DL_A1.py

# -*- coding: utf-8 -*-
"""
Created on Wed Dec 28 15:22:31 2016
DL Assignment 1.
@author: Seagle
"""

# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle

# Config the matplotlib backend as plotting inline in IPython
# matplotlib inline

#%%
url = 'http://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None

def download_progress_hook(count, blockSize, totalSize):
  """A hook to report the progress of a download. This is mostly intended for users with
  slow internet connections. Reports every 1% change in download progress.
  """
  global last_percent_reported
  percent = int(count * blockSize * 100 / totalSize)

  if last_percent_reported != percent:
    if percent % 5 == 0:
      sys.stdout.write("%s%%" % percent)
      sys.stdout.flush()
    else:
      sys.stdout.write(".")
      sys.stdout.flush()
      
    last_percent_reported = percent
        
def maybe_download(filename, expected_bytes, force=False):
  """Download a file if not present, and make sure it's the right size."""
  if force or not os.path.exists(filename):
    print('Attempting to download:', filename) 
    filename, _ = urlretrieve(url + filename, filename, 
                              reporthook=download_progress_hook)
    print('\nDownload Complete!')
  statinfo = os.stat(filename)
  if statinfo.st_size == expected_bytes:
    print('Found and verified', filename)
  else:
    raise Exception(
      'Failed to verify ' + filename + '. Can you get to it with a browser?')
  return filename

train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)
test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)

#%%
num_classes = 10
np.random.seed(133)

def maybe_extract(filename, force=False):
  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz
  if os.path.isdir(root) and not force:
    # You may override by setting force=True.
    print('%s already present - Skipping extraction of %s.' % (root, filename))
  else:
    print('Extracting data for %s. This may take a while. Please wait.' % root)
    tar = tarfile.open(filename)
    sys.stdout.flush()
    tar.extractall()
    tar.close()
  data_folders = [
    os.path.join(root, d) for d in sorted(os.listdir(root))
    if os.path.isdir(os.path.join(root, d))]
  if len(data_folders) != num_classes:
    raise Exception(
      'Expected %d folders, one per class. Found %d instead.' % (
        num_classes, len(data_folders)))
  print(data_folders)
  return data_folders
  
train_folders = maybe_extract(train_filename)
test_folders = maybe_extract(test_filename)

#%% Display image
# This section is very slow.
import random
import hashlib

def disp_samples(data_folders, sample_size):
  for folder in data_folders:
    print(folder)
    image_files = os.listdir(folder)
    image_sample = random.sample(image_files, sample_size)
    for image in image_sample:
      image_file = os.path.join(folder, image)
      i = Image(filename=image_file)
      display(i)

disp_samples(train_folders, 1)

#%%      
display(Image(filename="notMNIST_small/A/Q0NXaWxkV29yZHMtQm9sZEl0YWxpYy50dGY=.png"))

display(Image(filename="notMNIST_small/C/QmVlc2tuZWVzQy5vdGY=.png"))

#%% Convert to tensor

image_size = 28  # Pixel width and height.
pixel_depth = 255.0  # Number of levels per pixel.

def load_letter(folder, min_num_images):
  """Load the data for a single letter label."""
  image_files = os.listdir(folder)
  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
                         dtype=np.float32)
  print(folder)
  num_images = 0
  for image in image_files:
    image_file = os.path.join(folder, image)
    try:
      image_data = (ndimage.imread(image_file).astype(float) - 
                    pixel_depth / 2) / pixel_depth
      if image_data.shape != (image_size, image_size):
        raise Exception('Unexpected image shape: %s' % str(image_data.shape))
      dataset[num_images, :, :] = image_data
      num_images = num_images + 1
    except IOError as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
    
  dataset = dataset[0:num_images, :, :]
  if num_images < min_num_images:
    raise Exception('Many fewer images than expected: %d < %d' %
                    (num_images, min_num_images))
    
  print('Full dataset tensor:', dataset.shape)
  print('Mean:', np.mean(dataset))
  print('Standard deviation:', np.std(dataset))
  return dataset
        
def maybe_pickle(data_folders, min_num_images_per_class, force=False):
  dataset_names = []
  for folder in data_folders:
    set_filename = folder + '.pickle'
    dataset_names.append(set_filename)
    if os.path.exists(set_filename) and not force:
      # You may override by setting force=True.
      print('%s already present - Skipping pickling.' % set_filename)
    else:
      print('Pickling %s.' % set_filename)
      dataset = load_letter(folder, min_num_images_per_class)
      try:
        with open(set_filename, 'wb') as f:
          pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
      except Exception as e:
        print('Unable to save data to', set_filename, ':', e)
  
  return dataset_names

train_datasets = maybe_pickle(train_folders, 45000)
test_datasets = maybe_pickle(test_folders, 1800)

#%% Display the normalized image A
pickle_file = train_datasets[0]  # index 0 should be all As, 1 = all Bs, etc.
with open(pickle_file, 'rb') as f:
    letter_set = pickle.load(f)  # unpickle
    sample_idx = np.random.randint(len(letter_set))  # pick a random image index
    sample_image = letter_set[sample_idx, :, :]  # extract a 2D slice
    plt.figure()
    plt.imshow(sample_image)  # display it
    

pickle_file = train_datasets[8]  # index 0 should be all As, 1 = all Bs, etc.
with open(pickle_file, 'rb') as f:
    letter_set = pickle.load(f)  # unpickle
    sample_idx = np.random.randint(len(letter_set))  # pick a random image index
    sample_image = letter_set[sample_idx, :, :]  # extract a 2D slice
    plt.figure()
    plt.imshow(sample_image)  # display it
#%% A new section on display images
def disp_8_img(imgs, titles):
  """Display subplot with 8 images or less"""
  for i, img in enumerate(imgs):
    plt.subplot(2, 4, i+1)
    plt.title(titles[i])
    plt.axis('off')
    plt.imshow(img)

def disp_sample_pickles(data_folders):
  folder = random.sample(data_folders, 1)
  pickle_filename = ''.join(folder) + '.pickle'
  try:
    with open(pickle_filename, 'rb') as f:
      dataset = pickle.load(f)
  except Exception as e:
    print('Unable to read data from', pickle_filename, ':', e)
    return
  # display
  plt.suptitle(''.join(folder)[-1])
  for i, img in enumerate(random.sample(list(dataset), 8)):
    plt.subplot(2, 4, i+1)
    plt.axis('off')
    plt.imshow(img)
    
disp_sample_pickles(train_folders)
disp_sample_pickles(test_folders)
#%% Verify the data is balanced across all cases.
def disp_number_images(data_folders):
  for folder in data_folders:
    pickle_filename = ''.join(folder) + '.pickle'
    try:
      with open(pickle_filename, 'rb') as f:
        dataset = pickle.load(f)
    except Exception as e:
      print('Unable to read data from', pickle_filename, ':', e)
      return
    print('Number of images in ', folder, ' : ', len(dataset))
    
disp_number_images(train_folders)
disp_number_images(test_folders)
#%%
def make_arrays(nb_rows, img_size):
  if nb_rows:
    dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)
    labels = np.ndarray(nb_rows, dtype=np.int32)
  else:
    dataset, labels = None, None
  return dataset, labels

def merge_datasets(pickle_files, train_size, valid_size=0):
  num_classes = len(pickle_files)
  valid_dataset, valid_labels = make_arrays(valid_size, image_size)
  train_dataset, train_labels = make_arrays(train_size, image_size)
  vsize_per_class = valid_size // num_classes
  tsize_per_class = train_size // num_classes
    
  start_v, start_t = 0, 0
  end_v, end_t = vsize_per_class, tsize_per_class
  end_l = vsize_per_class+tsize_per_class
  for label, pickle_file in enumerate(pickle_files):       
    try:
      with open(pickle_file, 'rb') as f:
        letter_set = pickle.load(f)
        # let's shuffle the letters to have random validation and training set
        np.random.shuffle(letter_set)
        if valid_dataset is not None:
          valid_letter = letter_set[:vsize_per_class, :, :]
          valid_dataset[start_v:end_v, :, :] = valid_letter
          valid_labels[start_v:end_v] = label
          start_v += vsize_per_class
          end_v += vsize_per_class
                    
        train_letter = letter_set[vsize_per_class:end_l, :, :]
        train_dataset[start_t:end_t, :, :] = train_letter
        train_labels[start_t:end_t] = label
        start_t += tsize_per_class
        end_t += tsize_per_class
    except Exception as e:
      print('Unable to process data from', pickle_file, ':', e)
      raise
    
  return valid_dataset, valid_labels, train_dataset, train_labels
  
  
            
train_size = 200000
valid_size = 10000
test_size = 10000

valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(
  train_datasets, train_size, valid_size)
_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)

print('Training:', train_dataset.shape, train_labels.shape)
print('Validation:', valid_dataset.shape, valid_labels.shape)
print('Testing:', test_dataset.shape, test_labels.shape)  

#%%
def randomize(dataset, labels):
  permutation = np.random.permutation(labels.shape[0])
  shuffled_dataset = dataset[permutation,:,:]
  shuffled_labels = labels[permutation]
  return shuffled_dataset, shuffled_labels
train_dataset, train_labels = randomize(train_dataset, train_labels)
test_dataset, test_labels = randomize(test_dataset, test_labels)
valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)

#%%

print('Training:', train_dataset.shape, train_labels.shape)
print('Validation:', valid_dataset.shape, valid_labels.shape)
print('Testing:', test_dataset.shape, test_labels.shape)  


pretty_labels = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 
                 7: 'H', 8: 'I', 9: 'J'}
                 
                 
np.random.seed(100)
def disp_sample_dataset(dataset, labels):
  items = random.sample(range(len(labels)), 8)
  for i, item in enumerate(items):
    plt.subplot(2, 4, i+1)
    plt.axis('off')
    plt.title(pretty_labels[labels[item]])
    plt.imshow(dataset[item])
    
disp_sample_dataset(train_dataset, train_labels)
disp_sample_dataset(test_dataset, test_labels)
disp_sample_dataset(valid_dataset, valid_labels)

#%%
pickle_file = 'notMNIST.pickle'

try:
  f = open(pickle_file, 'wb')
  save = {
    'train_dataset': train_dataset,
    'train_labels': train_labels,
    'valid_dataset': valid_dataset,
    'valid_labels': valid_labels,
    'test_dataset': test_dataset,
    'test_labels': test_labels,
    }
  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
  f.close()
except Exception as e:
  print('Unable to save data to', pickle_file, ':', e)
  raise

#%%
statinfo = os.stat(pickle_file)
print('Compressed pickle size:', statinfo.st_size)

#%% Problem 6 Try logistic regression
train_size= 50
X_data = train_dataset[:train_size].reshape(train_size, 784)
Y_data = train_labels[:train_size]
lgr =LogisticRegression()
lgr.fit(X_data, Y_data)

#%%
test_size = 20
X_test = test_dataset[:test_size].reshape(test_size, 784)
print(lgr.score(X_test, test_labels[:test_size]))

#%%
import sklearn.metrics
X_test_pred = lgr.predict(X_test)
sklearn.metrics.confusion_matrix(test_labels[:test_size], X_test_pred)