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@@ -2,294 +2,209 @@
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"cells": [
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{
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"cell_type": "code",
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- "execution_count": 1,
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+ "execution_count": 4,
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"metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "dev-deid-600@aou-res-deid-vumc-test.iam.gserviceaccount.com df0ac049-d5b6-416f-ab3c-6321eda919d6 2018-09-25 08:18:34.829000+00:00 DONE\n"
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- ]
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- }
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- ],
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+ "outputs": [],
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"source": [
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+ "\"\"\"\n",
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+ " This notebook is intended to show how to use the risk framework:\n",
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+ " There are two basic usages:\n",
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+ " 1. Experiment\n",
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+ " \n",
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+ " Here the framework will select a number of random fields other than the patient id and compute risk for the selection.\n",
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+ " This will repeat over a designated number of runs.\n",
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+ " \n",
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+ " The parameters to pass to enable this mode are id=<patient id>,nun_runs=<number of runs>\n",
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+ " 2. Assessment\n",
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+ " \n",
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+ " 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",
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+ " The parameters to enable this mode are id=<patient id>,quasi_id=<list of quasi ids>\n",
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+ "\"\"\"\n",
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+ "import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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- "from google.cloud import bigquery as bq\n",
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"\n",
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- "client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
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- "# pd.read_gbq(query=\"select * from raw.observation limit 10\",private_key='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
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- "jobs = client.list_jobs()\n",
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- "for job in jobs :\n",
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- "# print dir(job)\n",
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- " print job.user_email,job.job_id,job.started, job.state\n",
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- " break"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 33,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "xo = ['person_id','date_of_birth','race']\n",
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- "xi = ['person_id','value_as_number','value_source_value']"
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+ "\n",
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+ "#\n",
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+ "#-- Loading a template file\n",
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+ "# The example taken a de-identification white-paper\n",
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+ "# http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf\n",
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+ "#\n",
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+ "\n",
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+ "import pandas as pd\n",
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+ "import numpy as np\n",
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+ "from io import StringIO\n",
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+ "csv = \"\"\"\n",
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+ "id,sex,age,profession,drug_test\n",
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+ "1,M,37,doctor,-\n",
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+ "2,F,28,doctor,+\n",
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+ "3,M,37,doctor,-\n",
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+ "4,M,28,doctor,+\n",
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+ "5,M,28,doctor,-\n",
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+ "6,M,37,doctor,-\n",
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+ "\"\"\"\n",
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+ "f = StringIO()\n",
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+ "f.write(unicode(csv))\n",
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+ "f.seek(0)\n",
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+ "MY_DATAFRAME = pd.read_csv(f) "
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]
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},
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{
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"cell_type": "code",
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- "execution_count": 10,
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+ "execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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- "def get_tables(client,id,fields=[]):\n",
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- " \"\"\"\n",
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- " getting table lists from google\n",
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- " \"\"\"\n",
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- " r = []\n",
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- " ref = client.dataset(id)\n",
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- " tables = list(client.list_tables(ref))\n",
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- " for table in tables :\n",
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- " ref = table.reference\n",
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- " schema = client.get_table(ref).schema\n",
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- " names = [f.name for f in schema]\n",
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- " x = list(set(names) & set(fields))\n",
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- " if x :\n",
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- " r.append({\"name\":table.table_id,\"fields\":names})\n",
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- " return r\n",
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- " \n",
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- "def get_fields(**args):\n",
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- " \"\"\"\n",
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- " This function will generate a random set of fields from two tables. Tables are structured as follows \n",
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- " {name,fields:[],\"y\":}, with \n",
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- " name table name (needed to generate sql query)\n",
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- " fields list of field names, used in the projection\n",
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- " y name of the field to be joined.\n",
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- " @param xo candidate table in the join\n",
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- " @param xi candidate table in the join\n",
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- " @param join field by which the tables can be joined.\n",
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- " \"\"\"\n",
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- " # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
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- "# xo = args['xo']['fields']\n",
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- "# xi = args['xi']['fields']\n",
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- "# zi = args['xi']['name']\n",
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- "# return list(set([ \".\".join([args['xo']['name'],name]) for name in xo]) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n",
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- " xo = args['xo']\n",
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- " fields = [\".\".join([args['xo']['name'],name]) for name in args['xo']['fields']]\n",
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- " if not isinstance(args['xi'],list) :\n",
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- " x_ = [args['xi']]\n",
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- " else:\n",
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- " x_ = args['xi']\n",
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- " for xi in x_ :\n",
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- " fields += (['.'.join([xi['name'], name]) for name in xi['fields'] if name != args['join']])\n",
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- " return fields\n",
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- "def generate_sql(**args):\n",
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+ "\"\"\"\n",
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+ " Here's the pandas_risk code verbatim. \n",
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+ " NOTE: \n",
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+ "\"\"\"\n",
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+ "@pd.api.extensions.register_dataframe_accessor(\"deid\")\n",
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+ "class deid :\n",
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" \"\"\"\n",
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- " This function will generate the SQL query for the resulting join\n",
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+ " This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe\n",
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" \"\"\"\n",
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+ " def __init__(self,df):\n",
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+ " self._df = df\n",
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" \n",
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- " xo = args['xo']\n",
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- " x_ = args['xi']\n",
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- " xo_name = \".\".join([args['prefix'],xo['name'] ]) if 'prefix' in args else xo['name']\n",
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- " SQL = \"SELECT :fields FROM :xo.name \".replace(\":xo.name\",xo_name)\n",
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- " if not isinstance(x_,list):\n",
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- " x_ = [x_]\n",
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- " f = []#[\".\".join([args['xo']['name'],args['join']] )] \n",
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- " INNER_JOINS = []\n",
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- " for xi in x_ :\n",
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- " xi_name = \".\".join([args['prefix'],xi['name'] ]) if 'prefix' in args else xi['name']\n",
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- " JOIN_SQL = \"INNER JOIN :xi.name ON \".replace(':xi.name',xi_name)\n",
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- " value = \".\".join([xi['name'],args['join']])\n",
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- " f.append(value) \n",
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+ " def risk(self,**args):\n",
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+ " \"\"\"\n",
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+ " @param id name of patient field \n",
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+ " @params num_runs number of runs (default will be 100)\n",
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+ " @params quasi_id \tlist of quasi identifiers to be used (this will only perform a single run)\n",
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+ " \"\"\"\n",
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" \n",
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- " ON_SQL = \"\"\n",
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- " tmp = []\n",
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- " for term in f :\n",
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- " ON_SQL = \":xi.name.:ofield = :xo.name.:ofield\".replace(\":xo.name\",xo['name'])\n",
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- " ON_SQL = ON_SQL.replace(\":xi.name.:ofield\",term).replace(\":ofield\",args['join'])\n",
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- " tmp.append(ON_SQL)\n",
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- " INNER_JOINS += [JOIN_SQL + \" AND \".join(tmp)]\n",
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- " return SQL + \" \".join(INNER_JOINS)\n",
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- "def get_final_sql(**args):\n",
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- " xo = args['xo']\n",
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- " xi = args['xi']\n",
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- " join=args['join']\n",
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- " prefix = args['prefix'] if 'prefix' in args else ''\n",
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- " fields = get_fields (xo=xo,xi=xi,join=join)\n",
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- " k = len(fields)\n",
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- " n = np.random.randint(2,k) #-- number of fields to select\n",
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- " i = np.random.randint(0,k,size=n)\n",
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- " fields = [name for name in fields if fields.index(name) in i]\n",
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- " base_sql = generate_sql(xo=xo,xi=xi,prefix)\n",
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- " SQL = \"\"\"\n",
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- " SELECT AVERAGE(count),size,n as selected_features,k as total_features\n",
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- " FROM(\n",
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- " SELECT COUNT(*) as count,count(:join) as pop,sum(:n) as N,sum(:k) as k,:fields\n",
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- " FROM (:sql)\n",
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- " GROUP BY :fields\n",
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- " ) \n",
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- " order by 1\n",
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- " \n",
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- " \"\"\".replace(\":sql\",base_sql)\n",
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- "# sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
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- "# fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
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- " \n",
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- " \n",
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- "# sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
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- "# sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
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- "# return sql\n",
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- " \n",
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- " "
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 33,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race','value_as_number']}\n",
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- "xi = [{\"name\":\"measurement\",\"fields\":['person_id','value_as_number','value_source_value']}] #,{\"name\":\"observation\",\"fields\":[\"person_id\",\"value_as_string\",\"observation_source_value\"]}]\n",
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- "# generate_sql(xo=xo,xi=xi,join=\"person_id\",prefix='raw')\n",
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- "fields = get_fields(xo=xo,xi=xi,join='person_id')\n",
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- "ofields = list(fields)\n",
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- "k = len(fields)\n",
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- "n = np.random.randint(2,k) #-- number of fields to select\n",
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- "i = np.random.randint(0,k,size=n)\n",
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- "fields = [name for name in fields if fields.index(name) in i]"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 34,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "['person.race', 'person.value_as_number', 'measurement.value_source_value']"
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- ]
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- },
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- "execution_count": 34,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "fields\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 55,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "'SELECT person_id,value_as_number,measurements.value_source_value,measurements.value_as_number,value_source_value FROM person INNER JOIN measurements ON measurements.person_id = person_id '"
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- ]
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- },
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- "execution_count": 55,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
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- "xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
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- "generate_sql(xo=xo,xi=xi)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 59,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
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- ]
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- },
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- "execution_count": 59,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "\"\"\"\n",
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- " We are designing a process that will take two tables that will generate \n",
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- "\"\"\"\n",
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- "import itertools\n",
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- "list(itertools.combinations(['a','b','c'],2))"
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+ " id = args['id']\n",
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+ " if 'quasi_id' in args :\n",
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+ " num_runs = 1\n",
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+ " columns = list(set(args['quasi_id'])- set(id) )\n",
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+ " else :\n",
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+ " num_runs = args['num_runs'] if 'num_runs' in args else 100\n",
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+ " columns = list(set(self._df.columns) - set([id]))\n",
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+ " r = pd.DataFrame() \n",
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+ " k = len(columns)\n",
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+ " for i in range(0,num_runs) :\n",
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+ " #\n",
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+ " # let's chose a random number of columns and compute marketer and prosecutor risk\n",
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+ " # Once the fields are selected we run a groupby clause\n",
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+ " #\n",
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+ " if 'quasi_id' not in args :\n",
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+ " n = np.random.randint(2,k) #-- number of random fields we are picking\n",
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+ " ii = np.random.choice(k,n,replace=False)\n",
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+ " cols = np.array(columns)[ii].tolist()\n",
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+ " else:\n",
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+ " cols \t= columns\n",
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+ " n \t= len(cols)\n",
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+ " x_ = self._df.groupby(cols).count()[id].values\n",
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+ " r = r.append(\n",
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+ " pd.DataFrame(\n",
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+ " [\n",
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+ " {\n",
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+ " \"selected\":n,\n",
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+ " \"marketer\": x_.size / np.float64(np.sum(x_)),\n",
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+ " \"prosecutor\":1 / np.float64(np.min(x_))\n",
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+ "\n",
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+ " }\n",
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+ " ]\n",
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+ " )\n",
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+ " )\n",
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+ " g_size = x_.size\n",
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+ " n_ids = np.float64(np.sum(x_))\n",
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+ "\n",
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+ " return r"
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]
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},
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{
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"cell_type": "code",
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- "execution_count": 6,
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+ "execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>marketer</th>\n",
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+ " <th>prosecutor</th>\n",
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+ " <th>selected</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>0.500000</td>\n",
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+ " <td>1.0</td>\n",
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+ " <td>2</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>0.500000</td>\n",
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+ " <td>1.0</td>\n",
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+ " <td>3</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>0.500000</td>\n",
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+ " <td>1.0</td>\n",
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+ " <td>3</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>0.333333</td>\n",
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+ " <td>1.0</td>\n",
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+ " <td>2</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>0.333333</td>\n",
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+ " <td>0.5</td>\n",
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+ " <td>2</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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"text/plain": [
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- "array([1, 3, 0, 0])"
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+ " marketer prosecutor selected\n",
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+ "0 0.500000 1.0 2\n",
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+ "0 0.500000 1.0 3\n",
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+ "0 0.500000 1.0 3\n",
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+ "0 0.333333 1.0 2\n",
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+ "0 0.333333 0.5 2"
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]
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},
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- "execution_count": 6,
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+ "execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#\n",
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- "# find every table with person id at the very least or a subset of fields\n",
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+ "# Lets us compute risk here for a random any random selection of quasi identifiers\n",
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+ "# We will run this experiment 5 times\n",
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"#\n",
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- "np.random.randint(0,4,size=4)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 90,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "['a']"
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- ]
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- },
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- "execution_count": 90,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "list(set(['a','b']) & set(['a']))"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 120,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "x_ = 1"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 10,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "x_ = pd.DataFrame({\"group\":[1,1,1,1,1], \"size\":[2,1,1,1,1]})"
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+ "MY_DATAFRAME.deid.risk(id='id',num_runs=5)"
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]
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},
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{
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"cell_type": "code",
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- "execution_count": 12,
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+ "execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@@ -313,35 +228,37 @@
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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- " <th>size</th>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>group</th>\n",
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- " <th></th>\n",
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+ " <th>marketer</th>\n",
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+ " <th>prosecutor</th>\n",
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+ " <th>selected</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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- " <th>1</th>\n",
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- " <td>1.2</td>\n",
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+ " <th>0</th>\n",
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+ " <td>0.5</td>\n",
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+ " <td>1.0</td>\n",
|
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+ " <td>3</td>\n",
|
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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- " size\n",
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- "group \n",
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- "1 1.2"
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+ " marketer prosecutor selected\n",
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+ "0 0.5 1.0 3"
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]
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},
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- "execution_count": 12,
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+ "execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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- "x_.groupby(['group']).mean()\n"
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+ "#\n",
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+ "# In this scenario we are just interested in sex,profession,age\n",
|
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+ "#\n",
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|
+ "MY_DATAFRAME.deid.risk(id='id',quasi_id=['age','sex','profession'])"
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]
|
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|
},
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{
|
