LinearClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# from IPython.display import clear_output # used for google col.\n",
    "from six.moves import urllib\n",
    "\n",
    "import tensorflow.compat.v2.feature_column as fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0, 1, 2, 2.5, 3]\n",
    "y = [1, 2, 3.2, 3.5, 4.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23f4b530888>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "line_inter = np.poly1d(np.polyfit(x,y,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23f4db63148>,\n",
       " <matplotlib.lines.Line2D at 0x23f4a8bc1c8>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAD4CAYAAADB9HwiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbO0lEQVR4nO3deXhV9b3v8fcv80hCBoaQkJCBmTCKIMoQrFarUoviULXW2joBZ2xvb59z7S1tb3v69PS0CVBwarW1Vqu1VaunQ8IkMsggiIpmJyEkYUhIICQh487v/pHQIgYJsJO1dvbn9Tw8ZK+9SL4/VnhnZ2Wzl7HWIiIi7hPk9AAiItIzBVpExKUUaBERl1KgRURcSoEWEXGpEF+9o6SkJJuRkeGrdyciEhB27tx5zFqb3NN9Pgt0RkYGO3bs8NW7ExEJCMaY8nPdp1McIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiFysvS/Af0+E/xvf9fveF3z67n32NDsRkYCy9wV4dTm0N3fdrq/oug2Qu8QnH0KPoEVELkbhin/E+bT25q7tPqJAi4hcjPrKC9t+ERRoEZGLEZd6YdsvggItInIRjlz2DdpM+Mc3hkbCwkd99jEUaBGRC/DhkQaW/mYXs/+UyLe8X+VE2DAsBuLS4MZ8n/2AEPQsDhGRXtlXVU9BUTF/fu8o0WHBPDgvi/uvvJr4mO/32cdUoEVEPsU7FScoKCymcH81sREhLM/L5r4rRxEfFdbnH1uBFhHpwY4DdeQXedj4UQ3xUaH822dGc88VGcRFhvbbDAq0iEg3ay1bS+vILyxmS2ktidFh/K/PjuXu2enEhPd/LhVoEQl41lo2FR+joKiYtw8cJzk2nP/43DjuvHwkUWHOZVKBFpGAZa1l3YfV5Bd6eKfiBMPjIvjOTRO47bI0IkKDnR5PgRaRwNPZafnL+0dZua6YfVUnGREfyfdvnsgt01MJD3E+zKcp0CISMLydljf2HWZlkYf9RxpIT4ziR7fkcvPUEYQGu++/hSjQIjLgdXg7eW3vYQqKiimpaSIrOZr/vm0yN+amEOLCMJ+mQIvIgNXu7eTl3VWsXufhQO0pxgyNZeWdU7lu4nCCg4zT452XAi0iA05rh5eXdlaxer2HyuPNTEgZxJq7pnPN+KEE+UGYT1OgRWTAaGn38vzbFazZUMLh+hYmp8XznZsmkDd2CMb4T5hPU6BFxO81t3l5dls5j20spbqhlRnpg/nh4lzm5iT5ZZhPU6BFxG81tnbw663lPL6xlNqmNmZlJvDT26cwOzPRr8N8mgItIn7nZEs7T28+wJObyzhxqp2rcpJYvjCHyzISnB7NpxRoEfEbJ0618dTmA/xicxkNLR3kjR3Csrxspo4c7PRofUKBFhHXq21s5ck3y3hmSzmNrR1cM34oy/JymJQa5/RofUqBFhHXqm5o4fGNpfx660FaOrxcP2k4SxdkM274IKdH6xcKtIi4zpH6FtZsKOG57Qdp93Zy0+QUluZlkz0k1unR+pUCLSKuUXn8FD9fX8LvdlTitZYvTB3BwwuyGZUU7fRojlCgRcRx5bVNrF5Xwku7KjEGbpmexsPzs0hLiHJ6NEcp0CLimJKaRlat8/DHdw4RHGS48/KRPDgvi5T4SKdHcwUFWkT63UdHGygo8vDa3kOEhwRx7xUZfG1uJkMHRTg9mqso0CLSb947VM/KIg9v7DtCVFgwX5ubyVevyiQpJtzp0VxJgRaRPren4gQFRcX87YNqYsNDWLogm/uuHEVCdJjTo7maAi0ifWZneR35hR42fFRDXGQo/3L1aO6dk0FcZKjTo/kFBVpEfG5raS0FRcVs9tSSEB3G168dwz2z04mNCIW9L0DhCqivhLhUWPgo5C5xemRXUqBFxCestWz21JJfWMz2A3UkxYTzrevH8sXL04kO707N3hfg1eXQ3tx1u76i6zYo0j1QoEXkklhrWf9hDflFxew+eIKhg8L59o3juWPmSCJCz7pCduGKf8T5tPbmru0K9Cco0CJyUay1/PX9oxQUeXi3qp4R8ZF89/MTuXV66ifDfFp95YVtD3AKtIhckM5Oyxv7jlBQVMz+Iw2MTIjiPxdP4uapqYSFnOcK2XGpXac1etoun6BAi0iveDstr+09xMoiD8XVjWQmRfNft05m0ZQUQoLPE+bTFj768XPQAKGRXdvlExRoEflUHd5O/vDOIVav81B6rImcITHk3zGVz00aTvCFXiH79HlmPYujVxRoEelRW0cnv99Vyar1Hirqmhk3fBA//+I0rp0wjKALDfOZcpcoyL2kQIvIx7S0e/ndjgrWbCil6kQzualxPHrDBK4eN2RAXIjVnyjQIgJAc5uX57YfZO3GEo6ebGXayHi+f/NE5o1OVpgdokCLBLim1g6e3VbOYxtLOdbYxuWjEvjJkilckZWoMDtMgRYJUA0t7TyzpZwnNpVy/FQ7V2YnsSwvm8szE50eTbop0CIBpv5UO794q4yn3izjZEsHC8YkszQvh+npg50eTc6iQIsEiLqmNp58s5Rn3iqnobWDz4wfyrK8bHJT450eTc5BgRbxB5fwCnA1Da08samUX20tp7ndy3UTh7F0QQ7jUwb18dByqRRoEbe7yFeAO3qyhbUbSvnN9nLaOjq5cXIKSxdkkzM0th+GFl9QoEXc7gJfAa7qRDNr1pfw/I4KvJ2Wz08ZwSMLsshMjumngcVXFGgRt+vlK8BV1J1i9XoPL+7s2r54WioPz89mZGJUX08ofUSBFnG787wCXNmxJlat8/Dy7iqCjeH2y0by4PwsRsRH9vOg4msKtIjbneMV4A7P+AY//O1uXt1ziNDgIO6Znc4Dc7MYFhfh3KziUwq0iNud9QpwbTEpPBt9LyteTyQy9ChfvSqT+6/KJDk23Nk5xecUaBF/kLuEdxOupaComL+8f5SYhhAenp/OV67MJCE6zOnppI8o0CIut+vgcQoKi1n3YQ2DIkL4p4U53DdnFHFRoU6PJn1MgRZxqe1ldRQUFbOp+BiDo0L5+rVjuHt2OoMiFOZAoUCLuIi1li0ltfyssJhtZXUkxYTxv68by12z0okO1z/XQKMjLuIC1lo2fFRDQZGHneXHGRIbzqM3jOeOmSOJDDvHFbJlwFOgRRxkraXwg2oKiorZU1lPSlwE3100gVtnpBERqjAHOgVaxAGdnZY/v3eEgiIP7x8+SVpCJD/4wiQWT0slLKSXV8iWAU+BFulH3k7Ln949zMqiYj462siopGh+fOtkFk1JITRYYZaPU6BF+kGHt5NX9hxi5ToPpTVNZA+J4We3T+GG3BSCL+UK2TKgKdAifaito5OXd1eyen0J5bWnGDssllV3TuO6icMIUpjlPBRokT7Q2uHldzsq+fn6EqpONDNpRByP3T2dq8cNVZil1xRoER9qaffy3PaDrN1QypGTLUwdGc/3Pj+R+WOSdYVsuWAKtIgPnGrr4NmtB1m7sZRjja3MzEjgx7dOZk52osIsF02BFrkEja0dPLPlAE9sKqOuqY052YmszJvKrMxEp0eTAUCBFrkI9c3t/HLzAZ7aXEZ9czvzRiezfGE209MTnB5NBhAFWuQCHG9q46nNZfxy8wEaWju4etwQluXlMDkt3unRZABSoEV64VhjK09sKuNXWw7Q1ObluonDWJqXzYSUOKdHkwFMgRb5FNUnW1i7sZRnt5XT2tHJDbkpLF2QzZhhsU6PJgFAgRbpwaETzazdUMJzb1fg7bQsmpLCIwuyyUqOcXo0CSAKtMgZKupOsXp9CS/urMBaWDwtlYcXZJGeGO30aBKAFGgR4MCxJlat8/Dy7iqCjGHJjDQemp9F6uAop0eTAKZAS0DzVDeyap2HP75TRWhwEHfNSueBeZkMj4t0ejQRBVoC0/4jJyko8vD6u4eJCAnmK1eO4qtzMxkSG+H0aCJ/p0BLQNlXVU9BUTF/fu8o0WHBPDQvi69cOYrEmHCnRxP5BAVaAsLug8cpKPJQtL+a2IgQli/M4b45GcRHhTk9msg5KdAyoL19oI78wmI2FR8jPiqUf/vMaL40J4NBEaFOjyZyXgq0DDjWWraU1lJQ6GFLaS2J0WF887qx3DUrnZhwfcqL/9BnqwwY1lo2FR8jv7CYHeXHSY4N5z8+N447Lx9JVJg+1cX/6LNW/J61lqL91eQXedhTcYLhcRF856YJ3HZZGhGhwU6PJ3LRFGjxW52dlr+8f5SComLeO3SS1MGR/L+bJ7F4+gjCQxRm8X8KtPgdb6fl9XcPs7LIw4dHG8hIjOJHt+Ry89QRhAYHOT2eiM8o0OI3OrydvLr3ECuLPJTUNJGVHM1Pb5vCDbnDCVGYZQBSoMX12r2dvLy7itXrPByoPcXYYbGsvHMq100cTrCukC0DmAItrtXa4eXFnZX8fH0JlcebmZAyiDV3Teea8UMJUpglACjQ4jot7V6ef7uCNRtKOFzfwuS0eFYsmsCCMUN0hWwJKAq0uEZzm5dnt5WzdmMpNQ2tXJYxmP9cnMtVOUkKswQkBVoc19jawa+2lPPEplJqm9qYnZlI/u1TmZWZoDBLQFOgxTEnW9p5evMBntxcxolT7cwdnczyvGxmZCQ4PZqIKyjQ0u9OnGrjqTfL+MVbB2ho6WDh2CEsW5jDlLR4p0cTcRUFWvpNbWMrT7xZxjNvHaCpzcu1E4ayLC+HiSPinB5NxJUUaOlz1Q0tPL6xlF9vPUhLh5fPTRrO0rxsxg4b5PRoIq6mQEufOVzfzNoNpTy3/SDt3k4WTRnBIwuyyB4S6/RoIn5BgRafqzx+ip+vL+F3OyrptJYvTBvBw/OzyUiKdno0Eb+iQIvPlNc2sWqdh9/vqsIYuHVGGg/NyyItIcrp0UT8kgItl6ykppFVRR7+uOcQwUGGL14+kgfmZZESH+n0aCJ+TYGWi/bhkQZWrvPw2t5DhIcE8eUrMvja3EyGDIpwejSRAUGBlgv23qF6Cgo9/M97R4gOC+aBuVncf9UokmLCnR5NZEBRoKXX9lScoKComL99UE1seAjL8rK5b84oBkeHOT2ayICkQMt57SyvI7/Qw4aPaoiLDOVfPzOaL12RQVxkqNOjiQxoCrSc09bSWvILi3mrpJaE6DC+8dkx3D0rndgIhVmkPyjQ8jHWWj7861Mkbv0hM73V/JdJ4oNp/8Kszz9IVJg+XUT6k/7FCdAV5vUf1rDrT4/x0MmfEWXawMBwjjG8+HuwfwjkLnF6TJGAokAHuM5Oy18/OMrKIg/vVtWzNeKXXXE+U3szFK5QoEX6mQIdoDo7LW/sO0JBUTH7jzQwMiGKHy3OZeifjvX8B+or+3dAEVGgA4230/La3kMUFHnwVDeSmRzNT5ZM5qbJKYQEB8GbqVBf8ck/GJfa/8OKBDgFOkC0ezv5w+4qVq8voexYE6OHxlBwx1SunzSc4DOvkL3wUXh1eddpjdNCI7u2i0i/UqAHuLaOTl7aVcnq9R4q6poZP3wQa+6axjXjhxEU1MP1/k6fZy5c0XVaIy61K846/yzS7xToAaql3csLOypYs76EQ/UtTE6N49s3TGDhuCHnvxBr7hIFWcQFFOgBprnNy2+2H2TthhKqG1qZnj6YHyzOZW5Okq6QLeJnFOgBoqm1g19vLefxTaUca2xjVmYCP71tCrOzEhVmET+lQPu5hpZ2ntlSzhObSjl+qp2rcpJYlpfDzFEJTo8mIpdIgfZT9afaeWpzGb/YXMbJlg7yxg5haV4200YOdno0EfERBdrP1DW18eSbpTz9VjmNrR1cM34oy/JymJQa5/RoIuJjCrSfqGlo5YlNpfxqaznN7V6unzicpXnZjBs+yOnRRKSPKNAud/RkC2s2lPDc9oO0dXRy0+QUHlmQTc7QWKdHE5E+pkC7VNWJZtasL+H5HRV4Oy03Tx3BIwuyGZUU7fRoItJPFGiXOVh7itXrPby0q+vFiW6ZnspD87IZmRjl8GQi0t8UaJcorWlk1boS/vBOFcFBhjtmjuSBeVmMiI90ejQRcYgC7bDiow2sXOfh1T2HCAsJ4kuzM3hgXiZDB0U4PZqIOEyBdsj7h06ycl0xb+w7QmRoMF+dm8n9V2aSHBvu9Ggi4hIKdD97t7Ke/KJi/vr+UWLDQ3hkfjb3XTmKhOgwp0cTEZdRoPvJroPHKSgsZt2HNQyKCOGfr87hy1eMIi5KV8gWkZ4p0H1sW2ktBUUe3vQcY3BUKF+/dgz3zE4nNkJhFpFPp0D3AWstb5XUkl9YzLayOpJiwvjW9WP54uXpRIfrr1xEeke18CFrLRs+qiG/sJhdB08wdFA4j94wnjtmjiQyLNjp8UTEzyjQPmCt5W8fVFNQVMzeynpS4iL47qIJ3DojjYhQhVlELo4CfQk6Oy1/fu8IBUUe3j98krSESH74hUl8YVoqYSFBTo8nIn5Ogb4I3k7Ln949zMqiYj462siopGh+fOtkFk1JITRYYRYR31CgL0CHt5M/vnOIVes9lNY0kTMkhp/dPoUbclMI7ukK2SIil0CB7oW2jk5e3l3JqnUlHKw7xdhhsaz+4jQ+O2EYQQqziPQRBfpTtHZ4eWFHJWvWl1B1oplJI+J47O7pXD1uqMIsIn1Oge5BS7uX57YfZO2GUo6cbGHqyHi+d/NE5o9O1hWyRaTfKNBnONXWwbNbD7J2YynHGluZOSqBH986mTnZiQqziPQ7BRpoaGnnmS3lPPlmGXVNbczJTmRl3lRmZSY6PZqIBLCADnR9czu/3HyApzaXUd/czvwxySzLy2F6+mCnRxMRCcxAH29q48k3y3j6rQM0tHZw9bihLMvLZnJavNOjiYj8XUAF+lhjK49vKuXXW8ppavNy3cRhLM3LZkJKnNOjiYh8QkAEuvpkC2s3lvLstnJaOzq5MTeFpXnZjB4a6/RoIiLnNKADfehEM2s2lPDbtyvwdloWTUnhkQXZZCXHOD2aiMh5DchAV9SdYvX6El7cWYG1sHhaKg8vyCI9Mdrp0UREem1ABfrAsSZWrfPw+91VBBvDbZel8eC8LFIHRzk9mojIBRsQgfZUN7CyyMMrew4RGhzE3bPSeXBeFsPiIpweTUTkovl1oPcfOUlBkYfX3z1MREgw91+Vyf1XjWJIrMIsIv7PLwO9r6qe/MJi/vL+UWLCQ3hoXhZfuXIUiTHhTo8mIuIzfhXo3QePU1DkoWh/NbERISxfmMN9czKIjwpzejQREZ/zi0C/faCO/MJiNhUfIz4qlH+/ZjT3XJHBoIhQp0cTEekzrg20tZYtpbXkFxaztbSOxOgwvnndWO6alU5MuGvHFhHxGedLt/cFKFwB9ZUQl4pd+CgbIxZQUFjMjvLjDIkN5//cMJ47Z44kMkxXyBaRwOFsoPe+AK8uh/bmrtv1FbT+fikvtX2FqtirWbFoAktmpBERqjCLSOBxNtCFK/4R524RtPKDQS8T8u/fIzxEYRaRwOVsoOsre9wc3XIEFGcRCXBBjn70uNQL2y4iEkCcDfTCRyE08uPbQiO7touIBDhnA527BG7Mh7g0wHT9fmN+13YRkQDn/NPscpcoyCIiPXD2EbSIiJyTAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIupUCLiLiUAi0i4lIKtIiISynQIiIudUGBNl0UdRGRfnDe2BpjMowxHxhjVgO7gLS+H0tERHr7aHgM8Iy1dqq1tvz0RmPM14wxO4wxO2pqavpmQhGRANXbQJdba7eevdFa+5i1doa1dkZycrKPRxMRCWy9DXRTn04hIiKfoB/4iYi4lAItIuJSIefbwVp7AJjY96OIiMiZ9AhaRMSlFGgREZdSoEVEXEqBFhFxKWOt9c07MqYGKD/vjueWBBzzyTDOGijrAK3FjQbKOkBrOS3dWtvj//TzWaAvlTFmh7V2htNzXKqBsg7QWtxooKwDtJbe0CkOERGXUqBFRFzKTYF+zOkBfGSgrAO0FjcaKOsAreW8XHMOWkREPs5Nj6BFROQMCrSIiEv1a6CNMZ81xnxojPEYY77Zw/3hxpjnu+/fZozJ6M/5LkQv1nKvMabGGPNO96/7nZjzfIwxTxljqo0x+85xvzHG5Hevc68xZlp/z9hbvVjLfGNM/RnH5NH+nrE3jDFpxph13Zeae88Y80897OMXx6WXa/GX4xJhjNlujNnTvZbv9LCPbxtmre2XX0AwUAJkAmHAHmD8Wfs8DKzpfvt24Pn+mq8P1nIvsNLpWXuxlrnANGDfOe6/HngDMMAsYJvTM1/CWuYDrzk9Zy/WMRyY1v12LPBRD59ffnFcerkWfzkuBojpfjsU2AbMOmsfnzasPx9BzwQ81tpSa20b8Ftg0Vn7LAKe7n77RWChMcb044y91Zu1+AVr7Uag7lN2WUTX9Sit7brsWbwxZnj/THdherEWv2CtPWyt3dX9dgPwATDirN384rj0ci1+ofvvurH7Zmj3r7OfZeHThvVnoEcAFWfcruSTB+rv+1hrO4B6ILFfprswvVkLwOLubz9fNMb469XQe7tWfzG7+1vUN4wxE5we5ny6v0WeStejtTP53XH5lLWAnxwXY0ywMeYdoBr4q7X2nMfFFw3rz0D39FXk7K8+vdnHDXoz56tAhrU2F/gb//iq6m/85Zj0xi66XvdgMlAA/MHheT6VMSYGeAn4Z2vtybPv7uGPuPa4nGctfnNcrLVea+0UIBWYaYw5+2ImPj0u/RnoSuDMR5GpwKFz7WOMCQHicOe3rOddi7W21lrb2n3zcWB6P83ma705bn7BWnvy9Leo1trXgVBjTJLDY/XIGBNKV9Cetdb+vodd/Oa4nG8t/nRcTrPWngDWA5896y6fNqw/A/02kGOMGWWMCaPrBPorZ+3zCvCl7rdvAYps99l2lznvWs46H3gTXefe/NErwD3dzxqYBdRbaw87PdTFMMYMO30+0Bgzk67P/1pnp/qk7hmfBD6w1v7kHLv5xXHpzVr86LgkG2Piu9+OBK4G9p+1m08bdt5rEvqKtbbDGLMU+DNdz4J4ylr7njFmBbDDWvsKXQfyV8YYD11fdW7vr/kuRC/XstwYcxPQQdda7nVs4E9hjHmOrp+iJxljKoFv0/XDD6y1a4DX6XrGgAc4BXzZmUnPrxdruQV4yBjTATQDt7v0AcAc4G7g3e7znQDfAkaC3x2X3qzFX47LcOBpY0wwXV9EXrDWvtaXDdN/9RYRcSn9T0IREZdSoEVEXEqBFhFxKQVaRMSlFGgREZdSoEVEXEqBFhFxqf8P86SSYODzmb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, line_inter(np.unique(x)))\n",
    "plt.plot(x,y, 'o', 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Datasets\n",
    "\n",
    "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data\n",
    "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>n_siblings_spouses</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
       "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
       "1         1  female  38.0                   1      0  71.2833  First        C   \n",
       "2         1  female  26.0                   0      0   7.9250  Third  unknown   \n",
       "3         1  female  35.0                   1      0  53.1000  First        C   \n",
       "4         0    male  28.0                   0      0   8.4583  Third  unknown   \n",
       "\n",
       "   embark_town alone  \n",
       "0  Southampton     n  \n",
       "1    Cherbourg     n  \n",
       "2  Southampton     y  \n",
       "3  Southampton     n  \n",
       "4   Queenstown     y  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = dftrain.pop('survived') # we remove the 'survived' column from the table and save it in another variable\n",
    "y_eval = dfeval.pop('survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>n_siblings_spouses</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
       "0    male  22.0                   1      0   7.2500  Third  unknown   \n",
       "1  female  38.0                   1      0  71.2833  First        C   \n",
       "2  female  26.0                   0      0   7.9250  Third  unknown   \n",
       "3  female  35.0                   1      0  53.1000  First        C   \n",
       "4    male  28.0                   0      0   8.4583  Third  unknown   \n",
       "\n",
       "   embark_town alone  \n",
       "0  Southampton     n  \n",
       "1    Cherbourg     n  \n",
       "2  Southampton     y  \n",
       "3  Southampton     n  \n",
       "4   Queenstown     y  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the survived column is removed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f4e5c24c8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dftrain.age.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sorted(np.unique(dftrain.age)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f4ea07dc8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dftrain['class'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f4ea94e48>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dftrain['deck'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'unknown'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(dftrain['deck'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data = ['sex' , 'class', 'deck', 'embark_town', 'alone']\n",
    "numerical_data = ['age' , 'n_siblings_spouses','parch','fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_columns = []\n",
    "for feature in categorical_data:\n",
    "    vocabolary = dftrain[feature].unique() # we make a list of unique values in each catego. data column of dftrain\n",
    "    features_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature, vocabolary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in numerical_data:\n",
    "    features_columns.append(tf.feature_column.numeric_column(feature, dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[VocabularyListCategoricalColumn(key='sex', vocabulary_list=('male', 'female'), dtype=tf.string, default_value=-1, num_oov_buckets=0),\n",
       " VocabularyListCategoricalColumn(key='class', vocabulary_list=('Third', 'First', 'Second'), dtype=tf.string, default_value=-1, num_oov_buckets=0),\n",
       " VocabularyListCategoricalColumn(key='deck', vocabulary_list=('unknown', 'C', 'G', 'A', 'B', 'D', 'F', 'E'), dtype=tf.string, default_value=-1, num_oov_buckets=0),\n",
       " VocabularyListCategoricalColumn(key='embark_town', vocabulary_list=('Southampton', 'Cherbourg', 'Queenstown', 'unknown'), dtype=tf.string, default_value=-1, num_oov_buckets=0),\n",
       " VocabularyListCategoricalColumn(key='alone', vocabulary_list=('n', 'y'), dtype=tf.string, default_value=-1, num_oov_buckets=0),\n",
       " NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='n_siblings_spouses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='parch', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='fare', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Input Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to return a function object that is the input function\n",
    "\n",
    "def make_input_fn (data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n",
    "    def input_fn ():\n",
    "        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n",
    "        if shuffle:\n",
    "            ds = ds.shuffle(1000) # to randomize the data\n",
    "        ds = ds.batch(batch_size).repeat(num_epochs) # split dataset into batches of 32 and repeat the proces for num_epochs=10\n",
    "        return ds # return a batch of the dataset\n",
    "    return input_fn #return a function object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input_fn = make_input_fn(dftrain, y_train)\n",
    "eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\Andi\\AppData\\Local\\Temp\\tmpdrd63nb2\n",
      "INFO:tensorflow:Using config: {'_model_dir': 'C:\\\\Users\\\\Andi\\\\AppData\\\\Local\\\\Temp\\\\tmpdrd63nb2', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000023F52451FC8>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "linear_est = tf.estimator.LinearClassifier(feature_columns=features_columns) # we create a Linear Classifier object from the/\n",
    "                                                                                # estimator module\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Andi\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From C:\\Users\\Andi\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\training\\training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Andi\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\feature_column\\feature_column_v2.py:518: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "WARNING:tensorflow:From C:\\Users\\Andi\\anaconda3\\lib\\site-packages\\tensorflow_estimator\\python\\estimator\\canned\\linear.py:308: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "WARNING:tensorflow:From C:\\Users\\Andi\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\ftrl.py:143: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into C:\\Users\\Andi\\AppData\\Local\\Temp\\tmpdrd63nb2\\model.ckpt.\n",
      "INFO:tensorflow:loss = 0.6931472, step = 0\n",
      "INFO:tensorflow:global_step/sec: 235.166\n",
      "INFO:tensorflow:loss = 0.28524733, step = 100 (0.408 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 200 into C:\\Users\\Andi\\AppData\\Local\\Temp\\tmpdrd63nb2\\model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.30818045.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.canned.linear.LinearClassifierV2 at 0x23f5321cd48>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_est.train(train_input_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2020-05-07T21:47:06Z\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\Andi\\AppData\\Local\\Temp\\tmpdrd63nb2\\model.ckpt-200\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Finished evaluation at 2020-05-07-21:47:08\n",
      "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.7651515, accuracy_baseline = 0.625, auc = 0.8337313, auc_precision_recall = 0.7851412, average_loss = 0.49858394, global_step = 200, label/mean = 0.375, loss = 0.48679593, precision = 0.6666667, prediction/mean = 0.44546735, recall = 0.74747473\n",
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 200: C:\\Users\\Andi\\AppData\\Local\\Temp\\tmpdrd63nb2\\model.ckpt-200\n"
     ]
    }
   ],
   "source": [
    "result = linear_est.evaluate(eval_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7651515\n"
     ]
    }
   ],
   "source": [
    "print(result['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.7651515, 'accuracy_baseline': 0.625, 'auc': 0.8337313, 'auc_precision_recall': 0.7851412, 'average_loss': 0.49858394, 'label/mean': 0.375, 'loss': 0.48679593, 'precision': 0.6666667, 'prediction/mean': 0.44546735, 'recall': 0.74747473, 'global_step': 200}\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\Andi\\AppData\\Local\\Temp\\tmpdrd63nb2\\model.ckpt-200\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "prediction = list(linear_est.predict(eval_input_fn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'logits': array([-2.0711577], dtype=float32),\n",
       "  'logistic': array([0.11193189], dtype=float32),\n",
       "  'probabilities': array([0.88806814, 0.11193192], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([0.38427716], dtype=float32),\n",
       "  'logistic': array([0.5949043], dtype=float32),\n",
       "  'probabilities': array([0.40509573, 0.5949043 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.6080738], dtype=float32),\n",
       "  'logistic': array([0.83314383], dtype=float32),\n",
       "  'probabilities': array([0.16685621, 0.8331438 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.3154095], dtype=float32),\n",
       "  'logistic': array([0.788417], dtype=float32),\n",
       "  'probabilities': array([0.21158303, 0.7884169 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-0.6246986], dtype=float32),\n",
       "  'logistic': array([0.3487136], dtype=float32),\n",
       "  'probabilities': array([0.6512864, 0.3487136], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.2158426], dtype=float32),\n",
       "  'logistic': array([0.7713311], dtype=float32),\n",
       "  'probabilities': array([0.2286689, 0.7713311], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([0.308608], dtype=float32),\n",
       "  'logistic': array([0.5765454], dtype=float32),\n",
       "  'probabilities': array([0.42345452, 0.5765454 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.8841196], dtype=float32),\n",
       "  'logistic': array([0.13191637], dtype=float32),\n",
       "  'probabilities': array([0.8680836, 0.1319164], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([0.7073186], dtype=float32),\n",
       "  'logistic': array([0.6698084], dtype=float32),\n",
       "  'probabilities': array([0.3301916, 0.6698084], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.1594023], dtype=float32),\n",
       "  'logistic': array([0.76122403], dtype=float32),\n",
       "  'probabilities': array([0.23877591, 0.7612241 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.0365788], dtype=float32),\n",
       "  'logistic': array([0.73818934], dtype=float32),\n",
       "  'probabilities': array([0.26181066, 0.73818934], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.8891209], dtype=float32),\n",
       "  'logistic': array([0.1313447], dtype=float32),\n",
       "  'probabilities': array([0.86865526, 0.13134474], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.8455676], dtype=float32),\n",
       "  'logistic': array([0.86360586], dtype=float32),\n",
       "  'probabilities': array([0.13639416, 0.86360586], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-0.01860996], dtype=float32),\n",
       "  'logistic': array([0.49534765], dtype=float32),\n",
       "  'probabilities': array([0.5046524 , 0.49534768], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.9037076], dtype=float32),\n",
       "  'logistic': array([0.8703106], dtype=float32),\n",
       "  'probabilities': array([0.12968943, 0.8703106 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-0.28944558], dtype=float32),\n",
       "  'logistic': array([0.42813963], dtype=float32),\n",
       "  'probabilities': array([0.5718604, 0.4281396], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.269982], dtype=float32),\n",
       "  'logistic': array([0.21926033], dtype=float32),\n",
       "  'probabilities': array([0.78073967, 0.21926033], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.7130548], dtype=float32),\n",
       "  'logistic': array([0.8472321], dtype=float32),\n",
       "  'probabilities': array([0.15276791, 0.84723204], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([0.04992001], dtype=float32),\n",
       "  'logistic': array([0.5124774], dtype=float32),\n",
       "  'probabilities': array([0.48752257, 0.5124774 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.9909986], dtype=float32),\n",
       "  'logistic': array([0.12015122], dtype=float32),\n",
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       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.850567], dtype=float32),\n",
       "  'logistic': array([0.8641937], dtype=float32),\n",
       "  'probabilities': array([0.13580634, 0.8641937 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.9680611], dtype=float32),\n",
       "  'logistic': array([0.12259725], dtype=float32),\n",
       "  'probabilities': array([0.8774027 , 0.12259731], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([1.3989947], dtype=float32),\n",
       "  'logistic': array([0.8020243], dtype=float32),\n",
       "  'probabilities': array([0.19797568, 0.8020243 ], dtype=float32),\n",
       "  'class_ids': array([1], dtype=int64),\n",
       "  'classes': array([b'1'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " ...]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.75900114\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(prediction[28]['probabilities'][0]) # probability of dying index [0]\n",
    "print(y_eval[28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex                        male\n",
       "age                          12\n",
       "n_siblings_spouses            1\n",
       "parch                         0\n",
       "fare                    11.2417\n",
       "class                     Third\n",
       "deck                    unknown\n",
       "embark_town           Cherbourg\n",
       "alone                         n\n",
       "Name: 28, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfeval.iloc[28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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