lectures/15_recommender-systems.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](img/330-banner.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Lecture 15: DBSCAN and Recommender Systems\n",
    "\n",
    "UBC 2021-22\n",
    "\n",
    "Instructor: Giulia Toti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "sys.path.append(\"code/.\")\n",
    "import matplotlib.pyplot as plt\n",
    "from plotting_functions import *\n",
    "from plotting_functions_unsup import *\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import cross_validate, train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# plt.style.use(\"seaborn\")\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Learning outcomes <a name=\"lo\"></a>\n",
    "\n",
    "From this lecture, students are expected to be able to:\n",
    "- Identify limitations of K-Means.\n",
    "- Explain how DBSCAN works at a high level.\n",
    "- Use `sklearn` to apply DBSCAN. \n",
    "- Explain the effect of epsilon and minimum samples hyperparameters in DBSCAN.  \n",
    "- Identify DBSCAN limitations.\n",
    "- State the problem of recommender systems. \n",
    "- Describe components of a utility matrix. \n",
    "- Create a utility matrix given ratings data. \n",
    "- Describe a common approach to evaluate recommender systems. \n",
    "- Implement some baseline approaches to complete the utility matrix. \n",
    "- Explain the idea of collaborative filtering. \n",
    "- Explain some serious consequences of recommendation systems. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Announcements\n",
    "\n",
    "- Homework 7 will be released later today (A short and sweet one)\n",
    "- Final exam announced: Apr 27, 15:30 pm, ESB 1013\n",
    "\n",
    "Unfortunately, it seems that the room assigned will not have outlets for all laptops like our classroom does. I am evaluating our options, and I need to know how many people do not have a laptop with a battery capable of lasting for the entire exam (~2 hours). If that is your case, or if you do not have a laptop for the final, please fill in this survey by next Monday, March 21st, 11:59pm.\n",
    "\n",
    "https://ubc.ca1.qualtrics.com/jfe/form/SV_bDuh89VDrGsZht4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Another useful clustering algorithm: DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means recap \n",
    "- We discussed K-Means clustering in the previous lecture. \n",
    "- Each cluster is represented by a center. \n",
    "- Given a new point, you can assign it to a cluster by computing the distances to all cluster centers and picking the cluster with the smallest distance. \n",
    "- It's a popular algorithm because \n",
    "    - It's easy to understand and implement.\n",
    "    - Runs relatively quickly and scales well to large datasets. \n",
    "    - `sklearn` has a more scalable variant called [`MiniBatchKMeans`](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html) which can handle very large datasets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means limitations\n",
    "\n",
    "- Relies on random initialization and so the outcome may change depending upon this initialization. \n",
    "- K-Means clustering requires to specify the number of clusters in advance.\n",
    "- Very often you do not know the centers in advance. The elbow method or the silhouette method to find the optimal number of clusters are not always easy to interpret. \n",
    "- Each point has to have a cluster assignment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means limitations: Shape of K-Means clusters\n",
    "\n",
    "- K-Means partitions the space based on the closest mean. \n",
    "- Each cluster is defined solely by its center and so it can only capture relatively simple shapes. \n",
    "- So the boundaries between clusters are linear; It fails to identify clusters with complex shapes. \n",
    "\n",
    "![](img/kmeans_boundaries.png)\n",
    "\n",
    "<!-- <img src=\"img/kmeans_boundaries.png\" alt=\"\" height=\"500\" width=\"500\">  -->\n",
    "    \n",
    "\n",
    "[Source](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means: failure case 1\n",
    "\n",
    "- K-Means performs poorly if the clusters have more complex shapes (e.g., two moons data below). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=200, noise=0.05, random_state=42)\n",
    "plot_X_k_means(X, k=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means: failure case 2\n",
    "\n",
    "- It assumes that all directions are equally important for each cluster and fails to identify non-spherical clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAEWCAYAAACZq3rNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACYx0lEQVR4nOydeXiTZfa/7ydLtyTdWKsDigUVWVo2RXFEkK2y6AAitOCCOo6DiKDOfAf3/ecoiIqjM4o6CGUR1BGxbILIoIxsbQFRMSAgtmxtadIlzfL8/kgTkjZJA3Tnua+rl/TNuzxvenlyct5zPh8hpUShUCgUCoVCoVDUjKahF6BQKBQKhUKhUDQVVPKsUCgUCoVCoVCEiUqeFQqFQqFQKBSKMFHJs0KhUCgUCoVCESYqeVYoFAqFQqFQKMJEJc8KhUKhUCgUCkWYqORZ0SgQQswUQrxb2/uGcS4phOgY5r5PCSEW1MZ1FQqFQhGcphpvhRD3CiHm1PM1PxZCDKvPa57vqORZUesIIe4QQuwSQpQKIfKFEG8JIeJDHSOlfEFKeXc45z+TfRsKIcQHQojnmst1FApFwyKE+EUIMcjn9/FCiEIhRP8A+0ohxFEhhM5nm04IcUwIcd6YOwghLq58L3Q1710r14sAHgNeDnR94eYNIcQPQogLz+C8Ayo/U4uEECeFEJ9UOf7/Ac/X5r0oQqOSZ0WtIoR4CHgJeASIA/oCFwFrKwNLoGPqJbApFApFc0AIcTvwJjBcSrkxyG5FQJrP7zcChXW8tGbFWXw23QT8IKU8EuBcAvgncD3QP9A+IfgeGCqljAcuAPYBb3lelFJ+B8QKIXqf4XoVZ4lKnhW1hhAiFngamCqlXCWltEspfwHG4U6gJ1bu95QQYpkQYoEQohi4o+ojOiHEbUKIg5Xfsh/3rbr47uvzzf52IcQhIcQJIcSjPue5UgjxbeU39jwhxNxgSXyA++kghNgohLAIIdYCLau8/lFlZf2UEOJrIUSXyu1/BDKAvwghrEKIFZXb/08IYa483/dCiD/4nKtj5bVOVd7DEp/XLhdCrBVCFAghfhRCjAt1HYVC0Xyp/P9+Fu5k6psQu34I3Obz+23A/CrnihNCzKuMjUeEEM8JIbSVryULIdZXxuATQoiFvk8QK2Pyw0KI3Mq4tUQIEVX5WkshxOeVcbdACLFJCBEw3xBCdPGJb0eFEDMD7HO9EOLXKtt8PxOuFEJsE0IUV55jduVuX1f+t6gyRl5duf9kIcRe4a7crxZCXORzXimEmCKE2AfsE25eFe6q/anK++0a5D1PAwJ9mdECHwC9geullEeDHB8QKeVRKeVvPpucQNV2w6+A4WdyXsXZo5JnRW1yDRAFfOy7UUppBbKAwT6bbwKWAfHAQt/9hRBXAP/AnRgm4a5g1/SI61rgMuAG4AkhROfK7U5gOu7E9+rK1/8c5v1kAtsrj30WuL3K61lAJ6A1sMNzH1LKf1X+++9SSqOUcmTl/mbg95X38zSwQAiRVPnas8AaIAH4HfAGgBDCAKytXEtrYALwDyFElxDXUSgUzZP7cMeKG6SU22rY91PgOiFEfGXS+3vgP1X2+TfgwJ2I9QCGAJ6WOAG8iLvS2RloBzxV5fhxwDCgA9AduKNy+0PAr0AroA0wE6jWLiKEMAHrgFWV1+kIfFnDfQXiNeA1KWUskAwsrdx+XeV/4ytj5LdCiJsr1zO6cn2bgEVVznczcBVwBe735DrgUtyfV7cCJ4OsoxvwY4DtC4HLgYFSSu+xQohrK79gBPu51mff9kKIIqAMeBj4e5Vr7AVSgqxLUcuo5FlRm7QETkgpHQFey8O/cvutlPJTKaVLSllWZd+xwAop5X+llBXAEwQIvFV4WkpZJqXMAXKoDCJSyu1Syi1SSkdlFfyfQLUewaoIIdoDfYDHpZQ2KeXXgF9lV0r5npTSIqW04f5QSRFCxAU7p5TyIynlb5X3vAT3o7crK1+2467OXyClLJdS/rdy+wjgFynl+5X3sANYXvkeKRSK84vBwBZgVxj7luOOWbcC44HPKrcBIIRog7tS+qCUskRKeQx4tXJfpJQ/SynXVsa/48BsqsfO1ytjWkHltVIrt9txFz4uqnwCuUlKGSiGjwDypZSzKuOeRUr5vzDurSp2oKMQoqWU0iql3BJi33uBF6WUeys/q14AUn2rz5WvF1R+NtkBE+7kV1Qelxfk3PGAJcD2IcBSKWWR78bKz7j4ED//9dn3UGXbRkvcfdU/VLmGpfL6inpAJc+K2uQE0FIE7hNLqnzdw+EQ57nA93UpZSnBv+l7yPf5dylgBBBCXFr5+DBfuFtEXqBK+0WINRRKKUt8th30/EMIoRVC/L/KNoxi4JfKl4KeW7hbUbI9VQWgq8/+f8Fd6flOCLFHCDG5cvtFwFW+1QjcFfm2YdyDQqFoXvwJdwX0XSGEAKiMF9bKn99X2X8+7naNai0buGOLHsjziS3/xP2ECyFEayHE4sp2jmJgAdXjW8C4i3tg7mdgjRBivxDi/4LcTzvcT+TOlbtwvy8/CCG2CiFGhNj3IuA1n3suwB17fZ9u+n7+rAfm4u4xPyqE+JdwtygGohB3ol2VEcCTPnH9rKn8ovJv4D9VPmtNuPvcFfWASp4Vtcm3gA334zAvla0Hafg/jgtVSc7D3brgOT4aaHGWa3oL9zf0TpWP9GbiDpQ1kQckVK7dQ3uff6fjbj0ZhLsN42LPciv/63d/lVWNd4D7gRaVFYTdnv2llPlSynuklBfgroz8Q7gl9A4DG6tUI4xSyvsCXUehUDRrjuFuPfs97tY2pJRdKmOCUUq5qcr+m3AXLtoA/63y2mHc8bqlT2yJlVJ2qXz9RdzxpXtl7JxIeLGTygryQ1LKS4CRwAwhxA0Bdj2Mu82iJkqAGM8vlX3ZrXyut09KOQF34v8SsKwydgeKj4eBe6vE1Ogq/eN+x0kpX5dS9gK64E7SHwmyztzK16vyDe734TUhRLrPffze54tPoJ+qX4Y86Crv1TeJ74z7qauiHlDJs6LWkFKewt3L+4YQYpgQQi+EuBj4CHf/24dhnmoZMFIIcY1wD/c9TZhBOwAmoBiwCiEux90zWCNSyoPANuBpIUREZe+Zb0+xCfcHz0ncQf2FKqc4Clzi87snkB8HEELcibvyTOXvtwghPF8YCiv3dQKfA5cKISZVvp96IUQfn57uqtdRKBTNmMrBsYHAMCHEqzXsK3HHrVFV2yYqWw/WALOEELFCCI1wDwl6WjNMgBX3sN2FBE8YqyGEGCHcQ9ACd/x1Vv5U5XOgrRDiQSFEpBDCJIS4KsB+PwFRQojhQgg97raFSJ/rTRRCtJJSujhdfXXijrcu/GPk28DfxOkB7zghxC0h7qWPEOKqyuuW4G59CXQvAF8QpC1QulVRRgP/EkKMrdy2yeeLT6CfTZVrGC2EuKzyb9QKdwvNzsoqtIf+uOdwFPWASp4VtYqU8u+4q7uv4A6a/8P9Tf+Gyt7gcM6xB5gKLMZdAbbgrriEdXwVHsZdJbbgrvwuCb27H+m4h0YKgCfxf+w5H3cbxxHcMkJVe+zmAVdUPhr8VEr5Pe4J+W9xJ7zdgM0++/cB/ieEsOLuTZwmpTwgpbTg7pcbD/yG+zHpS5z+4PC7zhncm0KhaKJIKQ/jTqDHCiFerGHfPZUxNRC3ARG4Y1gh7sKFZ4j5aaAncApYSZVB8BrohHsQ0Io75v1DSvlVgLVZcPdxj8Qd2/YBAwLsdwr3oPe7uGNuCe6CjIdhwJ7K+PkaML6yh7oUt/7x5soY2VdK+QnuGLq4sh1lN/6SflWJxf3ZUYg75p/E/fkWiBXA5UKICwK9KKVci7sH/QMhxJkMeF+Ie6jSgrvf3QX4qjX1AUqkW7JOUQ+IwD38CkXjQQhhxF1N6CSlPNDAy1EoFAqFIiDCLSV4hZTywXq85nJgnpTyi/q65vmOSp4VjZLKb+Vf4m7XmIW7AtwzyMS2QqFQKBQKRb2g2jYUjZWbcLcp/Ib7EeB4lTgrFAqFQqFoaFTlWaFQKBQKhUKhCBNVeVYoFAqFQqFQKMIkkJlFo6Vly5by4osvbuhlKBQKxRmzffv2E1LKVjXv2XxQMVuhUDRlgsXtBk2ehRDxuKVnuuLWtZ0spfw22P4XX3wx27Ztq6fVKRQKRe0hhDhY816NnzOJ2ypmKxSKpkywuN3QlefXgFVSyrGVZhgxNR2gUCgUigZFxW2FQnFe02DJc6U3/HXAHQBSygqgoqHWo1AoFIrQqLitUCgUDTsweAlu68z3hRA7hRDvVnrRKxQKhaJxouK2QqE472nI5FmH2/rzLSllD9x2m/9XdSchxB+FENuEENuOHz9e32sMG7PZzJSp04hv0QqNVkt8i1ZMmToNs9nc0EtTKBSK2qLGuN2UYvb0qX+mTYs4tFoNbVrEMX3qn1XMVigUNdKQyfOvwK9Syv9V/r4Md1D2Q0r5Lyllbyll71atGuegelZWFqm9+rBk51EMY1+k3UOfYBj7Ikt2HiW1Vx+ysrIaeokKhUJRG9QYt5tKzO7bK4Xo3Pl8k+HC9qiRbzJcROfOp2+vFBWzFQpFSBqs51lKmS+EOCyEuExK+SNwA/B9Q63nbDGbzYybkIFx5EwiL+zs3a5PSEJ/7ST0HXozbkIG2du3kpyc3IArVSgUinOjOcRts9nMbRNu4bMxkqvbnf4ITE4UvDAARnZ0MGrCLWzZnqNitkKhCEhDm6RMBRYKIXKBVOCFhl3OmTN7zutEdh3ilzj7EnlhZyK7DObV196o55UpFApFndCk4/bcObO4JwW/xNmXq9vpuDsF3nzt1XpemUKhaCo0KXvu3r17y8amGRrfohWGsS+iT0gKuo+9MI+SZTMpOnmsHlemUCgaE0KI7VLK3g29jvqkMcbsNi3i+CbDRXJi8NqRucBFv0wt+SeK6m9hCoWi0REsbjd05bnJU1xUgC6udch9dLGtsBQV1NOKFAqFQhGME0UWLooXIfdpHyc4UWSppxUpFIqmhkqez5HY+EQcp0JXlB3FxzHFJ9bTihQKhUIRjJbxJg4WhX7ieuiUpGW8qZ5WpFAomhoqeT5HMtLTse1ZF3If2+61TMxIr6cVKRQKhSIY6ekZzMsJnTy/myNJT59YTytSKBRNDZU8nyMzHnwA2+412I7sDfi67chebHvWMn3a1ICvK31ohUKhqD/uf/Ah3smBbw87Ar7+7WEH7+bAlGnTA76u9KEVCoVKns+R5ORkli5aiHXFC1g3zcdemId0OrAX5mHdNB/rihdYumhhQMkjpQ+tUCgU9UtycjLzF33EqOWCv21wYi5wYXdKzAUu/rbByajlgvmLPgoas5U+tEKhUGobtYTZbObV195gwcJMLEUFmOITmZiRzvRpUwMGYbPZTGqvPtX0oT3YjuzFuuIFpQ+tUDQTlNpG48JsNvPma6+SmbmAE0UWWsabSE+fyJRp04PG7L69UqrpQ3v49rCDUcuF0odWKJoRweK2Sp4biClTp7Fk51GM104Kuo9103zG90pi7utz6m9hCoWiTlDJc9Nm+tQ/E507nxcGaIPu87cNTmwpdzD79bn1uDKFQlFXKKm6RsbCzEwiuwwKuU9k18EsWJhZTytSKBQKRTAyMxdyV0poibu7UwSZmQvqaUUKhaKhUMlzA6H0oRUKhaLpoPShFQqFB5U8NxBnqw+t1DkUCoWi/jkXfWil0KFQNC9U8txAnI0+dCB1jsgh03nvkzV0urwLGo1KphUKhaIuOFt96KoKHd/fF8Pw9mW8/87bdOrYkRZxBpVIKxRNDJU8NxBnqg9tNpsZNyED48iZGK+dhD4hifIDOznxn5eIaNeNpMlzafewkrpTKBSKuuBs9KHNZjO3TbiFz8ZIXhig5aeTTq59v5S2RsH2PxqoeNzEd7driMr9t5K6UyiaENX1dhT1gkcfetyEDOxdBhPZdTC62FY4io9j270W2561fvrQs+e8TmTXIV5ZO3thHidWzqb1mMf9pO70CUnor52EvkNvxk3IUFJ3CoVCUQt49aEn3MLdKU7uThG0jxMcOiV5N0fybk51fei5c2ZxTwpc3U6HucDFbZ+W89n4aD+pu+REwYsDNIzq6GDUhFuU1J1C0QRQlecGJC0tjeztWxnfK4mSZTP5dfYYSpbNZHyvJLK3byUtLc27b1V1DsuOzzGmDA2oEQ0QeWFnIrsM5tXX3qjz+1AoFIrzgbS0NLZsz8GWcgf9MrVEv1hCv0wttpQ72LI9xy9mg79Cx9zvKrinpz6gRjS4E+y7U+DN116t8/tQKBTnhtJ5biJotFraPfQJQuPWGD38xkTaTnwZfUJS0GPshXmULJtJ0cnQg4kKhaLuUTrP5x9arQbbo0Z0GkGbVyx8M9lAcmLwmpW5wEW/TC35J4rqb5EKhSIoSue5iVNVncNVVhyW1N2pwhNqgFChUCgaAF+FjhOlMiypu2MnTyk1DoWikaOS5yaA2WymXbv2WLO/8G7TRMeGJXWniY5VA4QKhUJRj3ik6Wzl5by1rQKAljEiLKm71gbBNxkuonPnqyFChaKR0myT5+aih+yRp/tVm4Q1d51XncNwRX+suWtCHmvNXY3hiusxXjsJ48iZjJuQ0eTuX6FQnB80Fy1kX2m6j8fq+HeOnW8PO0jvqmfezoqQx767o4L0bnqSEzW8MEDLZ2Mkt024pcm9BwpFc6dZJs+B9JCbooSbrzxd3OD7aDniIY4tf5bCjR8Q3fFKrDmrQ0rdWXPWYOo5AlADhAqFovFSVQvZ9qixSVZfq0rTDeygY/7NUYxaXEaZXfKv7RWhpe522pnSJ8K7TQ0RKhSNk2Y3MGg2m0nt1QfjyJkBlShsR/ZiXfFCk5BwmzJ1Gkt2HsV47STvNnthHpYdn1OydyOuklMIXQTGHmmYegz3St1Zc1djzVlDy+EziE7u7XesGiBUKBoGNTAYGLPZTN9eKXw2RgZUovj2sINRy0WTkHCbPvXPROfO54UBWr/t5gIXb26t4P2dFVQ44U99Ivhz74jTUnc7Knh3p535N0eR1klf7Vg1RKhQNAzB4nazS54DJZxVsW6az/heScx9fU4tr7B2iW/RCsPYF0MqapQdzOH4x8+Dy4F0OtDExGLo3B9TzxHVjpNOB7/OHoPTGbjyoVAo6g6VPAcmWMLpy982OLGl3MHs1+fW9hJrlTYt4vgmwxVSUWP9AQd/WFKKwyUos7t7nNO76ZnSJyLgcXanJPrFEhwOZ10uXaFQBOC8UduoqocciMiug1mwMLOeVnT2FBcV1KioEfW7Lkh7OUKr44J7/km7+xeQeMM9ARNuR/FxTPGJdbVchUKhOGN8tZCDcXeKIDNzQT2t6Ow5UWSpUVHj9+21lFTAA1fHEBsTwebJBmYPjQqacB86JWkZb6qL5SoUirOk2SXP4SScuthWWIoK6mlFZ09VebpAOIqPo4mJJbrTNX5qHIGw7V7LxIz02lyiQqFQnBPhJJzt4wQniiz1tKKzx1eaLhiHTklaxgjuThEgYF5O6P3fzZGkp0+szWUqFIpzpNklz+EmnE2hApuRno5tz7qQ+1hzV2Po3J/YvmOw7Pgi5AChbc9apk+bWhdLVSgUirMi7ISzCVRf09Mzak6GKxU12scJLGV23skh9BBhDkyZNr0ulqtQKM6SZpc8h5NwNpUK7IwHH8C2e01YihpCoyM6KhLrihewbpqPvTAP6XRgL8zDumk+xf95nv7X/Z5eV/Zt0tJ9CoWieRFWwtlEqq/3P/hQzclwpaLGoVOSVgmxzF/0EaOWC/62wYm5wIXdKTEXuPjbBicjl0n6Xdefa67s2aTl+xSK5kazS57DSTibSgU2OTmZpYsWcnL5UxRueM8vIS7c+AHHlj9Ly+Ez0CckYdu9ljvvvJPs7VsZ3yuJkmUz+XX2GEqWzeTqVhUgXWwpiG7S0n0KhaL5EVbC2USqr8nJycxf9BHDFlXwyNpy/2R4XTmjFpcx/2Z3f7PnC0FaWhpbtudgS7mDfplaol8soV+mlp8SBuBywRWnNjZp+T6FojnS7NQ2wK0ZOm5CBpFdBhPZdbBXws22ey22PWtZumghaWlp9bDi2mH9+vUMGz4Kp9DiKrdWU9QIJb/XnKT7FIqmjFLbCE5WVha3TbiFu1Pcw4FeCbccybs5MH/RR00uZv9hxFAihJPCMnePs6+iRk3ye81Jvk+haMqcN2obAGlpaQErsON7JZG9fWuTCsIAAwcO5D8ff0RMhJYWV4+lbcbLJFx/J+CW3bOueIGlixYGDKKz57xOZNchARNnUOYpCoWi4QlWfbWl3MGW7TlNMmYvXv4Z6GN45PcGNk828NKgSMAtuzdquWD+oo+CJr5z58zinhQCJs6gzFMUioamWVaemytms5lXX3uDBQszsRQVYIpPZGJGOtOnTQ0ahENpRXsNV/ZswFVmIS6xBRnp6cx48AFVzVAoahlVeT7/MJvNvPnaq2RmLuBEkYWW8SbS0ycyZdr0kDE2lF60ucDF3O8qWLDLzslSSavEWNLTM7j/wYdU3FYoapnzxiRF4Y9Gq6XdQ58gNP4GBGXmbZxYORtjylCM3Yegi2uN49QxbHvWYdu9psm1tigUjR2VPCvCRavVYHvUiE7jL+GXtc/ObZ+Wc09PPXf1iOCieMHBIsm8HMk7TbC9RaFo7ASL24GfCSmaDR7pPt/Ks70wjxMrZ9N6zON+7Rz6hCT0105C36E34yZkqD5ohUKhaADc8n0ukhNPJ8/mAhe3fVrOZ+Oj/do5khMFLwyAkR0djJpwi+qDVijqgWbZ86w4TSDpPsuOzzGmDFV90AqFQtEICSTfN/e7Cu7pqVd90ApFI0Alz82cQNJ9Jd9vxNh9CPbCPAq+fIfDb0zk4N9HcfiNiRR8+Q72wrxqFuZms5kpU6cR36KV0olWKBSKOiSQfF/mbjuDLtExfVU5bV6xoH2mmDavWJi+yi2JB9VtzM1mM9On/pk2LeKUTrRCUYuoto1mjkcretyEDOxdBqNtn4LLVkL+/Bm4yq2IyGhiOl1DbN+xCI0Wa+4a8j98iBZp07wW5l7pv65DMIx9kbjK/uglO9cxv1cf1R+tUCgUtYhHL3rUhFsYc6mdCruDYptk0PxSYiNhdGc9f+0XgU4jmLezgr7zSph/cxSDLtF5bcw98n/3pMA3GYKL4o0cLHIxL2c+fXvNV/3RCsU50OADg0IILbANOCKlHBFqXzV8cvaYzWYenP4QK1etwph6I6YeN3qHBK25a7DmrKbl8BlEJ/fGdmQvx5Y9jRZJZGQEJSWltBn/HJEXdj6t0PH9RlxlxWiiDGhxserzzxg4cGBD36ZC0WhpLgODKmbXH++99x7T7/8T96QK7uvtMyC4s4J3dtiZf3MUaZ30fHvYwchFpVx/sY6sn52UOSTROsG6SdG0NmiY+10FmbvtnCh1a04PvFjL6oN6tmbvUv3RCkUIGvPA4DRgLxDb0Atp7nz19de0ufW5akOCCf1vJ6bjlRxb/ixtJ80i8sLOGLoNpuLI98jEdpiMCURe2NlPoaPtxJdPJ985qxg2YhT/Wa4qGQrFeYCK2fWA2WzmrzMeYNUEffUBwRuiGHmpjlGLy9hyl5ar2+m4IzWC/x5ykPsnA69/ZyNGJygql4xaXMI9PfV8M9ngl3y7fi7j4ekP8slnKxrwLhWKpkmD9jwLIX4HDAfebch1nA+EY5ZiTBmCZcfnAJh63Ij91FHK9m/z9kd7FDoS+t+OPiEJodG6k+/r76TFmKe4acwtrF+/vj5vS6FQ1CMqZtcfYRml9NDz5tYKAO7rHcEvRZLkRA2LdzsYnKzzqnO8cIPbElynESQnanjhhiiyMmJYs+pzFbMVirOgoQcG5wB/AVzBdhBC/FEIsU0Ise348eP1trDmxsLMTCK7DAq5j7H7UEr2bgRAF9sKV2kxrrJidHGtw1LoiO42lGHDR5GVlVXr61coFI2COaiYXS9kZi7krhQRcp+7e0aQucsOQPs4wYlSdxvmiVLJf36016jO8afeEfxhxFAVsxWKM6TBkmchxAjgmJRye6j9pJT/klL2llL2btWqVT2trvlRXFSALq51yH08CTOAo/g4mphYNNGxOE4d8yp0AEFVOqIu6YVTaLlpzC2Y4hOVKodC0YxQMbt+OVFk4aL40Mmzb8J86JS7nxmgZYwgc5eDu3pEAG6N6EAqHcM76YgQTiaNH8Odt2UoVQ6FIkwasvLcDxglhPgFWAwMFEIsCH2I4mzxmKWEwpMwA1hzV2Po3B/DFf2x5q7xVqDLzNvI//AhhC6CthNfpv3Dn9B24ssIXQQn/vMSrjIL0d2GItv3pt1Dn2AY+yJLdh4ltVcfVd1QKJo2KmbXI26jlNAD/b4J87s7KkjvpgcgvauegjLJRfGCrH12+s4rIVoP30w2YHvMxDeTDUTr4dZlZRSUSm7v4uSHDUv5JsOF7VEj32S4iM6dT99eKSpuKxQBaLDkWUr5Nynl76SUFwPjgfVSyokNtZ7mTiCzlKp4Embbkb1Yc9Zg6jkCU88RWHNWo4kyUH54d/C+5/6303rM4wh9BFGX9KJs/1bva8ZrJ2EcOZNxEzJUJUOhaKKomF2/BDJKqYonYf72sIN3d9qZ0sddab7/yggitfD1QWfIvufPxkcTpYfhnXQcKHT6vz5Ay2djJLdNuEXFbYWiCg3d86yoJwKZpfi3X4zEsu0zyg/mkr/0cVoOn+G2605IouXwGUink6KN/66x79mYeiPHP34eV8kpf9MV5VqoUCgUYRPIKKVq+8Vr/6tg/QEHQxaUMv9md3IMkJyoYVhHLX9bV15j3/N9vSMYvaSUYyWymumKci1UKALTKJJnKeVXNemFNjaamuOexyzFuuIFrJvmY8lZU9l+oa9sv/iUpMlvEnVxKsIlsZ887D02Ork3rUY/iv34L96+52CYetyI0Olp/8in3naO/A8fosy8rZproUKhaJo0xZgNTctxz2uUslzwtw1O3tvhNkOJ0p1uv9h1n5FBl+hwSdh73H+G8+XB0ew65vL2PQfjvt4RRGgFFY+fbufoO6+ErH3uQcSqroUKhaKRJM9NjaysLFJ79WHJzqMYxr7YZHp709LSyN6+lbSO0RR++c/K9os7/NovEgfeRZvxz1G0aQEnV8/FXpiHdDrQxbZGOuzhDR2WWaq1c5xYORvpcnhdCxUKhaI+ycrKom+vFKJz5zeZ3t60tDS2bM/h6MV/YOoqG5+Nj+bFQf7tF68MiWLdpBge/8rGn1aUYS5wYXe62z3KHYQ1dFhQJqu1c9z2qbsC3T5OeF0LFQqFm8ZgktKkMJvNjJuQgXHkzGpmI/prJ6Hv0JtxEzLI3r61UTo3JScnExsbR+KVN4dsvzD1GkHZwVzyFz6Cq7QYTUwsIiIax6lj6BOSgp7fd+jQ93zGlCEU/+9jTPGJtXo/CoVCURNms5lJ48ewYqyobjgyAEZ2dDBqwi1s2Z7T6OJ2cnIycbEmpl0dw9XttAH3ubqdjqlXRrD+gIN+75V4nQRjI+FgkSQ5MXgC7Tt06Hs+j4b0lD4RtIw31eo9KRRNHVV5PkNmz3mdiCsGhUw89Vfc0Kh7e8PSfE4ZhtNynHb3L+Civ3xGu/sXYOw+GGvO6pDHWXZ+gavc6tfvDG4N6bKfvmFiRnqt3YdCoVCEwyMzpnFnF2dow5FG3NsbjubzPT0jOHRKkv+wCccTseQ/bOLO1Aje2VER8ri3tlVQWF6939mjIf1ujiQ9Xc2FKhS+qOT5DPlwwQKiuoXu+43uNpT5HzbeHrFQms+eIcL8BQ+7h/5eT6dg3T8pO5iDtJdj2b7Cb+jQF9uRvZTsWkfSnXOr9TvrYlvhqihl+rSpdXlrCoVC4YfZbGZV1hf8qXfo3t/G3NsbSvPZM0R4zXslHCuRtHrZwrSsMtYfcFBil7zxXYXf0KEv3x528EF2Bbl/MlTrd/ZoSL+bA1OmTa/L21MomhyqbeMMsZwqJCGMvl/rqcJ6WtGZ49F8rtp+UWbexomVszGmDKXtxFfQxbXGceoYlp1fcHz5M0gJsb1HcmzZMxi6DcLU40Z0sa1wFB/Hmp2FZedKpL2Co5n/h+GK/ph6jiCm45UcW/4sLW/6KwZTXKN7JKpQKJo3c+fMwuaQ4RmONNLeXrfms6ta+0XWPju3fepW1PhmsoGL4gUHiyRvbatg5KJSpIQHr9IzclEpt6dG8OfeEbSPExw6JfnX9gre3l6BAK74RwktYwTpXfX848ZIbvu0nCVjo4nUCeYv+kjFbYWiCkLK0DqSjYnevXvLbdu2NegaNBFRJN05N2Tfr70wj7z378dVUV6PKwufKVOnsWTnUYzXTvJusxfmkf/hQ7Qe83jAlhTbkb0cXfwY6CLQxsQitBE4SwtxlZ5CaCPQt76Y+OtuI6pdVxynjmHNXYM1ZzUth8+g/Nfd2A7v4u7Rw5j7+px6vFOFovEghNgupezd0OuoTxpDzG7TIg5XuYUtdxm8Um6BMBe46JepJf9EUf0tLkymT/0z0bnzeWHA6Z5nc4GLvvNK+Gx8dMB2lG8POxg0v5QoPbSKFkTpBflWyfFSiUEPQrjNVB6+JtKbdM/bWcE7O+wMuFjLwVOSroMmMO+DD+vzVhWKRkWwuK3aNs4Q6XJhzV0Tch9rdhZSykYrYRdI89my4/MaNZxNvUYS06kv0clX4rScIP73tyF0EbQZ/xxJk2YRfVFKQJWNqIt7UHHslxpbNpqa/J9CoWj8nCiykN5Nx7ydoXt//7GtgsKiU41Svi6Q5vPc7ypq1HCeelUEN1+mZ8Rlen4tdvH+TVHER0GEFlZlxPDWiOiAxinrDzjIPepk5uNP1bi2piT/p1DUFip5PkOMRiPW7FUh+34tOatoPfbJM5Kwq8/Esarms70wj5Lvv6pRw9mYMpSy/VtJHHgXrcc+QeH6dzGm3hjaNCVlCGX7/oe0V3BF91Q0Gi2m+ES6pfYiNqGF915HjLqJ7j16Nzn5P4VC0bhpGW/ipsv0vLPDHrL399+e3t8w5evqM2msqvlsLnCRudvu1XA+VS4Zv6yUU+X+T5Lv6RnByn0OXhkSxYoJMdz2aTklFe7tIYcne0bgkjB+7E20SjCh0QjiDZHEGSL97vW9995rcvJ/CkVtoJLnM+S2SZMwXNKDY8ufpXDjB14dZHthHgXr53Fs2dO0GvkXvypsTfbUDaEb7dF8Ht8riZJlM3GVFoen4VxaDLgTY4TbFCUUxu5DKdm7EREZQ8uJr9Jy9GOU2hwciuqAadxLtHvoEyKHTOeLNeuIvelRjNdO8tOdVtbeCoXiXEhPz2DdL4L5N0cxanEZf1tX7tVCNhe4+OvackYtLuPDP0RzWUttWNbUDaEZ7dF8tqXcQb9MLcdLTvdxf/ajnSV7HKz4ye53jGfoD07Lz2mEW0kjFPf0jEArYEDMPl76vYMW0YL7esCOuyK891r+vw+Y+qe7+GyM5IUBWmXtrTivUMnzGTLjwQeQR3aROOQ+pMNO/sJHODRrNHn/nobtyPe0ve1VopOrtzUGs6f21Y2u78QxOTmZua/PoejkMYQ+AsepYyH3r6rhLCvKwjZNMXZzS+Od/GIOrcc+QcL1d3rvtezn7zD1GB6ygq2svRUKxdngaXmIjxJsucuAzQn93ish6nkLvf9lxe6CLXcZSOuk9zsumHyd2Wzmtgm3NEjSmJyczOzX55J/oohWibEcLHInxh9972BospaPvvevrFfVcL67Z0TYxik2J9zbK4K/fhnYnCVK42TqlTVUsBux/J9CcS6o5PkM8bQ8lH/1LyIiImmb8TLtH/oYodHRcsTDIQcJA9lTz57zOrrkayj54b8cfmMiB/8+yk8jub4Sx7B6uXNXY+jc3/u7Jjo2rIRbaPWYeo4I2ldd8v3GGltGlLW3QqE4G3xbHt7NkUzpE8Hh6UYSowXb/mhk9tCooIOEgeTr5s6ZxdhLXSzd46DNKxa0zxT7aSTXR9I4Km0wxwqK6fiGFfF0Mf874uSdkdFs+dWJeLrY+zNyUSnp3U5/KWgfJ4jU4U26g3HolKRFtAjZV5252849NVSwG7P8n0JxLqjk+Syo2vLw6+wxuMosYVVhiwtP+lUkPvjgA4q//xqhi6DtxJdp//An1TSS6yNxDKeX25qzBlPPEd5t0Zf0wbLzi5Dntez8gqgOPdEnJAVNkl1l4bWMKGtvhUJxNlRteYh+sYSTpeHJ1x0vOOUXs9//4AM+2l1GtB6+mWzA9pipmkZyXSeN0/8ykxax0Wy4PQb5ZCxHHzbRLk7D0YdNyCdjWX9bDInRcMTiYkqf0wnuoVMSnQb+tT308OQ/t1cwsbver6+6KifCfP8aq/yfQnEuqOT5LPFteXA6HcQltgivChsR7e1jNpvNlJXb3G0M/W/3a9nwVauQLkedJ46herkLN37g1moePsO/si4E1uysGk1TEgZMBoInyeFWsJW1t0KhOFt8Wx4cDqdf20MwDp2SxEYKbx+z2WzGbitjxYQYXrghKqBSxW2flmN3yjpNGgcMGMBHn65k1BIbX/3i36qx4YCDPywpxeGCxWOi/arq/9hWwYVGmLs1tHHK+9l2pvSJCJkgt4wRYb1/ytpb0RxRyXMtkZGejm3PupD7WHNXY+g2yNvH/OQzz2LqeSOamHgKvnzndNvG6+nk/ftBji57Ble5hfz5D6GLiqnWQ1ebCh3VerkXPMyhV/5A/ocPIR122k6a5dfLbTuyl9K9X6M1teLo4scoWD+v2vDk0cWPEdNlgDfhDpYkG67oX2PLiG33WmXtrVAoao309Azm5YRO/t7dUcGdqXpvH/PzzzzJn3tH0NqgYfqqcm/bRquXLfT5l5URi0opKJNc9W4JxihdwFhcWyodAwYMYNbrbzF6SZnf9jFLS7n+Yh07/mj06+P+9rCDt7dV0LWNjl5tNQz6sJSH1/gPTz68ppxBH5YysauO5ERNyAQ5vau+Rvk/Ze2taK6o5LmWCKSd7Itv24Onj/mjZcvRJbYj/8OH/Ns2Js0i6qIUBNBq9OMk3fEaMd2H+Clv1LZCR7Ve7omv0Gr0YyAlQuMW5q9aiY64sDOOojzQaLEd+d6dcM8aTf6Ch7Ed+R40Wqw5WZxYOYfDb0zEZSsJ2OZh6jkCa87qkO+dbc9aZe2tUChqjUDayb58e9jBuzvdFVhPH/PHyz6ic0sNfeeV+LVtbLnLwIAOOgTwn/HR7LjXyB9TNdWUN2pbpSM6OpqrLjbwyjc22r5iYfa3Nrq30eJ0uRNeT1L8t3VuRZHBl+hYbXaQfdSFXgObDzm4el4J0c9buHpeCZsPOdBr4O0ddib/pwybw+1WGPD9uzKiRvk/Ze2taK4oh8FaJCsri3ETMtBcej1GX+vq3NVYc9bQcvgMb/XWXpjHb/+6B010bEhXv2PLn6XtpFnoE5LcCfiKF/jPx8u4afRYjCNnBj3OuuIFsrdvPWNbVbPZzKuvvcGChZlYigrQRUTijIhBOuy4yixoYmIxdHZbbxdueJ/yg9m0SJuK7cgPlHy/EVdZMZroWAxX9Cfywss5uXIOhiv6E9v3FqTLydEFj9B67BPV1l1m3saJz1/B2G2w33tn270W2561LF20kLS0tDO6F4WiMaEcBhsfWVlZ3DbhFm6/ws59PtbV7+6o4N2ddubfHOWt3poLXHR8w0rLGBHS1W/U4jKvm+G3hx2MWi7Ysj0HgL69UvhsjAx+bOW+4cbtUUMH8uVXG+nSuTPtLk7myzUrsdmdROrA6YIyu7u9Ir2b3tv73PlNK5E6GNFJx/pfnJwolV5r7vuvjOBYiYtBH7oHDSd01XPrsrKg9zv7GxuPf2Vjat8Y7knVnH7/ciTv5sD8RR+puK1o0gSL2yp5rmXMZjMdL7scTaTRnUj6JJu+/cLS6eDQnFuJ7T2ShP53BD1f4cYPkA47iTfcA4B103ySSn4mz9jJz167KtZN8xnfK+mc7bDNZjOpvfoETNQPzbmVqItTsR3ahTFlKMbuQ9DFtfaz545s3w2dqaV3/WXmbZxYORtj98EYU4Z5k+SyXaux7VrDwBsGsmnTZixFBZjiE5mYkc70aVPP+EuAQtHYUMlz48RsNnPFZR1JiBKnE8nKZNO3X9julMT9PwvTrorgxUFRQc/3t3Xl2Jwwe6h7n79tcGJLuQMpXdUstqsdW7nv7NfnhrX2jFtu5sprrmPqtAfRaDQ4nU7eeG0OX61bxeZvvg2YqEc9W0yUHv7cJ4K7ekRUs+aef3MUX/3ixO5y30PWPju3fVrO3T303N3z9BeMf+108t4uDS/Nfp3d2TvIzFzAiSILLeNNpKdPZMq06SpuK5o8KnmuR+JbtMIw9sWQsnX2wjzy3ptC0uQ3a9wvf+EjtLt/wenj3r+fpDvn1nhcybKZFJ0MPYgXDp6KemSXwUR2HexNeH+b92c0+qiAlWSorJwvewYpoP0Di/zWZtnxOdZd65AVpWiiTGilk/feeZtv/7eVhZmZFBcVEBufSEZ6OjMefEAFYUWTRyXPjZc2LeL4JsMVVLIO3JXnbm9Z2XWfscb9+r1XQv7DptO/Z2qRUoZ1jX6ZWvJPFJ31vXjwVNXvTnFLxrWPE2w65GTkolLWTYoJWTlfMjaa9OVlfvfw5tYKMnfZOV4qMUYAGh1v/ut9tv/vGzIzF/okzhnc/+BDKmYrmgXB4rbqea4DwhketO1ei3Taz8jVz/O7tFfUq7RbIGm+kmUzQUqMqcNqsOceiqzwH2jRJySReMM9JN0+B01MHO0eyCSqQ08m//FPIXu469PCXKFQnD+ENTyYI7E5wzMY8bj6eX8vsnCiyFKv0m6B5PlGL3dwX+8ajE166PnsR7vfPSQnapg9NIr8h038dL+RaJ1gaLKWKX+cHLJ/uz4tzBWK+kQlz3VAOMODtj1rMcUlnLGrn6P4eNhugLUp7VZVmq/o5DGERlOjuYkxZShCBH5M6fliYC/Mo+RANi3GPBXUZXH0uPF07Z7Ke5+swVJWgZRgKavgvU/W0D21Z53Y4SoUivODsIYHcyAxzhiePJuPq59Hrq1lvKnepd2qyvNFRkZxX+8ajE16RpC5y+F3D760jxMcL5GsN9tYNUEf1GUxY9xo+qR2o/x/HzC8fRkto+FEYTHvv/M2PbtdznvvvVdr96lQ1Dcqea4DPMoV1hUvYN0030/CzbppPtYVL7B00UImTZwYnrydj6ufbfdaulzRJazKdl1Lu4VbOZeOCq9joi+eLwaWHZ/XWMEWSV2wOZxEtOtG20mzvKokEe26Ue5wMXrsOFXNUCgUZ4WvC+HfNjj95Nv+tsHJqOWC+Ys+YuLESWHJ2/m6+nnk2sKtbteltFvY1e9SSblDel0TfTl0SmKKhHt6hq5gT+7monuijWV7ymlrFF5lku1/NHBfTw1T/3SXSqAVTRaVPNcRwVodxvdKInv7VtLS0s5I3s7zu23PWv768HSKd3ze4NJuZ1I593VM9OD5YlCTPbe9MA/b4d20GfdMYDOZsU9ic7p46plna+3eFArF+UWgNod+mVpsKXewZXsOaWlpZyRv5/09B0bcPJqiU6d47dvSBpV2C7f63dog2P5Ho59rood3d7il6+6uwZr7xk46tua5+Gx8dDVDmf83KIp1k2KYfv+fVNFD0SRRA4MNTFZWFqPHjSei8w3+8nbZWVhz19LixgfRt2zvlWx7ZMaDvDx7DuLCbpTs3+lWuUgZ6qdaYf/+y3qRdpsydRoLtx4h/vo7g+7jqxbiK73nKi3y/vu3f/0RU68RlOzd5Cd151EoKfjyHYQugoT+t3vP6xk69MjjiYgopNNJxoRbefrJJ9SwiqLRoQYGmwdZWVlkjBvNnd1c/NlH3u7t7RW8v7OC92+K5opWWq9c29QZf+GN2X/nnhToGOfkr1/auKuHnnt8lCvqS9pt+tQ/o9/5Pn8fFDzxraoW4iu/d6zExajFZZwslUy9Ss/i3Y5qUneegchpWWXotYJXhrjPYy5wMfe7CjJ3273HXBQn+P6khnvuuUcNGSoaJUpto5FiNpu5onsquot6Un4oF1dpMZpoE9rY1jhO5SPLSxBaPRkZExg66AYm//FPtBjzFJEXdj6dQO7d6D4uyohWOlm18jMGDhxYL2vv3qM3sTc9GpZONUDhV+9Tfng3jsI8Wg6fAcDxT1/E1Hskxu5Dq0ndtRw+gxNfzKHtxJe95/DK3VWRx7Ps/AJrzioidVo+XrY04IeQ2Wxm9pzXlaKHot5RyXPzwGw207P7FQzv4PLqJCdGCy6OF+wvdFFUDpE6GJ8+kQGDhjLlj3eyasLpFgdf5YoTpZJInWB8egYzH3+qzmOQ2Wzmqp7dWDFWhKVT7eH/1pWz8RcHPxdKpl6p56XNFTxwZQR39wwsdZfWSU+rly3e83jk7u7pqfeTx3t3RwWvf1dBrwt07C2KDPjlwWw2M3fOLKXooWgQVPLcSJl4+x2s+KGYhAF3Bd2ncP08eseVsGnzZqK7DyMhRKW3tvSdwyVo5TyAMQxUSu19MI22t83CaS3g+MfP02bc0yGTb1e5hfYPf4rQaN3SfR8+FNpYZtnTROk05Gbv8AuuXsm9rkOI7DLIm3Tb9qzDtnuNMmJR1CkqeW4eTL59Ii32fcTLQ4JrPT+8ppzdhmv5dvPX3NtDw98Hh9CFPkNt53PFUzmf3M1VozGMB3OBi17/sjL3xiimfFHOqozQUndb7jJw6VwrtsfcbSJ955WENJYZ9GEp/++GCJ75Vu9nEuOR27snBe5KEacT9RzJO8qERVEPqOS5kRJpMNFy4qthaELfj9BH0HbS7HrTdw4Xs9nMU88+x8KFmUinI6gxDFSaw7zyB4ROj3S5MPUaSeLAEF8cNn6AZcdKku54PWgLR7VjNrxHxZHvuWv0UO+XiFBmL3BurowKRTio5Ll5EGeIZMddETVrQr9tJUYH/7s7DF3oWtJ2Dhez2cwLzz7F8o+WUlxaQWtDYGMYD3anJPI5C3otTL0ywtuKEYi/rSvnYJGLT390sOs+I3O/qyBaDy/cEPyYR9a6K9s3dIz2fpEwm8217sioUJwpSue5kVJRWhK2YoWrPLx9a0vfOVySk5P58IP3iY2N5YJ7/km7+xeQeMM9AZN8R/FxRGQMSZPfRBNlwNTjxpDnNnYfinS5sOz8AqDG4UIAY2oaFYV5LFiY6d02e87r7opzCEWPyC6DefW1N2q6XYVCcR5jKa0IS7HC5oDC8jB1oWtJ2zlckpOTmffBhxSV2GiVGMvmyQZmD40KmuR75Pfio0RYUnf/+cmBVqvlnR3uHue7eoQ+5k+9Ithz3MXdKYLMTLch2Nw5s7gnhdCa1Cnw5muvhnHHCkXtopLnBkbo9GEpVgidHk107DnrO9el0Ug45jDWnFUYuw1Cn5CEq8wS1pcB4bRj3/sltiN7cZUVh2csU2bx+xKxMDOTyC6DQh4X2XWwX8KtUCgUVYnUEZZiRaQWWsaIc9Z2rmujkbAk9HZUMLG7nhOlMrwvDk7Ba//4F298VxH2MWV2/y8SmZkLuSsl9HG+ybZCUZ+o5LmB0UdEYM1dHXIfa84q0OgxXNEfa+6akPuG0nfOysoitVefkC5+50JY0nu5a73Se54vA/bCPAq+fIfDb0zk4N9HcfiNiV5daEfxcWITWvDx0sUcXfIYIiIqPHm8aJPfl4jiooJGWbVXKBRNi8iICK9cWzDe2VFBhBbSu+qZtzP0vqG0nbOysujbKyWki9+5cibye54vA+YCF9NXldPmFQvaZ4pp84rFqwl96JSkZUIskydP5o235xGpDe/LRrTe/4tEfTsyKhRngkqeG5hbxozGsvOLkAmnZWcWURenYuo5AmvO6rPSdzabzYybkIFx5MygLn7jJmScUzXD1xymcP08P3OYgvXzOLbsGVoOn+Ft5zBc0Z/Cjf8m/8OHELoI2k582W1+MvFlry60ZdN8Jmakk5aWRvr4W9FEGrwtHMGwZmcRkZDk9yUiNj6x3l0ZFQpF8+MPY8by1raKkMnmW9sqGHyJjvuvjOCdHfaz0nY2m83cNuEWPhsjg7r43TbhlnOuQPsaxDy8ptzPIOb/1pUzanEZ8292t3Skd9Uz88ty+s4rIVqP1/jkm8kGryb03zbYvV8GJk+eTKdOnXh7e+gvEG9vr6BLK43fF4mGcGRUKMJFJc8NzNNPPkGkVsOxZU9TuPEDv4SzcOMHHFv2NBqcRBrj0Sck0XL4DI4tf7b6vhve4+Typ1i6aGHA4Yn66vn1mMO0rzhI/r8f5NCs0eQvfATLjhW0mfiyn/JGdMcrKTdvpfWYxwObn4x5HOvP3zH65lGn3yuXjZJda2uobq9Bc+o3vy8R4bSU1Icro0KhaNo89uQzSG0kIxaV8rd1/snm39aVM2JRKXapoaVRT3Kihvk3RzFqcVm1fR9ZW86wRXbmL/ooYMyuz55fj0HMxrJL6f1OCdHPW+j2lpVyh2TLXQav+sbIy3R8vs8R0PjkhRui+Gx8NCt/tDHi5tHec786923+sTX0l41/bK1gUned3xeJxuDIqFAEQ6ltNAKysrIYO248rrgLqCjMw1VmQRNtIiIhCc2p33jjtVeZNuNhr1JE2cEcir56H/uJw0hHBUKnR2g0/HveO0yc6A4kVfWM0epJunNuvSl1VFW3OPzGRD+tZsCtnKHVk3D9HUHPU/TVe5TtWoO9vJTY+ESu7XcN69Z9SYVLYkpNw5gyzCuPF0rnWaltKBoapbbRfHDLvY3hsng75gInBWVurefkRC0/Ful55bW5/HXGA16lCHOBi+c32fh4rx2bE2wOiNLB8JGj+H8vzyY5ObmannGUFnL/ZKhXpQ5fhYubl5TxzWT/609fVU6kDv7foODKGX/50s6X1k6YD/3m1dK/tGMye3b+jz/3ieBPvfyNZf6xtYKeF+j4oYrOs1LbUDQGgsXtwF9pFfVKWloaudk7ePW1N1iwMBNLucAUFcHE0UOZPm0qycnJJCUlMW5CBiUXdKXkQDbG1GG0HPXX06Yi2V9w2513c+99Uxg4cADr128gOiUNw9gXiYtrzaFXbq7Xnl9PC8e4CRnYuwwm+pLeWHPX+EnMlXy/kbYTXw55HkNKGpZdX9LuoU9wnDrGlj3r0Gq1XN/vKv67eRWW7SuQDjtCp0en05MxfhxPPfF4tWBadT2RXQd7k26Pe2Owqr1CoVD4kpaWxtbsXbz52qscyFwANnfB4+o/TGTBtOnemD1qwi3cneKkU5yTFT85uK+3v7HIW9tW0b3zClJ69uaHPbv5U08N32QILoo3Evlc/ff8els4JtxCcoKWd3dU8KJPopy52843kw0hz3FvDy1v/msv8bf/g7jKz6f9e9bh0u/h86Mt+MfbhyizS6J0ICVExcTQZ9SdfFD5vgVay90pTu5OEVUcGUXQqr1CUdeoynMTYv369QwbMcrrMFgV25G9HF38GNJhQ+giiOrQi4QBk9EnJAWs/FalLjSizWYzr772Bv+e/yElJSW0Gf+cd+0H/z6K9g9/gtBogx4vnQ4OzRrNRX/5zO8+z7ZK7FnPgoWZWIoKMMUnMjEj3fslRaGoK1Tl+fzDbDbz/DNPsWTRQtZNCmESMr8Up4QbO2l5eXA0yYka2rxiqVb5rXb+OtKIDrZu7TPF2B4zodMET+rtTknU81ba/WWF3/azjdtms5k3X3uVzMwFPg6DE5lSJdlWKOqCRmeSIoRoB8wH2gIu4F9SytdCHXO+B+IpU6exZOdRjNdOCrpP4VfvIx12TL1GutsYdn7hbu2IjMGUMoyEAXeetvX+fiOusmI00bEYruiP1mUno1+nOnMn9Dr8VVZ+8xc8TNuJr9SY0OcvfIR29/vLEdW3k6JCca40h+T5TOP2+R6zAaZP/TPRufN5YUDwIsH/rSvneIkkIVrw1rYKyuwQGwl/7BXB3wdHYS5wMfc7t2byiVK35nJ6Vz1lLi0xfSfXmTuhx+Hv7hS3LNw175WEldB3f89Fq/uXVntNxW1FU6NWTVKEEIPPfUk4gIeklJ2BvsAUIcQVtXDeZks4WsXGlGGU/PA1+oQkEgfeRZvxz6GJNpEw6F4sOVkUf/dJYHULrZ5TuV/y3nvzal3/2YNnmHB8ryRKls1E2kqxZtegnJG7GkPn/tW2K01mhSJ8ailmg4rbZ0w4esX39Ixg5T4HrwyJYt2kGFrECObeGMW/tlcw+xtbQHWLSC18mF3GvPfm1Yn+M5weJLSl3EG/TC2nbJK3t4VWznhrh5PozgMCvqbitqK5cLZqG/PO9cJSyjwp5Y7Kf1uAvcCF53re5kxxYXhaxa7SYu/vkRd2xpgyBPtRMwkD7qZo04LA6hbX30Gb8c9hcwraTv5Hreo/+5KcnMzc1+dQdPIY+37ci+vHr0IrZ+Ss8epCV71PpcmsUITNOcdsUHH7TDGbzZwoLA6vd7nU/RT46nY67u6hZ0eei9lDInn8K1tAdYv/N9idaOtd5Xx/X0yt6z97SE5OZvbrc8k/UcSeH37mg+/1IZUz3t4hiex5c8DXVdxWNBeCJs9CiM+C/KwAWtTmIoQQFwM9gP8FeO2PQohtQohtx48fr83LNimysrJAG54boSYm1m+bsftQSvZuxH7iIKbeI0PK1RlTh2LNzqpV/edg+OpCWzfN95fe++p9ji1/1k8Xuup9Kk1mheI09RmzK693MQHitorZbjwGJ6aIMF0GY04n2Hf3jCBzl51dxyQPXBkRUq7unp4R/HO7vdb1nwPhqwn9tw1OP+m9h9aUM3iRE8Pw/wvaiqfitqK5EKry/Hvgn8CsAD/W2lqAEMIILAcelFIWV31dSvkvKWVvKWXvVq1a1dZlmxQeg5PojlfV6DAYqM3BU40u+X4jxu5DQx7vSbQ91Jb+czCqtnL8OnsMJxdMp+LXPbSdNMtPF9qXQJrMdWk9rlA0AeolZkPouK1itr/ByZ2pYbgM7qggvZve+7unEp25287dPSNCHutJtD3Upv5zIKq2ckS/WEK/TC1flV2OvvOgoDEbVNxWNB9CJc9bgFIp5cYqP18BP9bGxYUQetwBeKGU8uPaOGdzxGNwEn/dpBodBgO1OXiq0a6y4jNu+4C671PzbeVwOh3syc1GZz2Kq7Qo4P6BnBSrWo+3nfwPHBek8vY78+jYsRPGuAQVkBXNnTqP2aDidjj4GpyE5TJYaX/twVOJPlEqz6jlw8PdKYLMzAVBjjh3fFs5HA4n+SeKWLrsYxzmb87IAdc3bkcOmY6xx3AsZRX848036HR5F0bedLOK2YpGSdDkWUqZJqXcEOS16871wkIIgbsPb6+Ucva5nq854xkU9HMY/Or96m6EQdocPNVoTXTsWbV91HefWqh2Duum+VhXvOCnyVzVetxRcISjC/+C1phA29vn0P6RT4kb/zKLd+TXeg+3QtFYqOuYDSpuh4vvkKDHZXDkotJq9td/q2J/7cFTiW4Zc+YtH+BOqI8XnKrXxPNc4rY2qTMn/vMSQh9F20mzaP/wpyRNnsvX+Rq69+itYrai0dGQ9tz9gEnAQCFEduXPjQ24nkZLcdHpQcHo5N60nTQL6XSQ/+EMDr3yB/Leux/HqWMB2xx8q9GGK/qfVduHo/g4Uqur1wAWqJ2jZNlMxvdKInv7Vj/3QF/rcXthHidWzg44FGn6/W112sOtUJwHqLgdBieK/A1O0jrp+d/dRg4UOun+lpWo5yz0nVeCzYmf/TX4V6LTu555ywe4E+qoCH29FwvOJm5rYuKDxuyEAXcRe9OjKmYrGh3KJKUJEN+iFYaxLwYcwrAX5lG4YR7lv+zEmJqGqcdwr3OeNXc11pw1tBw+g+jk3m7N5PnTaT32yaAmK8eWP0vbSbP8rlW48QMcp46h+W1Xo7Sv9n1/Cr58B6GL8HMyrIrSGlU0BM1B5/lMOV9jdpsWcXyT4Qqoh2wucPHwmnLW7Hfwp94R/Ln3abvqd3dU8O5OO/NvjiKtkx5zgYsr37Xy+YSY4BbVi8vYcpe/9vLD6+x8YBuI/vLrz9pQqq7xxG3Ljs9VzFY0WmpV51lRv2Skp2Pbsy7ga/qEJFqPfoy4bjdwsf0QJctmcnj2aPLev5+Kw7tpedNfibo41f3oLHc10uXk2LKnKdzwXljqFp7KdfzvJ9Xp4OC54FuZdw9FDgm5v9IaVSgUdUl6egbzcgIXppITNXwyPoZJqdFsKrvUPXT3gpXub5fw1UEXS8ZGM+gSHeYCF2/tcFLi1DMo087D6+x+LR9/XRu45cNXLq6uB77PBU/cVjFb0RQJrH9TBSFENNBeSllrQyeK8Jnx4APM79UHfYfeQSvGDvO3fOJTXfDYUP/jraeRTgeamFgMnfuTdMfrAOR98CDW3V/iKrOgiTLgsldg7HI9usQLkU5Htcq1PiEJug5mwcKZje7bf2x8Io5Tx9AnJIU9FKm0RhXNGRWzG5b7H3yIvr3mM7KjI2jFePlPGrZs/9QvZr/52quMeOtNbE5BVEw00Z0H0uKOmwH4YMen/OPtL7A5ICoqApxOxnfRcWkLLXan5NApyVs7nLy9Q/rJxUU28ritYraiKVJj8iyEGAm8AkQAHYQQqcAzUspRdbw2RSWeQYxxEzKwV1pbe1ozbLvXYtuz1m8Qw3PM3NfnsGDhwoAtH9JeRrtpnyA0bsvYgy+NROgiyV/4CK7SYm+y7dvC0VgDWEZ6Okt2rkN/7STvUGQoy2+lNapozqiY3fB49ZAn3MLdKU7uThGnWzNyJO/mCOYv+qhazJ79+lzeW7iEhAAxW3/DfZzcnkX7h91x216Yx4fz7mHxT3rKS8u8yXbcpJv9jm3scVvFbEVTJJy2jaeAK4EiACllNnBxXS1IEZgzGcTwJVjLhybK6Ke8oYmJw9RrJO3uX8BFf/mMdvcvIPGGe/wCWmMNYDMefADb7jXYjuwNaygykNZoY0bpoCrOkKdQMbvBCaaHbEu5gy3bc844ZtsL8xD6KG/c1ick4YyMJz7jNdr9ZQWt7l+K8Yb7qiWhjT1uR7XvrmK2oskRTvLskFKeqvOVKGqkqh5y0cljzH19TshBEN/E0kOZeRvS6cCas8q7rSklnVUDU68r+3J9/+so/vRZtC471uxVIbVGy3JXUXSqqEkEtqr61e0e+qTOrNMVzQYVsxsJgfSQZ78+96xidv6HD6Fv0Q5r7mrv9qYStwMlk7PnvM5rs1+BX7Ox7FxZoz706JtHNYmEVMXs84NwkufdQoh0QCuE6CSEeAP4po7Xpaglqmpvlh3M4cTK2bRIm4o1d603YJl6jqjRgKUxBLBggenbk1EgNFx7oZ4oneDo4scoXD+vmtZo0cdP4wJWmcsbfWCrql/tK+FU19bpiiaNitlNmGAxu/WYx2k58mGsOWvOKG43dLEgVDI5bcbDvPHaqwwfMpijSx6jYP27AfWhH5nxIDeNHtvoE1IVs88fapSqE0LEAI8CnnHY1cBzUsryOl5bNc5X2aPawDNA+M6894jsMojEgXdRZt7GiZWzMaYMwdh9KPYThzj5xRyM3QdjTE2r1lf9yIwHeXn2HLemcpdB6OJa4zh1DNueddh2r2HpooVBH0XW1j2k9uqDceTMoIOTHlkmgFdfe4MFCzOxFBVgik9k5Igb+fiT/xB706M1Ht8YZJ2mTJ3Gkp1HMV47Keg+SsKp6VBfUnUqZjcPAsVs4Izidkn2F6DRYEhJa5Ixe2JGOqNvHsVNo8eGdY6GjtsqZjc/gsXtkMmzEEILrJZSDqrLxYWLCsTnTlXNaHthHpYdn1OydyOu0mJElAGdsSWOot8QTkejCmDnGpgm3n4HK3+yEtf/zrM6vr4Jpe/twV6YR8mymRSdDO0cqWh46iN5VjG7+REoDlSL25ExaFxOYmJiKCkuajTFgnON2WazmZvHjONQ5EUkDLjrrM5Rn6iY3fw4K51nKaUTKBVCxNXZyhT1iq8mMriHThJvuMc7KNj+gUUk3f4qwunw66te/sl/vC5+gagPPVGPTXkoquqBenrtjHEJZGYuIqb7sBqPf3fevEbRV1f1bxWIxjpJr2gYVMxufgSKA1Xjdrv7FyAdFRQXnvDG7djYOKK7D23SMbtjx07s2bMbY2poE8vIroP5x9tvq5itqDfC6XkuB3YJIeYJIV73/NT1whR1g0dbMxSBprPPJgjWNmcamDy9dot35BM3/mWk0xHW8bay0kbRV3e2fyvFeY+K2c2I8zlmt3/kU6TTHtY5pMOuYrai3ggneV4JPA58DWz3+VE0QUK5FXoINJ1dXNjw36jPJDD5Dm6Yfn8b+oQkr55oTcdrYmIbxaDH2f6tFOc9KmY3I87nmC00WjTRcWHHbRWzFfVFjSYpUsp/18dCFPWDx61QtLwY25EfKPl+I66yYjTRsRiu6E/khZdj27OW6fO3eo/JysoCrT6okL23/27PBlwuF/EtWpGRns6MBx+o1V46XzOUYHgC0+w5r1drM/HIOiX0vz3o8dbc1Rg696+2PfLCzpRfPpCnnn2ODz94/9xuJEzCcZas+rdSKFTMbl6czzEbzj5uq5itqEtqrDwLIQ4IIfZX/amPxSlqn+TkZB6Z8SAnv5iD0OpoO/Fl2j/8CW0nvozQ6jj5xRwemfGgn2XsuAkZRHe8KqCeqEd/VOgiaDtpFu0f+dT72Kx7j96MGHVTrfUPB9I/9cUbmKZNDfjIMhxZJ2vOGkw9RwR8PSZlGAszF9Xbo8CqklWBJJyqOksqFCpmNy/O55gN5xa3VcxW1BXhSNW18Pk1CrgFSJRSPlGXCwuEmtw+d8xmM11TexJ/8+NBvxkXffosu7N3kJyc7J2WjuwyiPwPH6L1mNPH2Qvzqm2req6jHz1Jqz88SlS7rrUikZSVlcW4CRlEhrApT0tLQ6PV0u6h0/bjHqrKPHmOt+xcScnu9bQcPoPo5MCCCNLp4NCs0Rhj4+pVFskjWVVVwmn6tKkqCDch6lGqTsXsZsT5HrPBJ253H4Ix5XTctuasxpq7JmjcVjFbca6clVRdiJP9V0p5ba2s7AxQgfjcmXj7Haz4oTik7E/h+nmMuiKODz943096x5KzhsL172JMGYqpx41Ytn+G0EeS0P+O4Ofa8B7OMgstb5zm3XauEknhBKZQkkHeR5bff4WrtBiNIQ5ZUU6rMY8TfVFK0OvaC/PIX/gICSlDGoUskqJpUV/Jc5Brq5jdRFEx2429MI9T3y6ldO/XSKcdodUT0/k64q4eF/IYFbMV58JZSdVVHtjT56e3EOJPgKlOVqmocz5atrxG2R9jjxv56KNlwOlp6TLzNoo2foDhiv64yq3kL3gEa85qjN2Hhj5Xahqle7+mzHz6A/RcJZLCsSkPNbjhkXkydh+MqfcoWv/hUXQ6LY5DOSGv6+mrq+sJdYXiXFAxu3mhYrYbfUISWkMcxtRhtM14CZ1eR6QpIaSmsorZiroiHLWNWT4/LwI9gXF1uShF3VFRWhKmXFsJ4J6WLj+822sP22LoFFreOI12UxeELf0mnXZOrJyNvTDPu72ug1k4vXbW7NVoXXasK17g7bmvU7FnbVh9dUqnU9HIUTG7GaFithsVsxWNiRrVNoC7pJR+wyZCiA51tB5FHSN0wSewPTiKjyO0eqZMnUZZeTllX32AMaW62L5H+q2mc2liYjF2vQHLjs9JvOEewB2giwtPYjab66QHzDO4MW5CBvYqvXbWnV9g2fkF0VGRZPTrxPSlr5OcnExSUhLDb/oDpp4jMKYMO71/7mqsOe6+On1CEvbCvEal02k2m5k953UWZmZSXFRAbHxinUzOK5oMKmY3IxpTzD5VeIIpU6fVSWw5n2J2QUEBm7dsJndXLo5yB7ooHd27dadf334kJjaedSqCE87A4A4pZc8q27ZLKXvV6coCoPrnzp1Ig4mo7sNq6Hl7n+Idn9PiqpvRtuvO8WVPkzT5zWoBt+DLdxC6iJASQoUbP0A67Jh6jiB//nQMXW+olFo6hdDq0en1vD33dSZPnlxbt+jHmQ5uTLztdpav/46Konx3P3RMLIbO/TH1HOG9/8ZiBQs+wzhdhxDZZRC6uNa1MuSjqH3qcWBQxexmRGOK2Wh0aKOMROBg+UdL6iS2NPeYvW/fPhYvW8xPhp/YF7OPUl0pMY4YOpV24tKSSxk/djydOnVq6GUqKgkWt4NWnoUQlwNdgDghxGifl2JxT3ArmiC3jBlN5tJlxHS8Kui0tWXH57Qc/iCGy38PENThydRzBPkfPkRMxyuDnsuas4a2k2ahi22Fq7wEZ0khbSe+7E3yrDmruftPfwaokwTa02sXbtB8+skn+M9nfYj/w6ONXqfT11TAd636hCT0105C36E34yZk1OuUuaLhUDG7edKYYnbx1v9Q9NV7lOsjGT12HLtzs+ukAt1cY3ZBQQGLly1mXeI6CqJOt5GU6EvIjsvmUOQhWAZT7p2iKtCNnFA9z5cBI4B4YKTPT0/gnjpfmaJOePrJJ4jUaji27GkKN37gp0NZuPEDji59gqhLenqDMODn8GQvzKPgy3c4/MZEfnvnXqTLydHFj1H41fvVznVs+bPex2aO4uOIyBjKzNtwFBzxOkElXH8HbW59jj/d/0C9OkEFoynpdAYzFfAQbMjHbDYzZeq0WtNyVTQaVMxuhpxrzIbTcTt/wSO4yixnHbNtv+4mqkNPItp2wuZ08dQzzzbEW+JHU4rZm7ds5ifDT36Jsy8FUQX8FPMT32z5xn97QQErvljB8y89z9NPP83zLz3Pii9WUFCg+rgbinDaNq6WUn5bT+sJiXoEWDtkZWUxdtx4XHEXUFGYh6vMgibaRERCEuX5+7ngLv/HfZ5HfVG/61KpkTwUY/ch6OJauwdTVryCq6IMIV1Ihx0RGU3Mpf38JIQKN36As+QUZT//D4C2k2b5XaNw/Twm9W3fKB6rQePV6fTtbz5VcBJNtAlDlwF+jyh9sRfmUbJsJkUn3R+kqs2j4ajHtg0Vs5sZZxuzE/rf7qNt747b0uXk+MfP4rCcrDFml/zwX5xF+d7zamLiSbr9VfL+PR1XaZF3+6BhN7I2a2W9vR+BaKwx27e/2V5up0JTwSHjIX42/UyJvqTa/ga7gZEnR/LoXx8FVJtHQ3PWOs9CiCjgLtyPA72P/qSUddOkGgIViGuPYIHmH3PfoN3D/kL19sI88j54EKERtB77pLfSWc1wxNOKkbsGa85qr3C97cheji1/ljYZL5E3bwqxV41GOuzeQRTPNXyTPEV1giW+Vd9vX6TTwa+zx+B0OjCbzaT26lOtzcPDuWq5KkJTj8mzitnNkDON2fnzZ5A49M8UrHnLa4pypjE7fsBkCte8RetbniSqffdqayo/mIs162U+//Rjrr/++np8N5oGwRLfDtYOdLB0YGvLreTH5PsdI6RgzKExPPnEkxQUFPDmP9+s1ubhIbE8kUEFg1SbRx1y1jrPwIdAW2AosBH4HWCp3eUp6ptgupuxCYl+j/sAHAVHkNKJodtgP6cqjxRSQv870CcknW7F6H87rcc8zonPZ3Fy9Vzvo0Ch0bmnuLsPpWTvRr9rKCmh0Pj2NxuvnRT4/a4iLQXuyXnPlPnZtnkomhwqZjdDzjhmuxyc+OI1jN3dcftsYnbU77qATsfxT16k/FCu3zXKD+ZybNnTKnEOgm9/c3ZcNiX6EqSQlOhL2J2wm82tN9PnRB8MdoPfcTGOGHSR7nG0s23zUNQ94STPHaWUjwMlUsp/A8OBbnW7LEVDUVWo3hNwNTo9ph6nhfotOz4PKIXkIfLCzhi63YB1z1dEJ1+JLvFCr2C9LrYVrtJiv2ucXDUXqdWpHtwghJP4GlOGYNnxud922+61TMxIB2BhZiaRXQaFvI4yE2gWqJh9HhEsZrcZ9wwaXQTGlGHA2cdsY9dBtEh7gOMfv+C3//HP/o5AcvOYW1TcDkA4ie8B0wGSLf5P+TqVdCKlm9vpNndXLvti9oW8zj7DPnJ2hTb4UtQ+4eg82yv/WySE6ArkAxfX2YoUDcqMBx9gfq8+6Dv0JvLCzt6AW/y/5X7T2yXfb6TtxJdDnsvUYzglezagNSaQP3860uUk6Y7XvTqi4NP60X0wSXfO9T5GXLJzHfN79VE9uJUszMzEMPbFkPtEXdyD45+8UCkrVYwmyoAWF6Of/QxwO4/FhWGQoJ4ANHlUzD6PCBazIy/sjKvc6o3b5xKzS/dtQd+6A8Vb/0Px1k8w9bkJfYvfoYmIxnDDH4lTcbsaubty2dcidOJ7wHiAgb8NRCBoZ21HpCsSl3BRVl5GQUEBjnIHpbrSkOco1ZXisDlqc+mKMAgnef6XECIBeBz4DDACT9TpqhQNRlWh+pI9G2g7aRbW3LV+4vqusuKwnKpcZRYS+t9OTMcrObbsaeC0Zaq9MI/jK14hukNPrLu+pPi7T9BEx2K4olKjU0mteakp8T39JWQIph43er+ElO9aw02jx7J00UJijKENEuyFeZz6Zon7CYBGiz46BoCKshLiEloo45Wmg4rZ5xHBYjb4m6KcU8zetdY9OCidmHqN5NR/M8HlBI2W/AWPeGO2UUlkegkn8TXZTWjR4sTJhqQN3p7oA78e4Id//oBGryHGERNwsBDcw4WXF12OEydPP/00UuueYRNOoYxX6pga2zaklO9KKQullBullJdIKVtLKd+uj8UpGoa0tDSyt29lfK8kXGUWdHGtMVzRH2vuGu8+nqAcCt8Kc+SFnTGmDqNo04dYs1djTB3Gic9nI10Oyg5sw1V2Ck20iehLeiMdFeR/+BCu8hLVg1tJbHz1vkYPvr2MiQPv8utlNF13O8aRMxl7azo2mw1rzuqA5ygzbyP/w4fQGuJIunMu7R7+hJYTXyWq6xA0kUa0V6azZOdRUnv1ISsrqy5vVXGOqJh9/hEoZgN+cftcYrbTchJT75uI6XIDRf9diC7xd6DRgpQgXdiO/EDev6dTZt6mZicq0UXpiHHEBH3dYDfQ50Qfvm7zNbsTd/v1RGfHZbMucR0Ol4PLTl0W8Pi2pW0ZmDeQCm0Fay5Yw/KLlrOq7Sp+MvyEXdjZGbWTpT8v5c1/vsm+faEr4Iozp8bkWQjRRggxTwiRVfn7FUKIu+p+aYqGxDOcEpfYAsepY5h6jsCasxrbkb0A1ZLpQHgqzB6M3YdStu9/SOki7937sB/bT2zPESTd/hrtH/6UthNfQWtMoPTHzcT1vYUTK2ejbZ+ienCp3tfoSzi9jLrOAxCm1lhz13j/hh78Bomuv9N/kGjAZFqPfYKir94jsssgjCNnMm5ChuprbMSomH1+UjVmA35x+1xiNkDxlmUUrnsLjS6C6ItTSbrjddo9uIS2k2YRdVF3hIDjn72EvTBPzU4A3bt1p1NpcAm5jpaO/GL8JWRP9M/Gn2lnbUdiuX/l2JN4b269mV2Ju/wS712Ju9jUZhOdT3WmSBSxLnEdi5ctVprQtUw4A4MfAKuBCyp//wl4sI7Wo2hkeJI2fUISLYfP4NjyZync+AHRHa/0S6ar4nGqMvUc4d2mi22FdFaA0CB0kbQZ/xwJ1wee+i767wK0xhaU7F6venBx9zXadldPfMHdy2jsPiTk8cbUG3EU5WPqOdL7N/SYCZz6Zol3Ij8Q7kGiwZz6dqmqKjUNPkDF7PMW3y/avnFbOmxYs1edVcwWWj1otAhtBK3HPklC/9urx+2xT4LLxfGPn0O6HOd93O7Xtx+XllxaLfH10N7anv2m/SHPYTaakULS71g/uhZ2xWA3IKTg8qLLa068TT/TvbA7Nq1NKXLUAeEkzy2llEsBF4CU0gE463RVikaDb9IWndybtpNmIR12Tqx42etUVbB+XkinKg+O4uOIiBhiOl6FsceNIZM1U88RCH0EpT/+l6gYQ8D9zidCuWi5yk5V62X0dYI8+PdR5C94GCldFG/7Dy1v+ivSYSd/4SMcmjWa0h82eSfyg2HqcSOlP20GlCJHE0DF7POYql+0PXFb6CKR0lUZs989o5jdduLLCH0Epp41xO1eI0AXydEFfznv43ZiYiLjx45nUMEgUotSvYmvwW4gtSiVCFeEX0+0wW4gpSCFEYdGMOaXMYw4NIKOxR3RSz1bWm1BIzUMyBvAHw7+gXal7WpMvA+YDgCQbElWihx1QDgDgyVCiBaABBBC9AVO1emqFI2GqsMokV0Hk3D9nZh6jsC2ey1luavof4Fk7YLp2MpK0cTEYujcv5qDIIA1ZxXGboPCmvo2pgzDuvtL2tz6HCeXP4XZbD6vh0/gdF+j2yhhptcoISLa4DcI6Oso1nbiy97hQcvOL7DmrsayfQWtRz/mNak5+PdRYQ0SSVuZ99/ne1WpkaNi9nlMoJiti22FqecIIiIiK2M2ZxSz9QlJ4HRg7D405LU9cbv12CdU3AY6derElHun8M2Wb8jZlYPD5kAXqSOlWwrZudneYcC2pW3pc6IPB0wH/AYHO1g64BROkouT2dJmC7mJbq3tMb+MCUuFQyd1tLe2Z1fCLqXIUcuEkzzPwD2xnSyE2Ay0AsbW6aoUjYpgSdvEjHSmz99BcnJyeO51uWtpO2kWlu0rwpv6Li12VzN63Mirr73RaKy7GxJPX6PvezFl6jSW7FyH/tpJfv3Lvn8HfUISiQPvwnDZNRxd8hj2wjzvB6XvRH4wHMXHETq9998e4xVFo0TF7POc2o7ZANJhV3H7LEhMTGTEjSMYceMIv+0Syfc/fs9+035v/7JvG0aJvoTdibv5LeY3rjt6HQa7wau6YdPYQqpwgNtspUJTQYQrws94RVE7BG3bEEK0B5BS7gD6A9cA9wJdpJS5wY5TNE+CuVt5qgqh2goK1s/j2LJnvI8Ez3TqO6rrENUmEALfx7ThDA8aU9P8zFQMV/QPqsLhwZqzCrTu5NnXeEXReFAxW+FLbcZsABERreJ2LdKvbz+SLcl0KezCAdOBkP3LZpPZz0zlsPEwHawdQp6/g7UDedF5VGgq/IxXFLVDqJ7nT33+vURKuUdKuVtKaQ92wJkihBgmhPhRCPGzEOL/auu8iobBVy6pZNlMfp09hrz37seyfQVtJr5MdLLbHt499V1DsuYz9a3aBELj+RA8uuQxrLvW1jg8aOoxnJLvv/J+UMqKMizbV4QcJLJs/xydqSUlP2zCtmct06dNrYtbUZwbn/r8W8VsRY0EitlFix6uFrMBYi69BsvOL0KeT8Xt8ElMTCRCF8EFZRdwwHgg5L77Tfu52HKxt2/6t+jfSC5ODjqMmFieSMfijsRXxFMQUcClpZdyTd9r6uI2zltCJc/C59+X1PaFhRBa4E0gDbgCmCCEuKK2r6OoX6pWO1Z+9glCo0FotN593PJJgZUjoPrUt2oTqJm0tDTSx9+KtJWF/Wj10KzR5C98BCklEsGxZU/7qXDYC/Mo/Op9ji1/loRB9xJ1SS9OrpyD9VQRva7sq6x4Gx8qZivOmKoxu6S4iOiYGL+YDRB3za2U7Fqr4nYtkpqailZqw+pf1ku9d2DwquNXcTLiJNcdvY6uBV39hhG7FnSl37F+7EzcybHoY7S2tcZV4eKtd95ixRcrlGRdLRGqCUYG+XdtcSXws5RyP4AQYjFwE/B9HVxL0UCkpaUxcuQIvsrOImHAZMBfPsnYbRDG1DR0sa1wFB/Hmrsaa84av8eFpTmrVJtAEMxmM7PnvM7CzEyKCwsQOn14/cv6CJLunOvdz3BZP46veIWy/TuwbFuBdNoDDhIZLr2aY8ufJXLIdD783zbe6tyFG4cN5bVXZ5/Xg0GNBBWzFbXC6NF/4POcVcRff6d3mz4hiZYjHubYsmcwdBvkdjJVcfusKCgoYPOWzeTk5uAUzrD6l13CxYakDX77XVx8Md0LutPR0hGN1FChqeCQ8RDrk9Z79zsSc4R+x/qxMW4je37cw3fbvuOyjpdx47AblfPgOSCkDBxjhRBOoAR3NSMa8Hw1EoCUUsae04WFGAsMk1LeXfn7JOAqKeX9Vfb7I/BHgPbt2/c6ePDguVxWUc9kZWUxdtx4yuwO2tzytF8vrr0wj1PfLqV079d+yZqp5whvALYd2cvRJY+xb+8elZxVISsri3ETMojsOoTILoPQxbXmZNYbaGPiSBhwZ9DjrJvmk1TyM3nGThivneTdbi/M48RnLxHZrhuJA+/y227Z8Tkl32/EVVaMiIhCn3ABLUf9FVdpEUc/epJovY5lSxeTlpZWp/fclBFCbJdS9q55z7M+v4rZinMmVMyG0zKYtoPZSKdDxe0zZN++fSxetpifDD+xL2YfHYs74hROdifuDnpMalEqV0ZcyXf278iOy/Z7rdfxXlRoK9iVuMtvu8FuoKOlI+2s7Yh0RWIXdg6aDnIi4gS9T/YmQhfBhFsm0KlTcCMXRfC4HbRtQ0qplVLGSilNUkpd5b89v59TEPasKdBlA6zjX1LK3lLK3q1ataqFyyrqC7PZzLgJGcTe/DitRv6lmjkHgDYmDjQaNJExGLsNwtRzBLrYVn7aozjsKgBXwfPeGkfOxHjtJK9hQdw1t2Kt4dGqbc9aXpv9cjXTFX1CEo7iE5h63Ojd5rHtFroI2k58mfYPf0LS7a8R2a6b10Ld1ONGaJeqnAcbGBWzFedKTTHbXpiHZedKKn77gdirbkETZVRx+wwoKChg8bLFrEtcR3ZcNiX6En6O/ZkO1g4h+5cvLb2UEcNGBDRdSSpLqqb57LHudgonG5I28PFFH/PlBV/iFE56FvQkPzqfg/qDynnwHGhI7ZJfgXY+v/8O+K2B1qKoA2bPed1dFa2sXLSdNAvLjs/JX/gIrtJihFZPTOfrSLrjdQC/1zzVjJY3/RXbmjkNeBeNk6rvrYdQLTG23Wux7VnL0kULGThwYEAtWF/DlZpk7yIvuJRjn76IQCIddoQ+khtH3sQXK/6jPjSbJypmN3NqitmamFhcFeW0HvM40RelYOhyvYrbZ8DmLZv5yfBTNUm6rS230u9YPw6YDnDAeMCr89yppBOXll7K+LHj6dChA+PHjodl8FPMT+wz7KNUV0qkK7Ka2Uow6bsDxgPE2GO4oOwCtFJLhahgfuZ8bku/TbVwnCFB2zbq/MJC6HDbxt4AHAG2AulSyj3Bjundu7fctm1bPa1Qca7Et2iFYeyLQftvC758B6HTk9D/jqDnsG6az/heSV6tUL8e36ICYuMTyUhPZ8aDD5xXCVtN7623JeaHrxFOx2mN12lT/d4ns9lcqQWbiaWoAKnVeXuh3X+fCBL6317t/F4jlu6DMaYM8zNise/9ko9VC0c16rpto65RMbv5U1NcATixcg6aaJNfa1dVfOO2itmnef6l51nRYkXA/maD3UCyJZn21vZEuiLRR+lJ6ZbCNX2v8UtsCwoK/ExXnDhZc8Ea7zlTClLcbSAJ/m0gvkYsvgn6JZZLuKzsMtJvSVctHAE447aNuqbSMvZ+YDWwF1gaKggrmh7FRQUhlR/CUd2w7VnL6JtHMWXqNAymODpd3oUF3x3GMPZF2j30CYaxL7Jk51FSe/UhKyurrm6l0VHTe6tPSKLF0CkIpyOgxquHqpP29917L7Y96wAo+X5jNdk7e2Eexz5+gWOfvoi0l1P8v4/J+2AaJ7PeACBx4F3E3/w4Y29NVy0czQwVs5s/NcUVgNi+Y7Du/CJk3C7LXUXRqSIVs6vgKHcEVdYo0ZeQm5jLynYrEULw6F8fZcSNI6pVhD2mK4/+9VGefOJJ+vTuQ6fS00lvO2u7atJ37SztuOrYVWillstOXcbg3wYzIG8AMY4YdiXuYn2L9Sz6aJFq4TgDGtRyRkr5BRBaOFLRZImNTwyp/KBPSCK+/x0cXfIYpl4jMXYf6m0xsOz8AuvOL9BrYHDacCQaZEUZmmgj0uUCQGi06BOS0F87CX2H3oybkEH29q3nRTWjpvcWzk4qasaDDzC/Vx/0HXrjKiv2+yAtM2/j+GcvAYLY3pV/L5+Kc/786bQc8TDRyb3RXj6Am8eM49PlS8+Lv8f5gorZzZtw4orQ6ECrc7eGpQypErdXYtm+Ao1WT+bij1TMroIuSheWssaZuAH269uP7JxsDkUeoiCqoFobR+fCzlxWfBnmWDP7Tfv9Ks79jvXjx9gf2Zuwlx+if2DhkoVk3JqhWjjCoMEqz4rmT0Z6ureKGQxH0W/EXPZ7pMNO/oczOPTKH8j794O4yq2Yrh6H3QXGHiNIuuM12j/yKW0nzUboIsj/8CHKzKcfB0de2JnILoN59bU36vq2GgXhvLdn4wTo6zqmiYjyOorZC/M4vuIVhEZLm3FPk9D/Du+QoqcHuvXYJznx+SzshXmYetzI9z/8eF5WlxSKpko4ccWauxpjt0G0nTQL+8kj5L13v1szfsHDVBw7gNDoMPZUMTsQ3bt196sSB+JM3QATExMZP3Y8gwoGkVqUSoWmghhHDACtylpxWfFlfN3ma3Yl7qJEX4IUkhJ9CbsSd/F1m6+5rPgyWpW1Yr9pP/kn8nnzn2+yb9++c7rP8wGVPCvqDF/b6EB4RPXjrrm1Ulhf0Hbi32n/4BLirh6H5X/LaTP+ORKu90/UEvrfTusxj3Ni5WzshXne80V2HXze2MGG896erRNgWloa//l4GfFxceT9exoH/z6KvH8/iD7xAoypaaGtv7sPxrLjc/fwYUUZxpEzlQqHQtFECDdme4xQbL/uoc3457joL5/RduIrVOTtUzE7BP369guomOHBo6xxpm6AnTp1Ysq9U7i53c3opI4bfruBMb+M4Zqj12A2mWu0/u5a2JVSXSk6l451ieuUCkcYqORZUWf4VjGtm+b7O9etn8exZU97RfUtOz7HmDLUm5hV/b0qkRd2xpgyBMuOz73bzic72FDvrXXTfKwrXmDpooVn9Tg0KyuLm0aPhY6/J+n212j/8CcIjRZHUX6N1t/G1DRK9m7EUXwcTUysX3XJbDYzZeo04lu0QqPVEt+ilXIpVCgaESpm1y1Vq8S+zoCpRakMKhjE+LHjz6pt4uTJk3z/4/cciDvAlxd8yccXfYwQopqMXVX2m/YTZ48jxhFDhaaCgqgCfor5iW+2fENBQQErvljB8y89z9NPP+0eeFQuhUAD9zwrmj9paWlkb99aqegwE0tRAab4RK77fT/W79Xg/O177IkXUvL9V7Sd+Ir3uJLvN9J24sshz23sPpT8hY+QeMM9wPlnBxvsvZ2Ykc70+WfXR+irH+37IegqtwKEbf1tyc7C0Lk/4K4uvf/+Q/x7/nwiuw7BMPZF4ip7pZfsXMf8Xn1YumihUudQKBoBKmbXLZ4qsa9ihi5SF1BZI1x89aN9q8zhWn9rpZZLLJdwyHgIgH2GfezM3snOnJ1uM5cW+7y90j/8/APZOdmMHzv+vFbnUMmzos7xKDp45OY8nJZJm4mr1H84reqwWiA8iZqHs+nxbeoEe2/PlmD60ZroWJCusIYUNVFGrDtW0mrsEwBIl4PSsnJiOvWlcOdqXN98hCY6FsMVla5k5+HgkELRmFExu27xKGaMuHFErZwvkH40ELb1t1M4SbYks7n1ZgCEFFTYK/gt5jd+d+p3JBcmY9PYOGw8zD7TPg5FHoJlMOXeKeftcKFq21A0GL4yaXGJLbzDaeBO1nx/D4SnNQDOrcdXcZqFmZlEdhlUbbvhiv7o4pOw5q4Jebxl5xe4HHaikntT9vN3ABSs/SdCq0MX38brUth24sveISJXecl5NzikUDRFVMxunOTuymVfTPUhv2J9MZdYLgl57CWWS5BSkhedR1KZuzCSejIVKSSl+lKvQ+GGpA04hZOBeQOJcEV4WzvOV1TyrGgUVJ3yNlzRH2vu6pDHWHNWE31JHyyb/n1OPb6K0wTTeTX1HIG94AjW7KwaholWI4SG6A69KNm7EXthHhVH9tJm3NMYu7v7HX9983Z+e+derLlribooleMrXkHbPuW8GhxSKJo6KmY3HoLpR+9K2EWyJTnkgGKyJRkXLvKj82lvbY/BbqBlRUs2tdnEAeMBOlo6MvzwcIYdGcbFlos5GnWUPsf7cCTyCDm7cur61hotKnlWNAqqTnmHY6Bi2b4C14EtTOh1Adnbt6qe2VrAo/NaFX1CEq1GPox0OTm69EkKv3rfb5iooHKYqNWov9Bm3FMUbfwAV8kpir7+EGOPG3GVl5D/4UMIXYRf9VkX3wZwYdn22Xk1OKRQNHVUzG48ePSjq3I8+jg/xv7IdUevo1tBN78BxW4F3bju6HX8GPsjm9tupnthdyJcEXQp7ILZZCbCFcHAvIE4hdOv+lyqL0UgSC5OxmFzNMDdNg4azJ77bFBWr82brKwsxk3IILLLYCK7DsZ+4hAnv5jjtoBOTfMK8Vt3fkHZ7jW8Pfd1Jk+e3NDLblZMmTqNJTuPYrx2UsDX7YV5/DZvCmg0COlCOuyIyGhiLu1H3NXjvP3QhV+9T/GOzxESWo19ghP/eYnWYx4POIlvO7KXo0sewxBjwFJ0sk7vryFp6vbcZ4OK2c0bFbMbByu+WMHSn5eSHZcd8PVRv4zCrrUT6YpEK7U4hZNT+lPsTtjN8ejjAHQt6ErH4o4IIfhv6//S93hfNrfeHFDmLrE8keuOXkekPpLH/vZYXd5agxMsbquBQUWjIdCUd4wpjosqfuGXpf9HSXERpvhEJmWkM31RjnrcVwf4OgwGSnRdpUUIAUl3vhFycNCYMgzL9hXgdFC2b0vNElapaVxsd096r1+/nmkzHmHP93uQ9gqEPoIuV3ThtdkvM3DgwNq5UYVCcc6omN046Ne3Hzuyd3hdBqsihODrtl+HHBw8YDpAsiUZrdRyQekFHDAdqFEf+pootx71gQMH+HzV5xw/fhyN1OASLlq1asWIYSPo0KFD7dxkI0NVnhUKhR/vvfced//pz5h6jsCYMux09Sh3NdacNbjKLLR/5FOERhv0HBUnD5P3/lSE0CAddjTRJgxdBrjVNQIk3fbCPE4t+Qt/mfEgTz33wulrV0raWXNWYdnxOU89NpMnnniiLm+/zlCVZ4VCURcUFBQw9625lLvKOWA6wAHjAa+0XAdrBy47dRkfX/QxUgTP94wVRgb/NhgpJFqppUJTwSHjIX42/Rww6TbYDQzLH8ZVva7im63fYDZVt/9OtiQzqP8g+vfvX5e3X6cEi9uq51nRZFGmG3XD9p05xHUbhHQ6yF/4iNt6d+EjSIedtpNmISKiQ07Vl5m3cXTBXzD1HEnS5DcrLXpnBbTo9aCLbUXJqSKeeu4F2tz6HAnX3+nvUHb9nbS59Tmeeu4F1q9fX5e3r1Ao6ggVs+uGzVs2s8+0j/VJ69FIDQPyBvCHg39gQN4ANFLjZ9kdiLalbRmQP4CfY39m7QVr+fiij1mftN6rrtG2tG21Y0p1peCEb7Z+E9L+e93GdRw4cKAub79BUG0biiaJt9dOmW7UOgszMzGMfRF9QpLXzMAPIbDmriah/x3VXrIX5nFi5Wxaj33Cr03DY9Eb0/FKji1/lraTZvlVoB3FxxH6SEw9hods7zD1GM6DDz1C7s7t53yfCoWi/lAxu+7I3ZXLvhb7KNGXkJuYS25irt/rAsEllkvYlbir2rEGu4E+J/pU628u0ZewO2E3v0X/Rr+j/TgZdZLvWn2HQ+MeEoxxxOAQDvab9tfY3rFy1Uruv+/+WrzjhkdVnhVNDl8XPOO1k/wqlMZrJ2EcOZNxEzJUNeMsCSZX50HaSoNO1Vt2fI6xe3WTFQ+BLHoBrLmrkVJiTB0Wcm3G1DR279kTxl0oFIrGgorZdUswqToPP5t+DipZ19HSkQPG0P3NJ6NOckHZBVxQeoF3ewdrB4QMz/772PHQ+t9NEZU8K5ocwVzwPERe2FmZbpwDweTqPGhi4ojvfwfHlj9L4cYP/CTrrLvWYkwZGvL8xu5DKdm70fu7Wx96DThsYTmUSXvFmd2QQqFoUFTMrluCSdV5KNGXIJH0O9aProVd/STrLrJcxAFT6LYKrdSSH5XP70p+B7jVNjpYOqAlPPtvjWx+qWbzuyNFsyeYC54vkV0HK9ONs6Sq+UFVDFf0x1F4hLaTZiEddv++aFtZ2Ba99sI8Cjd+wLHlz9Jy+AyELjIshzKhjzir+1IoFA2Ditl1S/du3elU2inkPsX6Yo7EHKnWE62X+moJ8DVHr2HsL2O9P3EVcWxvuZ1EWyJjfxnLwPyBRLoicQhHyKQd3O0dLuE653tsbKieZ0WTo7iogLgwEjRlunF21CRXF3nh5ZxcOYeYTn1JvOEev77ow29MxHHqWEgZO0fxcYRWR/7CRzB07u/tf9a3bIc1ZxUJ198Z9FhrdhZdu3Q5txtUKBT1iorZdUu/vv3IzskOKlWXWJ6I0W7E6DCyufVmv57oEYdGEOOI8VPU2Be7jxa2FmxptcWrAw3weXt3u12rslb0Pd4Xm8YWtJfawyWWS2jdKvTfvimiKs+KJkdNbQXgTtBM8YEtSRWhSU5OZumihVhXvIB103y/tozCDe9RsPofmK4czbFlz1C44T2/13WxrbDs/CLk+a25qzGmptHu/gUk3nCPN9GOv/5OLNs/D+pQVvLDJop3rGT//v1qUl+haEKomF23JCYmMn7seAYVDCK1KLWak2C/Y/3Y2moruQm5XHv0Wj+3wbzoPC6xXOJ3vuPRx9nSagt9j/elVVkrv9c8ifOWVlvY2WJnSPvvC60XklycTGFRIU8//TTPv/Q8K75YQUFB0/+SpCrPiiZHRno6S3auQx/EBQ/AtnstEzPS63FVzQtf84P5H/4f1lOFoNMjXS4QguJvF4NGT8kPX2PdtQ5XuRVNtAmdqQXW7C8wXHZNUDdBa/Yq2t72arXXNLoIpNPO0cWPYeo53M+hrPCr9ynfv5343iOJ6T6UFmpSX6FoMqiYXfd06tSJKfdO4Zst35CzKweHzYHUSFwuF1K6+52dwunVYR6QN4AIVwQO4UCDhiMxR/yq1sejj7Ot5TauOn6Vt+IMcOWJK9nWcpu3In3QcJDrjl5XTee5W0E32pa3ZX/cfsxGs3f7Dz//QHZONuPHjqdTp9CtJo0ZlTwrmhw1tRXYjuzFtmct0+dvbYDVNR+Sk5MZnjaUf8+fT+I1txDZZZDXtMS2Zx1luasZ2L8fmzZ/g8VWgikqgonjRtIztTvTZjyMvdKy12uykrMaa84qpMvprj53H1rNgAUpaTn6MYo2vIdlx0qkowJ0erQaLW3GP1dN/k5/7ST0HXozbkIG2du3KgczhaIRomJ2/ZCYmMiIG0dwWafLWLxsMT8ZfmJfzD5v4tqppBOXWC4h3h6PS7rQReno060PF7S9gIjVEfwU8xP7DKf371DcgWJ9MR1PdaTzqc78EPcDRRFF6J16DHYDHSwduKjkIvbG7qVdaTuvQ6ELF2hgY5uN1eTvsuPc7SUsgyn3TiExsWk+bVAOg4omiVcztEqCZtu9FtuetaoSWQuYzWZSe/XBOHJm8CryihcCJq1ms7nSsjcTS1EBUqsj5vLriLt6HOCWtCvZuxFXaTGamFgMnfsT3fFKTqx4mXb3L/C7xsnlT2HqMRzT728LulbrpvmM75XE3Nfn1M7N1wHKYVBxPqNidv1QUFDAm/98k3WJ64L2Pw8qGFQtcS0oKPCrWjtxUqotJdoZTVFEEfuN+7ni1BVEO6Nx4cIpnORF53FB6QWsuGiF3/mvP349+037yY7PDrrO1KJUbu10KyNuHFGr91/bBIvbKnlWNFmqJmim+EQmZqQzfdrUsCqQZrOZ2XNeZ2FmJsVFBcTGJ5KRns6MBx9QFUxgytRpLNl5FGOIR63hJq3hnKtw4wdIh53EG+7BXpjnTrC/34ir9FRY9t4ly2ZSdLLx6omq5FlxvqNidt2z4osVLP15Kdlx2UH3CSdxXfHFCtbnrOcX4y/8bPoZBCChY3FHEisS+a7Vd3Qt7IpGashNzMVgN9DR0pF21nZEuiLDsvceeXIkj/710Vq467pDJc8KhQ++bldV2xFsu9eoKggQ36KV12kwGOEmrYGq2H4JctkphFZPzOXXEfm7Kyja+AHGlKEYuw/x/m2suWuw5qwmvv8d2E8crDyuGE10LIbOv8e6YyUul7NW34PaRCXPCsXZo2J2eDz/0vOsaLEiYMLqIZzENVgF25Mkt7e2J8IVQYWmghORJ2hpa8kB0wEOGA+cbvuwdqCDpQO74ncR54jzJtY2jY3DhsN0snTiySefrNX7r22CxW3V86w47/B1u1I9tMGpTXkpj4LHuAkZlF8+EE3ChZUJ8hDaTnzZ+0FY/N3HFK77Z8D+5oT+t6ONjqVw3T8x9R7pd5w1dzVo9TzzzDM88cQT53zvCoWi8aBidvjU5DYIbuMSh80Rch+PggfL4MfoH/nZ+DMmu4k+J/rwi/EX1ietp1RXSsvyllx97Gr+2+a/Ae29yzXlpBam8rPpZzYkbTidWFs64BAONm7cSP/+/Wvl3usTJVWnOO9QblfhUdvyUh4Fj5KcLAq//CetxzxOQv87/Kx6hS4CU6+RAf829sI8Tm35iDbjn6t2XEL/O2gz/jmeeu4F1q9ff1b3q1AoGicqZodPTW6D4DYu0UXWXDv1KHh0tHZk4G8D6Xu8L5tbb2ZX4i63a6GQXFB2Aftj9wfsrzbYDXQ+1Zmv23zN7sTd3mNK9CXsTtzN122+Zt3GdRw4ENrhsDGikmfFeYdyuwqPmpwG4czlpZKTk7Hb7Zh6jQr4QVjy/cag9t6WHZ9jTBka8gPU1GM4Dz70SNjrUSgUjR8Vs8MnHLfBTiWdSOmWEtb5EhMTEU7h7l+O/blaktzO2o4DxsDJb0dLRw6YDgRMrAEKogowm8ysXLUyrLU0JlTyrDjvKC4qCMtC+nx3u5rx4APYdq8JalrilZeaNvWMzis0GozdhwR8zVVWHPRvU/L9xqDHeTCmprF7z54zWo9CoWjcqJgdPv369uPSkkuDGpcklidyaemlXNP3mrDPqYvS0b6kfcAkOdIVGbRNJFRi7WG/aT9Hjx9tcsYpKnlWnHcot6vwCOU0aN00H+uKF1i6aOEZ9xhKR0XQD0JNdGzQv02oxNqDLrYV0l5BVlbWGa1JoVA0XlTMDp9QboOpRakMKhjE+LHjz0hfuXu37kS4IgImyTaNLWibSKjE2kOprhSt1PLmP99k3759Ya+poVHJs+K8oy7aEZornj7l8b2SKFk2k19nj6Fk2UzG90oie/vWs5puN8UlBP0gNFzRH2vumoCvhUqsPTiKjyN0EYybkKFsuxWKZoKK2WeGp1f51k63MvLkSMYcGsPIkyO5tdOtTLl3yhk7+/Xr2w+XcAVMkg8bD9PB2iHgcaESaw8xjhicwsm6xHUsXra4yVSglVSd4rzjXMw/FOfOlKnTWLw9D9N1t1d7zV6YR/6HD9F6zOPV/jYFX76D0OpJuP6OoOcu3PAeZYd2EXtJjxr1p+tbM1ZJ1SkUZ4eK2Q3Ph5kfknU0i12Ju/y2G+wGBuYNZHPrzdV6m1MKUnDiZHfi7qDn7VbQjZblLdlwwYaw9KcLCgrYvGUzubtycZQ70EXp6N6tO/369qsTt8JgcVtVnhXnHXXVjqAIjxkPPkDF9+sC9lLrE5Iwpgzh6OLHKPzqfb+/jXTYsGxfEbIH27JjJQnX31Hj8FBWVhapvfqwZOdRDGNfpN1Dn2AY+yJLdh4ltVcf1fahUDQiVMxueIYPG87lZZdX66Uu0ZeQm5DLdUevo2tBV782EY1LQ0dLx5D918mWZHYnuJPrfYZ95OzKCbqGffv28eY/32Tpz0tZ0WIFyy9azooWblOY+m77UDrPivMSTzuC2+1qpr/b1XxVvahLfDWf7QGsestysojtNhCX00H+wkf8LLwTBt3LseXPYuw2CGNqmvc4a3YWlh0rib1qLNEXpSCdjqDDQ0ozVqFoeqiY3bAkJiYy4ZYJiGWCn2J+Yp9hn1ezOdmazCHDITRoGJA3wGuecsh4iJ2JO+l3rB+/GH9hv2m/95hLLJeQbEnmx9gfOR59HAitP11QUMDiZYurmbaU6EvIjsvmUOQhWEY12/G6QrVtKBSKBiGQVe/IETey5KNltJo0J6izob0wj1PfLqV070akw47QRaBr2Z6E6+8g+qIU7z7BnA9r03b8TFBtGwqFoqlTUFDAN1u+IWdXDg6bA12kDrvdzqq2q4K6GhrsBi4vupx2pe3QSi1O4eSU/hS7E3Z7E2fPfsGcD2vLdvxMaVQOg0KIl4GRQAVgBu6UUhY1xFoUCkXDkJyczNzX53gTVI/9rr28NKSqhj4hiRZDp1Cy+0su+uuKgPuEGh5amJmJYeyLIdfmbvuYWavJc1NHxW2FQpGYmMiIG0d4E9R9+/axMHNhSFWNEn0JO1ru4OKDF7P84uVB9wulP527K5d9LUK3ZXjaPmozeQ5GQ/U8rwW6Sim7Az8Bf2ugdSgUikaAbyuFJjouLFUNTZQx4Gs16U8rzdizRsVthULhxdNKUaGpCFtV42z1p2vLdry2aJDkWUq5RkrpucMtwO8aYh0KhaJx4Gu/G0quzoM1ZxXS6aBw4wdnPDwUrmasPipayd35oOK2QqHwZfOWzfxk+IlDxkNB5eo8dLB2ID8qn37H+lUbLAxHfzpc23FthPas7+dMaAwDg5OBJQ29CIVC0XD4tlKYeo4g/8OHiOl4ZXBZqty1tBr9KGU/f0f+godxlRYj9BFkpE/gqcrhoWBSdCOG38iqPevQh+p5zlmFSPgdqb36sHTRwrPSs27mqLitUJzn+LZSDMwbyG/RvwW04k4sT6SDpQPrk9YDcHnR5Qz+bTBaqUUXqSO1eyrX9L2GxMTEoFJ0l3W6jB9+/SFkz/MllkuwO+zs27fvjLWsz5Q6S56FEOuAtgFeelRK+Z/KfR4FHMDCEOf5I/BHgPbt29fBShUKRUNTXFRAXGUrhT4hiZbDZ7hVNVKGYOw+1KuqYdm5kpLd62k5fAbRF6UQfVEKQqdHOuxEREQSFxtHcnKyt386susQDGNfJC6uNY5Tx1iycx1luatxOR3oO/QOmZy3nTQLV2nReaW8URtxW8VsheL8wNNKIYVka8ut9DvWjwOmAxwwHvCqanSwdKCjpSNbWm3xDhTadDb2m/aj0Wj8Bvz27dvH4mWL+cnwE/tanFbz+OHnH+hk7UQn2YlDkYeCJugXWy/mf4n/Q7dMV+eqGw2mtiGEuB34E3CDlDJ0I0slanJboWiexLdohWHsi34KG/bCPCw7Pqdk70a3XF20Cel0kHT7aSUO25G9HFv+LG0nzQKgZNlMtn/3bY2GCkWfPosGiOgymJiUYacl73JXY81Z407Ok90D1rWlvNEc1DbONG6rmK1QNF+ef+l5VrRY4U2KDXYDyZZk2lvbe+Xq8qLzuKD0AlZc5B7uTixPpN+xft4qtEddo6CggDf/+WY1KToPieWJDDw5EJfTxT7jPj/Zuw7WDnSwdGBry63kx+TXqupGY1PbGAb8FegfbuKsUCiaLxnp6SzZ6d9KoU9IIvGGe0i84R7A7R7oLLOgi23l7m/2SXT1CUlebWff/ulARF7Ymagug+l/IWStWk3xrnW4yixeLem2k2b5JfFKecONitsKhcKX7t2688PPp1spSvQl5CbmkpuY692na0FXDhoPYrAb/JLcEn0JQgrvgJ+nfzpQ4gxQEFXAz4afSbYkE+GMqKYnvT5pvTeJrw/VjYZS25gLmIC1QohsIcTbDbQOhULRCJjx4APYdq8J7R6YnUXpz1s4NGs0+QsfQTrstJ00y1shdhQfxxSfyMLMTCK7DAp5vZiUYXz22edUlJbQJv0lTL1GggTL9hXkL3iEgi/fwV6YByjlDR9U3FYoFF769e3HpSWXhlTQ6GjpSEdLRwbkDUAjNaxPWk9+TD7gHvDTRbpruLm7ctkXU7MUndPl5Me4HzlsPEyFpoJIVyTtre3paOmIwW4A6kd1o0Eqz1LKjg1xXYVC0TipyXWweOdKjB37kDjyL0HP4dF2/sebb3j7p4Ohi22FdNpBo+Xogkcwpg6j7cSX0VX2Rltz15D/4UO0HD4DXeKFmOLr3rGqsaPitkKh8CUxMZHxY8fDMqq5Dna0duTi4ovZ0mqLN1muiq+uc7hSdFqpZUD+AA6YDrAhaYNf68bAvIFsbbkVi97iTcrrisagtqFQKBQh7XdHP/sZN40ei+3I3qB9zLY9a5k+fysLFi7EcepYUIdCqNSJjjYh7TZaj32imk13Qv/biel4JceWP0ts52uDGq4oFArF+UynTp2Ycu+Uaq6Dl116Gd/v/Z4KTUXA46rqOnuk6IK5FMJprejNrTdXs+jenbCb36J/o9+xfuQZ8oKardQWKnlWKBSNhqqug76Eqkzb9qz1ajsH6p+uijV3NdrY1kRflBKyN9rYfTAlOauYvvT12rpFhUKhaFZUdR30sK/rPsQyUa0q3amkE5eWXuqn69y9W3f2/LiHXYm7gl7nEsslFOmLQvZFHzAe4NKS4GYrtYVKnhUKRZMgVGV6+vzTUnIzHnyA+b36hJaiy1mDlBLjqOBtIADGlGE49355XsjUKRQKRW0SrCqd0i3Fq+vsoV/ffny37TuOxBwJqraRbElmc+vNIa95wHSAK8qvqFOZOlDJs0KhaEKEqkz77rN00UKG3/QHTD1HYAwiRXds+TNh2XSXWS21fBcKhUJxfhCsKh1ovwhdRGCt6EqVDq3UciLqRMjzlOpKcdldtXkLAVHJs0KhaHakpaWRPv5Wlq//jvzdX7p1oqtI0WmiY6v1Rnu1pb/fiKusGE2UEV1UDGazWVWfFQqFog5JTU3l4x8/RiM1AaXoBuQNqNYXbbAb6GjpSDtrOyJdkVRoKpAaSUFBQZ1Wn1XyrFAomiVPP/kE//msD/F/eDRg+0ZU++5Ydn5B4sC7ACgzb+PEytkYU4b6KW+U5q5SNt0KhUJRx/Tr24/snGzWJa7z04r2cCzqGJdYLvH2RbctbUufE32qKW8kW5N5859vMn7s+Dqz6VbJs0KhaJaEkr+z5qyi7MAOhADDZdegiYnnxMrZtB7zeDXljbj+d2Lr2Pe8sulWKBSK+iaU9F2yJZk2ZW0QQnAk5gg2rY0+J/oEVN7ITcjl1+hfYRl1ZtPdYPbcZ4OyelUoFGeK2WyuHDLMxFJUQLTRREWFHcMV/dG07kjRxg/Qxbcl6qLuJPS/I+h5ztWmuznYc58pKmYrFIozpaCgoNqQ4UXtL2L/gf0ciThCi7IWWHVWjkcdZ3fi7qDnqQ2b7mBxWyXPCoXivMM3oS4uPAlaHUmT3wypDW0vzKNk2UyKTh47q2uq5FmhUCjOHk9SnZ2Tjc1uY+0Fa0PqQhvsBkaeHMmjf330rK8ZLG43lD23QqFQNBge1Y6ik8dwuZzgcoSlvHGq8AQarZb4Fq2YMnUaZrO5nlasUCgU5zce5Y7H/vYYOqkLy5HQXm7n6aef5vmXnmfFFysoKAisEX2mqORZoVCc98TGJ+I4Fbqi7HYljKXdQ59gGPsiS3YeJbVXH7KysupplQqFQqGA046EoYhxxGDT2Fh+0XJWtFjB0p+X8uY/32Tfvn3nfH2VPCsUivOejPR0bHvWhdzHmrsawxXXIzRa9AlJGK+dhHHkTMZNyFAVaIVCoahHunfrTqfS0EoaHawdOGQ8hBSSEn0J2XFuJY/FyxafcwVaJc8KheK8Z8aDD2DbvQbbkb0BX/e4Epp6+g+eRF7Ymcgug3n1tTfqY5kKhUKhwC1rd2nJpSSWB1bSSCxPpIOlA2aTf2GjIKqAn2J+4pst35zT9VXyrFAozhvMZjNTpk4jvkUrv95lgKWLFmJd8QLWTfOxF+YhnQ7shXkUfvU+x5Y/S8vhMwIOFEZ2HcyChZn1fSsKhULR7CkoKGDFFyt4/qXn/XqXAcaPHc+ggkGkFqVisBsQUmCwG+ha0JV+x/qxteXWgAOF+wz7yNmVc07rUjrPCoXivCArK4txEzKI7DoEw9gXias0QVmycx3zK01QsrdvrVThmImlqACp0WJMGeZ1JQyELrYVlqLaGUJRKBQKhZt9+/axeNlifjL8xL4WpzWff/j5B7Jzshk/djxT7p3iJ2vnwMF+437WJ60PqsRRqivFYXOc09pU8qxQKJo9ZrOZcRMyMI6cWc0ERX/tJPQdentNUOa+Pser5RzfohWRna7yt+yOjsVwRX9MPUegT0jCUXwcU3zd2cAqFArF+UZBQQGLly1mXeK6aiYo2XHZHIo85DVBGXHjCK+W8/MvPU9eTJ6fZbdNY+Ow8TA/m36mRF9CjCMGXeS5pb+qbUOhUDR7Zs95nciuQwLadEPg3mWz2Ux8XBzHlz0DQkPbiS/T/uFPaDvxZYQugvwPH6LMvA3b7rVMzEivr1tRKBSKZs/mLZv5yfCTX+LsS7De5QvaXsA1x67BhYsNSRv4+KKP2ZC0ASdOBuYNpG1pWzqVdCKlW8o5rU8lzwqFotmzMDOTyC6DQu7j27uclZVF99SeHPotjzbjnyNx4F3oE5K8ShsJ/W+n9ZjHOfH5K5TlrmL6tKn1cRsKhUJxXpC7K5d9MaEl5ar2Lu/YsYP9B/ezqc0m/n979x4bV3nmcfz7M3YgsQ2J17BQkpKp40hAIJDEiKSiNKEEAhFp1ahKy1ZBrfoHbRHbQq+o//Si3i/SLupuV1t127J0WQi3pdvEFW1VpYImocRJFG5JIAUSQeRCbOfG4Kd/zIwxZjw5wfYcz5zfR4o04zNzzuPX8ZMn77znfba1bWOgaWBop43tbdvZeMZGug500TnQyZJLl4wpPi/bMLO6d/CVXk5L0ASl75XeoSUezLyI1un/WHG2uuWCK3nPO4KOjo6JCNvMLJPyR/KJmqCU1i739vZy/6/v55nWZyrOVj/b8izXnHkNbW1jW2rnmWczq3tJm6C0Tm8bWuJxZG8PLRcur/ielouv4Y9/3DieoZqZZV7SJiiltcsbH9nI64Ovs6d1T8X37G7dzXN7nxtzfC6ezazuJWmCUlq7XFriMXj4YKKW3d5pw8xsfCVpgjJ87XLPtp7ELbvHutMGuHg2swxI0gTl6I5uPnPzTRx8pZfG086gYeqpiWerzcxs/CRpgjL30Nyhtcv5I3mONhw9odnqsfCaZzOrex0dHdx15x186MPX89r5V3LyvCtpPPV08gdf5uj2bg5v28DS976HhZdcCg2N5F99iebzLqe/ZwMzLl876nm904aZ2fhra2tjzeo1cDc8Ne0pnm5+Y5/nzoFOOgc6yc3O8eP/+DH5I3nyyrNv6j5y/Tm2z9g+6nk7+jrGvNMGuHg2s4xYsWLFW5qgtE5v47LL3s3DMcgjvVNpXv1NBrc8QH/PeloXrGT/L25h2pxLyt40ODRb/fNNKXw3Zmb1rbOz8y1NUBpPbuScd57D7j27WX9g/VDzlAUHFhAKcn05Xpz6YtmbBtuOtDFnYM6Yd9oAUESM+STVsmjRoti8eXPaYZhZndi1axcXLex6U/OU1/62j/2/uIUzPvgVBo8McOChH9AyfzktF141NFvd/5dfk3/id9z9P//NihUrEl1L0paIWDSR389k45xtZuOpt7eX2//99rc0T2l+rZll+5ax87SdnPvquexp3cOelj1Ds9W5vhxz+ubwgZUfYMGCBYmvN1re9syzmWVWueYpTTPOov3az/LSPV+jZf5y2ld9gcNPP8r+X97K4OE+dFIT1167gh/dudlb1JmZVdFozVMGmgbY1L6JrgNdvDDtBU7On8zSfUuZMjiFvPI0NjSyauWqEyqcK/ENg2aWWaM1T5nasYgzP/p9Iv8aBx74Dn1bHgSJ5nlX0NzczIP33evC2cysyio1T9k/bT8Pn/UwCjHr0CymDE7hWMMxmhqauPmTN49b4Qwuns0sw0o7a5TTNOMs2q74BDM/+V8gMevTv+QfrvoUh/perXKUZmYGx2+eMtA0wGPtj9EQDaybvY6HZj2EBjXmpigjuXg2s8xK2jylYdqpQ4+9NZ2ZWTqSNk851nBs6PF4bE03kotnM8usJM1T+nvW03zu5YC3pjMzS1OS5im5/hx7W/YCb26kMp5cPJtZZiVpntK/dQOtC1a+qZGKmZlVX5LmKbm+HLtad72lkcp48m4bZpZZlZqn9G/9Df093Uy//AaObu/m6I5u7rrzDt8oaGaWkkrNU3J9OXL9OXpm9NA50MncQ3NZs3rNuK93BhfPZpZx5ZqnNJ0ylQDiyCFe//Od/NP1H+EzP9/kwtnMLGXlmqfESQEBGhSLBxYz/4L5LLl0yYQUzuAmKWZmVeEmKWZmtWW0vO01z2ZmZmZmCbl4NjMzMzNLyMWzmZmZmVlCNbXmWdLLwHOjHG4HDlQxnMnIY1DgcfAYlEymcTgnIk5PO4hqcs4+Lo9BgcfBY1Ay2cahbN6uqeK5Ekmbs3YzzkgegwKPg8egxOMwefln4zEo8Th4DEpqZRy8bMPMzMzMLCEXz2ZmZmZmCdVT8fyTtAOYBDwGBR4Hj0GJx2Hy8s/GY1DicfAYlNTEONTNmmczMzMzs4lWTzPPZmZmZmYTysWzmZmZmVlCdVU8S7pJ0pOSdkj6TtrxpEnSrZJCUnvasVSbpO9KekJSj6R7JU1PO6ZqknR18ffgGUlfTDueapM0S9LvJO0s5oKb047JRue8XZDlnA3ZzttZz9lQe3m7bopnSUuBVcCFEXE+8L2UQ0qNpFnAlcDetGNJSTcwLyIuBJ4CvpRyPFUj6STgdmAFcB7wYUnnpRtV1eWBWyLiXOBS4FMZHIOa4Lxd4JwNZDRvO2cPqam8XTfFM3Aj8K2IOAoQES+lHE+afgh8Hsjk3aARsSEi8sWnjwAz04ynyi4BnomI3RFxDPgVheIkMyJiX0Q8VnzcB+wEzk43KhuF83ZBpnM2ZDpvZz5nQ+3l7XoqnucCl0l6VNIfJHWlHVAaJF0HvBARW9OOZZL4GPD/aQdRRWcDfx32/HkmcQKaaJJmAxcDj6YcipWX+bztnF1WlvK2c/YItZC3G9MO4ERI+i1wZplDt1H4XmZQmO7vAu6S9K6ow734jjMOXwaWVzei6qs0BhFxf/E1t1H4KOiOasaWMpX5Wt39DiQhqQW4B/jniDiYdjxZ5bztnF3ivF2Wc/YwtZK3a6p4joj3jXZM0o3AumLS/bOkQaAdeLla8VXLaOMg6QIgB2yVBIWPvR6TdElE7K9iiBOu0t8FAElrgZXAFfX2D/FxPA/MGvZ8JvBiSrGkRlIThQR8R0SsSzueLHPeds4ucd4uyzm7qJbydj0t27gPWAYgaS4wBTiQZkDVFhHbIuKMiJgdEbMp/FIuqMckXImkq4EvANdFxKG046myTUCnpJykKcAa4IGUY6oqFaqQ/wR2RsQP0o7HKrqPDOdt5+w3ZDhvZz5nQ+3l7Xoqnn8KvEvSdgoL7tdm6H+u9mb/CrQC3ZIel/RvaQdULcUbbj4NrKdww8VdEbEj3aiq7t3AR4FlxZ//45KuSTsoK8t520oymbeds4fUVN52e24zMzMzs4TqaebZzMzMzGxCuXg2MzMzM0vIxbOZmZmZWUIuns3MzMzMEnLxbGZmZmaWkItnqzmSXh+2lc3jxVaeJ3qO90s6bwLCK51/raSni3/WTtR1zMwmO+dsqzfeqs5qjqT+iGgZ4zl+BvxfRNx9Au9pLO7JebzXtQGbgUUU2qxuARZGxN/eZrhmZjXLOdvqjWeerS5IWijpD5K2SFov6azi1z8haZOkrZLukTRN0hLgOuC7xVmQDkm/l7So+J52Sc8WH98g6X8lPQhskNQs6afFc/5F0qoy4VwFdEdEbzH5dgNXV2MczMxqgXO21TIXz1aLpg77+O9eSU3AvwCrI2Ihha5l3yi+dl1EdEXEfArdmz4eEX+i0P70cxFxUUTsOs71FlPofLYMuA14OCK6gKUUknnziNefDfx12PPni18zM8si52yrK41pB2D2NhyOiItKTyTNA+ZRaOsKcBKwr3h4nqSvA9OBFgotUE9Ud0T0Fh8vB66TdGvx+SnAOykk+aGQypzD66PMLKucs62uuHi2eiBgR0QsLnPsZ8D7I2KrpBuA945yjjxvfBJzyohjAyOu9cGIeLJCPM+PuM5M4PcVXm9mliXO2VbTvGzD6sGTwOmSFgNIapJ0fvFYK7Cv+DHh9cPe01c8VvIssLD4eHWFa60HblJxukTSxaO8ZrmkGZJmUJj5eDuzJ2Zm9cg522qai2ereRFxjELy/LakrcDjwJLi4a8Aj1K4AeSJYW/7FfC54g0kHcD3gBsl/Qlor3C5rwFNQI+k7cXnI+PpLX59U/HPV4d9hGhmlmnO2VbrvFWdmZmZmVlCnnk2MzMzM0vIxbOZmZmZWUIuns3MzMzMEnLxbGZmZmaWkItnMzMzM7OEXDybmZmZmSXk4tnMzMzMLKG/A71dABUjvUjhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate some random cluster data\n",
    "X, y = make_blobs(random_state=170, n_samples=200)\n",
    "rng = np.random.RandomState(74)\n",
    "transformation = rng.normal(size=(2, 2))\n",
    "X = np.dot(X, transformation)\n",
    "plot_X_k_means(X, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means: failure case 3 \n",
    "\n",
    "- Again, K-Means is unable to capture complex cluster shapes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = datasets.make_circles(n_samples=200, noise=0.06, factor=0.4)[0]\n",
    "plot_X_k_means(X, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Can we do better than this? \n",
    "- Another clustering algorithm called DBSCAN is able to tackle some of these cases. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### DBSCAN\n",
    "\n",
    "- **D**ensity-**B**ased **S**patial **C**lustering of **A**pplications with **N**oise\n",
    "- Intuitively, it's based on the idea that clusters form dense regions in the data and so it works by identifying \"crowded\" regions in the feature space. \n",
    "- It can address some of the limitations of K-Means we saw above. \n",
    "    - It does not require the user to specify the number of clusters in advance. \n",
    "    - It can identify points that are not part of any clusters. \n",
    "    - It can capture clusters of complex shapes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's try `sklearn`'s DBSCAN.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "X, y = make_moons(n_samples=200, noise=0.08, random_state=42)\n",
    "dbscan = DBSCAN(eps=0.2)\n",
    "dbscan.fit(X)\n",
    "plot_X_dbscan(X, dbscan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0,  1,  1,  1,  0,  1,  1,  0,  0,  1,  1,  0,  1,  1,  0,\n",
       "        1,  0,  0,  1,  0,  1,  0,  0,  0,  0,  0,  1,  1,  0,  0,  0,  0,\n",
       "        1,  1,  1,  1,  1,  0,  0,  1,  0,  0,  1,  1,  0,  0,  1,  0,  1,\n",
       "        1,  0,  1,  1,  0,  1,  0,  1,  0,  1,  0,  0,  0,  0,  1,  0,  1,\n",
       "        0,  1,  0,  1,  1,  0,  1,  1,  0,  1, -1,  1,  0,  0,  0,  1,  1,\n",
       "        0,  1,  0,  1,  0,  0,  1,  1,  0,  0,  1,  1,  0,  0,  1,  0,  0,\n",
       "        1,  1,  1,  1,  0,  0,  1,  1,  1,  0,  1,  1,  0,  1,  0,  0,  1,\n",
       "        0,  0,  1,  1,  1,  0,  0,  1,  1,  0,  0,  1,  0,  0,  1,  0,  1,\n",
       "        0,  1,  0,  1,  1,  1,  1,  0,  0,  0,  0,  1,  0,  0,  1,  1,  0,\n",
       "        0,  0,  0,  1,  1,  0,  0,  1,  1,  1,  1,  0,  0,  1,  0,  1,  0,\n",
       "        0,  0,  0,  0,  1,  1,  1,  0,  1,  1,  0,  0,  1,  1,  1,  1,  0,\n",
       "        1,  0,  0,  1,  1,  0,  1,  0,  1,  0, -1,  1,  1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- DBSCAN is able to capture half moons shape\n",
    "- We don't not have to specify the number of clusters. \n",
    "    - That said, it has two other non-trivial hyperparameters to tune. \n",
    "- There are two examples which have not been assigned any label (noise examples). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "One more example of DBSCAN clusters capturing complex cluster shapes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = datasets.make_circles(n_samples=200, noise=0.06, factor=0.4)[0]\n",
    "dbscan = DBSCAN(eps=0.3, min_samples=3)\n",
    "dbscan.fit(X)\n",
    "plot_X_dbscan(X, dbscan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How does it work?\n",
    "\n",
    "- Iterative algorithm.  \n",
    "- Based on the idea that clusters form dense regions in the data. \n",
    "\n",
    "![](img/DBSCAN_search.gif)\n",
    "\n",
    "<!-- <img src=\"img/DBSCAN_search.gif\" alt=\"\" height=\"900\" width=\"900\">  -->\n",
    "\n",
    "[Source](https://dashee87.github.io/data%20science/general/Clustering-with-Scikit-with-GIFs/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Two main hyperparameters\n",
    "- `eps`: determines what it means for points to be \"close\"\n",
    "- `min_samples`: determines the number of **neighboring points** we require to consider in order for a point to be part of a cluster"
   ]
  },
  {
   "attachments": {
    "DBSCAN.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "There are three kinds of points.\n",
    "\n",
    "- **Core points** are the points that have at least `min_samples` points in the neighborhood.\n",
    "\n",
    "- **Border points** are the points with fewer than `min_samples` points in the neighborhood, but are connected to a core point. \n",
    "\n",
    "- **Noise points** are the points which do not belong to any cluster. In other words, the points which have less that `min_samples` point within distance `eps` of the starting point are noise points. \n",
    "\n",
    "![DBSCAN.png](attachment:DBSCAN.png)\n",
    "\n",
    "Picture from \"Introduction to Data Mining\", by Tan, Steinbach and Kumar.\n",
    "\n",
    "Let's see it in action: https://www.youtube.com/watch?v=h53WMIImUuc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Effect of `eps` hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc811cf1d63946ef95654cbac38efde8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='eps', max=12, min=1), Output()), _dom_classes=('widget-i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(random_state=40, centers=2, n_samples=6)\n",
    "interactive(lambda eps=1: plot_dbscan_with_labels(X, eps), eps=(1, 12, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1     2     3    4     5\n",
       "0  0.0  3.0   9.0   7.0   2.0  8.0 \n",
       "1  3.0  0.0   12.0  10.0  4.0  11.0\n",
       "2  9.0  12.0  0.0   2.0   8.0  1.0 \n",
       "3  7.0  10.0  2.0   0.0   6.0  1.0 \n",
       "4  2.0  4.0   8.0   6.0   0.0  7.0 \n",
       "5  8.0  11.0  1.0   1.0   7.0  0.0 "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(euclidean_distances(X, X)).round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Effect of `min_samples` hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "809a2499ad024c4da4813261e481e295",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='min_samples', max=5, min=1), Output()), _dom_classes=('w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive(\n",
    "    lambda min_samples=1: plot_dbscan_with_labels(X, eps=1.0, min_samples=min_samples),\n",
    "    min_samples=(1, 5, 1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Observations\n",
    "\n",
    "- Increasing `eps` ($\\uparrow$) means more points will be included in a cluster. \n",
    "    - `eps` = 1.0 either creates more clusters or more noise points, whereas eps=3.0 puts all points in one cluster with no noise points.  \n",
    "- Increasing `min_samples` ($\\uparrow$) means points in less dense regions will either be labeled as their own cluster or noise. \n",
    "- In general, it's not trivial to tune these hyperparameters. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### K-Means vs. DBSCAN\n",
    "\n",
    "- In DBSCAN, you do not have to specify the number of clusters! \n",
    "    - Instead, you have to tune `eps` and `min_samples`. \n",
    "- Unlike K-Means, DBSCAN doesn't have to assign all points to clusters. \n",
    "    - The label is -1 if a point is unassigned.\n",
    "- Unlike K-Means, there is no `predict` method. \n",
    "    - DBSCAN only really clusters the points you have, not \"new\" or \"test\" points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Illustration of hyperparameters `eps` and `min_samples` (extra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_samples: 2 eps: 1.000000  cluster: [-1  0  0 -1  0 -1  1  1  0  1 -1 -1]\n",
      "min_samples: 2 eps: 1.500000  cluster: [0 1 1 1 1 0 2 2 1 2 2 0]\n",
      "min_samples: 2 eps: 2.000000  cluster: [0 1 1 1 1 0 0 0 1 0 0 0]\n",
      "min_samples: 2 eps: 3.000000  cluster: [0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "min_samples: 3 eps: 1.000000  cluster: [-1  0  0 -1  0 -1  1  1  0  1 -1 -1]\n",
      "min_samples: 3 eps: 1.500000  cluster: [0 1 1 1 1 0 2 2 1 2 2 0]\n",
      "min_samples: 3 eps: 2.000000  cluster: [0 1 1 1 1 0 0 0 1 0 0 0]\n",
      "min_samples: 3 eps: 3.000000  cluster: [0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "min_samples: 5 eps: 1.000000  cluster: [-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]\n",
      "min_samples: 5 eps: 1.500000  cluster: [-1  0  0  0  0 -1 -1 -1  0 -1 -1 -1]\n",
      "min_samples: 5 eps: 2.000000  cluster: [-1  0  0  0  0 -1 -1 -1  0 -1 -1 -1]\n",
      "min_samples: 5 eps: 3.000000  cluster: [0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_dbscan()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Evaluating DBSCAN clusters \n",
    "- We cannot use the elbow method to examine the goodness of clusters created with DBSCAN. \n",
    "- But we can use the silhouette method because it's not dependent on the idea of cluster centers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(random_state=100, centers=3, n_samples=300)\n",
    "dbscan = DBSCAN(eps=2, min_samples=5)\n",
    "dbscan.fit(X)\n",
    "plot_X_dbscan(X, dbscan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Yellowbrick is designed to work with K-Means and not with DBSCAN.\n",
    "# So it needs the number of clusters stored in n_clusters\n",
    "# It also needs `predict` method to be implemented.\n",
    "# So I'm implementing it here so that we can use Yellowbrick to show Silhouette plots.\n",
    "n_clusters = len(set(dbscan.labels_))\n",
    "dbscan.n_clusters = n_clusters\n",
    "dbscan.predict = lambda x: dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.cluster import SilhouetteVisualizer\n",
    "\n",
    "visualizer = SilhouetteVisualizer(dbscan, colors=\"yellowbrick\")\n",
    "visualizer.fit(X)  # Fit the data to the visualizer\n",
    "visualizer.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Summary: Pros and cons\n",
    "\n",
    "- Pros\n",
    "    - Can learn arbitrary cluster shapes\n",
    "    - Can detect outliers \n",
    "- Cons\n",
    "    - Cannot `predict` on new examples.  \n",
    "    - Needs tuning of two non-obvious hyperparameters \n",
    "\n",
    "There is an improved version of DBSCAN called [`HDBSCAN` (hierarchical DBSCAN)](https://github.com/scikit-learn-contrib/hdbscan). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### DBSCAN: failure cases\n",
    "\n",
    "- DBSCAN is able to capture complex clusters. But this doesn't mean that `DBSCAN` always works better. It has its own problems! \n",
    "- DBSCAN doesn't do well when we have clusters with different densities. \n",
    "    - You can play with the hyperparameters but it's not likely to help much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_varied, y_varied = make_blobs(\n",
    "    n_samples=200, cluster_std=[1.0, 5.0, 1.0], random_state=10\n",
    ")\n",
    "plot_k_means_dbscan_comparison(X_varied)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```{note}\n",
    "There is also \"hierarchical\" clustering. We don't have time to talk about it. But if you want to know more about it, check out the notes from last year [here](https://github.com/UBC-CS/cpsc330/blob/v2.0/lectures/19_clustering.ipynb).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Recommender systems motivation <a name=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What is a recommender system? \n",
    "\n",
    "- A recommender or a recommendation system **recommends** a particular product or service to users they are likely to consume. \n",
    "\n",
    "![](img/recommendation_system.png)\n",
    "\n",
    "<!-- <img src=\"img/recommendation_system.png\" alt=\"\" height=\"900\" width=\"900\">  -->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example: Recommender Systems\n",
    "- A client goes to Amazon to buy products. \n",
    "- Amazon has some information about the client. They also have information about other clients buying similar products. \n",
    "- What should they recommend to the client, so that they buy more products? \n",
    "- There's no \"right\" answer (no label). \n",
    "- The whole idea is to understand user behavior in order to recommend them products they are likely to consume. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Why should we care about recommendation systems? \n",
    "\n",
    "- Almost everything we buy or consume today is in some way or the other influenced by recommendation systems. \n",
    "    - Music (Spotify), videos (YouTube), news, books and products (Amazon), movies (Netflix), jokes, restaurants, dating , friends (Facebook), professional connections (Linkedin)\n",
    "- Recommendation systems are at the core of the success of many companies. \n",
    "    - Amazon\n",
    "    - [Netflix](https://help.netflix.com/en/node/100639)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### An example Capstone project: [QxMD](https://qxmd.com/)\n",
    "\n",
    "- Present personalized journal article recommendations to health care professionals.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What kind of data we need to build recommendation systems? \n",
    "\n",
    "- **User ratings data** (most common)\n",
    "- **Features related to items or users** \n",
    "- Customer purchase history data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Main approaches\n",
    "\n",
    "- Collaborative filtering \n",
    "    - \"Unsupervised\" learning \n",
    "    - We only have labels $y_{ij}$ (rating of user $i$ for item $j$). \n",
    "    - We learn features.  \n",
    "- Content-based recommenders \n",
    "    - Supervised learning\n",
    "    - Extract features $x_i$ of users and/or items and building a model to predict rating $y_i$ given $x_i$. \n",
    "    - Apply model to predict for new users/items. \n",
    "- Hybrid \n",
    "    - Combining collaborative filtering with content-based filtering\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Netflix prize\n",
    "\n",
    "![](img/netflix.png)\n",
    "\n",
    "[Source](https://netflixtechblog.com/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Netflix prize\n",
    "\n",
    "- 100M ratings from 0.5M users on 18k movies.\n",
    "- Grand prize was \\$1M for first team to reduce squared error at least by 10%.\n",
    "- Winning entry (and most entries) used collaborative filtering:\n",
    "    - Methods that only looks at ratings, not features of movies/users.\n",
    "- A simple collaborative filtering method that does really well:\n",
    "   - Now adopted by many companies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Recommender systems problem "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Problem formulation\n",
    "\n",
    "- Most often the data for recommender systems come in as **ratings** for a set of items from a set of users. \n",
    "- We have two entities: $N$ **users** and $M$ **items**. \n",
    "- **Users** are consumers. \n",
    "- **Items** are the products or services offered.  \n",
    "    - E.g., movies (Netflix), books (Amazon), songs (spotify), people (tinder)  \n",
    "    \n",
    "![](img/utility_matrix.png)\n",
    "\n",
    "<!-- <img src=\"img/utility_matrix.png\" alt=\"\" height=\"900\" width=\"900\">  -->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Utility matrix \n",
    "\n",
    "- A **utility matrix** is the matrix that captures **interactions** between $N$ **users** and $M$ **items**. \n",
    "- The interaction may come in different forms: \n",
    "    - ratings, clicks, purchases\n",
    "\n",
    "![](img/utility_matrix.png)\n",
    "\n",
    "<!-- <img src=\"img/utility_mat.png\" alt=\"\" height=\"900\" width=\"900\">  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Utility matrix\n",
    "\n",
    "- Below is a toy utility matrix. Here $N$ = 6 and $M$ = 5.  \n",
    "- Each entry $y_{ij}$ ($i^{th}$ row and $j^{th}$ column) denotes the rating given by the user $i$ to item $j$. \n",
    "- We represent users in terms of items and items in terms of users. \n",
    "\n",
    "![](img/utility_matrix.png)\n",
    "\n",
    "<!-- <img src=\"img/utility_matrix.png\" alt=\"\" height=\"900\" width=\"900\">  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Sparsity of utility matrix\n",
    "\n",
    "- The utility matrix is very sparse because usually users only interact with a few items. \n",
    "- For example: \n",
    "    - all Netflix users will have rated only a small percentage of content available on Netflix\n",
    "    - all amazon clients will have rated only a small fraction of items among all items available on Amazon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What do we predict? \n",
    "Given a utility matrix of $N$ users and $M$ items, **complete the utility matrix**. In other words, **predict missing values in the matrix**. \n",
    "\n",
    "![](img/utility_matrix.png)\n",
    "\n",
    "<!-- <img src=\"img/utility_matrix.png\" alt=\"\" height=\"900\" width=\"900\">  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Once we have predicted ratings, we can recommend items to users they are likely to rate higher. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example dataset: [Jester 1.7M jokes ratings dataset](https://www.kaggle.com/vikashrajluhaniwal/jester-17m-jokes-ratings-dataset?select=jester_ratings.csv)\n",
    "\n",
    "- We'll use a sample of [Jester 1.7M jokes ratings dataset](https://www.kaggle.com/vikashrajluhaniwal/jester-17m-jokes-ratings-dataset) to demonstrate different recommendation systems. \n",
    "\n",
    "The dataset comes with two CSVs\n",
    "- A CSV containing ratings (-10.0 to +10.0) of 150 jokes from 59,132 users. \n",
    "- A CSV containing joke IDs and the actual text of jokes. \n",
    "\n",
    "> Some jokes might be offensive. Please do not look too much into the actual text data if you are sensitive to such language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Recommendation systems are most effective when you have a large amount of data.\n",
    "- But we are only taking a sample here for speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "filename = \"data/jester_ratings.csv\"\n",
    "ratings_full = pd.read_csv(filename)\n",
    "ratings = ratings_full[ratings_full[\"userId\"] <= 4000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>userId</th>\n",
       "      <th>jokeId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-9.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-9.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>-6.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  jokeId  rating\n",
       "0  1       5       0.219 \n",
       "1  1       7      -9.281 \n",
       "2  1       8      -9.281 \n",
       "3  1       13     -6.781 \n",
       "4  1       15      0.875 "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_key = \"userId\"\n",
    "item_key = \"jokeId\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dataset stats "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 141362 entries, 0 to 141361\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   userId  141362 non-null  int64  \n",
      " 1   jokeId  141362 non-null  int64  \n",
      " 2   rating  141362 non-null  float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 4.3 MB\n"
     ]
    }
   ],
   "source": [
    "ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ratings: 141362\n",
      "Average rating:  1.200\n",
      "Number of users (N): 3635\n",
      "Number of items (M): 140\n",
      "Fraction non-nan ratings: 0.278\n"
     ]
    }
   ],
   "source": [
    "def get_stats(ratings, item_key=\"jokeId\", user_key=\"userId\"):\n",
    "    print(\"Number of ratings:\", len(ratings))\n",
    "    print(\"Average rating:  %0.3f\" % (np.mean(ratings[\"rating\"])))\n",
    "    N = len(np.unique(ratings[user_key]))\n",
    "    M = len(np.unique(ratings[item_key]))\n",
    "    print(\"Number of users (N): %d\" % N)\n",
    "    print(\"Number of items (M): %d\" % M)\n",
    "    print(\"Fraction non-nan ratings: %0.3f\" % (len(ratings) / (N * M)))\n",
    "    return N, M\n",
    "\n",
    "\n",
    "N, M = get_stats(ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating utility matrix\n",
    "\n",
    "- Let's construct utility matrix with `number of users` rows and `number of items` columns from the ratings data. \n",
    "\n",
    "> Note we are constructing a non-sparse matrix for demonstration purpose here. In real life it's recommended that you work with sparse matrices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3635, 140)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_mapper = dict(zip(np.unique(ratings[user_key]), list(range(N))))\n",
    "item_mapper = dict(zip(np.unique(ratings[item_key]), list(range(M))))\n",
    "user_inverse_mapper = dict(zip(list(range(N)), np.unique(ratings[user_key])))\n",
    "item_inverse_mapper = dict(zip(list(range(M)), np.unique(ratings[item_key])))\n",
    "\n",
    "\n",
    "def create_Y_from_ratings(data, N, M):\n",
    "    Y = np.zeros((N, M))\n",
    "    Y.fill(np.nan)\n",
    "    for index, val in data.iterrows():\n",
    "        n = user_mapper[val[user_key]]\n",
    "        m = item_mapper[val[item_key]]\n",
    "        Y[n, m] = val[\"rating\"]\n",
    "\n",
    "    return Y\n",
    "\n",
    "\n",
    "Y_mat = create_Y_from_ratings(ratings, N, M)\n",
    "Y_mat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Utility matrix for the example problem\n",
    "- Rows represent users.\n",
    "- Columns represent items (jokes in our case).\n",
    "- Each cell gives the rating given by the user to the corresponding joke. \n",
    "- Users are features for jokes and jokes are features for users.\n",
    "- We want to predict the missing entries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.219</td>\n",
       "      <td>-9.281</td>\n",
       "      <td>-9.281</td>\n",
       "      <td>-6.781</td>\n",
       "      <td>0.875</td>\n",
       "      <td>-9.656</td>\n",
       "      <td>-9.031</td>\n",
       "      <td>-7.469</td>\n",
       "      <td>-8.719</td>\n",
       "      <td>-9.156</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-9.688</td>\n",
       "      <td>9.938</td>\n",
       "      <td>9.531</td>\n",
       "      <td>9.938</td>\n",
       "      <td>0.406</td>\n",
       "      <td>3.719</td>\n",
       "      <td>9.656</td>\n",
       "      <td>-2.688</td>\n",
       "      <td>-9.562</td>\n",
       "      <td>-9.125</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.844</td>\n",
       "      <td>-9.844</td>\n",
       "      <td>-7.219</td>\n",
       "      <td>-2.031</td>\n",
       "      <td>-9.938</td>\n",
       "      <td>-9.969</td>\n",
       "      <td>-9.875</td>\n",
       "      <td>-9.812</td>\n",
       "      <td>-9.781</td>\n",
       "      <td>-6.844</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.812</td>\n",
       "      <td>-4.500</td>\n",
       "      <td>-4.906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.906</td>\n",
       "      <td>4.750</td>\n",
       "      <td>-5.906</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>-4.031</td>\n",
       "      <td>3.875</td>\n",
       "      <td>6.219</td>\n",
       "      <td>5.656</td>\n",
       "      <td>6.094</td>\n",
       "      <td>5.406</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3630</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.812</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3631</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.844</td>\n",
       "      <td>7.531</td>\n",
       "      <td>-9.719</td>\n",
       "      <td>-9.344</td>\n",
       "      <td>3.875</td>\n",
       "      <td>9.812</td>\n",
       "      <td>8.938</td>\n",
       "      <td>8.375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3632</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.906</td>\n",
       "      <td>3.969</td>\n",
       "      <td>-2.312</td>\n",
       "      <td>-0.344</td>\n",
       "      <td>-8.844</td>\n",
       "      <td>4.188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.875</td>\n",
       "      <td>-9.156</td>\n",
       "      <td>-9.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.312</td>\n",
       "      <td>1.281</td>\n",
       "      <td>-3.531</td>\n",
       "      <td>2.125</td>\n",
       "      <td>-5.812</td>\n",
       "      <td>5.562</td>\n",
       "      <td>-6.062</td>\n",
       "      <td>0.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3635 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1      2      3      4      5      6      7      8      9  \\\n",
       "0     0.219 -9.281 -9.281 -6.781  0.875 -9.656 -9.031 -7.469 -8.719 -9.156   \n",
       "1    -9.688  9.938  9.531  9.938  0.406  3.719  9.656 -2.688 -9.562 -9.125   \n",
       "2    -9.844 -9.844 -7.219 -2.031 -9.938 -9.969 -9.875 -9.812 -9.781 -6.844   \n",
       "3    -5.812 -4.500 -4.906 NaN    NaN    NaN    NaN    NaN    NaN    NaN      \n",
       "4     6.906  4.750 -5.906 -0.406 -4.031  3.875  6.219  5.656  6.094  5.406   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "3630 NaN    -9.812 -0.062 NaN    NaN    NaN    NaN    NaN    NaN    NaN      \n",
       "3631 NaN    -9.844  7.531 -9.719 -9.344  3.875  9.812  8.938  8.375 NaN      \n",
       "3632 NaN    -1.906  3.969 -2.312 -0.344 -8.844  4.188 NaN    NaN    NaN      \n",
       "3633 NaN    -8.875 -9.156 -9.156 NaN    NaN    NaN    NaN    NaN    NaN      \n",
       "3634 NaN    -6.312  1.281 -3.531  2.125 -5.812  5.562 -6.062  0.125 NaN      \n",
       "\n",
       "      ...  130  131    132  133  134  135  136  137  138  139  \n",
       "0     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "...   ...  ..   ..   ..     ..   ..   ..   ..   ..   ..   ..   \n",
       "3630  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3631  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3632  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3633  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3634  ... NaN  NaN   4.188 NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "[3635 rows x 140 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Y_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Baseline Approaches\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Recall that our goal is to predict missing entries in the utility matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.219</td>\n",
       "      <td>-9.281</td>\n",
       "      <td>-9.281</td>\n",
       "      <td>-6.781</td>\n",
       "      <td>0.875</td>\n",
       "      <td>-9.656</td>\n",
       "      <td>-9.031</td>\n",
       "      <td>-7.469</td>\n",
       "      <td>-8.719</td>\n",
       "      <td>-9.156</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-9.688</td>\n",
       "      <td>9.938</td>\n",
       "      <td>9.531</td>\n",
       "      <td>9.938</td>\n",
       "      <td>0.406</td>\n",
       "      <td>3.719</td>\n",
       "      <td>9.656</td>\n",
       "      <td>-2.688</td>\n",
       "      <td>-9.562</td>\n",
       "      <td>-9.125</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.844</td>\n",
       "      <td>-9.844</td>\n",
       "      <td>-7.219</td>\n",
       "      <td>-2.031</td>\n",
       "      <td>-9.938</td>\n",
       "      <td>-9.969</td>\n",
       "      <td>-9.875</td>\n",
       "      <td>-9.812</td>\n",
       "      <td>-9.781</td>\n",
       "      <td>-6.844</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.812</td>\n",
       "      <td>-4.500</td>\n",
       "      <td>-4.906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.906</td>\n",
       "      <td>4.750</td>\n",
       "      <td>-5.906</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>-4.031</td>\n",
       "      <td>3.875</td>\n",
       "      <td>6.219</td>\n",
       "      <td>5.656</td>\n",
       "      <td>6.094</td>\n",
       "      <td>5.406</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3630</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.812</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3631</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.844</td>\n",
       "      <td>7.531</td>\n",
       "      <td>-9.719</td>\n",
       "      <td>-9.344</td>\n",
       "      <td>3.875</td>\n",
       "      <td>9.812</td>\n",
       "      <td>8.938</td>\n",
       "      <td>8.375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3632</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.906</td>\n",
       "      <td>3.969</td>\n",
       "      <td>-2.312</td>\n",
       "      <td>-0.344</td>\n",
       "      <td>-8.844</td>\n",
       "      <td>4.188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.875</td>\n",
       "      <td>-9.156</td>\n",
       "      <td>-9.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.312</td>\n",
       "      <td>1.281</td>\n",
       "      <td>-3.531</td>\n",
       "      <td>2.125</td>\n",
       "      <td>-5.812</td>\n",
       "      <td>5.562</td>\n",
       "      <td>-6.062</td>\n",
       "      <td>0.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3635 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1      2      3      4      5      6      7      8      9  \\\n",
       "0     0.219 -9.281 -9.281 -6.781  0.875 -9.656 -9.031 -7.469 -8.719 -9.156   \n",
       "1    -9.688  9.938  9.531  9.938  0.406  3.719  9.656 -2.688 -9.562 -9.125   \n",
       "2    -9.844 -9.844 -7.219 -2.031 -9.938 -9.969 -9.875 -9.812 -9.781 -6.844   \n",
       "3    -5.812 -4.500 -4.906 NaN    NaN    NaN    NaN    NaN    NaN    NaN      \n",
       "4     6.906  4.750 -5.906 -0.406 -4.031  3.875  6.219  5.656  6.094  5.406   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "3630 NaN    -9.812 -0.062 NaN    NaN    NaN    NaN    NaN    NaN    NaN      \n",
       "3631 NaN    -9.844  7.531 -9.719 -9.344  3.875  9.812  8.938  8.375 NaN      \n",
       "3632 NaN    -1.906  3.969 -2.312 -0.344 -8.844  4.188 NaN    NaN    NaN      \n",
       "3633 NaN    -8.875 -9.156 -9.156 NaN    NaN    NaN    NaN    NaN    NaN      \n",
       "3634 NaN    -6.312  1.281 -3.531  2.125 -5.812  5.562 -6.062  0.125 NaN      \n",
       "\n",
       "      ...  130  131    132  133  134  135  136  137  138  139  \n",
       "0     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4     ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "...   ...  ..   ..   ..     ..   ..   ..   ..   ..   ..   ..   \n",
       "3630  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3631  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3632  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3633  ... NaN  NaN  NaN    NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3634  ... NaN  NaN   4.188 NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "[3635 rows x 140 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Y_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Evaluation\n",
    "\n",
    "- We'll try a number of methods to do this. \n",
    "- Although there is no notion of \"accurate\" recommendations, we need a way to evaluate our predictions so that we'll be able to compare different methods.\n",
    "- Although we are doing unsupervised learning, we'll split the data and evaluate our predictions as follows.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data splitting \n",
    "\n",
    "- We split the ratings into train and validation sets. \n",
    "- It's easier to split the ratings data instead of splitting the utility matrix.\n",
    "- Don't worry about `y`; we're not really going to use it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((113089, 3), (28273, 3))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ratings.copy()\n",
    "y = ratings[user_key]\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "X_train.shape, X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now we will create utility matrices for train and validation splits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "train_mat = create_Y_from_ratings(X_train, N, M)\n",
    "valid_mat = create_Y_from_ratings(X_valid, N, M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3635, 140), (3635, 140))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mat.shape, valid_mat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `train_mat` has only ratings from the train set and `valid_mat` has only ratings from the valid set.\n",
    "- During training we assume that we do not have access to some of the available ratings. We predict these ratings and evaluate them against ratings in the validation set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Questions for you\n",
    "\n",
    "- How do train and validation utility matrices differ? \n",
    "- Why are utility matrices for train and validation sets are of the same shape?\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Answer:**\n",
    "\n",
    "- The training matrix `train_mat` is of shape N by M but only has ratings from `X_train` and all other ratings missing. \n",
    "- The validation matrix `valid_mat` is also of shape N by M but it only has ratings `X_valid` and all other ratings missing. \n",
    "- They have the same shape because both have the same number of users and items; that's how we have constructed them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Evaluation\n",
    "\n",
    "- Now that we have train and validation sets, how do we evaluate our predictions?\n",
    "- You can calculate the error between actual ratings and predicted ratings with metrics of your choice. \n",
    "    - Most common ones are MSE or RMSE. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The `error` function below calculates RMSE and `evaluate` function prints train and validation RMSE.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error(X1, X2):\n",
    "    \"\"\"\n",
    "    Returns the root mean squared error.\n",
    "    \"\"\"\n",
    "    return np.sqrt(np.nanmean((X1 - X2) ** 2))\n",
    "\n",
    "\n",
    "def evaluate(pred_X, train_X, valid_X, model_name=\"Global average\"):\n",
    "    print(\"%s train RMSE: %0.2f\" % (model_name, error(pred_X, train_X)))\n",
    "    print(\"%s valid RMSE: %0.2f\" % (model_name, error(pred_X, valid_X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Baselines\n",
    "\n",
    "Let's first try some simple approaches to predict missing entries. \n",
    "\n",
    "1. Global average baseline\n",
    "2. [$k$-Nearest Neighbours imputation](https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html)    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Global average baseline\n",
    "\n",
    "- Let's examine RMSE of the global average baseline. \n",
    "- In this baseline we predict everything as the global average rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "      <td>1.20741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0        1        2        3        4        5        6        7  \\\n",
       "0  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "1  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "2  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "3  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "4  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "\n",
       "         8        9  ...      130      131      132      133      134  \\\n",
       "0  1.20741  1.20741  ...  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "1  1.20741  1.20741  ...  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "2  1.20741  1.20741  ...  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "3  1.20741  1.20741  ...  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "4  1.20741  1.20741  ...  1.20741  1.20741  1.20741  1.20741  1.20741   \n",
       "\n",
       "       135      136      137      138      139  \n",
       "0  1.20741  1.20741  1.20741  1.20741  1.20741  \n",
       "1  1.20741  1.20741  1.20741  1.20741  1.20741  \n",
       "2  1.20741  1.20741  1.20741  1.20741  1.20741  \n",
       "3  1.20741  1.20741  1.20741  1.20741  1.20741  \n",
       "4  1.20741  1.20741  1.20741  1.20741  1.20741  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg = np.nanmean(train_mat)\n",
    "pred_g = np.zeros(train_mat.shape) + avg\n",
    "pd.DataFrame(pred_g).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global average train RMSE: 5.75\n",
      "Global average valid RMSE: 5.77\n"
     ]
    }
   ],
   "source": [
    "evaluate(pred_g, train_mat, valid_mat, model_name=\"Global average\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### [$k$-nearest neighbours imputation](https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html)\n",
    "\n",
    "- Can we try $k$-nearest neighbours type imputation? \n",
    "- Impute missing values using the mean value from $k$ nearest neighbours found in the training set. \n",
    "- Calculate distances between examples using features where neither value is missing. \n",
    "\n",
    "![](img/utility_matrix.png)\n",
    "\n",
    "<!-- <img src=\"img/utility_matrix.png\" alt=\"\" height=\"900\" width=\"900\">  -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "imputer = KNNImputer(n_neighbors=10)\n",
    "train_mat_imp = imputer.fit_transform(train_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.9406</td>\n",
       "      <td>-9.2810</td>\n",
       "      <td>-9.2810</td>\n",
       "      <td>-6.7810</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>-9.6560</td>\n",
       "      <td>-9.0310</td>\n",
       "      <td>-7.4690</td>\n",
       "      <td>-8.7190</td>\n",
       "      <td>-9.1560</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.5311</td>\n",
       "      <td>1.8968</td>\n",
       "      <td>0.6905</td>\n",
       "      <td>-3.1218</td>\n",
       "      <td>1.2843</td>\n",
       "      <td>-2.6063</td>\n",
       "      <td>-0.1812</td>\n",
       "      <td>-1.3937</td>\n",
       "      <td>1.7625</td>\n",
       "      <td>-0.4092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.3405</td>\n",
       "      <td>9.9380</td>\n",
       "      <td>9.5310</td>\n",
       "      <td>9.9380</td>\n",
       "      <td>0.4060</td>\n",
       "      <td>3.7190</td>\n",
       "      <td>9.6560</td>\n",
       "      <td>-2.6880</td>\n",
       "      <td>4.3438</td>\n",
       "      <td>-9.1250</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2437</td>\n",
       "      <td>3.1719</td>\n",
       "      <td>5.0251</td>\n",
       "      <td>5.1812</td>\n",
       "      <td>8.2407</td>\n",
       "      <td>5.9311</td>\n",
       "      <td>5.8375</td>\n",
       "      <td>6.3812</td>\n",
       "      <td>1.1687</td>\n",
       "      <td>6.2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.8440</td>\n",
       "      <td>-3.5750</td>\n",
       "      <td>-7.2190</td>\n",
       "      <td>-2.0310</td>\n",
       "      <td>-9.9380</td>\n",
       "      <td>-9.9690</td>\n",
       "      <td>-9.8750</td>\n",
       "      <td>-9.8120</td>\n",
       "      <td>-9.7810</td>\n",
       "      <td>-6.8440</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.4186</td>\n",
       "      <td>-3.1156</td>\n",
       "      <td>-1.5655</td>\n",
       "      <td>-5.6250</td>\n",
       "      <td>0.3720</td>\n",
       "      <td>-4.0439</td>\n",
       "      <td>-6.0500</td>\n",
       "      <td>-5.5563</td>\n",
       "      <td>-5.4125</td>\n",
       "      <td>-5.5874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.8120</td>\n",
       "      <td>-1.6499</td>\n",
       "      <td>-4.9060</td>\n",
       "      <td>-2.7781</td>\n",
       "      <td>0.5530</td>\n",
       "      <td>-3.8594</td>\n",
       "      <td>1.7031</td>\n",
       "      <td>-0.3687</td>\n",
       "      <td>1.8469</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0344</td>\n",
       "      <td>2.1469</td>\n",
       "      <td>2.8875</td>\n",
       "      <td>1.6845</td>\n",
       "      <td>1.2437</td>\n",
       "      <td>-0.0156</td>\n",
       "      <td>1.2595</td>\n",
       "      <td>3.8219</td>\n",
       "      <td>3.1971</td>\n",
       "      <td>5.0249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.3157</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>1.8658</td>\n",
       "      <td>-0.4060</td>\n",
       "      <td>1.7937</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>6.2190</td>\n",
       "      <td>1.9220</td>\n",
       "      <td>6.0940</td>\n",
       "      <td>5.4060</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2844</td>\n",
       "      <td>1.1313</td>\n",
       "      <td>4.0157</td>\n",
       "      <td>3.0344</td>\n",
       "      <td>4.0406</td>\n",
       "      <td>0.5218</td>\n",
       "      <td>4.3594</td>\n",
       "      <td>4.0968</td>\n",
       "      <td>3.9250</td>\n",
       "      <td>3.9657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3630</th>\n",
       "      <td>-0.7750</td>\n",
       "      <td>-9.8120</td>\n",
       "      <td>-0.0620</td>\n",
       "      <td>-2.8218</td>\n",
       "      <td>-4.1470</td>\n",
       "      <td>-4.8281</td>\n",
       "      <td>2.2718</td>\n",
       "      <td>-2.8782</td>\n",
       "      <td>-1.0125</td>\n",
       "      <td>0.0688</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.6844</td>\n",
       "      <td>3.0531</td>\n",
       "      <td>2.8687</td>\n",
       "      <td>1.5281</td>\n",
       "      <td>4.5002</td>\n",
       "      <td>-0.1878</td>\n",
       "      <td>2.0031</td>\n",
       "      <td>4.0908</td>\n",
       "      <td>2.3563</td>\n",
       "      <td>5.0406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3631</th>\n",
       "      <td>2.5188</td>\n",
       "      <td>-5.0625</td>\n",
       "      <td>-0.4001</td>\n",
       "      <td>-9.7190</td>\n",
       "      <td>-9.3440</td>\n",
       "      <td>-1.6408</td>\n",
       "      <td>-4.1187</td>\n",
       "      <td>8.9380</td>\n",
       "      <td>8.3750</td>\n",
       "      <td>-0.9314</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.0344</td>\n",
       "      <td>7.9155</td>\n",
       "      <td>3.4282</td>\n",
       "      <td>4.2968</td>\n",
       "      <td>6.7968</td>\n",
       "      <td>7.3999</td>\n",
       "      <td>1.8500</td>\n",
       "      <td>5.8219</td>\n",
       "      <td>5.1812</td>\n",
       "      <td>2.8437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3632</th>\n",
       "      <td>0.1749</td>\n",
       "      <td>-1.9060</td>\n",
       "      <td>3.9690</td>\n",
       "      <td>-1.3844</td>\n",
       "      <td>-0.3440</td>\n",
       "      <td>-8.8440</td>\n",
       "      <td>4.1880</td>\n",
       "      <td>-1.5564</td>\n",
       "      <td>5.0593</td>\n",
       "      <td>0.3343</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.0126</td>\n",
       "      <td>2.8344</td>\n",
       "      <td>2.4499</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>-0.4062</td>\n",
       "      <td>1.4375</td>\n",
       "      <td>3.9750</td>\n",
       "      <td>-1.2220</td>\n",
       "      <td>2.8375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>-2.7749</td>\n",
       "      <td>-6.4907</td>\n",
       "      <td>-6.1594</td>\n",
       "      <td>-9.1560</td>\n",
       "      <td>-7.1437</td>\n",
       "      <td>-6.5406</td>\n",
       "      <td>2.2531</td>\n",
       "      <td>-1.5563</td>\n",
       "      <td>-3.7406</td>\n",
       "      <td>-0.6406</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.6938</td>\n",
       "      <td>4.8061</td>\n",
       "      <td>4.9968</td>\n",
       "      <td>-0.1626</td>\n",
       "      <td>2.4187</td>\n",
       "      <td>-0.7750</td>\n",
       "      <td>4.6781</td>\n",
       "      <td>1.7658</td>\n",
       "      <td>0.4595</td>\n",
       "      <td>0.1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>-0.0812</td>\n",
       "      <td>-6.3120</td>\n",
       "      <td>1.2810</td>\n",
       "      <td>-3.5310</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>-5.8120</td>\n",
       "      <td>5.5620</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>-1.1874</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.8156</td>\n",
       "      <td>4.1812</td>\n",
       "      <td>4.1880</td>\n",
       "      <td>3.7280</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>2.1033</td>\n",
       "      <td>2.8156</td>\n",
       "      <td>5.5312</td>\n",
       "      <td>3.8283</td>\n",
       "      <td>4.1219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3635 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0       1       2       3       4       5       6       7       8  \\\n",
       "0    -5.9406 -9.2810 -9.2810 -6.7810  0.8750 -9.6560 -9.0310 -7.4690 -8.7190   \n",
       "1     2.3405  9.9380  9.5310  9.9380  0.4060  3.7190  9.6560 -2.6880  4.3438   \n",
       "2    -9.8440 -3.5750 -7.2190 -2.0310 -9.9380 -9.9690 -9.8750 -9.8120 -9.7810   \n",
       "3    -5.8120 -1.6499 -4.9060 -2.7781  0.5530 -3.8594  1.7031 -0.3687  1.8469   \n",
       "4     1.3157  4.7500  1.8658 -0.4060  1.7937  3.8750  6.2190  1.9220  6.0940   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "3630 -0.7750 -9.8120 -0.0620 -2.8218 -4.1470 -4.8281  2.2718 -2.8782 -1.0125   \n",
       "3631  2.5188 -5.0625 -0.4001 -9.7190 -9.3440 -1.6408 -4.1187  8.9380  8.3750   \n",
       "3632  0.1749 -1.9060  3.9690 -1.3844 -0.3440 -8.8440  4.1880 -1.5564  5.0593   \n",
       "3633 -2.7749 -6.4907 -6.1594 -9.1560 -7.1437 -6.5406  2.2531 -1.5563 -3.7406   \n",
       "3634 -0.0812 -6.3120  1.2810 -3.5310  2.1250 -5.8120  5.5620  0.2218  0.1250   \n",
       "\n",
       "           9  ...     130     131     132     133     134     135     136  \\\n",
       "0    -9.1560  ... -4.5311  1.8968  0.6905 -3.1218  1.2843 -2.6063 -0.1812   \n",
       "1    -9.1250  ...  2.2437  3.1719  5.0251  5.1812  8.2407  5.9311  5.8375   \n",
       "2    -6.8440  ... -4.4186 -3.1156 -1.5655 -5.6250  0.3720 -4.0439 -6.0500   \n",
       "3     0.0593  ... -2.0344  2.1469  2.8875  1.6845  1.2437 -0.0156  1.2595   \n",
       "4     5.4060  ... -0.2844  1.1313  4.0157  3.0344  4.0406  0.5218  4.3594   \n",
       "...      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "3630  0.0688  ... -6.6844  3.0531  2.8687  1.5281  4.5002 -0.1878  2.0031   \n",
       "3631 -0.9314  ... -4.0344  7.9155  3.4282  4.2968  6.7968  7.3999  1.8500   \n",
       "3632  0.3343  ... -4.0126  2.8344  2.4499  2.9312  2.3750 -0.4062  1.4375   \n",
       "3633 -0.6406  ... -4.6938  4.8061  4.9968 -0.1626  2.4187 -0.7750  4.6781   \n",
       "3634 -1.1874  ... -3.8156  4.1812  4.1880  3.7280  3.0750  2.1033  2.8156   \n",
       "\n",
       "         137     138     139  \n",
       "0    -1.3937  1.7625 -0.4092  \n",
       "1     6.3812  1.1687  6.2532  \n",
       "2    -5.5563 -5.4125 -5.5874  \n",
       "3     3.8219  3.1971  5.0249  \n",
       "4     4.0968  3.9250  3.9657  \n",
       "...      ...     ...     ...  \n",
       "3630  4.0908  2.3563  5.0406  \n",
       "3631  5.8219  5.1812  2.8437  \n",
       "3632  3.9750 -1.2220  2.8375  \n",
       "3633  1.7658  0.4595  0.1843  \n",
       "3634  5.5312  3.8283  4.1219  \n",
       "\n",
       "[3635 rows x 140 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(train_mat_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN imputer train RMSE: 0.00\n",
      "KNN imputer valid RMSE: 4.79\n"
     ]
    }
   ],
   "source": [
    "evaluate(train_mat_imp, train_mat, valid_mat, model_name=\"KNN imputer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Finding [nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html)\n",
    "\n",
    "- We can look at nearest neighbours of a query item. \n",
    "- Here our columns are jokes, and users are features for jokes, and we'll have to find nearest neighbours of columns vectors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.9406</td>\n",
       "      <td>-9.2810</td>\n",
       "      <td>-9.2810</td>\n",
       "      <td>-6.7810</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>-9.6560</td>\n",
       "      <td>-9.0310</td>\n",
       "      <td>-7.4690</td>\n",
       "      <td>-8.7190</td>\n",
       "      <td>-9.1560</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.5311</td>\n",
       "      <td>1.8968</td>\n",
       "      <td>0.6905</td>\n",
       "      <td>-3.1218</td>\n",
       "      <td>1.2843</td>\n",
       "      <td>-2.6063</td>\n",
       "      <td>-0.1812</td>\n",
       "      <td>-1.3937</td>\n",
       "      <td>1.7625</td>\n",
       "      <td>-0.4092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.3405</td>\n",
       "      <td>9.9380</td>\n",
       "      <td>9.5310</td>\n",
       "      <td>9.9380</td>\n",
       "      <td>0.4060</td>\n",
       "      <td>3.7190</td>\n",
       "      <td>9.6560</td>\n",
       "      <td>-2.6880</td>\n",
       "      <td>4.3438</td>\n",
       "      <td>-9.1250</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2437</td>\n",
       "      <td>3.1719</td>\n",
       "      <td>5.0251</td>\n",
       "      <td>5.1812</td>\n",
       "      <td>8.2407</td>\n",
       "      <td>5.9311</td>\n",
       "      <td>5.8375</td>\n",
       "      <td>6.3812</td>\n",
       "      <td>1.1687</td>\n",
       "      <td>6.2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.8440</td>\n",
       "      <td>-3.5750</td>\n",
       "      <td>-7.2190</td>\n",
       "      <td>-2.0310</td>\n",
       "      <td>-9.9380</td>\n",
       "      <td>-9.9690</td>\n",
       "      <td>-9.8750</td>\n",
       "      <td>-9.8120</td>\n",
       "      <td>-9.7810</td>\n",
       "      <td>-6.8440</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.4186</td>\n",
       "      <td>-3.1156</td>\n",
       "      <td>-1.5655</td>\n",
       "      <td>-5.6250</td>\n",
       "      <td>0.3720</td>\n",
       "      <td>-4.0439</td>\n",
       "      <td>-6.0500</td>\n",
       "      <td>-5.5563</td>\n",
       "      <td>-5.4125</td>\n",
       "      <td>-5.5874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.8120</td>\n",
       "      <td>-1.6499</td>\n",
       "      <td>-4.9060</td>\n",
       "      <td>-2.7781</td>\n",
       "      <td>0.5530</td>\n",
       "      <td>-3.8594</td>\n",
       "      <td>1.7031</td>\n",
       "      <td>-0.3687</td>\n",
       "      <td>1.8469</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0344</td>\n",
       "      <td>2.1469</td>\n",
       "      <td>2.8875</td>\n",
       "      <td>1.6845</td>\n",
       "      <td>1.2437</td>\n",
       "      <td>-0.0156</td>\n",
       "      <td>1.2595</td>\n",
       "      <td>3.8219</td>\n",
       "      <td>3.1971</td>\n",
       "      <td>5.0249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.3157</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>1.8658</td>\n",
       "      <td>-0.4060</td>\n",
       "      <td>1.7937</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>6.2190</td>\n",
       "      <td>1.9220</td>\n",
       "      <td>6.0940</td>\n",
       "      <td>5.4060</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2844</td>\n",
       "      <td>1.1313</td>\n",
       "      <td>4.0157</td>\n",
       "      <td>3.0344</td>\n",
       "      <td>4.0406</td>\n",
       "      <td>0.5218</td>\n",
       "      <td>4.3594</td>\n",
       "      <td>4.0968</td>\n",
       "      <td>3.9250</td>\n",
       "      <td>3.9657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3630</th>\n",
       "      <td>-0.7750</td>\n",
       "      <td>-9.8120</td>\n",
       "      <td>-0.0620</td>\n",
       "      <td>-2.8218</td>\n",
       "      <td>-4.1470</td>\n",
       "      <td>-4.8281</td>\n",
       "      <td>2.2718</td>\n",
       "      <td>-2.8782</td>\n",
       "      <td>-1.0125</td>\n",
       "      <td>0.0688</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.6844</td>\n",
       "      <td>3.0531</td>\n",
       "      <td>2.8687</td>\n",
       "      <td>1.5281</td>\n",
       "      <td>4.5002</td>\n",
       "      <td>-0.1878</td>\n",
       "      <td>2.0031</td>\n",
       "      <td>4.0908</td>\n",
       "      <td>2.3563</td>\n",
       "      <td>5.0406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3631</th>\n",
       "      <td>2.5188</td>\n",
       "      <td>-5.0625</td>\n",
       "      <td>-0.4001</td>\n",
       "      <td>-9.7190</td>\n",
       "      <td>-9.3440</td>\n",
       "      <td>-1.6408</td>\n",
       "      <td>-4.1187</td>\n",
       "      <td>8.9380</td>\n",
       "      <td>8.3750</td>\n",
       "      <td>-0.9314</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.0344</td>\n",
       "      <td>7.9155</td>\n",
       "      <td>3.4282</td>\n",
       "      <td>4.2968</td>\n",
       "      <td>6.7968</td>\n",
       "      <td>7.3999</td>\n",
       "      <td>1.8500</td>\n",
       "      <td>5.8219</td>\n",
       "      <td>5.1812</td>\n",
       "      <td>2.8437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3632</th>\n",
       "      <td>0.1749</td>\n",
       "      <td>-1.9060</td>\n",
       "      <td>3.9690</td>\n",
       "      <td>-1.3844</td>\n",
       "      <td>-0.3440</td>\n",
       "      <td>-8.8440</td>\n",
       "      <td>4.1880</td>\n",
       "      <td>-1.5564</td>\n",
       "      <td>5.0593</td>\n",
       "      <td>0.3343</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.0126</td>\n",
       "      <td>2.8344</td>\n",
       "      <td>2.4499</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>-0.4062</td>\n",
       "      <td>1.4375</td>\n",
       "      <td>3.9750</td>\n",
       "      <td>-1.2220</td>\n",
       "      <td>2.8375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>-2.7749</td>\n",
       "      <td>-6.4907</td>\n",
       "      <td>-6.1594</td>\n",
       "      <td>-9.1560</td>\n",
       "      <td>-7.1437</td>\n",
       "      <td>-6.5406</td>\n",
       "      <td>2.2531</td>\n",
       "      <td>-1.5563</td>\n",
       "      <td>-3.7406</td>\n",
       "      <td>-0.6406</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.6938</td>\n",
       "      <td>4.8061</td>\n",
       "      <td>4.9968</td>\n",
       "      <td>-0.1626</td>\n",
       "      <td>2.4187</td>\n",
       "      <td>-0.7750</td>\n",
       "      <td>4.6781</td>\n",
       "      <td>1.7658</td>\n",
       "      <td>0.4595</td>\n",
       "      <td>0.1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>-0.0812</td>\n",
       "      <td>-6.3120</td>\n",
       "      <td>1.2810</td>\n",
       "      <td>-3.5310</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>-5.8120</td>\n",
       "      <td>5.5620</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>-1.1874</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.8156</td>\n",
       "      <td>4.1812</td>\n",
       "      <td>4.1880</td>\n",
       "      <td>3.7280</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>2.1033</td>\n",
       "      <td>2.8156</td>\n",
       "      <td>5.5312</td>\n",
       "      <td>3.8283</td>\n",
       "      <td>4.1219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3635 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0       1       2       3       4       5       6       7       8  \\\n",
       "0    -5.9406 -9.2810 -9.2810 -6.7810  0.8750 -9.6560 -9.0310 -7.4690 -8.7190   \n",
       "1     2.3405  9.9380  9.5310  9.9380  0.4060  3.7190  9.6560 -2.6880  4.3438   \n",
       "2    -9.8440 -3.5750 -7.2190 -2.0310 -9.9380 -9.9690 -9.8750 -9.8120 -9.7810   \n",
       "3    -5.8120 -1.6499 -4.9060 -2.7781  0.5530 -3.8594  1.7031 -0.3687  1.8469   \n",
       "4     1.3157  4.7500  1.8658 -0.4060  1.7937  3.8750  6.2190  1.9220  6.0940   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "3630 -0.7750 -9.8120 -0.0620 -2.8218 -4.1470 -4.8281  2.2718 -2.8782 -1.0125   \n",
       "3631  2.5188 -5.0625 -0.4001 -9.7190 -9.3440 -1.6408 -4.1187  8.9380  8.3750   \n",
       "3632  0.1749 -1.9060  3.9690 -1.3844 -0.3440 -8.8440  4.1880 -1.5564  5.0593   \n",
       "3633 -2.7749 -6.4907 -6.1594 -9.1560 -7.1437 -6.5406  2.2531 -1.5563 -3.7406   \n",
       "3634 -0.0812 -6.3120  1.2810 -3.5310  2.1250 -5.8120  5.5620  0.2218  0.1250   \n",
       "\n",
       "           9  ...     130     131     132     133     134     135     136  \\\n",
       "0    -9.1560  ... -4.5311  1.8968  0.6905 -3.1218  1.2843 -2.6063 -0.1812   \n",
       "1    -9.1250  ...  2.2437  3.1719  5.0251  5.1812  8.2407  5.9311  5.8375   \n",
       "2    -6.8440  ... -4.4186 -3.1156 -1.5655 -5.6250  0.3720 -4.0439 -6.0500   \n",
       "3     0.0593  ... -2.0344  2.1469  2.8875  1.6845  1.2437 -0.0156  1.2595   \n",
       "4     5.4060  ... -0.2844  1.1313  4.0157  3.0344  4.0406  0.5218  4.3594   \n",
       "...      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "3630  0.0688  ... -6.6844  3.0531  2.8687  1.5281  4.5002 -0.1878  2.0031   \n",
       "3631 -0.9314  ... -4.0344  7.9155  3.4282  4.2968  6.7968  7.3999  1.8500   \n",
       "3632  0.3343  ... -4.0126  2.8344  2.4499  2.9312  2.3750 -0.4062  1.4375   \n",
       "3633 -0.6406  ... -4.6938  4.8061  4.9968 -0.1626  2.4187 -0.7750  4.6781   \n",
       "3634 -1.1874  ... -3.8156  4.1812  4.1880  3.7280  3.0750  2.1033  2.8156   \n",
       "\n",
       "         137     138     139  \n",
       "0    -1.3937  1.7625 -0.4092  \n",
       "1     6.3812  1.1687  6.2532  \n",
       "2    -5.5563 -5.4125 -5.5874  \n",
       "3     3.8219  3.1971  5.0249  \n",
       "4     4.0968  3.9250  3.9657  \n",
       "...      ...     ...     ...  \n",
       "3630  4.0908  2.3563  5.0406  \n",
       "3631  5.8219  5.1812  2.8437  \n",
       "3632  3.9750 -1.2220  2.8375  \n",
       "3633  1.7658  0.4595  0.1843  \n",
       "3634  5.5312  3.8283  4.1219  \n",
       "\n",
       "[3635 rows x 140 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(train_mat_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### $k$-nearest neighbours on a query joke\n",
    "- Let's transpose the matrix.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "item_user_mat = train_mat_imp.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>jokeId</th>\n",
       "      <th>jokeText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A man visits the doctor. The doctor says \"I have bad news for you.You have\\ncancer and Alzheimer's disease\". \\nThe man replies \"Well,thank God I don't have cancer!\"\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>This couple had an excellent relationship going until one day he came home\\nfrom work to find his girlfriend packing. He asked her why she was leaving him\\nand she told him that she had heard awful things about him. \\n\\n\"What could they possibly have said to make you move out?\" \\n\\n\"They told me that you were a pedophile.\" \\n\\nHe replied, \"That's an awfully big word for a ten year old.\" \\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Q. What's 200 feet long and has 4 teeth? \\n\\nA. The front row at a Willie Nelson Concert.\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Q. What's the difference between a man and a toilet? \\n\\nA. A toilet doesn't follow you around after you use it.\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Q.\\tWhat's O. J. Simpson's Internet address? \\nA.\\tSlash, slash, backslash, slash, slash, escape.\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jokeId  \\\n",
       "0  1        \n",
       "1  2        \n",
       "2  3        \n",
       "3  4        \n",
       "4  5        \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                   jokeText  \n",
       "0  A man visits the doctor. The doctor says \"I have bad news for you.You have\\ncancer and Alzheimer's disease\". \\nThe man replies \"Well,thank God I don't have cancer!\"\\n                                                                                                                                                                                                                                    \n",
       "1  This couple had an excellent relationship going until one day he came home\\nfrom work to find his girlfriend packing. He asked her why she was leaving him\\nand she told him that she had heard awful things about him. \\n\\n\"What could they possibly have said to make you move out?\" \\n\\n\"They told me that you were a pedophile.\" \\n\\nHe replied, \"That's an awfully big word for a ten year old.\" \\n  \n",
       "2  Q. What's 200 feet long and has 4 teeth? \\n\\nA. The front row at a Willie Nelson Concert.\\n                                                                                                                                                                                                                                                                                                               \n",
       "3  Q. What's the difference between a man and a toilet? \\n\\nA. A toilet doesn't follow you around after you use it.\\n                                                                                                                                                                                                                                                                                        \n",
       "4  Q.\\tWhat's O. J. Simpson's Internet address? \\nA.\\tSlash, slash, backslash, slash, slash, escape.\\n                                                                                                                                                                                                                                                                                                       "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jokes_df = pd.read_csv(\"data/jester_items.csv\")\n",
    "jokes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "id_joke_map = dict(zip(jokes_df.jokeId, jokes_df.jokeText))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query joke:  Q: If a person who speaks three languages is called \"tri-lingual,\" and\n",
      "a person who speaks two languages is called \"bi-lingual,\" what do call\n",
      "a person who only speaks one language?\n",
      "\n",
      "A: American! \n",
      "\n"
     ]
    },
    {
     "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>top recommendations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q: What is the difference between George  Washington, Richard Nixon,\\nand Bill Clinton?\\n\\nA: Washington couldn't tell a lie, Nixon couldn't   tell the truth, and\\nClinton doesn't know the difference.\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A man in a hot air balloon realized he was lost. He reduced altitude and spotted a woman below. He descended a bit more and shouted, \"Excuse me, can you help me? I promised a friend I would meet him an hour ago, but I don't know where I am.\" The woman below replied, \"You are in a hot air balloon hovering approximately 30 feet above the ground. You are between 40 and 41 degrees north latitude and between 59 and 60 degrees west longitude.\" \"You must be an engineer,\" said the balloonist. \"I am,\" replied the woman. \"How did you know?\" \"Well,\" answered the balloonist, \"everything you told me is technically correct, but I have no idea what to make of your information, and the fact is, I am still lost. Frankly, you've not been much help so far.\" The woman below responded, \"You must be in management.\" \"I am,\" replied the balloonist, \"but how did you know?\" \"Well,\" said the woman, \"you don't know where you are or where you are going. You have risen to where you are due to a large quantity of hot air. You made a promise that you have no idea how to keep, and you expect people beneath you to solve your problems. The fact is, you are in exactly the same position you were in before we met, but now, somehow, it's my fault!\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>If pro- is the opposite of con- then congress must be the opposite\\nof progress.\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arnold Swartzeneger and Sylvester Stallone are making a movie about\\nthe lives of the great composers.  \\nStallone says \"I want to be Mozart.\" \\nSwartzeneger says: \"In that case...  I'll be Bach.\"\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             top recommendations\n",
       "0  Q: What is the difference between George  Washington, Richard Nixon,\\nand Bill Clinton?\\n\\nA: Washington couldn't tell a lie, Nixon couldn't   tell the truth, and\\nClinton doesn't know the difference.\\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
       "1  A man in a hot air balloon realized he was lost. He reduced altitude and spotted a woman below. He descended a bit more and shouted, \"Excuse me, can you help me? I promised a friend I would meet him an hour ago, but I don't know where I am.\" The woman below replied, \"You are in a hot air balloon hovering approximately 30 feet above the ground. You are between 40 and 41 degrees north latitude and between 59 and 60 degrees west longitude.\" \"You must be an engineer,\" said the balloonist. \"I am,\" replied the woman. \"How did you know?\" \"Well,\" answered the balloonist, \"everything you told me is technically correct, but I have no idea what to make of your information, and the fact is, I am still lost. Frankly, you've not been much help so far.\" The woman below responded, \"You must be in management.\" \"I am,\" replied the balloonist, \"but how did you know?\" \"Well,\" said the woman, \"you don't know where you are or where you are going. You have risen to where you are due to a large quantity of hot air. You made a promise that you have no idea how to keep, and you expect people beneath you to solve your problems. The fact is, you are in exactly the same position you were in before we met, but now, somehow, it's my fault!\"\n",
       "2  If pro- is the opposite of con- then congress must be the opposite\\nof progress.\\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           \n",
       "3  Arnold Swartzeneger and Sylvester Stallone are making a movie about\\nthe lives of the great composers.  \\nStallone says \"I want to be Mozart.\" \\nSwartzeneger says: \"In that case...  I'll be Bach.\"\\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "\n",
    "def get_topk_recommendations(X, query_ind=0, metric=\"cosine\", k=5):\n",
    "    query_idx = item_inverse_mapper[query_ind]\n",
    "    model = NearestNeighbors(n_neighbors=k, metric=\"cosine\")\n",
    "    model.fit(X)\n",
    "    neigh_ind = model.kneighbors([X[query_ind]], k, return_distance=False).flatten()\n",
    "    neigh_ind = np.delete(neigh_ind, np.where(query_ind == query_ind))\n",
    "    recs = [id_joke_map[item_inverse_mapper[i]] for i in neigh_ind]\n",
    "    print(\"Query joke: \", id_joke_map[query_idx])\n",
    "\n",
    "    return pd.DataFrame(data=recs, columns=[\"top recommendations\"])\n",
    "\n",
    "\n",
    "get_topk_recommendations(item_user_mat, query_ind=8, metric=\"cosine\", k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Question**\n",
    "- Instead of imputation, what would be the consequences if we replace `nan` with zeros so that we can calculate distances between vectors? \n",
    "\n",
    "<br><br>\n",
    "**Answer**\n",
    "\n",
    "It's not a good idea replace ratings with 0, because 0 can be an actual rating value in our case. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What to do with predictions? \n",
    "- Once you have predictions, you can sort them based on ratings and recommend items with highest ratings.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Collaborative filtering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Collaborative filtering \n",
    "- One of the most popular approach for recommendation systems. \n",
    "- Approach used by the winning entry (and most of the entries) in the Netflix competition. \n",
    "- An unsupervised approach\n",
    "    - Only uses the user-item interactions given in the ratings matrix. \n",
    "- **Intuition**\n",
    "    - We may have similar users and similar items which can help us predict missing entries. \n",
    "    - Leverage social information to provide recommendations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Problem \n",
    "\n",
    "- Given a utility matrix with many missing entries, how can we predict missing ratings?  \n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} \n",
    "? & ? & \\checkmark  & ? & \\checkmark\\\\\n",
    "\\checkmark & ? & ?  & ? & ?\\\\\n",
    "? & \\checkmark & \\checkmark  & ? & \\checkmark\\\\\n",
    "? & ? & ?  & ? & ?\\\\\n",
    "? & ? & ? & \\checkmark & ?\\\\\n",
    "? & \\checkmark & \\checkmark  & ? & \\checkmark\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "> Note: rating prediction $\\neq$ Classification or regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Classification or regression\n",
    "\n",
    "- We have $X$ and targets for some rows in $X$. \n",
    "- We want to predict the last column (target column).  \n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} \n",
    "\\checkmark & \\checkmark & \\checkmark  & \\checkmark & \\checkmark\\\\\n",
    "\\checkmark & \\checkmark & \\checkmark  & \\checkmark & \\checkmark\\\\\n",
    "\\checkmark & \\checkmark & \\checkmark  & \\checkmark & \\checkmark\\\\\n",
    "\\checkmark & \\checkmark & \\checkmark  & \\checkmark & ?\\\\\n",
    "\\checkmark & \\checkmark & \\checkmark  & \\checkmark & ?\\\\\n",
    "\\checkmark & \\checkmark & \\checkmark  & \\checkmark & ?\\\\\n",
    "\\end{bmatrix}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Rating prediction \n",
    "\n",
    "- Ratings data has many missing values in the utility matrix. There is no special target column. We want to predict the missing entries in the matrix. \n",
    "- Since our goal is to **predict** ratings, usually the utility matrix is referred to as $Y$ matrix. \n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} \n",
    "? & ? & \\checkmark  & ? & \\checkmark\\\\\n",
    "\\checkmark & ? & ?  & ? & ?\\\\\n",
    "? & \\checkmark & \\checkmark  & ? & \\checkmark\\\\\n",
    "? & ? & ?  & ? & ?\\\\\n",
    "? & ? & ? & \\checkmark & ?\\\\\n",
    "? & \\checkmark & \\checkmark  & ? & \\checkmark\n",
    "\\end{bmatrix}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I'll show you how to use a package which implements collaborative filtering. \n",
    "- We don't have sufficient background to understand how it works under the hood.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rating prediction using the surprise package\n",
    "\n",
    "- We'll be using a package called [Surprise](https://surprise.readthedocs.io/en/stable/index.html). \n",
    "- The collaborative filtering algorithm we use in this package is called `SVD`. \n",
    "\n",
    "```\n",
    "conda install -c conda-forge scikit-surprise\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's try it out on our Jester dataset utility matrix.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import surprise\n",
    "from surprise import SVD, Dataset, Reader, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader()\n",
    "data = Dataset.load_from_df(ratings, reader)  # Load the data\n",
    "\n",
    "# I'm being sloppy here. Probably there is a way to create validset from our already split data.\n",
    "trainset, validset = surprise.model_selection.train_test_split(\n",
    "    data, test_size=0.2, random_state=42\n",
    ")  # Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 5.2893\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5.28926338380112"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 10\n",
    "algo = SVD(n_factors=k, random_state=42)\n",
    "algo.fit(trainset)\n",
    "svd_preds = algo.test(validset)\n",
    "accuracy.rmse(svd_preds, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- No big improvement over the global baseline (RMSE=5.77). \n",
    "- Probably because we are only considering a sample. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cross-validation for recommender systems\n",
    "\n",
    "- We can also carry out cross-validation and grid search with this package. \n",
    "- Let's look at an example of cross-validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
      "RMSE (testset)    5.2871  5.2903  5.2910  5.2473  5.3085  5.2849  0.0202  \n",
      "MAE (testset)     4.1977  4.2076  4.2179  4.1632  4.2330  4.2039  0.0234  \n",
      "Fit time          2.65    2.60    2.62    2.21    2.29    2.47    0.18    \n",
      "Test time         0.16    0.15    0.52    0.15    0.34    0.26    0.15    \n"
     ]
    }
   ],
   "source": [
    "from surprise.model_selection import cross_validate\n",
    "\n",
    "results = cross_validate(algo, data, measures=[\"RMSE\", \"MAE\"], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test_rmse    5.284852\n",
       "test_mae     4.203891\n",
       "fit_time     2.473700\n",
       "test_time    0.261754\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Jester dataset is available as one of the built-in datasets in this package and you can load it as follows and run cross-validation as follows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Dataset.load_builtin(\"jester\")\n",
    "\n",
    "results = cross_validate(algo, data, measures=[\"RMSE\", \"MAE\"], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(results).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What is content-based filtering? \n",
    "\n",
    "- Supervised machine learning approach\n",
    "- In collaborative filtering we assumed that we only have ratings data. \n",
    "- Usually there is some information on items and users available. \n",
    "- Examples\n",
    "    - Netflix can describe movies as action, romance, comedy, documentaries. \n",
    "    - Amazon could describe books according to topics: math, languages, history. \n",
    "    - Tinder could describe people according to age, location, employment.\n",
    "- Can we use this information to predict ratings in the utility matrix?   \n",
    "    - Yes!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hybrid filtering\n",
    "\n",
    "- Combining advantages of collaborative filtering and content-based filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Final comments and summary <a name=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Formulating the problem of recommender systems \n",
    "\n",
    "- We are given ratings data. \n",
    "- We use this data to create **utility matrix** which encodes interactions between users and items. \n",
    "- The utility matrix has many missing entries. \n",
    "- We defined recommendation systems problem as **matrix completion problem**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Collaborative filtering \n",
    "\n",
    "- Collaborative filtering is an unsupervised approach to recommendation systems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Evaluation \n",
    "\n",
    "- We split the data similar to supervised systems. \n",
    "- We evaluate recommendation systems using traditional regression metrics such as MSE or RMSE. \n",
    "- But real evaluation of recommender system can be very tricky because there is no ground truth. \n",
    "- We have been using RMSE due to the lack of a better measure.  \n",
    "- What we actually want to measure is the interest that our user has in the recommended items. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Precision at N\n",
    "\n",
    "- Another popular evaluation metric from information retrieval is precision at N. \n",
    "- Suppose you recommend top-N items, then we look at the proportion of the items that are relevant. \n",
    "$$\\text{Precision at } N = \\frac{r}{N}$$\n",
    "- For example, $r$ could be the number of elements in your list that the user rated.\n",
    "- In this way, you would know that your system is recommending items that the user had interest in!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Some thoughts on recommendation systems  \n",
    "- Be mindful of the consequences recommendation systems. \n",
    "- Companies such as Amazon,  Netflix, Facebook, Google (YouTube), which extensively use recommendation systems, are profit-driven and so they design these systems to maximize user attention; their focus is not necessarily human well-being. \n",
    "- There are tons of news and research articles on serious consequences of recommendation systems.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Some thoughts on recommendation systems  \n",
    "\n",
    "- Some weird stories which got media attention.   \n",
    "[How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did](https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/?sh=3171af136668)\n",
    "- More serious consequences are in political contexts. \n",
    "    - [Facebook Admits It Was Used to Incite Violence in Myanmar](https://www.nytimes.com/2018/11/06/technology/myanmar-facebook.html)\n",
    "    - [YouTube Extremism and the Long Tail](https://www.theatlantic.com/politics/archive/2018/03/youtube-extremism-and-the-long-tail/555350/)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### My advice\n",
    "\n",
    "- Ask hard and uncomfortable questions to yourself (and to your employer if possible) before implementing and deploying such systems.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Resources \n",
    "\n",
    "- [Collaborative filtering for recommendation systems in Python, by N. Hug](https://www.youtube.com/watch?v=z0dx-YckFko)\n",
    "- [An interesting talk: The paradox of choice](https://www.ted.com/talks/barry_schwartz_the_paradox_of_choice)\n",
    "- [How Netflix’s Recommendations System Works](https://help.netflix.com/en/node/100639)\n",
    "- [Hands on Recommendation Systems with Python](https://learning.oreilly.com/library/view/hands-on-recommendation-systems/9781788993753/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions for class discussion <a name=\"questions\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### True/False questions\n",
    "\n",
    "1. To evaluate rating predictions we split the data. The shapes of validation utility matrix and train utility matrix are the same. \n",
    "2. It would be reasonable to impute missing values in the utility matrix by taking the average of the ratings given to an item by similar users.  \n",
    "3. You might be able to predict missing ratings in the utility matrix using supervised machine learning methods. \n",
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Discuss a strange/scary experience you had with recommendation systems. "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda env:cpsc330]",
   "language": "python",
   "name": "conda-env-cpsc330-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}