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 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 data_frame_with_target(self, data_frame):
"""
:param data_frame:
:type data_frame: DataFrame
:return:
:rtype: SFrame
"""
data_sframe = SFrame(data_frame.toPandas())
sentiment_array = data_sframe.select_column('sentiment')
target_array = []
for x in sentiment_array:
try:
target_array.append(self.convert_func(x))
except Exception as ex:
print len(target_array), 'get_sentiments', x
target_array.append(3)
print ex
data_sframe.add_column(SArray(target_array, dtype=int), name='target')
print data_sframe
return data_sframe.dropna()
def create_sessions(sf=SFrame()):
assert(type(sf) == SFrame)
ip = []
user_script = []
err_msg = []
compile_err = []
of_interest = []
ignored = 0
for i in xrange(len(sf)):
count = sf['count'][i]
if count != len(sf['id'][i]):
ignored += 1
continue
tip = sf['ip'][i]
chunk_user_script = cut_dict_by_dt(sf['user_script'][i])
user_script += chunk_user_script
ip += [tip,] * len(chunk_user_script)
err_msg += cut_dict_by_dt(sf['err_msg'][i])
chunk_compile_err = cut_dict_by_dt(sf['compile_err'][i])
compile_err += chunk_compile_err
of_interest += set_of_interest_bit(chunk_compile_err)
print "DEBUG:", "ignored:", ignored
rst = SFrame()
rst.add_column(SArray(ip, dtype=str), name='ip')
rst.add_column(SArray(user_script, dtype=dict), name='user_script')
rst.add_column(SArray(err_msg, dtype=dict), name='err_msg')
rst.add_column(SArray(compile_err, dtype=dict), name='compile_err')
rst.add_column(SArray(of_interest, dtype=int), name='of_interest')
return rst
def build_data_graph():
file_path = "/Users/blahiri/healthcare/documents/recommendation_system/"
beneficiaries = SFrame.read_csv(file_path + "beneficiary_summary_2008_2009.csv")
bene_packed = beneficiaries.pack_columns(column_prefix = 'chron_', dtype = dict, new_column_name = 'chronic_conditions', remove_prefix = False)
#x is a row of bene_packed in the following lambda. We insert the desynpuf_id into the (key, value) tuple, convert the tuple to a list by calling list(),
#and the outer [] makes sure we emit a list of lists.
bene_chrons = bene_packed.flat_map(["chronic_condition_name", "chronic_condition_value", "desynpuf_id"],
lambda x:[list(k + (x['desynpuf_id'], )) for k in x['chronic_conditions'].iteritems()])
bene_chrons = bene_chrons[bene_chrons['chronic_condition_value'] == 1]
del bene_chrons['chronic_condition_value']
bene_chrons.rename({'chronic_condition_name': 'chronic_condition'})
g = SGraph()
bene_chrons['relation'] = 'had_chronic'
g = g.add_edges(bene_chrons, src_field = 'desynpuf_id', dst_field = 'chronic_condition')
print g.summary()
#Take out the distinct IDs of patients with chronic conditions to avoid repetition in query
bene_with_chrons = SFrame(None)
bene_with_chrons.add_column(bene_chrons['desynpuf_id'].unique(), 'desynpuf_id')
#Add edges to the graph indicating which patient had which diagnosed condition
tcdc = SFrame.read_csv(file_path + "transformed_claim_diagnosis_codes.csv")
cols_to_drop = ['clm_id', 'clm_from_dt', 'clm_thru_dt', 'claim_type', 'clm_thru_year']
for column in cols_to_drop:
del tcdc[column]
#Same patient can be diagnosed with same condition multiple times a year, so take distinct
tcdc = tcdc.unique()
#Take diagnosed conditions for only those patients who had some chronic condition in 2008 or 2009. It is possible that
#such a patient had no diagnosed condition, however.
bene_chrons_tcdc = bene_with_chrons.join(tcdc)
bene_chrons_tcdc['relation'] = 'diagnosed_with'
g = g.add_edges(bene_chrons_tcdc, src_field = 'desynpuf_id', dst_field = 'dgns_cd')
print g.summary()
#Add edges to the graph indicating which patient had which procedure
tcpc = SFrame.read_csv(file_path + "transformed_claim_prcdr_codes.csv", column_type_hints = {'prcdr_cd' : str})
cols_to_drop = ['clm_id', 'clm_from_dt', 'clm_thru_dt', 'claim_type', 'clm_thru_year']
for column in cols_to_drop:
del tcpc[column]
tcpc = tcpc.unique()
#Take procedures for only those patients who had some chronic condition in 2008 or 2009. It is possible that
#such a patient had no procedure, however.
bene_chrons_tcpc = bene_with_chrons.join(tcpc)
bene_chrons_tcpc['relation'] = 'underwent'
g = g.add_edges(bene_chrons_tcpc, src_field = 'desynpuf_id', dst_field = 'prcdr_cd')
print g.summary()
#Add edges to the graph indicating which patient had which medicine
pde = SFrame.read_csv(file_path + "prescribed_drugs.csv")
pde = pde.unique()
#Take medicines for only those patients who had some chronic condition in 2008 or 2009. It is possible that
#such a patient had no medicine, however.
bene_chrons_pde = bene_with_chrons.join(pde)
bene_chrons_pde['relation'] = 'had_drug'
g = g.add_edges(bene_chrons_pde, src_field = 'desynpuf_id', dst_field = 'substancename')
print g.summary()
return g
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 build_data_graph():
file_path = "/Users/blahiri/healthcare/documents/recommendation_system/"
beneficiaries = SFrame.read_csv(file_path +
"beneficiary_summary_2008_2009.csv")
bene_packed = beneficiaries.pack_columns(
column_prefix='chron_',
dtype=dict,
new_column_name='chronic_conditions',
remove_prefix=False)
#x is a row of bene_packed in the following lambda. We insert the desynpuf_id into the (key, value) tuple, convert the tuple to a list by calling list(),
#and the outer [] makes sure we emit a list of lists.
bene_chrons = bene_packed.flat_map(
["chronic_condition_name", "chronic_condition_value", "desynpuf_id"],
lambda x: [
list(k + (x['desynpuf_id'], ))
for k in x['chronic_conditions'].iteritems()
])
bene_chrons = bene_chrons[bene_chrons['chronic_condition_value'] == 1]
del bene_chrons['chronic_condition_value']
bene_chrons.rename({'chronic_condition_name': 'chronic_condition'})
g = SGraph()
bene_chrons['relation'] = 'had_chronic'
g = g.add_edges(bene_chrons,
src_field='desynpuf_id',
dst_field='chronic_condition')
print g.summary()
#Take out the distinct IDs of patients with chronic conditions to avoid repetition in query
bene_with_chrons = SFrame(None)
bene_with_chrons.add_column(bene_chrons['desynpuf_id'].unique(),
'desynpuf_id')
#Add edges to the graph indicating which patient had which diagnosed condition
tcdc = SFrame.read_csv(file_path + "transformed_claim_diagnosis_codes.csv")
cols_to_drop = [
'clm_id', 'clm_from_dt', 'clm_thru_dt', 'claim_type', 'clm_thru_year'
]
for column in cols_to_drop:
del tcdc[column]
#Same patient can be diagnosed with same condition multiple times a year, so take distinct
tcdc = tcdc.unique()
#Take diagnosed conditions for only those patients who had some chronic condition in 2008 or 2009. It is possible that
#such a patient had no diagnosed condition, however.
bene_chrons_tcdc = bene_with_chrons.join(tcdc)
bene_chrons_tcdc['relation'] = 'diagnosed_with'
g = g.add_edges(bene_chrons_tcdc,
src_field='desynpuf_id',
dst_field='dgns_cd')
print g.summary()
#Add edges to the graph indicating which patient had which procedure
tcpc = SFrame.read_csv(file_path + "transformed_claim_prcdr_codes.csv",
column_type_hints={'prcdr_cd': str})
cols_to_drop = [
'clm_id', 'clm_from_dt', 'clm_thru_dt', 'claim_type', 'clm_thru_year'
]
for column in cols_to_drop:
del tcpc[column]
tcpc = tcpc.unique()
#Take procedures for only those patients who had some chronic condition in 2008 or 2009. It is possible that
#such a patient had no procedure, however.
bene_chrons_tcpc = bene_with_chrons.join(tcpc)
bene_chrons_tcpc['relation'] = 'underwent'
g = g.add_edges(bene_chrons_tcpc,
src_field='desynpuf_id',
dst_field='prcdr_cd')
print g.summary()
#Add edges to the graph indicating which patient had which medicine
pde = SFrame.read_csv(file_path + "prescribed_drugs.csv")
pde = pde.unique()
#Take medicines for only those patients who had some chronic condition in 2008 or 2009. It is possible that
#such a patient had no medicine, however.
bene_chrons_pde = bene_with_chrons.join(pde)
bene_chrons_pde['relation'] = 'had_drug'
g = g.add_edges(bene_chrons_pde,
src_field='desynpuf_id',
dst_field='substancename')
print g.summary()
return g
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')