def print_digit_labels(digit_labels):
nb_digits = len(digit_labels)
nb_rows = int(floor(sqrt(nb_digits)))
nb_cols = int(ceil(nb_digits / nb_rows))
a = array([nb_cols * [''] for _ in range(nb_rows)], dtype=object)
k = -1
for i, j in product(range(nb_rows), range(nb_cols)):
k += 1
if k < nb_digits:
a[i, j] = digit_labels[k]
set_printoptions(threshold=nan)
printflush(a)
def parse_movielens_20m_data(
data_path='https://raw.githubusercontent.com/ChicagoBoothML/DATA___MovieLens___20M/master'):
printflush('Parsing Movies...', end=' ')
movies = read_csv(join(data_path, 'movies.csv'))
printflush('done!')
NB_RATINGS_FILES = 10
printflush('Parsing %i Ratings Files...' % NB_RATINGS_FILES, end=' ')
ratings = []
for i in range(NB_RATINGS_FILES):
ratings_file_name = 'ratings%02d.csv' % (i + 1)
ratings.append(read_csv(join(data_path, ratings_file_name)))
printflush(ratings_file_name, end=', ')
printflush('done!')
ratings = concat(ratings, ignore_index=True)
return dict(movies=movies, ratings=ratings)
def parse_MNIST_digits(
data_path='https://github.com/ChicagoBoothML/DATA___LeCun___MNISTDigits',
digits=arange(10)):
try:
status_code = head(data_path).status_code
valid_url = status_code < 400
except:
valid_url = False
if valid_url:
printflush('Cloning Data Repository ',
data_path,
' to Temporary Folder... ',
sep='',
end='')
temp_dir = mkdtemp()
Repo.clone_from(data_path, temp_dir)
data_path = temp_dir
to_delete = True
printflush('done!')
else:
to_delete = False
printflush('Parsing Data Set "MNIST Hand-Written Digits"... ', end='')
f = open(join(data_path, 'train-images.idx3-ubyte'), 'rb')
magic_number, nb_digits, nb_rows, nb_cols = unpack(">IIII", f.read(16))
nb_pixels = nb_rows * nb_cols
images = array(py_array("B", f.read()))
f.close()
f = open(join(data_path, 'train-labels.idx1-ubyte'), 'rb')
magic_number, nb_digits = unpack(">II", f.read(8))
labels = array(py_array("b", f.read()))
f.close()
indices = [i for i in range(nb_digits) if labels[i] in digits]
nb_digits = len(indices)
train_images = zeros((nb_digits, nb_rows, nb_cols), dtype=uint8)
train_labels = zeros((nb_digits, ), dtype=int8)
for i in range(nb_digits):
index = indices[i]
train_images[i] = array(
images[(index * nb_pixels):((index + 1) * nb_pixels)]).reshape(
(nb_rows, nb_cols))
train_labels[i] = labels[index]
f = open(join(data_path, 't10k-images.idx3-ubyte'), 'rb')
magic_number, nb_digits, nb_rows, nb_cols = unpack(">IIII", f.read(16))
nb_pixels = nb_rows * nb_cols
images = array(py_array("B", f.read()))
f.close()
f = open(join(data_path, 't10k-labels.idx1-ubyte'), 'rb')
magic_number, nb_digits = unpack(">II", f.read(8))
labels = array(py_array("b", f.read()))
f.close()
indices = [i for i in range(nb_digits) if labels[i] in digits]
nb_digits = len(indices)
test_images = zeros((nb_digits, nb_rows, nb_cols), dtype=uint8)
test_labels = zeros((nb_digits, ), dtype=int8)
for i in range(nb_digits):
index = indices[i]
test_images[i] = array(
images[(index * nb_pixels):((index + 1) * nb_pixels)]).reshape(
(nb_rows, nb_cols))
test_labels[i] = labels[index]
printflush('done!')
if to_delete:
printflush('Deleting Temporary Data Folder... ', end='')
for root, dirs, files in walk(data_path, topdown=False):
for file in files:
file_path = join(root, file)
chmod(file_path, S_IWUSR)
remove(file_path)
for dir in dirs:
rmdir(join(root, dir))
rmdir(data_path)
printflush('done!')
return dict(TrainImages=train_images,
TrainLabels=train_labels,
TestImages=test_images,
TestLabels=test_labels)
def parse_human_activity_recog_data(
data_path='https://raw.githubusercontent.com/ChicagoBoothML/DATA___UCI___HumanActivityRecognitionUsingSmartphones/master',
X_names_file_name='features.txt',
train_subfolder_name='train', X_train_file_name='X_train.txt', y_train_file_name='y_train.txt',
test_subfolder_name='test', X_test_file_name='X_test.txt', y_test_file_name='y_test.txt'):
"""
:param data_path: path of folder containing "Human Activity Recognition Using Smartphones" data set
:param X_names_file_name: name of file containing X features' names
:param train_subfolder_name: name of folder containing Training data
:param X_train_file_name: train_X matrix file
:param y_train_file_name: train_y labels in integer
:param test_subfolder_name: name of folder containing Test data
:param X_test_file_name: test_X matrix file
:param y_test_file_name: test_y labels in integer
:return: dict with the following:
feature_names: list of names of the X features
train_X: data frame containing the X features for model training
train_y: data frame containing class labels for model training
test_X: data frame containing X features for model testing
test_y: data frame containing class labels for model testing
"""
printflush('Parsing Data Set "UCI Human Activity Recognition Using Smartphones"...')
printflush(" Parsing Unique Input Features' (X's) Names... ", end='')
X_names_with_duplicates = read_csv(
join(data_path, X_names_file_name),
delim_whitespace=True, header=None, index_col=0).iloc[:, 0]
X_name_counts = Counter(X_names_with_duplicates)
X_unique_names = list(X_name_counts)
X_unique_names.sort()
printflush('done!')
printflush(' Parsing Train & Test Input Feature Data Sets... ', end='')
X_train_with_duplicates = read_csv(
join(data_path, train_subfolder_name, X_train_file_name),
delim_whitespace=True, header=None, index_col=False,
names=X_names_with_duplicates,
dtype=float, error_bad_lines=False, warn_bad_lines=True)
X_test_with_duplicates = read_csv(
join(data_path, test_subfolder_name, X_test_file_name),
delim_whitespace=True, header=None, index_col=False,
names=X_names_with_duplicates,
dtype=float, error_bad_lines=False, warn_bad_lines=True)
X_train = DataFrame(index=X_train_with_duplicates.index)
X_test = DataFrame(index=X_test_with_duplicates.index)
for x_name in X_unique_names:
if X_name_counts[x_name] == 1:
X_train[x_name] = X_train_with_duplicates[x_name]
X_test[x_name] = X_test_with_duplicates[x_name]
else:
X_train[x_name] = X_train_with_duplicates[x_name].iloc[:, 0]
X_test[x_name] = X_test_with_duplicates[x_name].iloc[:, 0]
printflush('done!')
printflush(' Removing Input Feature Data Rows with Missing (NaN) Values... ', end='')
for i in X_train.index:
if isnan(X_train.loc[i].sum()):
X_train.drop(i, inplace=True)
for i in X_test.index:
if isnan(X_test.loc[i].sum()):
X_test.drop(i, inplace=True)
printflush('done!')
printflush(' Parsing Train & Test Labels (y)... ', end='')
y_class_labels = 'Walking', 'WalkingUpstairs', 'WalkingDownstairs', 'Sitting', 'Standing', 'Laying'
y_train = read_csv(
join(data_path, train_subfolder_name, y_train_file_name),
sep=' ', header=None, dtype=int).iloc[:, 0].astype('category')
y_train.cat.rename_categories(y_class_labels, inplace=True)
y_train = y_train.loc[X_train.index]
y_test = read_csv(
join(data_path, test_subfolder_name, y_test_file_name),
sep=' ', header=None, dtype=int).iloc[:, 0].astype('category')
y_test.cat.rename_categories(y_class_labels, inplace=True)
y_test = y_test.loc[X_test.index]
printflush('done!')
return dict(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
def parse_MNIST_digits(data_path='https://github.com/ChicagoBoothML/DATA___LeCun___MNISTDigits', digits=arange(10)):
try:
status_code = head(data_path).status_code
valid_url = status_code < 400
except:
valid_url = False
if valid_url:
printflush('Cloning Data Repository ', data_path, ' to Temporary Folder... ', sep='', end='')
temp_dir = mkdtemp()
Repo.clone_from(data_path, temp_dir)
data_path = temp_dir
to_delete = True
printflush('done!')
else:
to_delete = False
printflush('Parsing Data Set "MNIST Hand-Written Digits"... ', end='')
f = open(join(data_path, 'train-images.idx3-ubyte'), 'rb')
magic_number, nb_digits, nb_rows, nb_cols = unpack(">IIII", f.read(16))
nb_pixels = nb_rows * nb_cols
images = array(py_array("B", f.read()))
f.close()
f = open(join(data_path, 'train-labels.idx1-ubyte'), 'rb')
magic_number, nb_digits = unpack(">II", f.read(8))
labels = array(py_array("b", f.read()))
f.close()
indices = [i for i in range(nb_digits) if labels[i] in digits]
nb_digits = len(indices)
train_images = zeros((nb_digits, nb_rows, nb_cols), dtype=uint8)
train_labels = zeros((nb_digits,), dtype=int8)
for i in range(nb_digits):
index = indices[i]
train_images[i] = array(images[(index * nb_pixels):((index + 1) * nb_pixels)]).reshape((nb_rows, nb_cols))
train_labels[i] = labels[index]
f = open(join(data_path, 't10k-images.idx3-ubyte'), 'rb')
magic_number, nb_digits, nb_rows, nb_cols = unpack(">IIII", f.read(16))
nb_pixels = nb_rows * nb_cols
images = array(py_array("B", f.read()))
f.close()
f = open(join(data_path, 't10k-labels.idx1-ubyte'), 'rb')
magic_number, nb_digits = unpack(">II", f.read(8))
labels = array(py_array("b", f.read()))
f.close()
indices = [i for i in range(nb_digits) if labels[i] in digits]
nb_digits = len(indices)
test_images = zeros((nb_digits, nb_rows, nb_cols), dtype=uint8)
test_labels = zeros((nb_digits,), dtype=int8)
for i in range(nb_digits):
index = indices[i]
test_images[i] = array(images[(index * nb_pixels):((index + 1) * nb_pixels)]).reshape((nb_rows, nb_cols))
test_labels[i] = labels[index]
printflush('done!')
if to_delete:
printflush('Deleting Temporary Data Folder... ', end='')
for root, dirs, files in walk(data_path, topdown=False):
for file in files:
file_path = join(root, file)
chmod(file_path, S_IWUSR)
remove(file_path)
for dir in dirs:
rmdir(join(root, dir))
rmdir(data_path)
printflush('done!')
return dict(TrainImages=train_images, TrainLabels=train_labels, TestImages=test_images, TestLabels=test_labels)
def parse_book_crossing_data(
data_path='https://raw.githubusercontent.com/ChicagoBoothML/DATA___BookCrossing/master'):
# Common NAs:
na_strings = [
'',
'na', 'n.a', 'n.a.',
'nan', 'n.a.n', 'n.a.n.',
'NA', 'N.A', 'N.A.',
'NaN', 'N.a.N', 'N.a.N.',
'NAN', 'N.A.N', 'N.A.N.',
'nil', 'Nil', 'NIL',
'null', 'Null', 'NULL']
printflush('Parsing Books...', end=' ')
books = read_csv(
join(data_path, 'BX-Books.csv'),
sep=';',
dtype=str,
na_values=na_strings,
usecols=['ISBN', 'Book-Title', 'Book-Author'],
error_bad_lines=False)
printflush('done!')
printflush('Parsing Users...', end=' ')
users = read_csv(
join(data_path, 'BX-Users.csv'),
sep=';',
dtype=str,
na_values=na_strings,
error_bad_lines=False)
users['User-ID'] = users['User-ID'].astype(int)
users['Age'] = users['Age'].astype(float)
printflush('done!')
printflush('Parsing Ratings...', end=' ')
ratings = read_csv(
join(data_path, 'BX-Book-Ratings.csv'),
sep=';',
dtype=str,
na_values=na_strings,
error_bad_lines=False)
ratings['User-ID'] = ratings['User-ID'].astype(int)
ratings['Book-Rating'] = ratings['Book-Rating'].astype(float)
printflush('done!')
return dict(books=books, users=users, ratings=ratings)
