sys.exit()
for f in edgesFiles:
content = SFrame.read_csv(path + f + '.csv',
na_values='null',
verbose=verbose)
if 'src' in content.column_names() and 'dst' in content.column_names():
g = g.add_edges(content, src_field='src', dst_field='dst')
elif 'source' in content.column_names(
) and 'target' in content.column_names():
g = g.add_edges(content, src_field='source', dst_field='target')
else:
print "Unknown src_id/dst_id field: ", content.column_names()
sys.exit()
print g.summary()
def parseResult(row, columns):
for c in columns:
if isinstance(c, list):
result = ''
found = False
for x in c:
if row[x] != None and row[x] != '':
result += row[x] + ' '
found = True
if found:
return result
else:
if row[c] != None and row[c] != '':
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 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
