tensorflow_cookbook：Ch 1: Getting Started with TensorFlow_(7)Data Source Information

7. Data Source Information

Here are the sources of the data sets used in this book. The following links are the original sources with explanations and citations. The script in this directory demonstrates how to access these data sets.

Iris Data

Low Birthweight Data

Housing Price Data

MNIST Dataset of Handwritten Digits

SMS Spam Data

Movie Review Data

William Shakespeare Data

German-English Sentence Data

# 07_data_gathering.py
# Data gathering
#----------------------------------
#
# This function gives us the ways to access
# the various data sets we will need

# Data Gathering
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Iris Data
from sklearn import datasets

iris = datasets.load_iris()
print(len(iris.data))
print(len(iris.target))
print(iris.data[0])
print(set(iris.target))

# Low Birthrate Data
import requests

birthdata_url = 'https://www.umass.edu/statdata/statdata/data/lowbwt.dat'
birth_file = requests.get(birthdata_url)
birth_data = birth_file.text.split('\r\n')[5:]
birth_header = [x for x in birth_data[0].split(' ') if len(x)>=1]
birth_data = [[float(x) for x in y.split(' ') if len(x)>=1] for y in birth_data[1:] if len(y)>=1]
print(len(birth_data))
print(len(birth_data[0]))

# Housing Price Data
import requests

housing_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
housing_header = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
housing_file = requests.get(housing_url)
housing_data = [[float(x) for x in y.split(' ') if len(x)>=1] for y in housing_file.text.split('\n') if len(y)>=1]
print(len(housing_data))
print(len(housing_data[0]))

# MNIST Handwriting Data
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print(len(mnist.train.images))
print(len(mnist.test.images))
print(len(mnist.validation.images))
print(mnist.train.labels[1,:])

# Ham/Spam Text Data
import requests
import io
from zipfile import ZipFile

# Get/read zip file
zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'
r = requests.get(zip_url)
z = ZipFile(io.BytesIO(r.content))
file = z.read('SMSSpamCollection')
# Format Data
text_data = file.decode()
text_data = text_data.encode('ascii',errors='ignore')
text_data = text_data.decode().split('\n')
text_data = [x.split('\t') for x in text_data if len(x)>=1]
[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]
print(len(text_data_train))
print(set(text_data_target))
print(text_data_train[1])

# Movie Review Data
import requests
import io
import tarfile

movie_data_url = 'http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz'
r = requests.get(movie_data_url)
# Stream data into temp object
stream_data = io.BytesIO(r.content)
tmp = io.BytesIO()
while True:
s = stream_data.read(16384)
if not s:
break
tmp.write(s)
stream_data.close()
tmp.seek(0)
# Extract tar file
tar_file = tarfile.open(fileobj=tmp, mode="r:gz")
pos = tar_file.extractfile('rt-polaritydata/rt-polarity.pos')
neg = tar_file.extractfile('rt-polaritydata/rt-polarity.neg')
# Save pos/neg reviews
pos_data = []
for line in pos:
pos_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode())
neg_data = []
for line in neg:
neg_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode())
tar_file.close()

print(len(pos_data))
print(len(neg_data))
print(neg_data[0])

# The Works of Shakespeare Data
import requests

shakespeare_url = 'http://www.gutenberg.org/cache/epub/100/pg100.txt'
# Get Shakespeare text
response = requests.get(shakespeare_url)
shakespeare_file = response.content
# Decode binary into string
shakespeare_text = shakespeare_file.decode('utf-8')
# Drop first few descriptive paragraphs.
shakespeare_text = shakespeare_text[7675:]
print(len(shakespeare_text))

# English-German Sentence Translation Data
import requests
import io
from zipfile import ZipFile
sentence_url = 'http://www.manythings.org/anki/deu-eng.zip'
r = requests.get(sentence_url)
z = ZipFile(io.BytesIO(r.content))
file = z.read('deu.txt')
# Format Data
eng_ger_data = file.decode()
eng_ger_data = eng_ger_data.encode('ascii',errors='ignore')
eng_ger_data = eng_ger_data.decode().split('\n')
eng_ger_data = [x.split('\t') for x in eng_ger_data if len(x)>=1]
[english_sentence, german_sentence] = [list(x) for x in zip(*eng_ger_data)]
print(len(english_sentence))
print(len(german_sentence))
print(eng_ger_data[10])