在构建估计器之前输入缺失值#

缺失值可以使用基本的 SimpleImputer .

在这个例子中，我们将研究不同的插补技术：

用常值0插补
通过每个特征的平均值与失踪程度指标辅助变量相结合进行插补
k最近邻插补
迭代插补

我们将使用两个数据集：糖尿病数据集由从糖尿病患者收集的10个特征变量组成，旨在预测疾病进展，以及加州住房数据集，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为缺失数据的新版本。的性能 RandomForestRegressor 然后将完整原始数据集的性能与使用不同技术估算的人为缺失值进行比较。

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

下载数据并设置缺失的值集#

首先我们下载这两个数据集。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个功能。加州住房数据集更大，有20640个条目和8个功能。它需要下载。为了加快计算，我们将仅使用前400个条目，但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)

指责缺失的数据和分数#

现在我们将编写一个函数，该函数将对不同估算的数据的结果进行评分。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: F401
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，估算器还具有 add_indicator 标记缺失值的参数，其中可能携带一些信息。

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)

估计分数#

首先，我们要估计原始数据的分数：

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")

将缺失的值替换为0#

现在我们将估计缺失值被0替换的数据的得分：

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")

kNN-imputation of the missing values#

KNNImputer 使用所需最近邻居数量的加权或未加权平均值来插补缺失值。

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")

用平均值输入缺失值#

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")

缺失值的迭代插补#

另一种选择是 IterativeImputer .这使用循环线性回归，将具有缺失值的每个特征依次建模为其他特征的函数。实现的版本假设高斯（输出）变量。如果您的功能明显不正常，请考虑将它们转换为看起来更正常，以可能提高性能。

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

你也可以尝试不同的技术。例如，中位数是一个更强大的估计数据与高幅度的变量，可能会主导结果（也称为“长尾”）。

Total running time of the script: （0分7.068秒）