{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from holisticai.datasets import load_dataset\n",
    "import pandas as pd\n",
    "\n",
    "from folktables import ACSDataSource, ACSIncome, ACSEmployment, ACSPublicCoverage, ACSMobility, ACSTravelTime\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1458542, 17)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "data = pd.read_parquet('data/acstraveltime/acstraveltime_dataset.parquet')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\"acsincome\", \"acspublic\", \"adult\", \"clinical_records\", \"law_school\", \"student\", \"us_crime\", \"german_credit\",\n",
    "            \"census_kdd\", \"bank_marketing\", \"compas_two_year_recid\", \"compas_is_recid\", \"diabetes\", \"mw_small\", \"mw_medium\"]\n",
    "\n",
    "tab = pd.DataFrame([], columns=['dataset', 'samples', 'features'])\n",
    "for data_name in datasets:\n",
    "    dataset = load_dataset(data_name)\n",
    "    samples = dataset['X'].shape[0]\n",
    "    features = dataset['X'].shape[1]\n",
    "    new = {\n",
    "        'dataset': data_name,\n",
    "        'samples': samples,\n",
    "        'features': features\n",
    "    }\n",
    "    tab = pd.concat([tab, pd.DataFrame([new])])\n",
    "\n",
    "print(tab.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
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      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def load_acs_data(path = 'datasets/', target_attr=\"income\", sensitive_attribute=\"sex\", survey_year=\"2018\",  states=[\"CA\"], horizon=\"1-Year\",survey='person'):\n",
    "    data_source = ACSDataSource(survey_year=survey_year, horizon=horizon, survey=survey, root_dir=path)\n",
    "    data = data_source.get_data(states=states, download=False)\n",
    "\n",
    "    if target_attr == \"acsincome\":\n",
    "        features, labels, _ = ACSIncome.df_to_pandas(data)\n",
    "        categorical_features = [\"COW\", \"SCHL\", \"MAR\", \"OCCP\", \"POBP\", \"RELP\", \"WKHP\"]\n",
    "    elif target_attr == \"acsemployment\":\n",
    "        features, labels, _ = ACSEmployment.df_to_pandas(data)\n",
    "        categorical_features = [\"AGEP\", \"SCHL\", \"MAR\", \"RELP\", \"DIS\", \"ESP\", \"CIT\", \"MIG\", \"MIL\", \"ANC\", \"NATIVITY\", \"DEAR\", \"DEYE\", \"DREM\"]\n",
    "    elif target_attr == \"acspubliccoverage\":\n",
    "        features, labels, _ = ACSPublicCoverage.df_to_pandas(data)\n",
    "        categorical_features = ['AGEP','SCHL','MAR','DIS','ESP','CIT','MIG','MIL','ANC','NATIVITY','DEAR','DEYE','DREM','PINCP','ESR','ST','FER']\n",
    "    elif target_attr == \"acsmobility\":\n",
    "        features, labels, _ = ACSMobility.df_to_pandas(data)\n",
    "        categorical_features = ['AGEP','SCHL','MAR','DIS','ESP','CIT','MIL','ANC','NATIVITY','RELP','DEAR','DEYE','DREM','GCL','COW','ESR','WKHP','JWMNP','PINCP']\n",
    "    elif target_attr == \"acstraveltime\":\n",
    "        features, labels, _ = ACSTravelTime.df_to_pandas(data)\n",
    "        categorical_features = ['AGEP','SCHL','MAR','DIS','ESP','MIG','RELP','PUMA','ST','CIT','OCCP','JWTR','POWPUMA','POVPIP']\n",
    "\n",
    "    else:\n",
    "        print( \"error\" )\n",
    "    \n",
    "\n",
    "    df = features\n",
    "    y = labels.astype(np.int32)\n",
    "    categorical_features.append(\"RAC1P\")\n",
    "    categorical_features.append(\"SEX\")\n",
    "\n",
    "    X = df\n",
    "    X[categorical_features] = X[categorical_features].astype(\"string\")\n",
    "\n",
    "\n",
    "    # Convert all non-uint8 columns to float32\n",
    "    string_cols = X.select_dtypes(exclude=\"string\").columns\n",
    "    X[string_cols] = X[string_cols].astype(\"float32\")\n",
    "\n",
    "    data = pd.concat([X, y], axis=1)\n",
    "    data.to_parquet(f'datasets/{target_attr}.parquet')\n",
    "\n",
    "\n",
    "states = [\"CA\", \"TX\", \"NY\", \"FL\", \"IL\", \"PA\", \"OH\", \"GA\", \"NC\", \"MI\", \"NJ\", \"VA\", \"WA\", \"AZ\", \"MA\", \"TN\", \"IN\", \"MO\", \"MD\", \"WI\", \"CO\", \"MN\", \"SC\", \"AL\", \"LA\", \"KY\", \"OR\", \"OK\", \"CT\", \"IA\", \"MS\", \"AR\", \"UT\", \"NV\", \"KS\", \"NM\", \"NE\", \"WV\", \"ID\", \"HI\", \"ME\", \"NH\", \"RI\", \"MT\", \"DE\", \"SD\", \"ND\", \"AK\", \"VT\", \"WY\"]\n",
    "for target in [\"acstraveltime\"]:\n",
    "    load_acs_data(target_attr=target, states=states)"
   ]
  }
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