{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42e833db-bf17-48c6-a4ea-7c5f1d310360",
   "metadata": {},
   "source": [
    "# Non-parametric lifetime models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e828b3dd-0bec-4b88-8731-0b857ba98176",
   "metadata": {},
   "source": [
    "A built-in dataset of circuit breaker lifetimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "264693a3-5de5-4457-9632-48ee20a0e452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34. 28. 12. ... 42. 42. 37.]\n",
      "[ True  True  True ... False False False]\n",
      "[33. 27. 11. ... 31. 31. 26.]\n"
     ]
    }
   ],
   "source": [
    "from relife.datasets import load_circuit_breaker\n",
    "\n",
    "dataset = load_circuit_breaker()\n",
    "print(dataset[\"time\"])\n",
    "print(dataset[\"event\"])\n",
    "print(dataset[\"entry\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cd6ca17-3980-4e90-bf2c-1b8fa35843a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from relife.lifetime_models import KaplanMeier, NelsonAalen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "713b3e2f-7265-43a0-b3f9-a8e28dc26f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<relife.lifetime_models._non_parametric_models.KaplanMeier at 0x7dff59d3c990>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaplan_meier = KaplanMeier()\n",
    "kaplan_meier.fit(\n",
    "    dataset[\"time\"],\n",
    "    event=dataset[\"event\"],\n",
    "    entry=dataset[\"entry\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98e52f10-2452-4c4f-8bb4-1c0968a89422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<relife.lifetime_models._non_parametric_models.NelsonAalen at 0x7dff59d4b2d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nelson_aalen = NelsonAalen()\n",
    "nelson_aalen.fit(\n",
    "    dataset[\"time\"],\n",
    "    event=dataset[\"event\"],\n",
    "    entry=dataset[\"entry\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87dfc944-1c3e-4dea-b489-0c3672c519bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 4))\n",
    "kaplan_meier.plot(\"sf\", ax=axs[0])\n",
    "nelson_aalen.plot(\"chf\", ax=axs[1])\n",
    "axs[0].set_title(\"Kaplan-Meier\")\n",
    "axs[1].set_title(\"Nelson-Aalen\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}