assignment4 - Network Analysis - Salary & Link Prediction.ipynb

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   "source": [
    "# Assignment 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "cell_type": "markdown",
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   "source": [
    "---\n",
    "\n",
    "## Part 1 - Random Graph Identification\n",
    "\n",
    "For the first part of this assignment you will analyze randomly generated graphs and determine which algorithm created them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "G1 = nx.read_gpickle(\"assets/A4_P1_G1\")\n",
    "G2 = nx.read_gpickle(\"assets/A4_P1_G2\")\n",
    "G3 = nx.read_gpickle(\"assets/A4_P1_G3\")\n",
    "G4 = nx.read_gpickle(\"assets/A4_P1_G4\")\n",
    "G5 = nx.read_gpickle(\"assets/A4_P1_G5\")\n",
    "P1_Graphs = [G1, G2, G3, G4, G5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": false,
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   },
   "source": [
    "<br>\n",
    "`P1_Graphs` is a list containing 5 networkx graphs. Each of these graphs were generated by one of three possible algorithms:\n",
    "* Preferential Attachment (`'PA'`)\n",
    "* Small World with low probability of rewiring (`'SW_L'`)\n",
    "* Small World with high probability of rewiring (`'SW_H'`)\n",
    "\n",
    "Anaylze each of the 5 graphs using any methodology and determine which of the three algorithms generated each graph.\n",
    "\n",
    "*The `graph_identification` function should return a list of length 5 where each element in the list is either `'PA'`, `'SW_L'`, or `'SW_H'`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_d = {}\n",
    "for idx, G_id in enumerate(['G1','G2','G3','G4','G5']):\n",
    "    graph_d[G_id] = {}\n",
    "    for node, degree in P1_Graphs[idx].degree():\n",
    "        if degree in graph_d[G_id].keys():\n",
    "            graph_d[G_id][degree] += 1\n",
    "        else:\n",
    "            graph_d[G_id][degree] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "d = graph_d['G1']\n",
    "degs = d.keys()\n",
    "node_count = d.values()\n",
    "ax.scatter(degs, node_count, alpha=0.7)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASkAAAESCAYAAAChJCPsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAYiklEQVR4nO3df1RT9/3H8VfkRwSWpBBLYo7Y0i7rL7Bz6KGgEzZ+OCfaznPGWtSjG+vsAdFMmY65TdvTJpWt4Fk5Uu08QnWU/rHS9o/VgtuajaOuEccq2NntlGNjJaY9TROwGBQ+3z883vMNVKrrTe8HfD3OueeUmzfxfWt5nmuIVCeEECAiktQ0rRcgIpoII0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikFqv1AtEyOjqKc+fOwWAwQKfTab0OEY0hhMDAwABsNhumTbv2/dKUjdS5c+eQlpam9RpE9Dm8Xi9mzZp1zcenbKQMBgOAK/8CjEajxtsQ0VihUAhpaWnK1+o1iRvkdrtFSUmJmDlzpgAg2traIh4fHR0V27dvFzNnzhTTp08XeXl5oqenJ2Lm4sWLYv369cJsNovExESxbNky4fV6I2Y+/vhjsWrVKmE0GoXRaBSrVq0SgUDguvcMBoMCgAgGgzd6iUT0Jbjer9EbfuH8woULuP/++9HQ0PCZj9fW1qKurg4NDQ3weDywWq0oKirCwMCAMuNwONDW1obW1lZ0dnZicHAQJSUlGBkZUWbKysrQ3d2NQ4cO4dChQ+ju7sbq1atvdF0imuy+SAkx5k5qdHRUWK1W8fTTTyvnLl68KEwmk3juueeEEEJ88sknIi4uTrS2tiozH3zwgZg2bZo4dOiQEEKIU6dOCQDi2LFjyszRo0cFAPHvf//7unbjnRSR3KJ2JzWRvr4++Hw+FBcXK+f0ej3y8vJw5MgRAEBXVxcuXboUMWOz2ZCRkaHMHD16FCaTCdnZ2crMAw88AJPJpMyMFQ6HEQqFIg4imvxUjZTP5wMAWCyWiPMWi0V5zOfzIT4+HsnJyRPOpKamjnv+1NRUZWYsl8sFk8mkHPzOHtHUEJU3c459X5IQ4nPfqzR25rPmJ3qempoaBINB5fB6vf/D5kQkG1UjZbVaAWDc3Y7f71furqxWK4aHhxEIBCacOX/+/Ljn//DDD8fdpV2l1+thNBojDiKa/FSNVHp6OqxWKzo6OpRzw8PDcLvdyM3NBQBkZWUhLi4uYqa/vx89PT3KTE5ODoLBIN566y1l5h//+AeCwaAyQ0Q3hxt+M+fg4CD++9//Kh/39fWhu7sbKSkpmD17NhwOB5xOJ+x2O+x2O5xOJxITE1FWVgYAMJlMKC8vx+bNm2E2m5GSkoLq6mpkZmaisLAQAHDPPffgO9/5Dh599FHs2bMHAPCTn/wEJSUluOuuu9S4biKaJG44UsePH8e3vvUt5eNNmzYBANasWYOmpiZs2bIFQ0NDqKioQCAQQHZ2Ntrb2yPeVVpfX4/Y2FiUlpZiaGgIBQUFaGpqQkxMjDLzhz/8ARs2bFC+C7h8+fJrvjeLbm7lTR6tV7hh+9bO13qFSUMnxNT8HzGEQiGYTCYEg0G+PjXFMVKT0/V+jfJHtRCR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikxkgRkdQYKSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSY6SISGqMFBFJjZEiIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikxkgRkdQYKSKSmuqRunz5Mn75y18iPT0dCQkJuOOOO/DEE09gdHRUmRFCYMeOHbDZbEhISEB+fj56e3sjniccDqOqqgozZsxAUlISli9fjrNnz6q9LhFJTvVI7dy5E8899xwaGhrwzjvvoLa2Fr/5zW/w7LPPKjO1tbWoq6tDQ0MDPB4PrFYrioqKMDAwoMw4HA60tbWhtbUVnZ2dGBwcRElJCUZGRtRemYgkFqv2Ex49ehQPPvggli5dCgC4/fbb8eKLL+L48eMArtxF7dq1C9u2bcOKFSsAAM3NzbBYLGhpacG6desQDAaxb98+HDhwAIWFhQCAgwcPIi0tDYcPH8bixYvVXpuIJKX6ndTChQvx5z//Ge+++y4A4F//+hc6Ozvx3e9+FwDQ19cHn8+H4uJi5XP0ej3y8vJw5MgRAEBXVxcuXboUMWOz2ZCRkaHMjBUOhxEKhSIOIpr8VL+T2rp1K4LBIO6++27ExMRgZGQETz31FB555BEAgM/nAwBYLJaIz7NYLDhz5owyEx8fj+Tk5HEzVz9/LJfLhccff1ztyyEijal+J/XSSy/h4MGDaGlpwYkTJ9Dc3Izf/va3aG5ujpjT6XQRHwshxp0ba6KZmpoaBINB5fB6vV/sQohICqrfSf3sZz/Dz3/+czz88MMAgMzMTJw5cwYulwtr1qyB1WoFcOVuaebMmcrn+f1+5e7KarVieHgYgUAg4m7K7/cjNzf3M39dvV4PvV6v9uUQkcZUv5P69NNPMW1a5NPGxMQob0FIT0+H1WpFR0eH8vjw8DDcbrcSoKysLMTFxUXM9Pf3o6en55qRIqKpSfU7qWXLluGpp57C7Nmzcd999+Gf//wn6urq8KMf/QjAlT/mORwOOJ1O2O122O12OJ1OJCYmoqysDABgMplQXl6OzZs3w2w2IyUlBdXV1cjMzFS+20dENwfVI/Xss8/iV7/6FSoqKuD3+2Gz2bBu3Tr8+te/Vma2bNmCoaEhVFRUIBAIIDs7G+3t7TAYDMpMfX09YmNjUVpaiqGhIRQUFKCpqQkxMTFqr0xEEtMJIYTWS0RDKBSCyWRCMBiE0WjUeh2KovImj9Yr3LB9a+drvYLmrvdrlH93j4ikxkgRkdQYKSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSY6SISGqMFBFJjZEiIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikxkgRkdQYKSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUohKpDz74AKtWrYLZbEZiYiK+/vWvo6urS3lcCIEdO3bAZrMhISEB+fn56O3tjXiOcDiMqqoqzJgxA0lJSVi+fDnOnj0bjXWJSGKqRyoQCGDBggWIi4vD66+/jlOnTuGZZ57BLbfcoszU1tairq4ODQ0N8Hg8sFqtKCoqwsDAgDLjcDjQ1taG1tZWdHZ2YnBwECUlJRgZGVF7ZSKSWKzaT7hz506kpaVh//79yrnbb79d+WchBHbt2oVt27ZhxYoVAIDm5mZYLBa0tLRg3bp1CAaD2LdvHw4cOIDCwkIAwMGDB5GWlobDhw9j8eLFaq9NRJJS/U7qtddew7x58/D9738fqampmDt3Lp5//nnl8b6+Pvh8PhQXFyvn9Ho98vLycOTIEQBAV1cXLl26FDFjs9mQkZGhzIwVDocRCoUiDiKa/FSP1HvvvYfGxkbY7Xa88cYbeOyxx7Bhwwa88MILAACfzwcAsFgsEZ9nsViUx3w+H+Lj45GcnHzNmbFcLhdMJpNypKWlqX1pRKQB1SM1OjqKb3zjG3A6nZg7dy7WrVuHRx99FI2NjRFzOp0u4mMhxLhzY000U1NTg2AwqBxer/eLXQgRSUH1SM2cORP33ntvxLl77rkH77//PgDAarUCwLg7Ir/fr9xdWa1WDA8PIxAIXHNmLL1eD6PRGHEQ0eSneqQWLFiA06dPR5x79913cdtttwEA0tPTYbVa0dHRoTw+PDwMt9uN3NxcAEBWVhbi4uIiZvr7+9HT06PMENHNQfXv7v30pz9Fbm4unE4nSktL8dZbb2Hv3r3Yu3cvgCt/zHM4HHA6nbDb7bDb7XA6nUhMTERZWRkAwGQyoby8HJs3b4bZbEZKSgqqq6uRmZmpfLePiG4Oqkdq/vz5aGtrQ01NDZ544gmkp6dj165dWLlypTKzZcsWDA0NoaKiAoFAANnZ2Whvb4fBYFBm6uvrERsbi9LSUgwNDaGgoABNTU2IiYlRe2UikphOCCG0XiIaQqEQTCYTgsEgX5+a4sqbPFqvcMP2rZ2v9Qqau96vUf7dPSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSY6SISGqMFBFJjZEiIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikxkgRkdQYKSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSi3qkXC4XdDodHA6Hck4IgR07dsBmsyEhIQH5+fno7e2N+LxwOIyqqirMmDEDSUlJWL58Oc6ePRvtdYlIMlGNlMfjwd69ezFnzpyI87W1tairq0NDQwM8Hg+sViuKioowMDCgzDgcDrS1taG1tRWdnZ0YHBxESUkJRkZGorkyEUkmapEaHBzEypUr8fzzzyM5OVk5L4TArl27sG3bNqxYsQIZGRlobm7Gp59+ipaWFgBAMBjEvn378Mwzz6CwsBBz587FwYMHcfLkSRw+fDhaKxORhKIWqcrKSixduhSFhYUR5/v6+uDz+VBcXKyc0+v1yMvLw5EjRwAAXV1duHTpUsSMzWZDRkaGMjNWOBxGKBSKOIho8ouNxpO2trbixIkT8Hg84x7z+XwAAIvFEnHeYrHgzJkzykx8fHzEHdjVmaufP5bL5cLjjz+uxvpEJBHV76S8Xi82btyIgwcPYvr06dec0+l0ER8LIcadG2uimZqaGgSDQeXwer03vjwRSUf1SHV1dcHv9yMrKwuxsbGIjY2F2+3G7373O8TGxip3UGPviPx+v/KY1WrF8PAwAoHANWfG0uv1MBqNEQcRTX6qR6qgoAAnT55Ed3e3csybNw8rV65Ed3c37rjjDlitVnR0dCifMzw8DLfbjdzcXABAVlYW4uLiImb6+/vR09OjzBDRzUH116QMBgMyMjIiziUlJcFsNivnHQ4HnE4n7HY77HY7nE4nEhMTUVZWBgAwmUwoLy/H5s2bYTabkZKSgurqamRmZo57IZ6IpraovHD+ebZs2YKhoSFUVFQgEAggOzsb7e3tMBgMykx9fT1iY2NRWlqKoaEhFBQUoKmpCTExMVqsTEQa0QkhhNZLREMoFILJZEIwGOTrU1NcedP47yLLbt/a+VqvoLnr/Rrl390jIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikxkgRkdQYKSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSY6SISGqMFBFJjZEiIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDXVI+VyuTB//nwYDAakpqbioYcewunTpyNmhBDYsWMHbDYbEhISkJ+fj97e3oiZcDiMqqoqzJgxA0lJSVi+fDnOnj2r9rpEJDnVI+V2u1FZWYljx46ho6MDly9fRnFxMS5cuKDM1NbWoq6uDg0NDfB4PLBarSgqKsLAwIAy43A40NbWhtbWVnR2dmJwcBAlJSUYGRlRe2UikphOCCGi+Qt8+OGHSE1NhdvtxqJFiyCEgM1mg8PhwNatWwFcuWuyWCzYuXMn1q1bh2AwiFtvvRUHDhzAD37wAwDAuXPnkJaWhj/96U9YvHjx5/66oVAIJpMJwWAQRqMxmpdIGitv8mi9wg3bt3a+1ito7nq/RqP+mlQwGAQApKSkAAD6+vrg8/lQXFyszOj1euTl5eHIkSMAgK6uLly6dClixmazISMjQ5kZKxwOIxQKRRxENPlFNVJCCGzatAkLFy5ERkYGAMDn8wEALBZLxKzFYlEe8/l8iI+PR3Jy8jVnxnK5XDCZTMqRlpam9uUQkQaiGqn169fj7bffxosvvjjuMZ1OF/GxEGLcubEmmqmpqUEwGFQOr9f7vy9ORNKIWqSqqqrw2muv4a9//StmzZqlnLdarQAw7o7I7/crd1dWqxXDw8MIBALXnBlLr9fDaDRGHEQ0+akeKSEE1q9fj5dffhl/+ctfkJ6eHvF4eno6rFYrOjo6lHPDw8Nwu93Izc0FAGRlZSEuLi5ipr+/Hz09PcoMEd0cYtV+wsrKSrS0tODVV1+FwWBQ7phMJhMSEhKg0+ngcDjgdDpht9tht9vhdDqRmJiIsrIyZba8vBybN2+G2WxGSkoKqqurkZmZicLCQrVXJiKJqR6pxsZGAEB+fn7E+f3792Pt2rUAgC1btmBoaAgVFRUIBALIzs5Ge3s7DAaDMl9fX4/Y2FiUlpZiaGgIBQUFaGpqQkxMjNorE5HEov4+Ka3wfVI3D75PanKS5n1SRERfBCNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSY6SISGqMFBFJjZEiIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJDVGioikxkgRkdQYKSKSGiNFRFJjpIhIaowUEUmNkSIiqTFSRCQ1RoqIpMZIEZHUGCkikhojRURSY6SISGrSR2r37t1IT0/H9OnTkZWVhb///e9ar0REX6JYrReYyEsvvQSHw4Hdu3djwYIF2LNnD5YsWYJTp05h9uzZWq83aZQ3ebRe4YbtWztf6xWkMdl+/9T+vZM6UnV1dSgvL8ePf/xjAMCuXbvwxhtvoLGxES6XK2I2HA4jHA4rHweDQQBAKBT68haW1PDQoNYr3LAb+X3j9cnleq/t6pwQYuJBIalwOCxiYmLEyy+/HHF+w4YNYtGiRePmt2/fLgDw4MFjkh1er3fCFkh7J/XRRx9hZGQEFosl4rzFYoHP5xs3X1NTg02bNikfj46O4uOPP4bZbIZOp4v6vp8lFAohLS0NXq8XRqNRkx2iaSpf31S+NkCO6xNCYGBgADabbcI5aSN11djACCE+Mzp6vR56vT7i3C233BLN1a6b0Wickv+hXzWVr28qXxug/fWZTKbPnZH2u3szZsxATEzMuLsmv98/7u6KiKYuaSMVHx+PrKwsdHR0RJzv6OhAbm6uRlsR0ZdN6j/ubdq0CatXr8a8efOQk5ODvXv34v3338djjz2m9WrXRa/XY/v27eP+GDpVTOXrm8rXBkyu69MJ8Xnf/9PW7t27UVtbi/7+fmRkZKC+vh6LFi3Sei0i+pJIHykiurlJ+5oUERHASBGR5BgpIpIaI0VEUmOkosDlcmH+/PkwGAxITU3FQw89hNOnT2u9lioaGxsxZ84c5Z3KOTk5eP3117VeK2pcLhd0Oh0cDofWq6hix44d0Ol0EYfVatV6rQkxUlHgdrtRWVmJY8eOoaOjA5cvX0ZxcTEuXLig9Wpf2KxZs/D000/j+PHjOH78OL797W/jwQcfRG9vr9arqc7j8WDv3r2YM2eO1quo6r777kN/f79ynDx5UuuVJqbSDy2gCfj9fgFAuN1urVeJiuTkZPH73/9e6zVUNTAwIOx2u+jo6BB5eXli48aNWq+kiu3bt4v7779f6zVuCO+kvgRXf7ZVSkqKxpuoa2RkBK2trbhw4QJycnK0XkdVlZWVWLp0KQoLC7VeRXX/+c9/YLPZkJ6ejocffhjvvfee1itNSOq/FjMVCCGwadMmLFy4EBkZGVqvo4qTJ08iJycHFy9exFe+8hW0tbXh3nvv1Xot1bS2tuLEiRPweCbXT8S8HtnZ2XjhhRfwta99DefPn8eTTz6J3Nxc9Pb2wmw2a73eZ2Kkomz9+vV4++230dnZqfUqqrnrrrvQ3d2NTz75BH/84x+xZs0auN3uKREqr9eLjRs3or29HdOnT9d6HdUtWbJE+efMzEzk5OTgzjvvRHNzc8TPY5MJ/1pMFFVVVeGVV17B3/72N6Snp2u9TtQUFhbizjvvxJ49e7Re5Qt75ZVX8L3vfQ8xMTHKuZGREeh0OkybNg3hcDjisamgqKgIX/3qV9HY2Kj1Kp+Jd1JRIIRAVVUV2tra8Oabb07pQAFXrvf//3z5yaygoGDcd7t++MMf4u6778bWrVunXKDC4TDeeecdfPOb39R6lWtipKKgsrISLS0tePXVV2EwGJQf3GcymZCQkKDxdl/ML37xCyxZsgRpaWkYGBhAa2sr3nzzTRw6dEjr1VRhMBjGvXaYlJQEs9k8JV5TrK6uxrJlyzB79mz4/X48+eSTCIVCWLNmjdarXRMjFQVXb5vz8/Mjzu/fvx9r16798hdS0fnz57F69Wr09/fDZDJhzpw5OHToEIqKirReja7D2bNn8cgjj+Cjjz7CrbfeigceeADHjh3DbbfdpvVq18TXpIhIanyfFBFJjZEiIqkxUkQkNUaKiKTGSBGR1BgpIpIaI0VEUmOkiEhqjBQRSY2RIiKpMVJEJLX/A52Mu7mkVcoMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "d = graph_d['G2']\n",
    "degs = d.keys()\n",
    "node_count = d.values()\n",
    "ax.bar(degs, node_count, alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "d = graph_d['G3']\n",
    "degs = d.keys()\n",
    "node_count = d.values()\n",
    "ax.bar(degs, node_count, alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR8AAAEXCAYAAACUBEAgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAXn0lEQVR4nO3dX2wU170H8O/s7D/8b/Hi2sHYLFSBAuGGPwYi3CLVlWLkICKSpkJ9MI6UVEKyFCFLrcr1Q6WokZ/K5YGFivYBkPpgtRI8NFwhP9CCsJSCC1KARK5bYjsYX8c47Hq9eP/MzH0wu9m113jXnp2zZ/f7kVbIu8PMmdXsVzNnzpmfYhiGASIii9lEN4CIShPDh4iEYPgQkRAMHyISguFDREIwfIhICIYPEQnB8CEiIeyiG5ArXdcxNjaGyspKKIoiujlENI9hGJienkZ9fT1stsXPb6QJH7/fD7/fj2g0in//+9+im0NESxgdHUVDQ8OinyuyTa8IBAJYvXo1RkdHUVVVJbo5RDRPMBhEY2Mjnj17Bo/Hs+hy0pz5JCQutaqqqhg+RAVsqW4RdjgTkRDShI/f78e2bduwd+9e0U0hIhNI1+cTDAbh8XgQCAReetml6wYGJ6YRCMfgKXNgc20lbDbeHSPKt2x/o9L1+WRjYHgKF/uHMTQRQjSuwWlX8WptBTqafWjyeUU3j4gg0WVXtgaGp/DJp1/g/uMAqtx2NFSXocptx4OxAD759AsMDE+JbiIRQaLwyabPR9cNXOwfxrNwDBvWlKHcZYdqU1DussPnLUPgeQyX+oeh61JdaRIVJWnCp7OzEw8fPsTt27cXXWZwYhpDEyHUVroW3OZTFAXfq3DhXxMhDE5M57u5RLQEacInG4FwDNG4BrdDzfi526EiGtcQCMcsbhkRzVdU4eMpc8BpVzEb0zJ+Phub63z2lDksbhkRzVdU4bO5thKv1lbgm1AE80cQGIaBb0IRbKqtwObaSkEtJKIEacInmw5nm01BR7MPnlUODE+FMROJQ9MNzETiGJ4Kw7PKgWPNPo73ISoARTnIMNM4n021FTjGcT5EeVfSgwybfF7saqzmCGeiAlaU4QPMXYJteYWz3okKlTR9PkRUXBg+RCSENOHDR2oQFZeivNtFROJk+xuV5syHiIoLw4eIhGD4EJEQDB8iEoLhQ0RCSBM+vNVOVFx4q52ITMVb7URU0Bg+RCQEw4eIhGD4EJEQDB8iEoLhQ0RCMHyISAiGDxEJIU34cIQzUXHhCGciMhVHOBNRQWP4EJEQDB8iEoLhQ0RCMHyISAiGDxEJwfAhIiEYPkQkBMOHiIRg+BCREAwfIhJCmvDhxFKi4sKJpYLouoHBiWkEwjF4yhzYXFsJm00R3SyiFcv2N2q3sE30wsDwFC72D2NoIoRoXIPTruLV2gp0NPvQ5POKbh6RJaS57CoWA8NT+OTTL3D/cQBVbjsaqstQ5bbjwVgAn3z6BQaGp0Q3kcgSDB8L6bqBi/3DeBaOYcOaMpS77FBtCspddvi8ZQg8j+FS/zB0XaorYaJlYfhYaHBiGkMTIdRWuqAo6f07iqLgexUu/GsihMGJaUEtJLIOw8dCgXAM0bgGt0PN+LnboSIa1xAIxyxuGZH1GD4W8pQ54LSrmI1pGT+fjc11PnvKHBa3jMh6DB8Lba6txKu1FfgmFMH8EQ6GYeCbUASbaiuwubZSUAuJrMPwsZDNpqCj2QfPKgeGp8KYicSh6QZmInEMT4XhWeXAsWYfx/tQSWD4WKzJ50X3oa14rd6D4GwcX38bRnA2ju31HnQf2spxPlQyOMhQgCafF7saqznCmUoaw0cQm03BllfknR5CtFK87CIiIRg+RCQEw4eIhLA8fEZHR/HjH/8Y27Ztw+uvv44///nPVjeBiAqA5R3Odrsdp0+fxs6dOzExMYHdu3fjrbfeQnl5udVNISKBLA+ftWvXYu3atQCA2tpaeL1eTE1NMXyISkzOl103btzA4cOHUV9fD0VRcOXKlQXLnD17Fhs3boTb7UZTUxNu3ryZcV137tyBrutobGzMueFEJLecw2dmZgY7duzAmTNnMn7e29uLEydOoLu7G3fv3sWBAwfQ1taGkZGRtOWePn2KY8eO4fz588trORFJbUXPcFYUBZcvX8aRI0eS773xxhvYvXs3zp07l3xv69atOHLkCHp6egAAkUgEb775Jn7xi1+gvb39pduIRCKIRCLJv4PBIBobG6V/hjNRscr2Gc6m3u2KRqMYGBhAa2tr2vutra3o7+8HMDd7+/3338dPfvKTJYMHAHp6euDxeJIvXqIRFQdTw2dychKapqGuri7t/bq6OoyPjwMAbt26hd7eXly5cgU7d+7Ezp078fnnny+6zpMnTyIQCCRfo6OjZjaZiATJy92u+Y8INQwj+d6PfvQj6Lqe9bpcLhdcLpep7SMi8Uw986mpqYGqqsmznISJiYkFZ0NEVNpMDR+n04mmpib09fWlvd/X14fm5uYVrZsVS4mKS86XXaFQCENDQ8m/Hz16hHv37sHr9WL9+vXo6upCe3s79uzZg/379+P8+fMYGRnB8ePHV9TQzs5OdHZ2JnvSaXGshkoyyDl87ty5g5aWluTfXV1dAICOjg5cuHABR48exdOnT/Hxxx/jyZMn2L59O65evQqfz2deq2lRrIZKspCmVrvf74ff74emaRgcHOQ4nwwS1VCfhWOorXTB7ZirlPFNKALPKgcf00qWyHacjzThk5DtjpUaXTdwovce7j8OYMOasrQ7joZhYHgqjO31HvzP0Z28BKO8EjLIkMRhNVSSDcOnSLAaKsmG4VMkWA2VZCNN+HCcz8uxGirJhh3ORSRxtyvwPIbvVfBuF4nBDucSxGqoJBMWDSwyrIZKsmD4FCFWQyUZSHPZxQ5nouLCDmciMhU7nImooDF8iEgIhg8RCcHwISIhpAkf3u0iKi6820VEpuLdLiIqaAwfIhKC4UNEQjB8iEgIhg8RCSFN+PBWO1Fx4a12yohVT2m5sv2N8nk+tACrnpIVpLnsImskngN9/3EAVW47GqrLUOW248FYAJ98+gUGhqdEN5GKBMOHknTdwMX+YTwLx7BhTRnKXXaoNgXlLjt83jIEnsdwqX8Yui7VlToVKIYPJbHqKVmJ4UNJrHpKVmL4UBKrnpKVGD6UxKqnZCVpwoeDDPPPZlPQ0eyDZ5UDw1NhzETi0HQDM5E4hqfC8Kxy4Fizj+N9yBQcZEgLZBrns6m2Asc4zoeywEGGtGysekpWYPhQRqx6SvkmTZ8PERUXhg8RCcHwISIhGD5EJATDh4iEYPgQkRAMHyISguFDREIwfIhICGnChxNLiYoLJ5aSKVjtghI4sZQsw2oXtBzSXHZRYWK1C1ouhg8tG6td0EowfGjZWO2CVoLhQ8vGahe0EgwfWjZWu6CVYPjQsrHaBa0Ew4eWbbnVLnTdwJfjQXz2n6f4cjzIDukSxXE+tCJNPi+6D21NjvOZDEXgtKvYXu/JWO2CY4IogSOcyRTZjHBOjAl6Fo6httIFt2Ouv+ibUASeVQ50H9rKACoCHOFMllqq2sX8MUGJW/PlLjvKnCqGp8K41D+MXY3VnJZRItjnQ5bgmCCaj+FDluCYIJqPl11kidQxQeWuhYfd/DFBnCVf/Bg+ZInEmKAHYwGUOdW0S6/EmKDt9R5srq3kHbESIeSy65133kF1dTXee+89EZsnAbIdE3R39FvOki8RQsLno48+wqVLl0RsmgRKjAl6rd6D4GwcX38bRnA2ju31HnQf2opdjdWcJV9ChFx2tbS04G9/+5uITZNgTT4vdjVWZ+zP+XI8mPUdsZfd1ic55Hzmc+PGDRw+fBj19fVQFAVXrlxZsMzZs2exceNGuN1uNDU14ebNm2a0lYpEYkzQG99fgy2vVCU7knlHrLTkHD4zMzPYsWMHzpw5k/Hz3t5enDhxAt3d3bh79y4OHDiAtrY2jIyMrLixVNw4S7605HzZ1dbWhra2tkU/P3XqFD744AN8+OGHAIDTp0/j2rVrOHfuHHp6enJuYCQSQSQSSf4dDAZzXgfJIZc7YiQ/Uzuco9EoBgYG0NramvZ+a2sr+vv7l7XOnp4eeDye5KuxsdGMplIBWu4seZKTqeEzOTkJTdNQV1eX9n5dXR3Gx8eTfx88eBA/+9nPcPXqVTQ0NOD27duLrvPkyZMIBALJ1+joqJlNpgKz1B0xjvMpHnm52zX/ToVhGGnvXbt2Let1uVwuuFwu09pGhe9ld8SoeJgaPjU1NVBVNe0sBwAmJiYWnA3lyu/3w+/3Q9Myd0ZScVlqljzJz9TLLqfTiaamJvT19aW939fXh+bm5hWtu7OzEw8fPnzpJRoRySPnM59QKIShoaHk348ePcK9e/fg9Xqxfv16dHV1ob29HXv27MH+/ftx/vx5jIyM4Pjx46Y2nIjklnP43LlzBy0tLcm/u7q6AAAdHR24cOECjh49iqdPn+Ljjz/GkydPsH37dly9ehU+n8+8VhOZhLPnxZHmMaqpfT6Dg4N8jCqtGGfP50e2j1GVJnwS+AxnMgOfJ50/2f5G+SRDKjmsMV8YGD5Ucvg86cIgTfj4/X5s27YNe/fuFd0UkhxnzxcGacKH43zILJw9XxikCR8is7DGfGFg+FDJ4ez5wsDwoZLE2fPiSVM6hxNLyWycPS8WBxkSkak4yJCIChrDh4iEYPgQkRAMHyISQprw4fQKouLCu11EZKpsf6PSjPMhygafTCgPhg8VDT6ZUC7S9PkQvUziyYT3HwdQ5bajoboMVW47HowF8MmnX2BgeEp0E2kehg9Jj08mlBPDh6THJxPKSZrw4a12WgyfTCgnacKHTzKkxfDJhHKSJnyIFsMnE8qJ4UPS45MJ5cTwoaLAJxPKh4MMqWjwyYRyYfhQUbHZFGx5hXP+ZMDLLiISguFDREIwfIhICGnChyOciYoLHyZGRKZi6RwiKmgMHyISguFDREIwfIhICIYPEQnB8CEiITi3i6SUzxI5LL9jDYYPSSefJXJYfsc6vOwiqeSzRA7L71iL4UPSyGeJHJbfsR7Dh6SRzxI5LL9jPWnChxNLKZ8lclh+x3rShA9L51A+S+Sw/I71pAkfonyWyGH5HesxfEga+SyRw/I71uPzfEg6mcbibKqtwLE8jfMxa92lItvfKMOHpMQRzoUr298oRziTlPJZIofld6zBPh8iEoLhQ0RCMHyISAiGDxEJwfAhIiEYPkQkBMOHiIRg+BCREAwfIhJCSPj89a9/xQ9+8ANs2rQJf/zjH0U0gYgEs3x6RTweR1dXF65fv46qqirs3r0b7777LrxeTtqj/LF6vlbq9irddkABpp/H07adTZsyrScYjuPb51F43A4EZmNYvcqB6nLnS/cp2/3PtByAvHx3lofPP/7xD7z22mtYt24dAOCtt97CtWvX8POf/9zqplCJsLoiRer2As+jmJ6NAwAq3XZ4Vjnxam0F3vi+F5/9Z+qlbcq0Hk03oBtAXNehG4BNAVx2G2oqXHi9YXXGfcp2/zMt5y13AFAwNRM1/bvL+bLrxo0bOHz4MOrr66EoCq5cubJgmbNnz2Ljxo1wu91oamrCzZs3k5+NjY0lgwcAGhoa8Pjx4+W1nmgJVlekSN2eogDB5zFE4zqimo7p2TgUGMll7nw1tWibMq1nNqYhHNUwG9MQ1wzougFNNxCNG5gMRZL/J3Wfst3/TMspCnD7q29x+6spKDBM/+5yDp+ZmRns2LEDZ86cyfh5b28vTpw4ge7ubty9excHDhxAW1sbRkZGAGDBU+IALHhgN5EZrK5Ikbo935oyTIYi0HSg3GVHuUNFXDcw+eIMIqbp0HQDZU51QZsu3voKF259tWA9CcaLl00BlBfvaLqBuGbgWTia3Kds9z8e1xcsZ7MpmAxFYAOg2hRMzkRhU2Dqd5dz+LS1teG3v/0t3n333Yyfnzp1Ch988AE+/PBDbN26FadPn0ZjYyPOnTsHAFi3bl3amc7XX3+NtWvXLrq9SCSCYDCY9iLKhtUVKVK3F45qmIlocNptUF5sz6naMP08jlBEh9uuYiYax0zku2dGJ9p0fyyIB0+CaetRVQWagbS+FkWZe2m6AbvNhploHBUue3Kfst3/vi//b8FyM5G5trkcKpyqLfm3md+dqXe7otEoBgYG0NramvZ+a2sr+vv7AQD79u3D/fv38fjxY0xPT+Pq1as4ePDgouvs6emBx+NJvhobG81sMhUxqytSpG4vpunQDQNqSlioCqAbBnTDgENVoBtATNfT1uF2qIjENERi6etRMHfVkBohRsq/CowXfUBKcp+y3f/xwOyC5VLbP9fu9Laa8d2ZGj6Tk5PQNA11dXVp79fV1WF8fBwAYLfb8bvf/Q4tLS3YtWsXfvnLX2LNmjWLrvPkyZMIBALJ1+joqJlNpiJmdUWK1O05VBtsigIt5bJEexEONkVBTDNgUwCHLf0nOBubO9twOdLXY2DujCP1IkdJ+deAAtuLcEvsU7b7/4rHvWC51PZrLzq2U9tqxneXl7td80/xDMNIe+/tt9/G22+/ndW6XC4XXC6Xqe2j0pCoSPFgLIAyp5p2DCYqUmyv95hWkSJ1e+u9ZSh3qZiejUO1qYBhIKrpqFxlh67rePY8Dm+ZE+Wu7842vmtTFQwAD58Ev1vP8zhUBYhpRsryc/+qNgVxXUeV24FQJI7/Wrc6uU/Z7P+bW+rwv5+Ppy1X7rKj3KUi+DwGRVFQ6bYn22rWd2fqmU9NTQ1UVU2e5SRMTEwsOBsiyjerK1Kkbm9kKoyaChdUBXN9OzENdpuCmnInnPa5fhS7qiAc1Ra0qeOHG/D+Dzekryfll6q8eOlG4tJLgWpTYFcVrC5zJvcp2/23220LltN1AzUVLujGXJ9STbkTugFTvztTw8fpdKKpqQl9fX1p7/f19aG5uXlF62bFUlqOJp8X3Ye24rV6D4KzcXz9bRjB2Ti213vQfWir6eN8UrdnGEDVKgecqg1O1YZKtx0GFOzxefHfL7a9WJsyrcftUFHmVOF2qLCrc+Gi2hQ47QpqKlzY8+L/pO5TtvufaTnDAPZtrMbeDV4YUEz/7nKuXhEKhTA0NAQA2LVrF06dOoWWlhZ4vV6sX78evb29aG9vx+9//3vs378f58+fxx/+8Ac8ePAAPp9vRY0FWL2ClocjnK0b4Zz1b9TI0fXr1xPDDNJeHR0dyWX8fr/h8/kMp9Np7N692/j73/+e62YWFQgEDABGIBAwbZ1EZJ5sf6PS1O3y+/3w+/3QNA2Dg4M88yEqUCwaSERCZPsb5fN8iEgI6SqWJk7UOM2CqDAlfptLXVRJFz7T03NzSTjNgqiwTU9Pw+PxLPq5dH0+uq5jbGwMlZWVaSM29+7di9u3b6ctGwwG0djYiNHR0YLtH8rU7kJad67ryGX5bJZdapnFPufxkJ/1Z/P/DcPA9PQ06uvrYbMt3rMj3ZmPzWZDQ0PDgvdVVV30gKqqqirYg+1l7S6Edee6jlyWz2bZpZZZ7HMeD/lZf7b//2VnPAlF0+Hc2dkpugnLks92m7HuXNeRy/LZLLvUMot9zuMhP+s3s33SXXblgrflKRWPh8JSNGc+mbhcLvzmN7/hrHgCwOOh0BT1mQ8RFa6iPvMhosLF8CEiIRg+RCQEw4eIhCjZ8GG9eJrvnXfeQXV1Nd577z3RTSkJJXm3Kx6PY9u2bWn14j/77DPWiy9x169fRygUwsWLF/GXv/xFdHOKXkme+aTWi6+srEzWi6fS1tLSgspKcypZ0NKkDB/Wi6f5VnpMkPWkDB/Wi6f5VnpMkAB5eoa0ZQAYly9fTntv3759xvHjx9Pe27Jli/HrX//aMAzDuHXrlnHkyJHkZx999JHxpz/9Ke9tJWss55hIuH79uvHTn/40300kwzCkPPN5mXzUiye5ZXNMkPWke57PUnKtF6/rOn71q1+9tF48yS2bYwIADh48iH/+85+YmZlBQ0MDLl++zCKVeVR04ZNgZr14Kg5LHRO842mtorvsYr14mo/HRGEquvDJZ714khOPicIk5WVXar14AHj06BHu3buXrBff1dWF9vZ27NmzJ1kvfmRkBMePHxfYasonHhMSEn27bTlE14unwsNjQj4lObeLiMQruj4fIpIDw4eIhGD4EJEQDB8iEoLhQ0RCMHyISAiGDxEJwfAhIiEYPkQkBMOHiIRg+BCREAwfIhKC4UNEQvw/PtzhdaCtMQAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "d = graph_d['G4']\n",
    "degs = d.keys()\n",
    "node_count = d.values()\n",
    "ax.scatter(degs, node_count, alpha=0.7)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "d = graph_d['G5']\n",
    "degs = d.keys()\n",
    "node_count = d.values()\n",
    "ax.bar(degs, node_count, alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0ff06107761a8aae493e00bd5375ddcf",
     "grade": false,
     "grade_id": "cell-c8c5e35d8114420d",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def graph_identification():\n",
    "    \n",
    "    # YOUR CODE HERE\n",
    "    # raise NotImplementedError()\n",
    "    return ['PA','SW_L','SW_L','PA','SW_H']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "34772a15ef373a444ae7597d2005254b",
     "grade": true,
     "grade_id": "cell-efb9da7e1c19accf",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "ans_one = graph_identification()\n",
    "assert type(ans_one) == list, \"You must return a list\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "24751ee5132e6ae445b370a534718f46",
     "grade": false,
     "grade_id": "cell-a8b02acc0a67f5cb",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "---\n",
    "\n",
    "## Part 2 - Company Emails\n",
    "\n",
    "For the second part of this assignment you will be working with a company's email network where each node corresponds to a person at the company, and each edge indicates that at least one email has been sent between two people.\n",
    "\n",
    "The network also contains the node attributes `Department` and `ManagmentSalary`.\n",
    "\n",
    "`Department` indicates the department in the company which the person belongs to, and `ManagmentSalary` indicates whether that person is receiving a managment position salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e722c34a6345262e25f7b6f3aa9ea24f",
     "grade": false,
     "grade_id": "cell-8318572a20542eb0",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 1005 nodes and 16706 edges\n"
     ]
    }
   ],
   "source": [
    "G = pickle.load(open('assets/email_prediction_NEW.txt', 'rb'))\n",
    "\n",
    "print(f\"Graph with {len(nx.nodes(G))} nodes and {len(nx.edges(G))} edges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "fce5fb3b46c870ef3a396d6272fb5741",
     "grade": false,
     "grade_id": "cell-e87009c4bfab6968",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Part 2A - Salary Prediction\n",
    "\n",
    "Using network `G`, identify the people in the network with missing values for the node attribute `ManagementSalary` and predict whether or not these individuals are receiving a managment position salary.\n",
    "\n",
    "To accomplish this, you will need to create a matrix of node features of your choice using networkx, train a sklearn classifier on nodes that have `ManagementSalary` data, and predict a probability of the node receiving a managment salary for nodes where `ManagementSalary` is missing.\n",
    "\n",
    "\n",
    "\n",
    "Your predictions will need to be given as the probability that the corresponding employee is receiving a managment position salary.\n",
    "\n",
    "The evaluation metric for this assignment is the Area Under the ROC Curve (AUC).\n",
    "\n",
    "Your grade will be based on the AUC score computed for your classifier. A model which with an AUC of 0.75 or higher will recieve full points.\n",
    "\n",
    "Using your trained classifier, return a Pandas series of length 252 with the data being the probability of receiving managment salary, and the index being the node id.\n",
    "\n",
    "    Example:\n",
    "    \n",
    "        1       1.0\n",
    "        2       0.0\n",
    "        5       0.8\n",
    "        8       1.0\n",
    "            ...\n",
    "        996     0.7\n",
    "        1000    0.5\n",
    "        1001    0.0\n",
    "        Length: 252, dtype: float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d02e4f542d1aa318bbf2d31a4190f873",
     "grade": false,
     "grade_id": "cell-5980e664930b216a",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, {'Department': 1, 'ManagementSalary': 0.0}),\n",
       " (1, {'Department': 1, 'ManagementSalary': nan}),\n",
       " (581, {'Department': 3, 'ManagementSalary': 0.0}),\n",
       " (6, {'Department': 25, 'ManagementSalary': 1.0}),\n",
       " (65, {'Department': 4, 'ManagementSalary': nan})]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(G.nodes(data=True))[:5] # print the first 5 nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Department</th>\n",
       "      <th>ManagementSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Department  ManagementSalary\n",
       "0             1               0.0\n",
       "1             1               NaN\n",
       "581           3               0.0\n",
       "6            25               1.0\n",
       "65            4               NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = [tup[0] for tup in list(G.nodes(data=True))]\n",
    "data = [tup[1] for tup in list(G.nodes(data=True))]\n",
    "df = pd.DataFrame(data=data, index=people)\n",
    "# df2 = pd.DataFrame(index=G.edges())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Department</th>\n",
       "      <th>ManagementSalary</th>\n",
       "      <th>degree</th>\n",
       "      <th>centrality</th>\n",
       "      <th>clustering</th>\n",
       "      <th>closeness</th>\n",
       "      <th>btwn1</th>\n",
       "      <th>btwn2</th>\n",
       "      <th>btwn3</th>\n",
       "      <th>btwn4</th>\n",
       "      <th>btwn5</th>\n",
       "      <th>btwn6</th>\n",
       "      <th>btwn7</th>\n",
       "      <th>btwn8</th>\n",
       "      <th>pr1</th>\n",
       "      <th>pr2</th>\n",
       "      <th>pr3</th>\n",
       "      <th>pr4</th>\n",
       "      <th>hub</th>\n",
       "      <th>auth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0.043825</td>\n",
       "      <td>0.276423</td>\n",
       "      <td>0.421991</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.001704</td>\n",
       "      <td>81.273250</td>\n",
       "      <td>0.001883</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>85.635385</td>\n",
       "      <td>0.001630</td>\n",
       "      <td>0.001202</td>\n",
       "      <td>0.001224</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.001268</td>\n",
       "      <td>0.000944</td>\n",
       "      <td>0.000944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>0.051793</td>\n",
       "      <td>0.265306</td>\n",
       "      <td>0.422360</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.001112</td>\n",
       "      <td>210.909613</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.000989</td>\n",
       "      <td>0.001875</td>\n",
       "      <td>1095.798511</td>\n",
       "      <td>0.002471</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.001472</td>\n",
       "      <td>0.001472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.031873</td>\n",
       "      <td>0.248276</td>\n",
       "      <td>0.412974</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>21.360500</td>\n",
       "      <td>0.001012</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>37.460393</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.000889</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0.000861</td>\n",
       "      <td>0.000861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115</td>\n",
       "      <td>0.114542</td>\n",
       "      <td>0.155183</td>\n",
       "      <td>0.475805</td>\n",
       "      <td>0.009122</td>\n",
       "      <td>0.015167</td>\n",
       "      <td>7481.274179</td>\n",
       "      <td>0.012756</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.021793</td>\n",
       "      <td>1542.423617</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.003045</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.003331</td>\n",
       "      <td>0.002554</td>\n",
       "      <td>0.002554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>0.090637</td>\n",
       "      <td>0.172880</td>\n",
       "      <td>0.461049</td>\n",
       "      <td>0.010574</td>\n",
       "      <td>0.010510</td>\n",
       "      <td>2476.509774</td>\n",
       "      <td>0.008385</td>\n",
       "      <td>0.008521</td>\n",
       "      <td>0.010197</td>\n",
       "      <td>5120.935523</td>\n",
       "      <td>0.009096</td>\n",
       "      <td>0.002868</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>0.002011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Department  ManagementSalary  degree  centrality  clustering  closeness  \\\n",
       "0             1               0.0      44    0.043825    0.276423   0.421991   \n",
       "1             1               NaN      52    0.051793    0.265306   0.422360   \n",
       "581           3               0.0      32    0.031873    0.248276   0.412974   \n",
       "6            25               1.0     115    0.114542    0.155183   0.475805   \n",
       "65            4               NaN      91    0.090637    0.172880   0.461049   \n",
       "\n",
       "        btwn1     btwn2        btwn3     btwn4     btwn5     btwn6  \\\n",
       "0    0.003431  0.001704    81.273250  0.001883  0.000124  0.001871   \n",
       "1    0.000228  0.001112   210.909613  0.001655  0.000989  0.001875   \n",
       "581  0.000063  0.001247    21.360500  0.001012  0.000213  0.001134   \n",
       "6    0.009122  0.015167  7481.274179  0.012756  0.003886  0.021793   \n",
       "65   0.010574  0.010510  2476.509774  0.008385  0.008521  0.010197   \n",
       "\n",
       "           btwn7     btwn8       pr1       pr2       pr3       pr4       hub  \\\n",
       "0      85.635385  0.001630  0.001202  0.001224  0.001246  0.001268  0.000944   \n",
       "1    1095.798511  0.002471  0.001395  0.001426  0.001458  0.001492  0.001472   \n",
       "581    37.460393  0.001130  0.000889  0.000894  0.000902  0.000913  0.000861   \n",
       "6    1542.423617  0.006649  0.003045  0.003146  0.003241  0.003331  0.002554   \n",
       "65   5120.935523  0.009096  0.002868  0.002857  0.002827  0.002776  0.002011   \n",
       "\n",
       "         auth  \n",
       "0    0.000944  \n",
       "1    0.001472  \n",
       "581  0.000861  \n",
       "6    0.002554  \n",
       "65   0.002011  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg_index = [person for person, degree in list(G.degree())]\n",
    "deg_degree = [degree for person, degree in list(G.degree())]\n",
    "df['degree'] = deg_degree\n",
    "df['centrality'] = pd.Series(nx.degree_centrality(G))\n",
    "df['clustering'] = pd.Series(nx.clustering(G))\n",
    "df['closeness'] = pd.Series(nx.closeness_centrality(G))\n",
    "df['btwn1'] = pd.Series(nx.betweenness_centrality(G, normalized=True, endpoints=False, k=10))\n",
    "df['btwn2'] = pd.Series(nx.betweenness_centrality(G, normalized=True, endpoints=True, k=10))\n",
    "df['btwn3'] = pd.Series(nx.betweenness_centrality(G, normalized=False, endpoints=False, k=10))\n",
    "df['btwn4'] = pd.Series(nx.betweenness_centrality(G, normalized=True, endpoints=True, k=10))\n",
    "df['btwn5'] = pd.Series(nx.betweenness_centrality(G, normalized=True, endpoints=False, k=25))\n",
    "df['btwn6'] = pd.Series(nx.betweenness_centrality(G, normalized=True, endpoints=True, k=25))\n",
    "df['btwn7'] = pd.Series(nx.betweenness_centrality(G, normalized=False, endpoints=False, k=25))\n",
    "df['btwn8'] = pd.Series(nx.betweenness_centrality(G, normalized=True, endpoints=True, k=25))\n",
    "df['pr1'] = pd.Series(nx.pagerank(G,alpha=0.8))\n",
    "df['pr2'] = pd.Series(nx.pagerank(G,alpha=0.85))\n",
    "df['pr3'] = pd.Series(nx.pagerank(G,alpha=0.9))\n",
    "df['pr4'] = pd.Series(nx.pagerank(G,alpha=0.95))\n",
    "df['hub'] = pd.Series(nx.hits(G)[0])\n",
    "df['auth'] = pd.Series(nx.hits(G)[1])\n",
    "# closeness, bewteeness, pagerank, scaled pagerank, hub/authority\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dept_dict = {node: dic['Department'] for node, dic in G.nodes(data=True)}\n",
    "dept_set = set(dept_dict.values())\n",
    "pairs = [(send,rec) for (send,rec,attr) in list(G.edges(data=True))]\n",
    "email_dict = {}\n",
    "for node in list(G.nodes()):\n",
    "    email_dict[node] = {}\n",
    "    for dept in dept_set:\n",
    "        email_dict[node][dept] = 0\n",
    "for send,rec in pairs:\n",
    "    rec_dept = dept_dict[rec]\n",
    "    email_dict[send][rec_dept] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for node, dic in email_dict.items():\n",
    "    dept = dic.keys()\n",
    "    count = dic.values()\n",
    "    df.loc[node,dept] = count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test = df[df['ManagementSalary'].map(np.isnan)]\n",
    "Test_index = Test.index\n",
    "TrainVal = df.drop(index=Test.index)\n",
    "# features_columns = list(TrainVal.columns).pop('ManagementSalary')\n",
    "features_columns = list(TrainVal.columns)\n",
    "target_column = features_columns.pop(1)\n",
    "X_trainval = TrainVal[features_columns]\n",
    "y_trainval = TrainVal[target_column]\n",
    "X_test = Test[features_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7923c33da469542ff6e642a304dce099",
     "grade": false,
     "grade_id": "cell-c1ae7e7278c56f0c",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0.100\n",
       "65     0.930\n",
       "18     0.125\n",
       "215    0.595\n",
       "283    0.835\n",
       "       ...  \n",
       "691    0.000\n",
       "788    0.010\n",
       "944    0.000\n",
       "798    0.000\n",
       "808    0.005\n",
       "Length: 252, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def salary_predictions():\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    \n",
    "    scaler = StandardScaler()\n",
    "    X_trainval_s = scaler.fit_transform(X_trainval)\n",
    "    X_test_s = scaler.transform(X_test)\n",
    "    \n",
    "    randfor = RandomForestClassifier()\n",
    "    grid_values = {\"n_estimators\":[10,15,25,50,100,200]}\n",
    "    cv = 4\n",
    "    randfor_grid = GridSearchCV(randfor, param_grid=grid_values, scoring='roc_auc', cv=cv)\n",
    "    randfor_grid.fit(X_trainval_s, y_trainval)\n",
    "    randfor = RandomForestClassifier(n_estimators=randfor_grid.best_params_['n_estimators'])\n",
    "    randfor.fit(X_trainval_s, y_trainval)\n",
    "    test_probs = pd.Series(randfor.predict_proba(X_test_s)[:,1],index=Test_index)\n",
    "    # raise NotImplementedError()\n",
    "    return test_probs\n",
    "salary_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "25615bc140cf5f8b7049928438419a81",
     "grade": true,
     "grade_id": "cell-bc9c23e7517908ab",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "ans_salary_preds = salary_predictions()\n",
    "assert type(ans_salary_preds) == pd.core.series.Series, \"You must return a Pandas series\"\n",
    "assert len(ans_salary_preds) == 252, \"The series must be of length 252\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "01ee118d34018c23ed12b93790c65e49",
     "grade": false,
     "grade_id": "cell-636bdc6669fbdf2f",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "### Part 2B - New Connections Prediction\n",
    "\n",
    "For the last part of this assignment, you will predict future connections between employees of the network. The future connections information has been loaded into the variable `future_connections`. The index is a tuple indicating a pair of nodes that currently do not have a connection, and the `Future Connection` column indicates if an edge between those two nodes will exist in the future, where a value of 1.0 indicates a future connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Future Connection</th>\n",
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       "      <th>(6, 840)</th>\n",
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      "text/plain": [
       "            Future Connection\n",
       "(6, 840)                  0.0\n",
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_connections = pd.read_csv('assets/Future_Connections.csv', index_col=0, converters={0: eval})\n",
    "future_connections.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
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     "cell_type": "markdown",
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   },
   "source": [
    "paris = [tup[0] for tup in list(G.edges(data=True))]\n",
    "data = [tup[1] for tup in list(G.edges(data=True))]\n",
    "df = pd.DataFrame(data=data, index=pairs)\n",
    "df.head()Using network `G` and `future_connections`, identify the edges in `future_connections` with missing values and predict whether or not these edges will have a future connection.\n",
    "\n",
    "To accomplish this, you will need to:      \n",
    "1. Create a matrix of features of your choice for the edges found in `future_connections` using Networkx     \n",
    "2. Train a sklearn classifier on those edges in `future_connections` that have `Future Connection` data     \n",
    "3. Predict a probability of the edge being a future connection for those edges in `future_connections` where `Future Connection` is missing.\n",
    "\n",
    "\n",
    "\n",
    "Your predictions will need to be given as the probability of the corresponding edge being a future connection.\n",
    "\n",
    "The evaluation metric for this assignment is the Area Under the ROC Curve (AUC).\n",
    "\n",
    "Your grade will be based on the AUC score computed for your classifier. A model which with an AUC of 0.75 or higher will recieve full points.\n",
    "\n",
    "Using your trained classifier, return a series of length 122112 with the data being the probability of the edge being a future connection, and the index being the edge as represented by a tuple of nodes.\n",
    "\n",
    "    Example:\n",
    "    \n",
    "        (107, 348)    0.35\n",
    "        (542, 751)    0.40\n",
    "        (20, 426)     0.55\n",
    "        (50, 989)     0.35\n",
    "                  ...\n",
    "        (939, 940)    0.15\n",
    "        (555, 905)    0.35\n",
    "        (75, 101)     0.65\n",
    "        Length: 122112, dtype: float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Edges in G:\t\t 16706\n",
      "Number of Non-Edges in G:\t 488446 \n",
      "\n",
      "Future Connections:\t\t 488446\n",
      "Future Connections 0 or 1:\t 366334\n",
      "Missing Future Connections:\t 122112\n"
     ]
    }
   ],
   "source": [
    "num_nonedges = len(list(nx.non_edges(G)))\n",
    "num_edges = len(list(G.edges()))\n",
    "print(\"Number of Edges in G:\\t\\t\",num_edges)\n",
    "print(\"Number of Non-Edges in G:\\t\",num_nonedges,'\\n')\n",
    "print(\"Future Connections:\\t\\t\",future_connections.shape[0])\n",
    "print(\"Future Connections 0 or 1:\\t\",future_connections.value_counts().values.sum())\n",
    "print(\"Missing Future Connections:\\t\",future_connections.shape[0]-future_connections.value_counts().values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "            Target\n",
       "(6, 840)       0.0\n",
       "(4, 197)       0.0\n",
       "(620, 979)     0.0\n",
       "(519, 872)     0.0\n",
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc = future_connections.copy()\n",
    "fc.rename(columns={'Future Connection':'Target'}, inplace=True)\n",
    "fc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['PA'] = [p for (v,u,p) in nx.preferential_attachment(G,fc.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['CN'] = fc.index.map(lambda pair: len(list(nx.common_neighbors(G, pair[0], pair[1]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['jacc'] = [p for (v,u,p) in nx.jaccard_coefficient(G,fc.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['RAI'] = [p for (v,u,p) in nx.resource_allocation_index(G,fc.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['AA'] = [p for (v,u,p) in nx.adamic_adar_index(G,fc.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['Node1Comm'] = [dept_dict[pair[0]] for pair in fc.index] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['Node2Comm'] = [dept_dict[pair[1]] for pair in fc.index] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['SharedComm'] = fc['Node1Comm'] == fc['Node2Comm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for node in G.nodes():\n",
    "    G.nodes[node]['community'] = dept_dict[node]     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['CN_SH'] = [p for (v,u,p) in nx.cn_soundarajan_hopcroft(G,fc.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['RAI_SH'] = [p for (v,u,p) in nx.ra_index_soundarajan_hopcroft(G,fc.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc['SharedComm'] = fc['SharedComm'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>PA</th>\n",
       "      <th>CN</th>\n",
       "      <th>jacc</th>\n",
       "      <th>RAI</th>\n",
       "      <th>AA</th>\n",
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       "      <th>Node2Comm</th>\n",
       "      <th>SharedComm</th>\n",
       "      <th>CN_SH</th>\n",
       "      <th>RAI_SH</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(6, 840)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2070</td>\n",
       "      <td>9</td>\n",
       "      <td>0.073770</td>\n",
       "      <td>0.136721</td>\n",
       "      <td>2.110314</td>\n",
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       "      <td>0.008437</td>\n",
       "      <td>0.363528</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>(519, 872)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>299</td>\n",
       "      <td>2</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.039726</td>\n",
       "      <td>0.507553</td>\n",
       "      <td>23</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>(382, 423)</th>\n",
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      "text/plain": [
       "            Target    PA  CN      jacc       RAI        AA  Node1Comm  \\\n",
       "(6, 840)       0.0  2070   9  0.073770  0.136721  2.110314         25   \n",
       "(4, 197)       0.0  3552   2  0.015504  0.008437  0.363528         21   \n",
       "(620, 979)     0.0    28   0  0.000000  0.000000  0.000000          4   \n",
       "(519, 872)     0.0   299   2  0.060606  0.039726  0.507553         23   \n",
       "(382, 423)     0.0   205   0  0.000000  0.000000  0.000000          7   \n",
       "\n",
       "            Node2Comm  SharedComm  CN_SH  RAI_SH  \n",
       "(6, 840)            4           0      9     0.0  \n",
       "(4, 197)            4           0      2     0.0  \n",
       "(620, 979)         23           0      0     0.0  \n",
       "(519, 872)          1           0      2     0.0  \n",
       "(382, 423)         10           0      0     0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test = fc[fc['Target'].map(np.isnan)]\n",
    "Test_index = Test.index\n",
    "TrainVal = fc.drop(index=Test.index)\n",
    "features_columns = list(TrainVal.columns)\n",
    "target_column = features_columns.pop(0)\n",
    "X_trainval = TrainVal[features_columns]\n",
    "y_trainval = TrainVal[target_column]\n",
    "X_test = Test[features_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "aa635313ab21384aefd0e005c28f8a9d",
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     "grade_id": "cell-63e0f775d43cc2e4",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
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   },
   "outputs": [],
   "source": [
    "def new_connections_predictions():\n",
    "    from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    \n",
    "    scaler = StandardScaler()\n",
    "    X_trainval_s = scaler.fit_transform(X_trainval)\n",
    "    X_test_s = scaler.transform(X_test)\n",
    "    \n",
    "    randfor = RandomForestClassifier()\n",
    "    grid_values = {\"n_estimators\":[10,100]}\n",
    "    cv = 2\n",
    "    randfor_grid = GridSearchCV(randfor, param_grid=grid_values, scoring='roc_auc', cv=cv)\n",
    "    randfor_grid.fit(X_trainval_s, y_trainval)\n",
    "    randfor = RandomForestClassifier(n_estimators=randfor_grid.best_params_['n_estimators'])\n",
    "    randfor.fit(X_trainval_s, y_trainval)\n",
    "    test_probs = pd.Series(randfor.predict_proba(X_test_s)[:,1],index=Test_index)\n",
    "    # raise NotImplementedError()\n",
    "    return test_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "1551f972fbe01646fda6b8679026970b",
     "grade": true,
     "grade_id": "cell-979b4a17d794f3d0",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "ans_prob_preds = new_connections_predictions()\n",
    "assert type(ans_prob_preds) == pd.core.series.Series, \"You must return a Pandas series\"\n",
    "assert len(ans_prob_preds) == 122112, \"The series must be of length 122112\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107, 348)    0.00000\n",
       "(542, 751)    0.00000\n",
       "(20, 426)     0.49000\n",
       "(50, 989)     0.00000\n",
       "(942, 986)    0.00000\n",
       "               ...   \n",
       "(165, 923)    0.00000\n",
       "(673, 755)    0.00000\n",
       "(939, 940)    0.00000\n",
       "(555, 905)    0.16714\n",
       "(75, 101)     0.00000\n",
       "Length: 122112, dtype: float64"
      ]
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     "execution_count": 46,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": null,
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