{
 "cells": [
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   "cell_type": "markdown",
   "id": "587ff3a2",
   "metadata": {},
   "source": [
    "# Do Car License & Access Predict Regular Transit?\n",
    "\n",
    "### Random Forest follow-up to the geography → transit memo\n",
    "\n",
    "**Course:** PSYCH 755 · University of Wisconsin–Madison  \n",
    "**Project:** CA persona / PRCA research framework  \n",
    "**Notebook role:** Geo-memo follow-up — alternative predictors of regular transit  \n",
    "\n",
    "---\n",
    "\n",
    "#### Research question\n",
    "\n",
    "> Do driver's license (Q20) and car access (Q21) predict whether a matched respondent takes public transportation regularly?\n",
    "\n",
    "| Element | Specification |\n",
    "|---|---|\n",
    "| **Outcome** | `regular_transit`: `Q26` ∈ {`4-8 days a month`, `8 or more days a month`} |\n",
    "| **Features** | `Q20`, `Q21` |\n",
    "| **Model** | Balanced Random Forest + stratified 5-fold CV (`seed=42`) |\n",
    "| **Benchmarks** | Chance (AUC = 0.50); geo RF (≈ 0.551); CA RF (≈ 0.590) |\n",
    "\n",
    "Companion memo: [`memos/car_access_predicts_transit.md`](../memos/car_access_predicts_transit.md)  \n",
    "Supporting code: [`src/ca_personas/transit_covariate_rf.py`](../src/ca_personas/transit_covariate_rf.py)  \n",
    "CLI: `ca-personas covariate-transit-rf --specs car_access`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3612100e",
   "metadata": {},
   "source": [
    "## 1. Analytic roadmap\n",
    "\n",
    "| Section | Content |\n",
    "|---|---|\n",
    "| **2. Setup** | Imports, paths, styling |\n",
    "| **3. Sample** | Matched File A/B/C cohort; complete-case features |\n",
    "| **4. Descriptive associations** | Regular-transit prevalence by feature level |\n",
    "| **5. Random Forest CV** | ROC-AUC and classification metrics vs benchmarks |\n",
    "| **6. Importances** | Gini (aggregated) + permutation importance |\n",
    "| **7. Written results** | Plain-language findings for the memo |\n",
    "| **8. Artifacts** | Export tables / JSON under `outputs/transit_covariate_rf/` |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d9ab934",
   "metadata": {},
   "source": [
    "## 2. Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0baf272d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:21.389058Z",
     "iopub.status.busy": "2026-07-25T15:41:21.388941Z",
     "iopub.status.idle": "2026-07-25T15:41:22.436648Z",
     "shell.execute_reply": "2026-07-25T15:41:22.435898Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data source: ../sibling_data File A/B/C\n",
      "Prolific: ['/tmp/sibling_data/PRCAProlificExport_FileA.csv', '/tmp/sibling_data/PRCAProlificExport_FileB.csv']\n",
      "Qualtrics: /tmp/sibling_data/PRCAQualtricsExport_FileC.csv\n",
      "Spec: car_access {'features': ['Q20', 'Q21'], 'label': 'Car license & access (Q20/Q21)', 'research_question': \"Do driver's license (Q20) and car access (Q21) predict whether a matched respondent takes public transportation regularly?\"}\n"
     ]
    }
   ],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from pathlib import Path\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "ROOT = Path.cwd().resolve()\n",
    "if ROOT.name == \"notebooks\":\n",
    "    ROOT = ROOT.parent\n",
    "if str(ROOT / \"src\") not in sys.path:\n",
    "    sys.path.insert(0, str(ROOT / \"src\"))\n",
    "\n",
    "from ca_personas.load import load_full_cohort\n",
    "from ca_personas.paths import default_prolific_paths, default_qualtrics_path, sibling_data_available\n",
    "from ca_personas.transit_covariate_rf import (\n",
    "    FEATURE_SPECS,\n",
    "    plot_comparison_memo_figure,\n",
    "    plot_family_memo_figure,\n",
    "    run_all_followup_analyses,\n",
    "    run_feature_family_analysis,\n",
    "    save_feature_family_artifacts,\n",
    "    save_followup_bundle,\n",
    ")\n",
    "\n",
    "plt.rcParams.update({\"axes.grid\": True, \"grid.alpha\": 0.25})\n",
    "plt.rcParams[\"figure.dpi\"] = 120\n",
    "\n",
    "if sibling_data_available():\n",
    "    PROLIFIC = default_prolific_paths()\n",
    "    QUALTRICS = default_qualtrics_path()\n",
    "    source = \"../sibling_data File A/B/C\"\n",
    "else:\n",
    "    staged = Path(\"/tmp/sibling_data\")\n",
    "    PROLIFIC = [\n",
    "        staged / \"PRCAProlificExport_FileA.csv\",\n",
    "        staged / \"PRCAProlificExport_FileB.csv\",\n",
    "    ]\n",
    "    QUALTRICS = staged / \"PRCAQualtricsExport_FileC.csv\"\n",
    "    source = \"/tmp/sibling_data File A/B/C\"\n",
    "\n",
    "SEED = 42\n",
    "N_SPLITS = 5\n",
    "N_PERM = 30\n",
    "print(\"Data source:\", source)\n",
    "print(\"Prolific:\", [str(p) for p in PROLIFIC])\n",
    "print(\"Qualtrics:\", QUALTRICS)\n",
    "\n",
    "SPEC_KEY = 'car_access'\n",
    "print('Spec:', SPEC_KEY, FEATURE_SPECS[SPEC_KEY])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6753233c",
   "metadata": {},
   "source": [
    "## 3. Sample & outcome\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5643fd9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:22.438908Z",
     "iopub.status.busy": "2026-07-25T15:41:22.438712Z",
     "iopub.status.idle": "2026-07-25T15:41:22.754308Z",
     "shell.execute_reply": "2026-07-25T15:41:22.753330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched analytic rows: 241\n",
      "{'n_prolific_raw': 262, 'n_prolific_unique': 262, 'n_qualtrics_raw': 273, 'n_qualtrics_with_pid': 255, 'n_qualtrics_complete_ca': 260, 'n_joined': 252, 'n_matched_both': 252, 'n_analytic': 241, 'n_dropped_missing_pid': 0, 'n_dropped_incomplete_ca': 11, 'n_dropped_unscorable_ca': 0, 'n_dropped_unjoined': 31, 'n_prolific_only': 10, 'n_qualtrics_only': 21, 'n_qualtrics_missing_pid': 18, 'waves': {'B': 153, 'A': 99}, 'notes': ['Normalized 19 DATA_EXPIRED Student status values to missing.', 'Full Prolific waves (File A/B) omit Ethnicity / Nationality / Language; demos tier uses Age, Sex, Country of residence, and Student status.', 'Analytic sample = Prolific∩Qualtrics with complete scorable PRCA group + interpersonal items.', 'Merge coverage (pre-CA filter): 252 matched Prolific∩Qualtrics; 21 Qualtrics-only (incl. 18 blank Q0 test rows; disregard); 10 Prolific-only (disregard).']}\n"
     ]
    }
   ],
   "source": [
    "participants, report = load_full_cohort(\n",
    "    prolific_paths=PROLIFIC,\n",
    "    qualtrics_path=QUALTRICS,\n",
    "    join_how=\"inner\",\n",
    ")\n",
    "print(\"Matched analytic rows:\", len(participants))\n",
    "print(report)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd7c8113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:22.756508Z",
     "iopub.status.busy": "2026-07-25T15:41:22.756387Z",
     "iopub.status.idle": "2026-07-25T15:41:27.122585Z",
     "shell.execute_reply": "2026-07-25T15:41:27.121433Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do driver's license (Q20) and car access (Q21) predict whether a matched respondent takes public transportation regularly?\n",
      "n                149.000000\n",
      "n_regular         58.000000\n",
      "n_not_regular     91.000000\n",
      "prevalence         0.389262\n",
      "dtype: float64\n"
     ]
    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant_id</th>\n",
       "      <th>Q20</th>\n",
       "      <th>Q21</th>\n",
       "      <th>Q26</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79a7690ded603d7a1fdd9aba77ef07ee3e4ebd7bbb9677...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4-8 days a month</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95ed67f67dae85a33b5f68f5e50bf019dffb7409de52a1...</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>8 or more days a month</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2132c4e72843f510a097f2fd65a46eae103825791e5693...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0-1 days a month</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59bf206766decf1eb20587204ac50399cd688aea1260cc...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>8 or more days a month</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0b7b608e95ac791a2e561ebd34498c617e36ed73f4aff1...</td>\n",
       "      <td>Yes</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                      participant_id  Q20  Q21  \\\n",
       "0  79a7690ded603d7a1fdd9aba77ef07ee3e4ebd7bbb9677...  Yes  Yes   \n",
       "1  95ed67f67dae85a33b5f68f5e50bf019dffb7409de52a1...  Yes  Yes   \n",
       "2  2132c4e72843f510a097f2fd65a46eae103825791e5693...  Yes  Yes   \n",
       "3  59bf206766decf1eb20587204ac50399cd688aea1260cc...  Yes   No   \n",
       "4  0b7b608e95ac791a2e561ebd34498c617e36ed73f4aff1...  Yes  Yes   \n",
       "\n",
       "                      Q26  y  \n",
       "0        4-8 days a month  1  \n",
       "1  8 or more days a month  1  \n",
       "2        0-1 days a month  0  \n",
       "3  8 or more days a month  1  \n",
       "4        0-1 days a month  0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis = run_feature_family_analysis(\n",
    "    participants,\n",
    "    spec_key=SPEC_KEY,\n",
    "    n_splits=N_SPLITS,\n",
    "    n_perm_repeats=N_PERM,\n",
    "    random_state=SEED,\n",
    ")\n",
    "frame = analysis[\"frame\"]\n",
    "print(analysis[\"summary\"][\"secondary_rq\"])\n",
    "print(pd.Series(analysis[\"summary\"][\"sample\"]))\n",
    "frame[[\"participant_id\", *analysis[\"features\"], \"Q26\", \"y\"]].head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ef5c7c",
   "metadata": {},
   "source": [
    "## 4. Descriptive associations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef876301",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:27.124649Z",
     "iopub.status.busy": "2026-07-25T15:41:27.124529Z",
     "iopub.status.idle": "2026-07-25T15:41:27.253996Z",
     "shell.execute_reply": "2026-07-25T15:41:27.253197Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>level</th>\n",
       "      <th>n</th>\n",
       "      <th>n_regular</th>\n",
       "      <th>n_not_regular</th>\n",
       "      <th>pct_regular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>129</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>0.364341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q20</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q21</td>\n",
       "      <td>Yes</td>\n",
       "      <td>123</td>\n",
       "      <td>38</td>\n",
       "      <td>85</td>\n",
       "      <td>0.308943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q21</td>\n",
       "      <td>No</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q21</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "  feature     level    n  n_regular  n_not_regular  pct_regular\n",
       "1     Q20       Yes  129         47             82     0.364341\n",
       "0     Q20        No   20         11              9     0.550000\n",
       "4     Q21       Yes  123         38             85     0.308943\n",
       "2     Q21        No   25         19              6     0.760000\n",
       "3     Q21  Not Sure    1          1              0     1.000000"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x336 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x336 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assoc = analysis[\"associations\"].copy()\n",
    "display(assoc)\n",
    "for feat in analysis[\"features\"]:\n",
    "    sub = assoc.loc[assoc[\"feature\"] == feat].sort_values(\"pct_regular\")\n",
    "    fig, ax = plt.subplots(figsize=(7.2, max(2.8, 0.45 * len(sub))))\n",
    "    ax.barh(sub[\"level\"].astype(str), sub[\"pct_regular\"], color=\"#2F6F7E\")\n",
    "    ax.axvline(frame[\"y\"].mean(), color=\"#B35C2E\", ls=\"--\", label=\"sample prevalence\")\n",
    "    ax.set_xlabel(\"Share regular transit (weekly+)\")\n",
    "    ax.set_title(f\"Regular-transit prevalence by {feat}\")\n",
    "    ax.legend(frameon=False)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750daca3",
   "metadata": {},
   "source": [
    "## 5. Random Forest cross-validated performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e477eccc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:27.256100Z",
     "iopub.status.busy": "2026-07-25T15:41:27.255997Z",
     "iopub.status.idle": "2026-07-25T15:41:27.331162Z",
     "shell.execute_reply": "2026-07-25T15:41:27.330499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>average_precision</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>brier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_forest_car_access</td>\n",
       "      <td>149</td>\n",
       "      <td>0.607332</td>\n",
       "      <td>0.515021</td>\n",
       "      <td>0.639447</td>\n",
       "      <td>0.47619</td>\n",
       "      <td>0.226675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prevalence_prob</td>\n",
       "      <td>149</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.389262</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.237737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>majority_class</td>\n",
       "      <td>149</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.389262</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.389262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model    n   roc_auc  average_precision  \\\n",
       "0  random_forest_car_access  149  0.607332           0.515021   \n",
       "1           prevalence_prob  149  0.500000           0.389262   \n",
       "2            majority_class  149  0.500000           0.389262   \n",
       "\n",
       "   balanced_accuracy       f1     brier  \n",
       "0           0.639447  0.47619  0.226675  \n",
       "1           0.500000  0.00000  0.237737  \n",
       "2           0.500000  0.00000  0.389262  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benchmarks — geo AUC 0.551 · CA AUC 0.590 · chance 0.500\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 624x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = analysis[\"metrics_table\"].copy()\n",
    "display(metrics[[\"model\", \"n\", \"roc_auc\", \"average_precision\", \"balanced_accuracy\", \"f1\", \"brier\"]])\n",
    "print(\"Benchmarks — geo AUC 0.551 · CA AUC 0.590 · chance 0.500\")\n",
    "roc = analysis[\"roc\"]\n",
    "fig, ax = plt.subplots(figsize=(5.2, 5.0))\n",
    "ax.plot(roc[\"fpr\"], roc[\"tpr\"], color=\"#2F6F7E\", lw=2.2,\n",
    "        label=f\"RF AUC = {analysis['metrics']['roc_auc']:.3f}\")\n",
    "ax.plot([0, 1], [0, 1], \"--\", color=\"#999999\", label=\"Chance\")\n",
    "ax.set_xlabel(\"False positive rate\")\n",
    "ax.set_ylabel(\"True positive rate\")\n",
    "ax.set_title(FEATURE_SPECS[SPEC_KEY][\"label\"])\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379875af",
   "metadata": {},
   "source": [
    "## 6. Feature importances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ce5dd84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:27.332341Z",
     "iopub.status.busy": "2026-07-25T15:41:27.332207Z",
     "iopub.status.idle": "2026-07-25T15:41:27.380655Z",
     "shell.execute_reply": "2026-07-25T15:41:27.380239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance_gini_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q21</td>\n",
       "      <td>0.858351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q20</td>\n",
       "      <td>0.141649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  importance_gini_sum\n",
       "0     Q21             0.858351\n",
       "1     Q20             0.141649"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance_perm_mean</th>\n",
       "      <th>importance_perm_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q21</td>\n",
       "      <td>0.182067</td>\n",
       "      <td>0.030873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q20</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.019133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  importance_perm_mean  importance_perm_std\n",
       "0     Q21              0.182067             0.030873\n",
       "1     Q20              0.013692             0.019133"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 780x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(analysis[\"gini_raw\"])\n",
    "display(analysis[\"permutation_importance\"])\n",
    "perm = analysis[\"permutation_importance\"].sort_values(\"importance_perm_mean\")\n",
    "fig, ax = plt.subplots(figsize=(6.5, max(2.5, 0.55 * len(perm))))\n",
    "ax.barh(perm[\"feature\"], perm[\"importance_perm_mean\"], xerr=perm[\"importance_perm_std\"],\n",
    "        color=\"#1F4E5F\", ecolor=\"#8FA3A8\")\n",
    "ax.set_xlabel(\"Permutation importance (mean AUC drop)\")\n",
    "ax.set_title(\"Permutation importance\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad36bf8f",
   "metadata": {},
   "source": [
    "## 7. Written results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcdde695",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:27.382358Z",
     "iopub.status.busy": "2026-07-25T15:41:27.382218Z",
     "iopub.status.idle": "2026-07-25T15:41:27.384951Z",
     "shell.execute_reply": "2026-07-25T15:41:27.384490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Car license & access (Q20/Q21) Random Forest CV ROC-AUC = 0.607. Stronger discrimination than the geo benchmark (≈0.55). Exceeds the CA-score benchmark (≈0.59).\n",
      "\n",
      "Sample: {'n': 149, 'n_regular': 58, 'n_not_regular': 91, 'prevalence': 0.38926174496644295}\n",
      "CV metrics: {'roc_auc': 0.6073323228495642, 'average_precision': 0.5150205538870885, 'balanced_accuracy': 0.6394467601364153, 'f1': 0.47619047619047616, 'brier': 0.22667540655726176}\n",
      "Caveats:\n",
      "- Same-wave self-reports; association ≠ causal effect on transit use.\n",
      "- Complete-case analysis for the listed features; N may be smaller than the full matched cohort when items are missing.\n",
      "- Regular transit is a thresholded Q26 label (weekly+), not continuous ridership.\n"
     ]
    }
   ],
   "source": [
    "summary = analysis[\"summary\"]\n",
    "print(summary[\"verdict\"][\"interpretation\"])\n",
    "print()\n",
    "print(\"Sample:\", summary[\"sample\"])\n",
    "print(\"CV metrics:\", {k: summary[\"cv_metrics\"][k] for k in\n",
    "      [\"roc_auc\", \"average_precision\", \"balanced_accuracy\", \"f1\", \"brier\"]})\n",
    "print(\"Caveats:\")\n",
    "for c in summary[\"caveats\"]:\n",
    "    print(\"-\", c)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c553451",
   "metadata": {},
   "source": [
    "## 8. Artifacts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b540281",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:27.386551Z",
     "iopub.status.busy": "2026-07-25T15:41:27.386416Z",
     "iopub.status.idle": "2026-07-25T15:41:27.566862Z",
     "shell.execute_reply": "2026-07-25T15:41:27.566120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote 11 artifacts to /workspace/outputs/transit_covariate_rf/car_access\n",
      "Memo figure: /workspace/memos/figures/car_access_predicts_transit_memo.png\n"
     ]
    }
   ],
   "source": [
    "out = ROOT / \"outputs\" / \"transit_covariate_rf\" / SPEC_KEY\n",
    "paths = save_feature_family_artifacts(analysis, out)\n",
    "fig_path = ROOT / \"memos\" / \"figures\" / f\"{SPEC_KEY}_predicts_transit_memo.png\"\n",
    "plot_family_memo_figure(analysis, output_path=fig_path)\n",
    "print(\"Wrote\", len(paths), \"artifacts to\", out)\n",
    "print(\"Memo figure:\", fig_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc77247",
   "metadata": {},
   "source": [
    "## 9. Limitations\n",
    "\n",
    "- Complete-case analysis for the listed items can shrink *N* relative to the full matched cohort (especially `Q20`/`Q21`).\n",
    "- Same-wave self-reports cannot establish whether mobility constraints *cause* transit use.\n",
    "- The outcome is a weekly+ threshold on `Q26`, not continuous ridership intensity.\n",
    "- Compare AUCs carefully across families when analytic *N* differs.\n"
   ]
  }
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