[sklearn] 官方例程－Imputing missing values before building an estimator 随机填充缺失值

官方链接：http://scikit-learn.org/dev/auto_examples/plot_missing_values.html#sphx-glr-auto-examples-plot-missing-values-py

该例程是为了说明对缺失值的随即填充训练出的estimator表现优于直接删掉有缺失字段值的estimator

例程代码及附加注释如下：

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import numpy as np

from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.model_selection import cross_val_score

# 设定随机数种子
rng = np.random.RandomState(0)
# 载入数据 波士顿房价
dataset = load_boston()
X_full, y_full = dataset.data, dataset.target
n_samples = X_full.shape[0]
n_features = X_full.shape[1]

# Estimate the score on the entire dataset, with no missing values
# 随机森林－－回归 random_state-随机种子 n_estimator 森林里树的数目
estimator = RandomForestRegressor(random_state=0, n_estimators=100)
# 交叉验证分类器的准确率
score = cross_val_score(estimator, X_full, y_full).mean()
print("Score with the entire dataset = %.2f" % score)

# Add missing values in 75% of the lines
missing_rate = 0.75
n_missing_samples = int(np.floor(n_samples * missing_rate))
# hstack 把两个数组拼接起来－行数需要一致
missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,
                                      dtype=np.bool),
                             np.ones(n_missing_samples,
                                     dtype=np.bool)))

# 打乱随机数组顺序
rng.shuffle(missing_samples)
missing_features = rng.randint(0, n_features, n_missing_samples)

# Estimate the score without the lines containing missing values
X_filtered = X_full[~missing_samples, :]
y_filtered = y_full[~missing_samples]
estimator = RandomForestRegressor(random_state=0, n_estimators=100)
score = cross_val_score(estimator, X_filtered, y_filtered).mean()
print("Score without the samples containing missing values = %.2f" % score)

# Estimate the score after imputation of the missing values
X_missing = X_full.copy()
X_missing[np.where(missing_samples)[0], missing_features] = 0
y_missing = y_full.copy()
estimator = Pipeline([("imputer", Imputer(missing_values=0,
                                          strategy="mean",
                                          axis=0)),
                      ("forest", RandomForestRegressor(random_state=0,
                                                       n_estimators=100))])
score = cross_val_score(estimator, X_missing, y_missing).mean()
print("Score after imputation of the missing values = %.2f" % score)

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补充：
A. numpy.where()用法：