AccidentalDrugDeaths.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DataLoader import DataLoader\n",
    "from Anonymisation import Anonymisation\n",
    "from Consistenter import Consistenter\n",
    "from GUM import GraduallyUpdateMethod\n",
    "from PostProcessor import RecordPostprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Race</th>\n",
       "      <th>Residence State</th>\n",
       "      <th>Death County</th>\n",
       "      <th>Location</th>\n",
       "      <th>Location if Other</th>\n",
       "      <th>Injury County</th>\n",
       "      <th>Injury State</th>\n",
       "      <th>...</th>\n",
       "      <th>Tramad</th>\n",
       "      <th>Morphine (Not Heroin)</th>\n",
       "      <th>Hydromorphone</th>\n",
       "      <th>Xylazine</th>\n",
       "      <th>Opiate NOS</th>\n",
       "      <th>Any Opioid</th>\n",
       "      <th>CardioCondition</th>\n",
       "      <th>RespiratoryCondition</th>\n",
       "      <th>ObesityCondition</th>\n",
       "      <th>DiabetesCondition</th>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "   Date  Age  Sex  Race  Residence State  Death County  Location  \\\n",
       "0     0    2    0     6                0             1         5   \n",
       "1     0    4    1     6                0             4         6   \n",
       "2     1    2    1     6                0             2         3   \n",
       "3     2    4    1     6                0             3         6   \n",
       "4     2    4    1     6                0             6         3   \n",
       "\n",
       "   Location if Other  Injury County  Injury State  ...  Tramad  \\\n",
       "0                  6              0             0  ...       0   \n",
       "1                  0              0             0  ...       0   \n",
       "2                  0              0             0  ...       0   \n",
       "3                  0              0             0  ...       0   \n",
       "4                  0              0             0  ...       0   \n",
       "\n",
       "   Morphine (Not Heroin)  Hydromorphone  Xylazine  Opiate NOS  Any Opioid  \\\n",
       "0                      0              0         0           0           0   \n",
       "1                      0              0         0           0           0   \n",
       "2                      0              0         0           0           0   \n",
       "3                      0              0         0           0           0   \n",
       "4                      0              0         0           0           0   \n",
       "\n",
       "   CardioCondition  RespiratoryCondition  ObesityCondition  DiabetesCondition  \n",
       "0                0                     0                 0                  0  \n",
       "1                0                     0                 0                  0  \n",
       "2                0                     0                 0                  0  \n",
       "3                0                     0                 0                  0  \n",
       "4                0                     0                 0                  0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl = DataLoader('AccidentalDrugDeaths/accidental_drug_deaths.csv', 'AccidentalDrugDeaths/data.yaml', 'AccidentalDrugDeaths/column_info.json', 'AccidentalDrugDeaths/loading_data.json')\n",
    "dl.data_loader()\n",
    "dl.all_indifs(dl.private_data)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon: 10, delta: 3e-11, rho: 0.8616407135665874\n",
      "one-way sigma: 13.412284072685887\n",
      "indif sigma: 1.395e-08\n",
      "multi-way sigma: 8.643617003684135\n"
     ]
    }
   ],
   "source": [
    "anon = Anonymisation(10,3e-11)\n",
    "anon.anonymiser(dl)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/privsyntest/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in divide\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/envs/privsyntest/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in multiply\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1 of 5 to make the marginals consistent and normalise them\n",
      "Iteration 2 of 5 to make the marginals consistent and normalise them\n",
      "Iteration 3 of 5 to make the marginals consistent and normalise them\n",
      "Iteration 4 of 5 to make the marginals consistent and normalise them\n",
      "Iteration 5 of 5 to make the marginals consistent and normalise them\n"
     ]
    }
   ],
   "source": [
    "c = Consistenter(anon, dl.all_attrs)\n",
    "c.make_consistent(iterations = 5)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1 of 100 completed to consist the marginal views\n",
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      "num_rec: 7628\n",
      "attrs: ['Age', 'Date', 'Sex', 'Race', 'Residence State', 'Death County', 'Location', 'Location if Other', 'Injury County', 'Injury State', 'Heroin', 'Cocaine', 'Fentanyl', 'Fentanyl Analogue', 'Oxycodone', 'Oxymorphone', 'Ethanol', 'Hydrocodone', 'Benzodiazepine', 'Methadone', 'Amphet', 'Tramad', 'Morphine (Not Heroin)', 'Hydromorphone', 'Xylazine', 'Opiate NOS', 'Any Opioid', 'CardioCondition', 'RespiratoryCondition', 'ObesityCondition', 'DiabetesCondition']\n",
      "domains: [ 9  9  2  7 11  9  7 10 15  3  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "  2  2  2  2  2  2  2]\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/finalsukjin/Desktop/Final PrivSyn/GUM.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.synthesize_records(attrs, domains, clusters, num_records)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Iteration 91 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 92 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 93 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 94 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 95 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 96 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 97 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 98 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 99 of 100 completed to generate the dataset using the noisy marginals\n",
      "Iteration 100 of 100 completed to generate the dataset using the noisy marginals\n"
     ]
    }
   ],
   "source": [
    "gum = GraduallyUpdateMethod(dl, c)\n",
    "gum.initialiser(view_iterations = 100)\n",
    "syn_data = gum.synthesize(iterations = 100, num_records = int(c.num_synthesize_records))\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "processor_priv = RecordPostprocessor(dl.private_data, dl.configpath, dl.datainfopath, dl.decode_mapping)\n",
    "processor_pub = RecordPostprocessor(syn_data, dl.configpath, dl.datainfopath, dl.decode_mapping)\n",
    "priv_data = processor_priv.post_process()\n",
    "pub_data = processor_pub.post_process()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marginal Difference Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12778130327845041"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 0\n",
    "for i in range(len(dl.all_attrs)):\n",
    "    for j in range(i+1, len(dl.all_attrs)):\n",
    "        attr1 = dl.all_attrs[i]\n",
    "        attr2 = dl.all_attrs[j]\n",
    "        priv_marg = dl.two_way_marginal(attr1, attr2, dl.private_data)/len(dl.private_data)\n",
    "        pub_marg = dl.two_way_marginal(attr1, attr2, syn_data)/len(syn_data)\n",
    "        t += (priv_marg - pub_marg).abs().sum().sum()\n",
    "t/(len(dl.all_attrs)*(len(dl.all_attrs)-1))"
   ]
  }
 ],
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