import xlwt
import xlrd
import os
import numpy as np
import pandas as pd
from collections import OrderedDict
from pathlib import Path
from copy import deepcopy
from sklearn.preprocessing import StandardScaler
from time import time
from sklearn.metrics.pairwise import pairwise_distances
from numpy.linalg import inv
from sklearn.metrics import accuracy_score, mean_absolute_error, f1_score, mutual_info_score
from sklearn.neighbors import NearestNeighbors
from numpy.linalg import LinAlgError
from scipy.spatial.distance import pdist, squareform
from scipy.special import gammaln
# from ycimpute import EM
import copy
np.seterr(divide='ignore',invalid='ignore')
class EM():
def __init__(self,max_iter=100,theta=1e-5):
self.max_iter = max_iter
self.theta = theta
def _init_parameters(self, X):
rows, cols = X.shape
mu_init = np.nanmean(X, axis=0)
sigma_init = np.zeros((cols, cols))
for i in range(cols):
for j in range(i, cols):
vec_col = X[:, [i, j]]
vec_col = vec_col[~np.any(np.isnan(vec_col), axis=1), :].T
if len(vec_col) > 0:
cov = np.cov(vec_col)
cov = cov[0, 1]
sigma_init[i, j] = cov
sigma_init[j, i] = cov
else:
sigma_init[i, j] = 1.0
sigma_init[j, i] = 1.0
return mu_init, sigma_init
def _e_step(self, mu,sigma, X):
samples,_ = X.shape
for sample in range(samples):
if np.any(np.isnan(X[sample,:])):
loc_nan = np.isnan(X[sample,:])
new_mu = np.dot(sigma[loc_nan, :][:, ~loc_nan],
np.dot(np.linalg.inv(sigma[~loc_nan, :][:, ~loc_nan]),
(X[sample, ~loc_nan] - mu[~loc_nan])[:,np.newaxis]))
nan_count = np.sum(loc_nan)
X[sample, loc_nan] = mu[loc_nan] + new_mu.reshape(1,nan_count)
return X
def _m_step(self,X):
rows, cols = X.shape
mu = np.mean(X, axis=0)
sigma = np.cov(X.T)
tmp_theta = -0.5 * rows * (cols * np.log(2 * np.pi) +
np.log(np.linalg.det(sigma)))
return mu, sigma,tmp_theta
def solve(self, X, missing_mask):
mu, sigma = self._init_parameters(X)
complete_X,updated_X = None, None
rows,_ = X.shape
theta = -np.inf
for iter in range(self.max_iter):
updated_X = self._e_step(mu=mu, sigma=sigma, X=copy.copy(X))
mu, sigma, tmp_theta = self._m_step(updated_X)
for i in range(rows):
tmp_theta -= 0.5 * np.dot((updated_X[i, :] - mu),
np.dot(np.linalg.inv(sigma), (updated_X[i, :] - mu)[:, np.newaxis]))
if abs(tmp_theta-theta)
