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256 lines
9.7 KiB
Python

"""
Health Information Privacy Lab
@TODO:
sample = args['sample'] if 'sample' in args else pd.DataFrame(self._df)
r['pop. marketer'] = handle_population.marketer()
r['pitman risk'] = handle_population.pitman()
r['pop. group size'] = np.unique(yi.population_group_size).size
#
# At this point we have both columns for either sample,population or both
#
r['field count'] = len(cols)
return pd.DataFrame([r])
class Risk :
"""
This class is an abstraction of how we chose to structure risk computation i.e in 2 sub classes:
- Sample computes risk associated with a sample dataset only
- Population computes risk associated with a population
"""
def __init__(self):
self.cache = {}
def set(self,key,value):
if id not in self.cache :
self.cache[id] = {}
self.cache[key] = value
class Sample(Risk):
"""
This class will compute risk for the sample dataset: the marketer and prosecutor risk are computed by default.
This class can optionally add pitman risk if the population size is known.
"""
def __init__(self):
Risk.__init__(self)
def marketer(self):
It identifies if there is at least one record that is unique
# sample_row_count = r.sample_group_size.size
return r.apply(lambda row: (row.sample_group_size / np.float64(row.population_group_size)) /np.float64(sample_row_count) ,axis=1).sum()