetes * -1
mses_california = mses_california * -1

绘制结果#

最后，我们将可视化分数

import matplotlib.pyplot as plt

n_bars = len(mses_diabetes)
xval = np.arange(n_bars)

colors = ["r", "g", "b", "orange", "black"]

# plot diabetes results
plt.figure(figsize=(12, 6))
ax1 = plt.subplot(121)
for j in xval:
    ax1.barh(
        j,
        mses_diabetes[j],
        xerr=stds_diabetes[j],
        color=colors[j],
        alpha=0.6,
        align="center",
    )

ax1.set_title("Imputation Techniques with Diabetes Data")
ax1.set_xlim(left=np.min(mses_diabetes) * 0.9, right=np.max(mses_diabetes) * 1.1)
ax1.set_yticks(xval)
ax1.set_xlabel("MSE")
ax1.invert_yaxis()
ax1.set_yticklabels(x_labels)

# plot california dataset results
ax2 = plt.subplot(122)
for j in xval:
    ax2.barh(
        j,
        mses_california[j],
        xerr=stds_california[j],
        color=colors[j],
        alpha=0.6,
        align="center",
    )

ax2.set_title("Imputation Techniques with California Data")
ax2.set_yticks(xval)
ax2.set_xlabel("MSE")
ax2.invert_yaxis()
ax2.set_yticklabels([""] * n_bars)

plt.show()

Imputation Techniques with Diabetes Data, Imputation Techniques with California Data

您也可以尝试不同的技术。例如，对于具有高幅度变量（可能支配结果，也称为“长尾”）的数据，中位数是更稳健的估计量。

脚本的总运行时间：（0 分钟 11.125 秒）