{
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"\"\"\"\n",
" This notebook is intended to show how to use the risk framework:\n",
" There are two basic usages:\n",
" 1. Experiment\n",
" \n",
" Here the framework will select a number of random fields other than the patient id and compute risk for the selection.\n",
" This will repeat over a designated number of runs.\n",
" \n",
" The parameters to pass to enable this mode are id=<patient id>,nun_runs=<number of runs>\n",
" 2. Assessment\n",
" \n",
" Here the framework assumes you are only interested in a list of quasi identifiers and will run the evaluation once for a given list of quasi identifiers.\n",
" The parameters to enable this mode are id=<patient id>,quasi_id=<list of quasi ids>\n",
"\"\"\"\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"\n",
"#\n",
"#-- Loading a template file\n",
"# The example taken a de-identification white-paper\n",
"# http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf\n",
"#\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from io import StringIO\n",
"csv = \"\"\"\n",
"id,sex,age,profession,drug_test\n",
"1,M,37,doctor,-\n",
"2,F,28,doctor,+\n",
"3,M,37,doctor,-\n",
"4,M,28,doctor,+\n",
"5,M,28,doctor,-\n",
"6,M,37,doctor,-\n",
"\"\"\"\n",
"f = StringIO()\n",
"f.write(unicode(csv))\n",
"f.seek(0)\n",
"MY_DATAFRAME = pd.read_csv(f) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
" Here's the pandas_risk code verbatim. \n",
" NOTE: \n",
"\"\"\"\n",
"@pd.api.extensions.register_dataframe_accessor(\"deid\")\n",
"class deid :\n",
" \"\"\"\n",
" This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe\n",
" \"\"\"\n",
" def __init__(self,df):\n",
" self._df = df\n",
" \n",
" def risk(self,**args):\n",
" \"\"\"\n",
" @param id name of patient field \n",
" @params num_runs number of runs (default will be 100)\n",
" @params quasi_id \tlist of quasi identifiers to be used (this will only perform a single run)\n",
" \"\"\"\n",
" \n",
" id = args['id']\n",
" if 'quasi_id' in args :\n",
" num_runs = 1\n",
" columns = list(set(args['quasi_id'])- set(id) )\n",
" else :\n",
" num_runs = args['num_runs'] if 'num_runs' in args else 100\n",
" columns = list(set(self._df.columns) - set([id]))\n",
" r = pd.DataFrame() \n",
" k = len(columns)\n",
" for i in range(0,num_runs) :\n",
" #\n",
" # let's chose a random number of columns and compute marketer and prosecutor risk\n",
" # Once the fields are selected we run a groupby clause\n",
" #\n",
" if 'quasi_id' not in args :\n",
" n = np.random.randint(2,k) #-- number of random fields we are picking\n",
" ii = np.random.choice(k,n,replace=False)\n",
" cols = np.array(columns)[ii].tolist()\n",
" else:\n",
" cols \t= columns\n",
" n \t= len(cols)\n",
" x_ = self._df.groupby(cols).count()[id].values\n",
" r = r.append(\n",
" pd.DataFrame(\n",
" [\n",
" {\n",
" \"selected\":n,\n",
" \"marketer\": x_.size / np.float64(np.sum(x_)),\n",
" \"prosecutor\":1 / np.float64(np.min(x_))\n",
"\n",
" }\n",
" ]\n",
" )\n",
" )\n",
" g_size = x_.size\n",
" n_ids = np.float64(np.sum(x_))\n",
"\n",
" return r"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>marketer</th>\n",
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"text/plain": [
" marketer prosecutor selected\n",
"0 0.500000 1.0 2\n",
"0 0.500000 1.0 3\n",
"0 0.500000 1.0 3\n",
"0 0.333333 1.0 2\n",
"0 0.333333 0.5 2"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#\n",
"# Lets us compute risk here for a random any random selection of quasi identifiers\n",
"# We will run this experiment 5 times\n",
"#\n",
"MY_DATAFRAME.deid.risk(id='id',num_runs=5)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>marketer</th>\n",
" <th>prosecutor</th>\n",
" <th>selected</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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"text/plain": [
" marketer prosecutor selected\n",
"0 0.5 1.0 3"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#\n",
"# In this scenario we are just interested in sex,profession,age\n",
"#\n",
"MY_DATAFRAME.deid.risk(id='id',quasi_id=['age','sex','profession'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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