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

"""
Explanation: Deep Learning
Assignment 1
The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
This notebook uses the notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.
End of explanation
"""


url = 'http://yaroslavvb.com/upload/notMNIST/'

def maybe_download(filename, expected_bytes):
  """Download a file if not present, and make sure it's the right size."""
  if not os.path.exists(filename):
    filename, _ = urllib.urlretrieve(url + filename, filename)
  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)

"""
Explanation: First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine.
End of explanation
"""


num_classes = 10

def extract(filename):
  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz
  if not os.path.exists(root):
      tar = tarfile.open(filename)
      tar.extractall()
      tar.close()
  data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root))]
  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 = extract(train_filename)
test_folders = extract(test_filename)

"""
Explanation: Extract the dataset from the compressed .tar.gz file.
This should give you a set of directories, labelled A through J.
End of explanation
"""


display(Image("notMNIST_small/A/MDEtMDEtMDAudHRm.png"), Image("notMNIST_small/D/MDEtMDEtMDAudHRm.png"))
display(Image("notMNIST_small/D/MDEtMDEtMDAudHRm.png"))
display(Image("notMNIST_small/C/MDEtMDEtMDAudHRm.png"))
display(Image("notMNIST_small/B/MDEtMDEtMDAudHRm.png"))

"""
Explanation: Problem 1
Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.
End of explanation
"""


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

def load(data_folders, min_num_images, max_num_images):
  dataset = np.ndarray(
    shape=(max_num_images, image_size, image_size), dtype=np.float32)
  labels = np.ndarray(shape=(max_num_images), dtype=np.int32)
  label_index = 0
  image_index = 0
  for folder in data_folders:
    print folder
    for image in os.listdir(folder):
      if image_index >= max_num_images:
        raise Exception('More images than expected: %d >= %d' % (
          num_images, max_num_images))
      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[image_index, :, :] = image_data
        labels[image_index] = label_index
        image_index += 1
      except IOError as e:
        print 'Could not read:', image_file, ':', e, '- it\'s ok, skipping.'
    label_index += 1
  num_images = image_index
  dataset = dataset[0:num_images, :, :]
  labels = labels[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)
  print 'Labels:', labels.shape
  return dataset, labels
train_dataset, train_labels = load(train_folders, 450000, 550000)
test_dataset, test_labels = load(test_folders, 18000, 20000)

"""
Explanation: Now let's load the data in a more manageable format.
We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. The labels will be stored into a separate array of integers 0 through 9.
A few images might not be readable, we'll just skip them.
End of explanation
"""


plt.imshow(train_dataset[np.random.randint(train_dataset.shape[0])])

"""
Explanation: Problem 2
Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.
End of explanation
"""


np.random.seed(133)
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)

"""
Explanation: Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.
End of explanation
"""


#plt.imshow(train_dataset[np.random.randint(train_dataset.shape[0])])
plt.pcolor(train_dataset[np.random.randint(train_dataset.shape[0])], cmap="Greys")

"""
Explanation: Problem 3
Convince yourself that the data is still good after shuffling!
End of explanation
"""


fig, ax = plt.subplots(1,2)
bins = np.arange(train_labels.min(), train_labels.max()+2)
ax[0].hist(train_labels, bins=bins)
ax[0].set_xticks((bins[:-1]+bins[1:])/2, [chr(k) for k in range(ord("A"), ord("J")+1)])
ax[0].set_title("Training data")

bins = np.arange(test_labels.min(), test_labels.max()+2)
ax[1].hist(test_labels, bins=bins)
ax[1].set_xticks((bins[:-1]+bins[1:])/2, [chr(k) for k in range(ord("A"), ord("J")+1)])
ax[1].set_title("Test data")
[chr(k) for k in range(ord("A"), ord("J")+1)]

print (bins[:-1]+bins[1:])/2
print train_labels.min(), train_labels.max()


"""
Explanation: Problem 4
Another check: we expect the data to be balanced across classes. Verify that.
End of explanation
"""


train_size = 200000
valid_size = 10000

valid_dataset = train_dataset[:valid_size,:,:]
valid_labels = train_labels[:valid_size]
train_dataset = train_dataset[valid_size:valid_size+train_size,:,:]
train_labels = train_labels[valid_size:valid_size+train_size]
print 'Training', train_dataset.shape, train_labels.shape
print 'Validation', valid_dataset.shape, valid_labels.shape

"""
Explanation: Prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune train_size as needed.
Also create a validation dataset for hyperparameter tuning.
End of explanation
"""


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

"""
Explanation: Finally, let's save the data for later reuse:
End of explanation
"""


def find_duplicates(d1, l1, d2, l2):
    """
    d1: data 1 - smaller
    d2: data 2 - larger
    """
    print d1.shape, d2.shape
    total_items = d1.shape[0] + d2.shape[0]
    overlap_items = 0
    for l in range(10):
        # Check only items with same labels
        idx1 = (l1 == l)
        idx2 = (l2 == l)
        m1 = d1[idx1]
        m2 = d2[idx2]
        for it1 in m1:
            overlap_items += np.sum(np.sum(np.abs(m2 - it1), axis=(-1,-2)) == 0)
    return overlap_items * 100. / total_items

print "Overlap between Validation and Test : ", find_duplicates(valid_dataset, valid_labels, test_dataset, test_labels)
np.sum(np.random.randn(3,4,5), axis=(-1,-2,-3))
#print "Overlap between Validation and Train : ", find_duplicates(valid_dataset, valid_labels, train_dataset, train_labels)
#print "Overlap between Test and Train : ", find_duplicates(test_dataset, test_labels, train_dataset, train_labels)

"""
Explanation: Problem 5
By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.
Measure how much overlap there is between training, validation and test samples.
Optional questions:
- What about near duplicates between datasets? (images that are almost identical)
- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.
End of explanation
"""


n_train = -1
X_train, y_train = train_dataset[:n_train].reshape(-1, train_dataset.shape[1]*train_dataset.shape[2]), train_labels[:n_train]
model = LogisticRegression(multi_class="multinomial", solver="lbfgs")
model.fit(X_train, y_train)



from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, classification_report

print "Predictions"
labels = [chr(k) for k in range(ord("A"), ord("J")+1)]
n_val = 10000
X_val, y_val = valid_dataset[:n_val].reshape(-1, valid_dataset.shape[1]*train_dataset.shape[2]), valid_labels[:n_val]
print X_val.shape, y_val.shape
y_pred = model.predict(X_val)
print "Confusion matrix: "
plt.pcolor(confusion_matrix(y_pred, y_val), cmap="Blues")
print "Score: ", classification_report(y_pred, y_val, target_names=labels)

n_vis = 10
n_cols = 5
n_rows = n_vis/ n_cols
idx = np.random.randint(valid_dataset.shape[0], size=n_vis)
X_vis, y_vis = valid_dataset[idx].reshape(-1, valid_dataset.shape[1]*valid_dataset.shape[2]), valid_labels[idx]
y_pred = model.predict(X_vis)
fig, ax = plt.subplots(n_rows, n_cols, sharex=True, sharey=True, figsize=(n_rows, n_cols))
fig.set_size_inches(10*n_rows, 5*n_cols)
for i, axi in enumerate(ax.flatten()):
    axi.pcolor(X_vis[i].reshape(valid_dataset.shape[1], valid_dataset.shape[2]), cmap="Blues")
    axi.set_title("True: %s, Predicted: %s" % (labels[y_vis[i]], labels[y_pred[i]]))




"""
Explanation: Problem 6
Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.
Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.
Optional question: train an off-the-shelf model on all the data!
End of explanation
"""


