def __init__(self):
self.sourceNames = BuildNdcWhiteList()
self.sourceLabels = AcquireOpenFda()
class BuildDrugCanon():
''' Determine which package or product NDC is considered _the_
representitive for the same proprietary name
'''
def __init__(self):
self.sourceNames = BuildNdcWhiteList()
self.sourceLabels = AcquireOpenFda()
def __str__(self):
return 'Determine Canonical Drugs'
# -------------------------------------------------------------------------
def _map(self, label):
''' A generator function that produces multiple elements from each
OpenFDA record
'''
if ('openfda' in label and 'product_ndc' in label['openfda'] and
'brand_name' in label['openfda']):
asString = json.dumps(label)
for x in label['openfda']['brand_name']:
brand = self.sourceNames.title(x)
for y in label['openfda']['product_ndc']:
ndc = ProductNdc.parse(y).format()
yield {'proprietary_name': brand,
'ndc':ndc,
'size':len(asString)}
def _reduce(self, dicts):
''' Selects the "best" element from a list
'''
if len(dicts) < 1:
return None
best = dicts[0]
for i in range(1, len(dicts)):
if best['size'] < dicts[i]['size']:
best = dicts[i]
return best
# -------------------------------------------------------------------------
def _mapLabels(self):
''' A generator function that internally calls `map` for each label
'''
for record in self.sourceLabels.acquire_labels():
for node in self._map(record):
yield node
def _reduceToCanon(self, partitions):
''' A generator that internally calls `reduce` for each entry
'''
for name in sorted(partitions):
result = self._reduce(partitions[name])
if result:
yield (name, result['ndc'])
# -------------------------------------------------------------------------
def run(self):
''' Use the size of a record in the FDA data set to determine which
package or product NDC is considered _the_ representitive for the same
proprietary name
'''
print('Loading White List')
whiteListFileName = io.relativeToAbsolute('../../data/product_ndc.txt')
records = []
with open(whiteListFileName) as f:
for row in csv.DictReader(f, dialect=csv.excel_tab):
# for some reason a weird 'None' column appears
records.append({k:v for k,v in row.items() if k})
partitions = {x['proprietary_name']: [] for x in records}
products = {x['product_ndc'] for x in records if x['proprietary_name']}
print('Mapping Labels')
for node in self._mapLabels():
nameKey = node['proprietary_name']
prodKey = node['ndc']
if nameKey in partitions and prodKey in products:
partitions[nameKey].append(node)
print('Reducing to Canon')
outFileName = io.relativeToAbsolute('../../data/canon_drugs.txt')
canon = {x for x in self._reduceToCanon(partitions)}
print('Updating NDC Whitelist')
for row in records:
tuple = (row['proprietary_name'], row['product_ndc'])
if tuple in canon:
# consume because multiple package codes map to this key
canon.remove(tuple)
row['is_canon'] = 'true'
else:
row['is_canon'] = 'false'
print('Saving')
tempName = io.relativeToAbsolute('../../data/product_ndc_canon.txt')
io.saveAsTabbedText(records, '../../data/product_ndc_canon.txt')
# no errors, rename
os.remove(whiteListFileName)
os.rename(tempName, whiteListFileName)
def __init__(self):
self.source = AcquireOpenFda()
self.whiteList = BuildNdcWhiteList()
self.features = self._buildFeatureSet()
