Refactored, including population risk assessment
This commit is contained in:
Steve L. Nyemba -- The Architect
parent
6863df382e
commit
c3066408c9
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@ -22,16 +22,108 @@
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"""
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"""
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import pandas as pd
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import pandas as pd
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import numpy as np
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import numpy as np
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import time
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@pd.api.extensions.register_dataframe_accessor("deid")
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@pd.api.extensions.register_dataframe_accessor("deid")
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class deid :
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class deid :
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"""
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"""
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This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe
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This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe
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"""
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"""
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def __init__(self,df):
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def __init__(self,df):
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self._df = df
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self._df = df.fillna(' ')
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def explore(self,**args):
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"""
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This function will perform experimentation by performing a random policies (combinations of attributes)
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This function is intended to explore a variety of policies and evaluate their associated risk.
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def risk(self,**args):
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@param pop|sample data-frame with popublation reference
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@param id key field that uniquely identifies patient/customer ...
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"""
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# id = args['id']
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pop= args['pop'] if 'pop' in args else None
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# if 'columns' in args :
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# cols = args['columns']
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# params = {"sample":args['data'],"cols":cols}
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# if pop is not None :
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# params['pop'] = pop
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# return self.evaluate(**params)
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# else :
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#
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# Policies will be generated with a number of runs
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#
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RUNS = args['num_runs'] if 'num_runs' in args else 5
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sample = args['sample'] if 'sample' in args else pd.DataFrame(self._df)
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k = sample.columns.size -1 if 'field_count' not in args else int(args['field_count'])
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columns = list(set(sample.columns.tolist()) - set([id]))
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o = pd.DataFrame()
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# pop = args['pop'] if 'pop' in args else None
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for i in np.arange(RUNS):
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n = np.random.randint(2,k)
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cols = np.random.choice(columns,n,replace=False).tolist()
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params = {'sample':sample,'cols':cols}
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if pop is not None :
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params['pop'] = pop
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r = self.evaluate(**params)
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#
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# let's put the policy in place
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p = pd.DataFrame(1*sample.columns.isin(cols)).T
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p.columns = sample.columns
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o = o.append(r.join(p))
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o.index = np.arange(o.shape[0]).astype(np.int64)
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return o
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def evaluate(self,**args) :
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"""
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This function will compute the marketer, if a population is provided it will evaluate the marketer risk relative to both the population and sample
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@param smaple data-frame with the data to be processed
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@param policy the columns to be considered.
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@param pop population dataset
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@params flag user defined flag (no computation use)
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"""
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if (args and 'sample' not in args) or not args :
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x_i = pd.DataFrame(self._df)
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elif args and 'sample' in args :
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x_i = args['sample']
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if (args and 'cols' not in args) or not args :
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cols = x_i.columns.tolist()
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# cols = self._df.columns.tolist()
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elif args and 'cols' in args :
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cols = args['cols']
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flag = args['flag'] if 'flag' in args else 'UNFLAGGED'
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# if args and 'sample' in args :
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# x_i = pd.DataFrame(self._df)
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# else :
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# cols = args['cols'] if 'cols' in args else self._df.columns.tolist()
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# x_i = x_i.groupby(cols,as_index=False).size().values
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x_i_values = x_i.groupby(cols,as_index=False).size().values
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SAMPLE_GROUP_COUNT = x_i_values.size
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SAMPLE_FIELD_COUNT = len(cols)
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SAMPLE_POPULATION = x_i_values.sum()
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SAMPLE_MARKETER = SAMPLE_GROUP_COUNT / np.float64(SAMPLE_POPULATION)
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SAMPLE_PROSECUTOR = 1/ np.min(x_i_values).astype(np.float64)
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if 'pop' in args :
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Yi = args['pop']
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y_i= pd.DataFrame({"group_size":Yi.groupby(cols,as_index=False).size()}).reset_index()
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# y_i['group'] = pd.DataFrame({"group_size":args['pop'].groupby(cols,as_index=False).size().values}).reset_index()
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# x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size().values}).reset_index()
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x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size()}).reset_index()
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SAMPLE_RATIO = int(100 * x_i.size/args['pop'].shape[0])
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r = pd.merge(x_i,y_i,on=cols,how='inner')
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r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1)
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r['sample %'] = np.repeat(SAMPLE_RATIO,r.shape[0])
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r['tier'] = np.repeat(flag,r.shape[0])
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r['sample marketer'] = np.repeat(SAMPLE_MARKETER,r.shape[0])
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r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]
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else:
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r = pd.DataFrame({"marketer":[SAMPLE_MARKETER],"prosecutor":[SAMPLE_PROSECUTOR],"field_count":[SAMPLE_FIELD_COUNT],"group_count":[SAMPLE_GROUP_COUNT]})
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return r
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def _risk(self,**args):
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"""
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"""
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@param id name of patient field
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@param id name of patient field
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@params num_runs number of runs (default will be 100)
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@params num_runs number of runs (default will be 100)
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@ -50,7 +142,7 @@ class deid :
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k = len(columns)
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k = len(columns)
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N = self._df.shape[0]
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N = self._df.shape[0]
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tmp = self._df.fillna(' ')
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tmp = self._df.fillna(' ')
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np.random.seed(1)
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np.random.seed(int(time.time()) )
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for i in range(0,num_runs) :
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for i in range(0,num_runs) :
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#
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#
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@ -85,6 +177,7 @@ class deid :
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[
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[
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{
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{
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"group_count":x_.size,
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"group_count":x_.size,
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"patient_count":N,
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"patient_count":N,
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"field_count":n,
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"field_count":n,
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"marketer": x_.size / np.float64(np.sum(x_)),
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"marketer": x_.size / np.float64(np.sum(x_)),
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231
src/risk.py
231
src/risk.py
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@ -146,7 +146,7 @@ class utils :
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return " ".join(SQL).replace(":fields"," , ".join(fields))
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return " ".join(SQL).replace(":fields"," , ".join(fields))
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class risk :
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class SQLRisk :
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"""
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"""
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This class will handle the creation of an SQL query that computes marketer and prosecutor risk (for now)
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This class will handle the creation of an SQL query that computes marketer and prosecutor risk (for now)
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"""
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"""
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@ -186,102 +186,163 @@ class risk :
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class UtilHandler :
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def __init__(self,**args) :
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"""
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@param path path to the service account file
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@param dataset input dataset name
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@param key_field key_field (e.g person_id)
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@param key_table
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"""
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self.path = args['path']
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self.client = bq.Client.from_service_account_json(self.path)
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dataset = args['dataset']
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self.key = args['key_field']
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self.mytools = utils(client = self.client)
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self.tables = self.mytools.get_tables(dataset=dataset,client=self.client,key=self.key)
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index = [ self.tables.index(item) for item in self.tables if item['name'] == args['key_table']] [0]
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if index != 0 :
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first = self.tables[0]
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aux = self.tables[index]
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self.tables[0] = aux
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self.tables[index] = first
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if 'filter' in args :
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self.tables = [item for item in self.tables if item['name'] in args['filter']]
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if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute','migrate'] :
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def create_table(self,**args):
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"""
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@param path absolute filename to save the create statement
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path = SYS_ARGS['path']
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"""
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client = bq.Client.from_service_account_json(path)
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create_sql = self.mytools.get_sql(tables=self.tables,key=self.key) #-- The create statement
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i_dataset = SYS_ARGS['i_dataset']
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# o_dataset = SYS_ARGS['o_dataset']
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key = SYS_ARGS['key']
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# table = SYS_ARGS['table']
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if 'path' in args:
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mytools = utils(client = client)
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f = open(args['path'],'w')
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tables = mytools.get_tables(dataset=i_dataset,client=client,key=key)
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# print len(tables)
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# tables = tables[:6]
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if SYS_ARGS['action'] == 'create' :
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#usage:
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# create --i_dataset <in dataset> --key <patient id> --o_dataset <out dataset> --table <table|file> [--file] --path <bq JSON account file>
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#
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create_sql = mytools.get_sql(tables=tables,key=key) #-- The create statement
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o_dataset = SYS_ARGS['o_dataset']
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table = SYS_ARGS['table']
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if 'file' in SYS_ARGS :
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f = open(table+'.sql','w')
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f.write(create_sql)
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f.write(create_sql)
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f.close()
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f.close()
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else:
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return create_sql
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job = bq.QueryJobConfig()
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def migrate_tables(self,**args):
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job.destination = client.dataset(o_dataset).table(table)
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"""
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job.use_query_cache = True
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This function will migrate a table from one location to another
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job.allow_large_results = True
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The reason for migration is to be able to reduce a candidate table to only represent a patient by her quasi-identifiers.
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job.priority = 'BATCH'
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@param dataset target dataset
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job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
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"""
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o_dataset = args['dataset'] if 'dataset' in args else None
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p = []
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for table in self.tables:
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sql = " ".join(["SELECT ",",".join(table['fields']) ," FROM (",self.mytools.get_filtered_table(table,self.key),") as ",table['name']])
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p.append(sql)
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if o_dataset :
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job = bq.QueryJobConfig()
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job.destination = self.client.dataset(o_dataset).table(table['name'])
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job.use_query_cache = True
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job.allow_large_results = True
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job.priority = 'INTERACTIVE'
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job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
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r = client.query(create_sql,location='US',job_config=job)
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r = self.client.query(sql,location='US',job_config=job)
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print [r.job_id,' ** ',r.state]
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print [table['full_name'],' ** ',r.job_id,' ** ',r.state]
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elif SYS_ARGS['action'] == 'migrate' :
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return p
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#
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#
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o_dataset = SYS_ARGS['o_dataset']
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# if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute','migrate'] :
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for table in tables:
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sql = " ".join(["SELECT ",",".join(table['fields']) ," FROM (",mytools.get_filtered_table(table,key),") as ",table['name']])
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print ""
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print sql
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print ""
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# job = bq.QueryJobConfig()
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# job.destination = client.dataset(o_dataset).table(table['name'])
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# job.use_query_cache = True
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# job.allow_large_results = True
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# job.priority = 'INTERACTIVE'
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# job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
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# r = client.query(sql,location='US',job_config=job)
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# path = SYS_ARGS['path']
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# client = bq.Client.from_service_account_json(path)
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# i_dataset = SYS_ARGS['i_dataset']
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# key = SYS_ARGS['key']
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# print [table['full_name'],' ** ',r.job_id,' ** ',r.state]
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# mytools = utils(client = client)
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# tables = mytools.get_tables(dataset=i_dataset,client=client,key=key)
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# # print len(tables)
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# # tables = tables[:6]
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# if SYS_ARGS['action'] == 'create' :
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# #usage:
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# # create --i_dataset <in dataset> --key <patient id> --o_dataset <out dataset> --table <table|file> [--file] --path <bq JSON account file>
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# #
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# create_sql = mytools.get_sql(tables=tables,key=key) #-- The create statement
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# o_dataset = SYS_ARGS['o_dataset']
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# table = SYS_ARGS['table']
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# if 'file' in SYS_ARGS :
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# f = open(table+'.sql','w')
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# f.write(create_sql)
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# f.close()
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# else:
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# job = bq.QueryJobConfig()
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# job.destination = client.dataset(o_dataset).table(table)
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# job.use_query_cache = True
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# job.allow_large_results = True
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# job.priority = 'BATCH'
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# job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
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# r = client.query(create_sql,location='US',job_config=job)
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# print [r.job_id,' ** ',r.state]
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# elif SYS_ARGS['action'] == 'migrate' :
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# #
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# #
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# o_dataset = SYS_ARGS['o_dataset']
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# for table in tables:
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# sql = " ".join(["SELECT ",",".join(table['fields']) ," FROM (",mytools.get_filtered_table(table,key),") as ",table['name']])
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# print ""
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# print sql
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# print ""
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# # job = bq.QueryJobConfig()
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# # job.destination = client.dataset(o_dataset).table(table['name'])
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# # job.use_query_cache = True
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# # job.allow_large_results = True
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# # job.priority = 'INTERACTIVE'
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# # job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
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# # r = client.query(sql,location='US',job_config=job)
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# # print [table['full_name'],' ** ',r.job_id,' ** ',r.state]
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pass
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# pass
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else:
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# else:
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#
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# #
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#
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# #
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tables = [tab for tab in tables if tab['name'] == SYS_ARGS['table'] ]
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# tables = [tab for tab in tables if tab['name'] == SYS_ARGS['table'] ]
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limit = int(SYS_ARGS['limit']) if 'limit' in SYS_ARGS else 1
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# limit = int(SYS_ARGS['limit']) if 'limit' in SYS_ARGS else 1
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if tables :
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# if tables :
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risk= risk()
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# risk= risk()
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df = pd.DataFrame()
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# df = pd.DataFrame()
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dfs = pd.DataFrame()
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# dfs = pd.DataFrame()
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np.random.seed(1)
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# np.random.seed(1)
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for i in range(0,limit) :
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# for i in range(0,limit) :
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r = risk.get_sql(key=SYS_ARGS['key'],table=tables[0])
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# r = risk.get_sql(key=SYS_ARGS['key'],table=tables[0])
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sql = r['sql']
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# sql = r['sql']
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dfs = dfs.append(r['stream'],sort=True)
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# dfs = dfs.append(r['stream'],sort=True)
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df = df.append(pd.read_gbq(query=sql,private_key=path,dialect='standard').join(dfs))
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# df = df.append(pd.read_gbq(query=sql,private_key=path,dialect='standard').join(dfs))
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# df = df.join(dfs,sort=True)
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# # df = df.join(dfs,sort=True)
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df.to_csv(SYS_ARGS['table']+'.csv')
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# df.to_csv(SYS_ARGS['table']+'.csv')
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# dfs.to_csv(SYS_ARGS['table']+'_stream.csv')
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# # dfs.to_csv(SYS_ARGS['table']+'_stream.csv')
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print [i,' ** ',df.shape[0],pd.DataFrame(r['stream']).shape]
|
# print [i,' ** ',df.shape[0],pd.DataFrame(r['stream']).shape]
|
||||||
time.sleep(2)
|
# time.sleep(2)
|
||||||
|
|
||||||
|
|
||||||
else:
|
# else:
|
||||||
print 'ERROR'
|
# print 'ERROR'
|
||||||
pass
|
# pass
|
||||||
|
|
||||||
# r = risk(path='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json', i_dataset='raw',o_dataset='risk_o',o_table='mo')
|
# # r = risk(path='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json', i_dataset='raw',o_dataset='risk_o',o_table='mo')
|
||||||
# tables = r.get_tables('raw','person_id')
|
# # tables = r.get_tables('raw','person_id')
|
||||||
# sql = r.get_sql(tables=tables[:3],key='person_id')
|
# # sql = r.get_sql(tables=tables[:3],key='person_id')
|
||||||
# #
|
# # #
|
||||||
# # let's post this to a designated location
|
# # # let's post this to a designated location
|
||||||
# #
|
# # #
|
||||||
# f = open('foo.sql','w')
|
# # f = open('foo.sql','w')
|
||||||
# f.write(sql)
|
# # f.write(sql)
|
||||||
# f.close()
|
# # f.close()
|
||||||
# r.get_sql(tables=tables,key='person_id')
|
# # r.get_sql(tables=tables,key='person_id')
|
||||||
# p = r.compute()
|
# # p = r.compute()
|
||||||
# print p
|
# # print p
|
||||||
# p.to_csv("risk.csv")
|
# # p.to_csv("risk.csv")
|
||||||
# r.write('foo.sql')
|
# # r.write('foo.sql')
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue