data_loader.py

# coding=utf-8
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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'''Data loader for UCI letter, spam and MNIST datasets.
'''

# Necessary packages
import numpy as np
from utils import binary_sampler
from keras.datasets import mnist


def data_loader (data_name, miss_rate):
  '''Loads datasets and introduce missingness.
  
  Args:
    - data_name: letter, spam, or mnist
    - miss_rate: the probability of missing components
    
  Returns:
    data_x: original data
    miss_data_x: data with missing values
    data_m: indicator matrix for missing components
  '''
  
  # Load data
  if data_name in ['letter', 'spam']:
    file_name = 'data/'+data_name+'.csv'
    data_x = np.loadtxt(file_name, delimiter=",", skiprows=1)
  elif data_name == 'mnist':
    (data_x, _), _ = mnist.load_data()
    data_x = np.reshape(np.asarray(data_x), [60000, 28*28]).astype(float)

  # Parameters
  no, dim = data_x.shape
  
  # Introduce missing data
  data_m = binary_sampler(1-miss_rate, no, dim)
  miss_data_x = data_x.copy()
  miss_data_x[data_m == 0] = np.nan
      
  return data_x, miss_data_x, data_m