来源 https://www.cnblogs.com/B-Hanan/articles/12774433.html
1 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']]
2 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. ]]
3 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. ]])
4 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])