def fit_transform(self, x):
x = np.array(x)
return em(x)
def test_impute_missing_values(self):
""" After imputation, no NaN's should exist"""
imputed = em(self.data_m)
self.assertFalse(np.isnan(imputed).any())
# 调用impyute包中的数据补齐方法，主要有Multivariate Imputation，Expectation Maximization
import numpy as np
import time
import copy
from impyute.imputations.cs import mice
from impyute.imputations.cs import em
# 加载数据集
path = 'E:\\大论文相关\\LMVFM\\data\\compare';
x = np.loadtxt(path + '\\australian\\30%\\missing_other.txt');
x1 = copy.deepcopy(x)
# Multivariate Imputation by Chained Equations
time1 = time.clock();
data_mice = mice(x);
time_mice = time.clock() - time1;
#Expectation Maximization
time1 = time.clock();
data_em = em(x1);
time_em = time.clock() - time1;
np.savetxt('data_em.txt',data_em,'%.2f');
np.savetxt('data_mice.txt',data_mice,'%.2f');
print(time_mice ,'----', time_em)
def test_return_type(self):
""" Check return type, should return an np.ndarray"""
imputed = em(self.data_m)
self.assertTrue(isinstance(imputed, np.ndarray))
from impyute.datasets import random_uniform # from impyute.imputations.cs import random_imputation # from impyute.imputations.cs import mean_imputation from impyute.imputations.cs import em raw_data = random_uniform(shape=(5, 5), missingness="mcar", th=0.2) complete_data = em(raw_data)