填充指的是利用现有知识/数据来确定缺失的数量值并填充的行为。我们有几个选择，最常见的是用此列其余部分的均值填充缺失值；
我们可以看到原始的数据集中有五行数据的plasma_glucose_concentration有缺失值；
print(pima['plasma_glucose_concentration'].isnull().sum())
# 5
我们可以先看下这5行的数据的具体情况
plasma_empty_index = pima[pima['plasma_glucose_concentration'].isnull()].index
print(pima.loc[plasma_empty_index])
# times_pregnant plasma_glucose_concentration diastolic_blood_pressure triceps_thickness serum_insulin bmi pedigree_function age onset_diabetes
# 75 1 None 48 20 None 24.7 0.140 22 0
# 182 1 None 74 20 23 27.7 0.299 21 0
# 342 1 None 68 35 None 32.0 0.389 22 0
# 349 5 None 80 32 None 41.0 0.346 37 1
# 502 6 None 68 41 None 39.0 0.727 41 1
我们使用列的均值进行填充之后，可以看到缺失值的行数为0，同时可以看到5行对应字段的值都是121.686763；
pima['plasma_glucose_concentration'].fillna(pima['plasma_glucose_concentration'].mean(), inplace=True)
print(pima['plasma_glucose_concentration'].isnull().sum())
print(pima.loc[plasma_empty_index])# 0
# times_pregnant plasma_glucose_concentration diastolic_blood_pressure triceps_thickness serum_insulin bmi pedigree_function age onset_diabetes
# 75 1 121.686763 48 20 None 24.7 0.140 22 0
# 182 1 121.686763 74 20 23 27.7 0.299 21 0
# 342 1 121.686763 68 35 None 32.0 0.389 22 0
# 349 5 121.686763 80 32 None 41.0 0.346 37 1
# 502 6 121.686763 68 41 None 39.0 0.727 41 1
我们可以直接使用sklearn的SimpleImputer来进行数据的填充；
from sklearn.impute import SimpleImputerimputer = SimpleImputer(strategy='mean')
pima_imputed = imputer.fit_transform(pima)
pima_imputed = pd.DataFrame(pima_imputed, columns=pima_column_names)
print(pima_imputed.loc[plasma_empty_index])
# times_pregnant plasma_glucose_concentration diastolic_blood_pressure triceps_thickness serum_insulin bmi pedigree_function age onset_diabetes
# 75 1.0 121.686763 48.0 20.0 155.548223 24.7 0.140 22.0 0.0
# 182 1.0 121.686763 74.0 20.0 23.000000 27.7 0.299 21.0 0.0
# 342 1.0 121.686763 68.0 35.0 155.548223 32.0 0.389 22.0 0.0
# 349 5.0 121.686763 80.0 32.0 155.548223 41.0 0.346 37.0 1.0
# 502 6.0 121.686763 68.0 41.0 155.548223 39.0 0.727 41.0 1.0
可以看到所有的列都已经填充，sklearn的填充处理类确实减少了繁琐的填充工作；
print(pima_imputed.isnull().sum())
# times_pregnant 0
# plasma_glucose_concentration 0
# diastolic_blood_pressure 0
# triceps_thickness 0
# serum_insulin 0
# bmi 0
# pedigree_function 0
# age 0
# onset_diabetes 0
# dtype: int64
如果我们直接使用0来填充缺失值，同样使用KNN模型得到的准确率是0.7357185298361768，有所降低的；
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCVpima_zero = pima.fillna(0)
X_zero = pima_zero.drop('onset_diabetes', axis=1)
print('learning from {} rows'.format(X_zero.shape[0]))
y_zero = pima_zero['onset_diabetes']knn_params = {'n_neighbors':[1, 2, 3, 4, 5, 6, 7]}
knn = KNeighborsClassifier()
grid = GridSearchCV(knn, knn_params)
grid.fit(X_zero, y_zero)
print(grid.best_score_, grid.best_params_)
# learning from 768 rows
# 0.7357185298361768 {'n_neighbors': 7}
scikit-learn有一个用于构建流水线的内置模块，其会对原始输入数据进行各种预处理；实际的训练过程中，是需要首先划分数据集，如果我们在应用算法之前直接对整个数据集填充值，我们就是在作弊，模型其实学不到任何模式。可以看见，模型的准确率是66%（并不好，但这不是重点）。重点是，训练集和测试集都是用整个X矩阵的均值填充的。这违反了机器学习流程的核心原则。当预测测试集的响应值时，不能假设我们已经知道了整个数据集的均值。
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifierX = pima[['serum_insulin']].copy()
y = pima['onset_diabetes'].copy()
entire_data_set_mean = X.mean()
X = X.fillna(entire_data_set_mean)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=99)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
score = knn.score(X_test, y_test)
print(score)
# 0.6666666666666666
我们使用训练样本的均值填充训练样本和测试样本的缺失值，然后进行训练；
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifierX = pima[['serum_insulin']].copy()
y = pima['onset_diabetes'].copy()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=99)
X_train_mean = X_train.mean()
X_train = X_train.fillna(X_train_mean)
X_test = X_test.fillna(X_train_mean)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
score = knn.score(X_test, y_test)
print(score)
# 0.6822916666666666
我们使用Pipeline结合SimpleImputer来使用均值填充数据，查看训练结果；
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCVknn_para = {'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}
knn = KNeighborsClassifier()
mean_impute = Pipeline([('imputer', SimpleImputer(strategy='mean')),('classify', knn)])
X = pima.drop('onset_diabetes', axis=1)
y = pima['onset_diabetes']
grid = GridSearchCV(mean_impute, knn_para)
grid.fit(X, y)
print(grid.best_score_, grid.best_params_)
# 0.7305407011289364 {'classify__n_neighbors': 7}
我们使用Pipeline结合SimpleImputer来使用中位数填充数据，查看训练结果；
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCVknn_para = {'classify__n_neighbors':[1, 2, 3, 4, 5, 6, 7]}
knn = KNeighborsClassifier()
mean_impute = Pipeline([('imputer', SimpleImputer(strategy='median')),('classify', knn)])
X = pima.drop('onset_diabetes', axis=1)
y = pima['onset_diabetes']
grid = GridSearchCV(mean_impute, knn_para)
grid.fit(X, y)
print(grid.best_score_, grid.best_params_)
# 0.7292589763177999 {'classify__n_neighbors': 7}
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