Sklearn笔记：缺失值处理

目录

• 单变量缺失
• 多元特征估计
• K-近邻法
• 标记推算值

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笔记：缺失值估算

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单变量缺失

import numpy as np
from sklearn.impute import SimpleImputer

help(SimpleImputer):

class SimpleImputer(_BaseImputer):Imputation transformer for completing missing values.

Parameters(参数设置)

missing_values(缺失值类型) : number, string, np.nan (default) or None

The placeholder for the missing values. All occurrences of missing_values will be imputed.

strategy : string, default='mean'

The imputation strategy.

• If "mean", then replace missing values using the mean along each column. Can only be used with numeric data.

• If "median", then replace missing values using the median along each column. Can only be used with numeric data.

• If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data.

• If "constant", then replace missing values with fill_value. Can be used with strings or numeric data.strategy="constant" for fixed value imputation.

fill_value : string or numerical value, default=None

When strategy == "constant", fill_value is used to replace all occurrences of missing_values.If left to the default, fill_value will be 0 when imputing numericaldata and "missing_value" for strings or object data types.

imp=SimpleImputer(missing_values=np.nan,strategy='mean')
imp.fit([[1,2],[np.nan,3],[7,6]])

SimpleImputer(add_indicator=False, copy=True, fill_value=None,
missing_values=nan, strategy='mean', verbose=0)

##SimpleImputer类支持稀疏矩阵
import scipy.sparse as sp
X=sp.csc_matrix([[1,2],[0,-1],[8,4]])
imp=SimpleImputer(missing_values=-1,strategy='mean')
imp.fit(X)

SimpleImputer(add_indicator=False, copy=True, fill_value=None,
missing_values=-1, strategy='mean', verbose=0)

X_test=sp.csc_matrix([[-1,2],[6,-1],[7,6]])
print(imp.transform(X_test))

(0, 0) 3.0
(1, 0) 6.0
(2, 0) 7.0
(0, 1) 2.0
(1, 1) 3.0
(2, 1) 6.0

print(imp.transform(X_test).toarray())

[[3. 2.]
[6. 3.]
[7. 6.]]

import pandas as pd
df=pd.DataFrame([['a','x'],
                [np.nan,'y'],
                ['a',np.nan],
                ['b','y']],dtype='category')
df

	0	1
0	a	x
1	NaN	y
2	a	NaN
3	b	y

imp=SimpleImputer(strategy='most_frequent')

print(imp.fit_transform(df))

[['a' 'x']
['a' 'y']
['a' 'y']
['b' 'y']]

多元特征估计

使用IterativeImputer类，它将每一个特征的缺失值建模为其它特性的函数，并使用该估计值进行估计。
工作模式：迭代循环
在每一步，都指定一个功能列出作为输出

import numpy as np
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer

imp=IterativeImputer(max_iter=10,random_state=0)
imp.fit([[1,2],[3,6],[4,8],[np.nan,3],[7,np.nan]])

IterativeImputer(add_indicator=False, estimator=None,
imputation_order='ascending', initial_strategy='mean',
max_iter=10, max_value=None, min_value=None,
missing_values=nan, n_nearest_features=None, random_state=0,
sample_posterior=False, skip_complete=False, tol=0.001,
verbose=0)

imp.transform([[1,2],[3,6],[4,8],[np.nan,3],[7,np.nan]])

array([[ 1. , 2. ],
[ 3. , 6. ],
[ 4. , 8. ],
[ 1.50004509, 3. ],
[ 7. , 14.00004135]])

X_test = [[np.nan, 2], [6, np.nan], [np.nan, 6]]
print(imp.transform(X_test))

[[ 1.00007297 2. ]
[ 6. 12.00002754]
[ 2.99996145 6. ]]

K-近邻法

这个KNNImputer类提供了使用k-最近邻方法填充缺失值的估算。默认情况下，支持缺失值的欧氏距离度量，nan_euclidean_distances，用于查找最近的邻居。每个缺失的特性都使用n_neighbors具有该功能值的最近邻居。

from sklearn.impute import KNNImputer

help(KNNImputer)：

Imputation for completing missing values using k-Nearest Neighbors.

(使用k近邻方法补全缺失值。)

Each sample's missing values are imputed using the mean value from n_neighbors nearest neighbors found in the training set. Two samples are close if the features that neither is missing are close.

(每个样本的缺失值是使用在训练集中找到的最近邻居的‘n_neighbors’的平均值来推算的.如果两个都不缺少的要素都不接近，则两个样本是接近的。)

Parameters：

missing_values : number, string, np.nan or None, default=np.nan

The placeholder for the missing values. All occurrences of missing_values will be imputed.

n_neighbors : int, default=5 Number of neighboring samples to use for imputation.

weights : {'uniform', 'distance'} or callable, default='uniform' Weight function used in prediction.

import numpy as np
from sklearn.impute import KNNImputer
nan = np.nan
X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]]
print(X)
imputer = KNNImputer(n_neighbors=2, weights="uniform")
imputer.fit_transform(X)

[[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]]

array([[1. , 2. , 4. ],
[3. , 4. , 3. ],
[5.5, 6. , 5. ],
[8. , 8. , 7. ]])

标记推算值

这个MissingIndicator转换器用于将数据集转换为相应的二进制矩阵，以指示数据集中是否存在缺失值。这种转换与计算相结合是很有用的。在使用估算时，保存有关哪些值丢失的信息可以提供信息。

from sklearn.impute import MissingIndicator

help(MissingIndicator):

class MissingIndicator(sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)

Binary indicators for missing values(缺失值的二进制指示符).

MissingIndicator(missing_values=nan, features='missing-only', sparse='auto', error_on_new=True)

X = np.array([[-1, -1, 1, 3],
              [4, -1, 0, -1],
              [8, -1, 1, 0]])
indicator = MissingIndicator(missing_values=-1)
mask_missing_values_only = indicator.fit_transform(X)
mask_missing_values_only

array([[ True, True, False],
[False, True, True],
[False, True, False]])

#只返回存在缺失值的列的索引
indicator.features_

array([0, 1, 2, 3])

#这个features参数可以设置为'all'若要返回所有特征，无论它们是否包含缺失的值
indicator = MissingIndicator(missing_values=-1, features="all")
mask_all = indicator.fit_transform(X)
mask_all

array([[ True, True, False, False],
[False, True, False, True],
[False, True, False, False]])

indicator.features_
#特征所在的列索引

array([0, 1, 2, 3])