def create_frame_from_file(file_name):
n_total_lines = 220000
sf = SFrame()
with open(file_name) as data:
dt = []
ip = []
py = []
script = []
id = []
for i, line in enumerate(data):
jo = json.loads(line)
dt += jo['dt']
ip += jo['ip']
py += jo['py']
id += [i]
script += jo['user_script']
if i % 100 == 0:
print float(i) / n_total_lines
sf = sf.add_column(SArray(id), name='id')
sf.add_column(SArray(dt), name='dt')
sf.add_column(SArray(ip), name='ip')
sf.add_column(SArray(py, dtype=str), name='py')
sf.add_column(SArray(script), name='user_script')
sf.save('python_tutor')
return sf
def get_rating_sf(self, samples, save_to=None):
sf = SFrame(self.ratings.ix[samples])
sf['userId'] = sf['userId'].apply(lambda uid: self.user_dict[uid])
sf['movieId'] = sf['movieId'].apply(lambda mid: self.movie_dict[mid])
if save_to is not None:
print "saving sframe to", save_to
sf.save(save_to)
return sf
def get_rating_sf(self, samples, save_to=None):
sf = SFrame(self.ratings.ix[samples])
sf['userId'] = sf['userId'].apply(lambda uid: self.user_dict[uid])
sf['movieId'] = sf['movieId'].apply(lambda mid: self.movie_dict[mid])
if save_to is not None:
print "saving sframe to", save_to
sf.save(save_to)
return sf
def process_frame(filename):
sf = gl.load_sframe(filename)
output_frame = SFrame()
#Setup our output frame
id = []
ip = []
sub_count = []
error_count = []
time_count = []
error_sequence_raw = []
error_sequence = []
#How many session ID's do we have?
sa = gl.SArray()
sa = sf['session_id']
test = sa.unique()
limit = len(test)
#Start grabbing each session
for i in range(1,limit):
#Test output
if (i % 100 == 0):
break
#Get the session and sort it by the date time
session_frame = sf.filter_by(i,"session_id")
#sorted_session = session_frame.sort("dt")
row = sf[0]
id += [i]
ip += [row['ip']]
sub_count += [len(row)]
#time_count += [fn_time_count(sorted_session)]
#error_count += [fn_error_count(sorted_session)]
#error_sequence_raw += [fn_error_sequence_raw(sorted_session)]
print len(id)
print len(ip)
print len(sub_count)
#print len(time_count)
output_frame = output_frame.add_column(SArray(id), name='id')
output_frame.add_column(SArray(ip), name='ip')
output_frame.add_column(SArray(sub_count),name='sub_count')
#output_frame.add_column(SArray(time_count),name='sub_length')
#output_frame.add_column(SArray(error_count),name='error_count')
#output_frame.add_column(SArray(error_sequence_raw,dtype=str),name='err_seq_raw')
output_frame.save('py2_session_analysis')
def resize_images(filename):
images = graphlab.image_analysis.load_images(filename,
format='auto',
with_path=False,
recursive=False,
ignore_failure=True,
random_order=True)
# firstImages = images[0:9]['image']
new_images = list()
new_images.append(
graphlab.image_analysis.resize(images['image'],
32,
32,
channels=4,
decode=True))
frame = SFrame(new_images)
frame.save('mini')
def append_images(json_file):
# we fill an SFrame with all the given metadata of the dogs
meta = SFrame.read_json(json_file, orient='records')
# this is the SFrame that we will fill with the data plus the image, which will be saved in the final file
image_list = SFrame(data=None)
# for each image in the images column in the meta SFrame, we add one line in the final SF with one image per line
for i in range(0, len(meta) - 1):
dogo = meta[i:i + 1]
for image in dogo['images'][0]:
# print image
dogo_clone = dogo.copy()
dogo_clone.add_column(SArray([(graphlab.Image(images_path + image))
]),
name='image')
dogo_clone.add_column(SArray([image]), name='image_filename')
image_list = image_list.append(SFrame(dogo_clone))
image_list.save(filename='prepared_data/')
def process_frame(frame_name):
#Setup columns for the new frame
session_id = []
ip_address = []
python_version = []
interest = []
submissions = []
#Load in the frame we're processing
frame = gl.load_sframe(frame_name)
#Sort the frame by IP and then DT ASC
sorted_frame = frame.sort(['ip','dt'])
#Previous IP to see if we're looking at a new IP address
previous_ip = 0
previous_py = 0
#Counters (for keys)
record_counter = 1
submission_counter = 1
#Dictionary to hold submissions
submissions_collection = {}
#Looping through all records to break this up into
#ip address and then 'session' chunks
for i in xrange(len(sorted_frame)):
if(i == 1):
print sorted_frame['ip'][i]
break;
if(i % 100 == 0):
print "processing record:" + str(i)
if((sorted_frame['ip'][i] != previous_ip)):
if(previous_ip != 0):
#Add in the record to the frame
session_id += str(record_counter)
ip_address += str(previous_ip)
python_version += str(previous_py)
interest += str(is_interesting(submissions_collection))
submissions += submissions_collection
#Reset all values
submissions_collection = {}
previous_ip = sorted_frame['ip'][i]
previous_py = sorted_frame['py'][i]
record_counter = record_counter + 1
submission_counter = 1
#Create and append the submission
d = {}
d['date-time'] = sorted_frame['dt'][i]
d['code_segment'] = sorted_frame['user_script'][i]
d['error_message'] = sorted_frame['err_msg'][i]
d['error_flag'] = sorted_frame['compile_err'][i]
submissions_collection[str(submission_counter)] = d
submission_counter = submission_counter + 1
else:
#Handling the very first record
previous_ip = sorted_frame['ip'][i]
previous_py = sorted_frame['py'][i]
#Create and append the submission
d = {}
d['date-time'] = sorted_frame['dt'][i]
d['code_segment'] = sorted_frame['user_script'][i]
d['error_message'] = sorted_frame['err_msg'][i]
d['error_flag'] = sorted_frame['compile_err'][i]
submissions_collection[str(submission_counter)] = d
submission_counter = submission_counter + 1
else:
#Create and append the submission
d = {}
d['date-time'] = sorted_frame['dt'][i]
d['code_segment'] = sorted_frame['user_script'][i]
d['error_message'] = sorted_frame['err_msg'][i]
d['error_flag'] = sorted_frame['compile_err'][i]
submissions_collection[str(submission_counter)] = d
submission_counter = submission_counter + 1
#Finally, create the frame and save it!
print ip_address
print len(session_id)
print len(ip_address)
print len(python_version)
print len(submissions)
rst = SFrame()
rst.add_column(SArray(session_id, dtype=str), name='session_id')
rst.add_column(SArray(ip_address, dtype=str), name='ip_address')
rst.add_column(SArray(python_version, dtype=str), name='python_version')
rst.add_column(SArray(submissions, dtype=dict), name='submissions')
rst.save("test_frame")
def get_sf_from_coo(coo, save_to):
sf = SFrame({'userId': coo.row, 'movieId': coo.col, 'rating': coo.data})
if save_to is not None:
print "saving sframe to", save_to
sf.save(save_to)
return sf
def main():
with open('../../Data/data_file_modified.txt') as data:
sf = SFrame()
# Data model format
# RecordID | Date/Time | IP Address | Python Version |
# User Script | Compile Flag | Compile Message
id = []
dt = []
ip = []
py = []
script = []
error = []
error_msg = []
for i, line in enumerate(data):
jo = json.loads(line)
# Two different version of Python script
# need to be compiled on different interpreters
if(jo['py'][0] == 3):
# Setup the data model we're using
id += [i]
dt += jo['dt']
ip += jo['ip']
py += jo['py']
script += jo['user_script']
# Run the script on the compile method
# and obtain any error message
flag = False
msg = ""
pattern = "is local and global"
try:
compile(jo['user_script'][0],'<string>','exec')
except SyntaxError, e:
if(re.search(pattern, str(e))):
msg = "Variable is Local and Global"
else:
msg = str(e)
flag = True
if(flag):
error += [1]
else:
error += [0]
# We need to chop off the error type
# and remove the (filename line number)
# to have any meaning here.
fix_msg = msg.partition('(')[0]
error_msg += [fix_msg.strip()]
sf = sf.add_column(SArray(id), name='id')
sf.add_column(SArray(dt), name='dt')
sf.add_column(SArray(ip), name='ip')
sf.add_column(SArray(py, dtype=str), name='py')
sf.add_column(SArray(script), name='user_script')
sf.add_column(SArray(error), name='compile_err')
sf.add_column(SArray(error_msg), name='err_msg')
sf.save('py3_error_frame_clean')
def get_sf_from_coo(coo, save_to):
sf = SFrame({'userId': coo.row, 'movieId': coo.col, 'rating': coo.data})
if save_to is not None:
print "saving sframe to", save_to
sf.save(save_to)
return sf
