{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "edd718da-1295-49c4-b556-3cc7b718f93c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Preparation and Quality\n",
    "Lecture Data Engineering and Analytics<br>\n",
    "Eva Zangerle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b126eda-5b79-4531-b8ea-72898d09dc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required packages\n",
    "import json\n",
    "import os\n",
    "from pprint import pprint\n",
    "from sys import getsizeof\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import sklearn.datasets\n",
    "import sklearn.preprocessing as preproc\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap\n",
    "from scipy import stats\n",
    "from sklearn import linear_model, preprocessing\n",
    "from sklearn.cluster import DBSCAN, KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_extraction import FeatureHasher, text\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import pairwise_distances_argmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5406f6f3-1c06-4f3b-aaaf-9ac6f2967729",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a870c325-706d-4c07-bb57-e3b84322e6e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Eva Zangerle\n",
      "\n",
      "Last updated: 2021-12-11 14:18:48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Eva Zangerle\" -u -d -t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de6ba96a-a20d-4c9e-bb38-46b80ab6ae1f",
   "metadata": {},
   "source": [
    "## Enhancing Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05832b33-0bb8-4496-a2a1-f23f8c7927b1",
   "metadata": {},
   "source": [
    "### Scaling and Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b0f558-ce94-4349-a525-d65e09db72f8",
   "metadata": {},
   "source": [
    "The following example is based on the online news popularity dataset (taken from the UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/online+news+popularity). The dataset provides set of features about articles published by Mashable in a period of two years and was originally used for predicting popularity of articles in social networks. In the following example, we are primarily interested in the word count for each article (`n_tokens_content`) and showcase the results of different scaling methods. This example is adapted from the FeatEng book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b966c0f-b892-408f-935f-d6fcb26db76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "news = pd.read_csv(\n",
    "    os.path.join(data_dir, \"OnlineNewsPopularity.csv\"),\n",
    "    delimiter=\", \",\n",
    "    engine=\"python\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071c9705-c8a4-486b-b124-0a55583d412f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> We use `, ` as a delimiter here. If we would use only the comma as a delimiter, we would be able to read the dataframe, but for instance, accessing a specific field would fails as the key is not recognized due to the trailing space. Furthermore, we specify the python parsing engine to allow separators of more than one character.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70e9a7e8-a3da-43be-b561-ac73ed655728",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>timedelta</th>\n",
       "      <th>n_tokens_title</th>\n",
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       "      <th>...</th>\n",
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       "      <th>0</th>\n",
       "      <td>http://mashable.com/2013/01/07/amazon-instant-...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.013699</td>\n",
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       "      <td>0.230769</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791946</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.913725</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.043137</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.286915</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.118750</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.100000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>711</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.575130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.393365</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.056872</td>\n",
       "      <td>0.009479</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>-0.800</td>\n",
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       "      <td>0.500000</td>\n",
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       "      <td>1500</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>0.503788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.665635</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.404896</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.041431</td>\n",
       "      <td>0.020716</td>\n",
       "      <td>0.666667</td>\n",
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       "      <td>0.80</td>\n",
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       "      <td>0.500000</td>\n",
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       "      <td>1200</td>\n",
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       "    <tr>\n",
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       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>0.415646</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.540890</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.682836</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.074627</td>\n",
       "      <td>0.012127</td>\n",
       "      <td>0.860215</td>\n",
       "      <td>0.139785</td>\n",
       "      <td>0.411127</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>-0.500</td>\n",
       "      <td>-0.050000</td>\n",
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       "      <td>0.136364</td>\n",
       "      <td>505</td>\n",
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       "    <tr>\n",
       "      <th>39639</th>\n",
       "      <td>http://mashable.com/2014/12/27/samsung-app-aut...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.529052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.684783</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.523121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037572</td>\n",
       "      <td>0.014451</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.333791</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.260000</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1800</td>\n",
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       "    <tr>\n",
       "      <th>39640</th>\n",
       "      <td>http://mashable.com/2014/12/27/seth-rogen-jame...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.696296</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.885057</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.405488</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039634</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.374825</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.211111</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900</td>\n",
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       "    <tr>\n",
       "      <th>39641</th>\n",
       "      <td>http://mashable.com/2014/12/27/son-pays-off-mo...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>0.516355</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.644128</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.076923</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>0.024887</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.307273</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.356439</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>1900</td>\n",
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       "    <tr>\n",
       "      <th>39642</th>\n",
       "      <td>http://mashable.com/2014/12/27/ukraine-blasts/</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>0.539493</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.692661</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.975073</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.236851</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.205246</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.012500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39643</th>\n",
       "      <td>http://mashable.com/2014/12/27/youtube-channel...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.701987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.471338</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063694</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.247338</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39644 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url  timedelta  \\\n",
       "0      http://mashable.com/2013/01/07/amazon-instant-...      731.0   \n",
       "1      http://mashable.com/2013/01/07/ap-samsung-spon...      731.0   \n",
       "2      http://mashable.com/2013/01/07/apple-40-billio...      731.0   \n",
       "3      http://mashable.com/2013/01/07/astronaut-notre...      731.0   \n",
       "4       http://mashable.com/2013/01/07/att-u-verse-apps/      731.0   \n",
       "...                                                  ...        ...   \n",
       "39639  http://mashable.com/2014/12/27/samsung-app-aut...        8.0   \n",
       "39640  http://mashable.com/2014/12/27/seth-rogen-jame...        8.0   \n",
       "39641  http://mashable.com/2014/12/27/son-pays-off-mo...        8.0   \n",
       "39642     http://mashable.com/2014/12/27/ukraine-blasts/        8.0   \n",
       "39643  http://mashable.com/2014/12/27/youtube-channel...        8.0   \n",
       "\n",
       "       n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
       "0                12.0             219.0         0.663594               1.0   \n",
       "1                 9.0             255.0         0.604743               1.0   \n",
       "2                 9.0             211.0         0.575130               1.0   \n",
       "3                 9.0             531.0         0.503788               1.0   \n",
       "4                13.0            1072.0         0.415646               1.0   \n",
       "...               ...               ...              ...               ...   \n",
       "39639            11.0             346.0         0.529052               1.0   \n",
       "39640            12.0             328.0         0.696296               1.0   \n",
       "39641            10.0             442.0         0.516355               1.0   \n",
       "39642             6.0             682.0         0.539493               1.0   \n",
       "39643            10.0             157.0         0.701987               1.0   \n",
       "\n",
       "       n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  \\\n",
       "0                      0.815385        4.0             2.0       1.0   \n",
       "1                      0.791946        3.0             1.0       1.0   \n",
       "2                      0.663866        3.0             1.0       1.0   \n",
       "3                      0.665635        9.0             0.0       1.0   \n",
       "4                      0.540890       19.0            19.0      20.0   \n",
       "...                         ...        ...             ...       ...   \n",
       "39639                  0.684783        9.0             7.0       1.0   \n",
       "39640                  0.885057        9.0             7.0       3.0   \n",
       "39641                  0.644128       24.0             1.0      12.0   \n",
       "39642                  0.692661       10.0             1.0       1.0   \n",
       "39643                  0.846154        1.0             1.0       0.0   \n",
       "\n",
       "       num_videos  average_token_length  num_keywords  \\\n",
       "0             0.0              4.680365           5.0   \n",
       "1             0.0              4.913725           4.0   \n",
       "2             0.0              4.393365           6.0   \n",
       "3             0.0              4.404896           7.0   \n",
       "4             0.0              4.682836           7.0   \n",
       "...           ...                   ...           ...   \n",
       "39639         1.0              4.523121           8.0   \n",
       "39640        48.0              4.405488           7.0   \n",
       "39641         1.0              5.076923           8.0   \n",
       "39642         0.0              4.975073           5.0   \n",
       "39643         2.0              4.471338           4.0   \n",
       "\n",
       "       data_channel_is_lifestyle  data_channel_is_entertainment  ...  \\\n",
       "0                            0.0                            1.0  ...   \n",
       "1                            0.0                            0.0  ...   \n",
       "2                            0.0                            0.0  ...   \n",
       "3                            0.0                            1.0  ...   \n",
       "4                            0.0                            0.0  ...   \n",
       "...                          ...                            ...  ...   \n",
       "39639                        0.0                            0.0  ...   \n",
       "39640                        0.0                            0.0  ...   \n",
       "39641                        0.0                            0.0  ...   \n",
       "39642                        0.0                            0.0  ...   \n",
       "39643                        0.0                            1.0  ...   \n",
       "\n",
       "       global_rate_positive_words  global_rate_negative_words  \\\n",
       "0                        0.045662                    0.013699   \n",
       "1                        0.043137                    0.015686   \n",
       "2                        0.056872                    0.009479   \n",
       "3                        0.041431                    0.020716   \n",
       "4                        0.074627                    0.012127   \n",
       "...                           ...                         ...   \n",
       "39639                    0.037572                    0.014451   \n",
       "39640                    0.039634                    0.009146   \n",
       "39641                    0.033937                    0.024887   \n",
       "39642                    0.020528                    0.023460   \n",
       "39643                    0.063694                    0.012739   \n",
       "\n",
       "       rate_positive_words  rate_negative_words  avg_positive_polarity  \\\n",
       "0                 0.769231             0.230769               0.378636   \n",
       "1                 0.733333             0.266667               0.286915   \n",
       "2                 0.857143             0.142857               0.495833   \n",
       "3                 0.666667             0.333333               0.385965   \n",
       "4                 0.860215             0.139785               0.411127   \n",
       "...                    ...                  ...                    ...   \n",
       "39639             0.722222             0.277778               0.333791   \n",
       "39640             0.812500             0.187500               0.374825   \n",
       "39641             0.576923             0.423077               0.307273   \n",
       "39642             0.466667             0.533333               0.236851   \n",
       "39643             0.833333             0.166667               0.247338   \n",
       "\n",
       "       min_positive_polarity  max_positive_polarity  avg_negative_polarity  \\\n",
       "0                   0.100000                   0.70              -0.350000   \n",
       "1                   0.033333                   0.70              -0.118750   \n",
       "2                   0.100000                   1.00              -0.466667   \n",
       "3                   0.136364                   0.80              -0.369697   \n",
       "4                   0.033333                   1.00              -0.220192   \n",
       "...                      ...                    ...                    ...   \n",
       "39639               0.100000                   0.75              -0.260000   \n",
       "39640               0.136364                   0.70              -0.211111   \n",
       "39641               0.136364                   0.50              -0.356439   \n",
       "39642               0.062500                   0.50              -0.205246   \n",
       "39643               0.100000                   0.50              -0.200000   \n",
       "\n",
       "       min_negative_polarity  max_negative_polarity  title_subjectivity  \\\n",
       "0                     -0.600              -0.200000            0.500000   \n",
       "1                     -0.125              -0.100000            0.000000   \n",
       "2                     -0.800              -0.133333            0.000000   \n",
       "3                     -0.600              -0.166667            0.000000   \n",
       "4                     -0.500              -0.050000            0.454545   \n",
       "...                      ...                    ...                 ...   \n",
       "39639                 -0.500              -0.125000            0.100000   \n",
       "39640                 -0.400              -0.100000            0.300000   \n",
       "39641                 -0.800              -0.166667            0.454545   \n",
       "39642                 -0.500              -0.012500            0.000000   \n",
       "39643                 -0.200              -0.200000            0.333333   \n",
       "\n",
       "       title_sentiment_polarity  abs_title_subjectivity  \\\n",
       "0                     -0.187500                0.000000   \n",
       "1                      0.000000                0.500000   \n",
       "2                      0.000000                0.500000   \n",
       "3                      0.000000                0.500000   \n",
       "4                      0.136364                0.045455   \n",
       "...                         ...                     ...   \n",
       "39639                  0.000000                0.400000   \n",
       "39640                  1.000000                0.200000   \n",
       "39641                  0.136364                0.045455   \n",
       "39642                  0.000000                0.500000   \n",
       "39643                  0.250000                0.166667   \n",
       "\n",
       "       abs_title_sentiment_polarity  shares  \n",
       "0                          0.187500     593  \n",
       "1                          0.000000     711  \n",
       "2                          0.000000    1500  \n",
       "3                          0.000000    1200  \n",
       "4                          0.136364     505  \n",
       "...                             ...     ...  \n",
       "39639                      0.000000    1800  \n",
       "39640                      1.000000    1900  \n",
       "39641                      0.136364    1900  \n",
       "39642                      0.000000    1100  \n",
       "39643                      0.250000    1300  \n",
       "\n",
       "[39644 rows x 61 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at data\n",
    "news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "280cf353-a469-4ea1-94f8-d74173600310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = news[\"n_tokens_content\"].hist()\n",
    "fig.set_xlabel(\"Word Count\")\n",
    "fig.set_ylabel(\"Articles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a88dfdf-9fae-44e9-8ba1-857f965b31c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# min max scaling\n",
    "news[\"minmax\"] = preproc.minmax_scale(news[\"n_tokens_content\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "975f64b2-33d6-4a3a-bbcf-b46c6c3c903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardization\n",
    "news[\"standardized\"] = preproc.StandardScaler().fit_transform(\n",
    "    news[[\"n_tokens_content\"]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9ce7284-8c2a-4d95-9962-c5eb867c3145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# l2 normalization\n",
    "news[\"normalized\"] = preproc.normalize(news[[\"n_tokens_content\"]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4bab441-0d1d-402c-8141-d81d49c66a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare different results\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(10, 15))\n",
    "# fig.tight_layout();\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_xlabel(\"Article word count\")\n",
    "ax1.set_ylabel(\"Number of articles\")\n",
    "news[\"minmax\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_xlabel(\"Min-max scaled word count\")\n",
    "ax2.set_ylabel(\"Number of articles\")\n",
    "news[\"standardized\"].hist(ax=ax3, bins=100)\n",
    "ax3.set_xlabel(\"Standardized word count\")\n",
    "ax3.set_ylabel(\"Number of articles\")\n",
    "news[\"normalized\"].hist(ax=ax4, bins=100)\n",
    "ax4.set_xlabel(\"L2-normalized word count\")\n",
    "ax4.set_ylabel(\"Number of articles\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fef108e-a831-4454-a8be-f9bb90bdd0d9",
   "metadata": {},
   "source": [
    "### Power Transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c154449a-6dc6-4707-b3fd-39ea0c2ae3e3",
   "metadata": {},
   "source": [
    "For investigating power transforms, we will look at an example we already discussed during our exploratory data analysis: the count of ratings per movie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2eba9b9-c768-46f7-9171-f94aa2c12896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 855598 entries, 0 to 855597\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   userID       855598 non-null  int64  \n",
      " 1   movieID      855598 non-null  int64  \n",
      " 2   rating       855598 non-null  float64\n",
      " 3   date_day     855598 non-null  int64  \n",
      " 4   date_month   855598 non-null  int64  \n",
      " 5   date_year    855598 non-null  int64  \n",
      " 6   date_hour    855598 non-null  int64  \n",
      " 7   date_minute  855598 non-null  int64  \n",
      " 8   date_second  855598 non-null  int64  \n",
      "dtypes: float64(1), int64(8)\n",
      "memory usage: 58.7 MB\n"
     ]
    }
   ],
   "source": [
    "# read in rating data\n",
    "ratings = pd.read_csv(\n",
    "    os.path.join(data_dir, \"hetrec/user_ratedmovies.dat\"), delimiter=\"\\t\"\n",
    ")\n",
    "ratings.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "513fc696-bc60-462c-9166-8a5f53f578b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# asymmetric, skewed distribution\n",
    "# number of ratings per movie\n",
    "rating_counts = ratings.groupby(\"movieID\")[\"rating\"].agg(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "061330ae-f08f-4b3f-8059-f7a575a2e3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of number of movies with given number of ratings\n",
    "# (binned value counts)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "axes[0].hist(rating_counts, bins=100)\n",
    "axes[0].set_ylabel(\"Number of Movies\")\n",
    "axes[0].set_xlabel(\"Number of Ratings\")\n",
    "axes[1].hist(rating_counts, log=True, bins=100)\n",
    "axes[1].set_ylabel(\"Number of Movies (log)\")\n",
    "axes[1].set_xlabel(\"Number of Ratings\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13a1272-f61c-486d-bd0a-7ad95dcd627e",
   "metadata": {},
   "source": [
    "Another example is the number of words in an article as already shown previously as part of the news popularity dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e89f79a5-ecfd-4d19-8499-9ed7b650c745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift + 1 due to log(0)\n",
    "news[\"log_n_tokens_content\"] = np.log10(news[\"n_tokens_content\"] + 1)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_xlabel(\"Number of Words in Article\")\n",
    "ax1.set_ylabel(\"Number of Articles\")\n",
    "news[\"log_n_tokens_content\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_xlabel(\"Log of Number of Words\")\n",
    "ax2.set_ylabel(\"Number of Articles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8dfed8b5-cf59-4186-8f22-39b0013bf844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38045297261832045"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# box-cox transform\n",
    "# again, +1 as boxcox expects data to be positive\n",
    "# log transform\n",
    "news[\"n_tokens_content_lmbda0\"] = stats.boxcox(\n",
    "    news[\"n_tokens_content\"] + 1, lmbda=0\n",
    ")\n",
    "\n",
    "# as close to normal distribution as possible (optimal box-cox transform)\n",
    "# If the lmbda parameter is None, the second returned argument\n",
    "# is the lambda that maximizes the log-likelihood function.\n",
    "values, lambda_param = stats.boxcox(news[\"n_tokens_content\"] + 1)\n",
    "news[\"n_tokens_content_opt\"] = values\n",
    "lambda_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "792ae313-f920-4ddd-8f2d-d5fd6921ba24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sqqr3TX9jVa0D1gFMTEzU6tWrdzGEwbz7nAt4x+XeJWqUXHfc6h8837hxI21/htQdz+9o8/yOrnE8t4NmGodX1WFJvgxQVbcl2XO+g6mq04DT5nu7kiRJi9mgCdk9zbivgh9MFHvfbuz3BmD/vuX9mrKBJFkDrFm1atVuhKBx1T/Q/+RDt3H8DAP/wcH/kqSFM+i0F6cBHwUeleQtwOeAt+7Gfi8DDkxyQNPSdgxw4aBvrqoNVbV22bJlc68sSZK0yM3ZQpbkAcDXgNcDz6Y35cWLq+qqQXaQ5FxgNbBvks3Am6rqzCQnAZfSm/birKq6cteqILXDKTMkSQtlzoSsqu5LcnpVPRX46s7uoKqOnaX8EuCSnd0e2GUpSZJGy6Bdlp9K8vPN9Beds8tSkiSNkkETsl8BPgx8P8ntSe5IcnuLcUmSJI2NOROyZgzZUc1M/Xvuwkz98y7JmiTrJicnuwpBkiRp3syZkFXVfcB7FiCWgdllKUmSRslQjiGTJEkaJY4hkyRJ6thAM/VX1UPaDmRnOO2FuuLcZJKkNgyUkCX5qZnKq+qz8xvOYKpqA7BhYmLixC72L0mSNJ8GvZflb/c9fzDwdGAT8Kx5j0iSJGnMDNpluaZ/Ocn+wLvaCEgaNnZjSpJ216AtZNNtBp44n4FIo2SmJA1M1CRJMxt0DNm7gWoWHwA8BfhSSzENEo+D+iVJ0sgYtIXsi33PtwHnVtU/txDPQBzUL0mSRsmgCdn5wPeq6l6AJEuS7F1V320vNEmSpPEw8Ez9wF59y3sBn5z/cCRJksbPoAnZg6tq69RC83zvdkKSJEkaL4MmZHcmOWxqIcnTgLvaCWluSdYkWTc5OdlVCJIkSfNm0ITsN4EPJ/mnJJ8DPgSc1FpUc6iqDVW1dtmyZV2FIEmSNG8GnRj2siRPAA5qiq6uqnvaC0saTU4iK0mayUAtZEleC+xTVVdU1RXA0iS/1m5okiRJ42HQaS9OrKrTpxaq6rYkJwJntBOWNN5sSZOk8TLoGLIlSTK1kGQJsGc7IUmSJI2XQVvILgU+lOT/NsuvAT7WTkiSJEnjZdCE7HeBE4GpcWOXAme2EpEkSdKY2WFClmQP4K3Aq4Drm+LHAtfS6+68t9XoZo/Lm4tLkqSRMdcYsj8DfgR4fFUdVlWHAQcAy4C3tx3cbJyHTJIkjZK5ErIX0LvC8o6pgub5rwLPazMwSZKkcTHXGLKqqpqh8N4k25VL2nkzTXEhSRovcyVkX0nyiqr6YH9hkpcDX20vLEnTOTeZJI2uuRKy1wIfSfJqYFNTNgHsBfxsm4FJkiSNix0mZFV1A3B4kmcBT2qKL6mqT7UemSRJ0pgY9ObinwY+3XIskiRJY2nQWydJkiSpJSZkkiRJHRv01kmLijP1S7PzakxJGj5D2ULmTP2SJGmUDGVCJkmSNEpMyCRJkjpmQiZJktSxoRzUL6nH+2BK0miwhUySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI45D5k0BrzhuCQtboumhSzJ45OcmeT8rmORJElaSK0mZEnOSrIlyRXTyo9KcnWSa5KcAlBV11bVCW3GI0mStBi13UJ2NnBUf0GSJcDpwNHAwcCxSQ5uOQ5JkqRFK1XV7g6SlcBFVXVIs3wE8Oaqem6zfCpAVf1xs3x+Vb1kB9tbC6wFWL58+dPWr1/favxbbp3kprta3YU6tHwvPL9zOHTFsq5D2GVbt25l6dKlXYehlnh+R9eontsjjzxyU1VNzPRaF4P6VwDX9y1vBg5P8gjgLcBTk5w6laBNV1XrgHUAExMTtXr16laDffc5F/COy732YVSdfOg2z+8crjtuddch7LKNGzfS9t8IdcfzO7rG8dwuml+iqroFeE3XcUiSJC20LhKyG4D9+5b3a8oGlmQNsGbVqlXzGZekATmNhiTNry6mvbgMODDJAUn2BI4BLtyZDVTVhqpau2zZ8I5tkSRJmtL2tBfnAp8HDkqyOckJVbUNOAm4FLgKOK+qrmwzDkmSpMWs1S7Lqjp2lvJLgEt2dbt2WUqSpFGyaGbq3xl2WUqSpFEylAmZJEnSKFk0017sDLsspcWnjSsv53ubXh0qabEayhYyuywlSdIoGcqETJIkaZSYkEmSJHXMMWSSdmimcVfDvB9JWoyGsoXMMWSSJGmUDGVCJkmSNEpMyCRJkjpmQiZJktQxB/VLao0TsUrSYIayhcxB/ZIkaZQMZUImSZI0SkzIJEmSOmZCJkmS1LGhTMiSrEmybnJysutQJEmSdttQJmQO6pckSaNkKBMySZKkUWJCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkd8+bikhbUTDccb+v9Jx+6jeN3c387G8s43jzd4yDtvqFsIXMeMkmSNEqGMiGTJEkaJSZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zJn6JY21+Z5lvo1Z650JfzztzF0p/DwMv6FsIXOmfkmSNEqGMiGTJEkaJSZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsf26DqAKUn2Ac4A7gY2VtU5HYckSZK0IFptIUtyVpItSa6YVn5UkquTXJPklKb454Dzq+pE4IVtxiVJkrSYtN1leTZwVH9BkiXA6cDRwMHAsUkOBvYDrm9Wu7fluCRJkhaNVFW7O0hWAhdV1SHN8hHAm6vquc3yqc2qm4HbquqiJOur6phZtrcWWAuwfPnyp61fv77V+LfcOslNd7W6C3Vo+V54fkfYuJ3fQ1csG2i9y2+Y3OX3LsT2BrV161aWLl26y/HM93rzbab97oxh/jzMdm7nS1fn9Mgjj9xUVRMzvdbFGLIV/LAlDHqJ2OHAacB7kjwf2DDbm6tqHbAOYGJiolavXt1epMC7z7mAd1y+aIbaaZ6dfOg2z+8IG7fze91xqwda7/hTLt7l9y7E9ga1ceNGZvoNGDSe+V5vvs20350xzJ+H2c7tfOnqnO7IovlLVVV3Aq/qOg5JkqSF1sW0FzcA+/ct79eUDSzJmiTrJid3rzlXkiRpMegiIbsMODDJAUn2BI4BLtyZDVTVhqpau2xZ+/29kiRJbWt72otzgc8DByXZnOSEqtoGnARcClwFnFdVV7YZhyRJ0mLW6hiyqjp2lvJLgEt2dbtJ1gBrVq1ataubkCRJWjSG8tZJdllKkqRRMpQJmSRJ0igZyoTMqywlSdIoGcqEzC5LSZI0Slq/dVKbknwb+HrLu9kXuLnlfag7nt/R5vkdbZ7f0TWq5/ZxVfXImV4Y6oRsIST54mz3ndLw8/yONs/vaPP8jq5xPLdD2WUpSZI0SkzIJEmSOmZCNrd1XQegVnl+R5vnd7R5fkfX2J1bx5BJkiR1zBYySZKkjpmQSZIkdcyEbAeSHJXk6iTXJDml63g0tyT7J/lMkq8kuTLJ65ryH0nyiST/2fz78KY8SU5rzvF/JDmsb1uvbNb/zySv7KpO2l6SJUm+nOSiZvmAJF9ozuOHkuzZlD+oWb6meX1l3zZObcqvTvLcjqqiaZI8LMn5Sb6a5KokR/j9HQ1Jfqv5u3xFknOTPNjv7g+ZkM0iyRLgdOBo4GDg2CQHdxuVBrANOLmqDgaeAby2OW+nAJ+qqgOBTzXL0Du/BzaPtcB7oZfAAW8CDgeeDrxp6kdAi8LrgKv6lv8EeGdVrQJuA05oyk8AbmvK39msR/OZOAZ4EnAUcEbznVf3/gL4WFU9AXgyvfPs93fIJVkB/AYwUVWHAEvofQf97jZMyGb3dOCaqrq2qu4G1gMv6jgmzaGqbqyqLzXP76D3x3wFvXP3V81qfwW8uHn+IuCD1fMvwMOS/DfgucAnqurWqroN+AS9L786lmQ/4PnA+5vlAM8Czm9WmX5+p877+cCzm/VfBKyvqu9X1deAa+h959WhJMuAnwLOBKiqu6vqO/j9HRV7AHsl2QPYG7gRv7s/YEI2uxXA9X3Lm5syDYmmifupwBeA5VV1Y/PSt4DlzfPZzrPnf/F6F/B64L5m+RHAd6pqW7Pcf65+cB6b1yeb9T2/i9MBwLeBDzRd0u9Psg9+f4deVd0AvB34Br1EbBLYhN/dHzAh00hKshT4O+A3q+r2/teqN9eL870MoSQvALZU1aauY1Er9gAOA95bVU8F7uSH3ZOA399h1XQZv4he0v0YYB9stbwfE7LZ3QDs37e8X1OmRS7JA+klY+dU1Uea4puargyaf7c05bOdZ8//4vSTwAuTXEdvGMGz6I05eljTDQL3P1c/OI/N68uAW/D8Llabgc1V9YVm+Xx6CZrf3+H3HOBrVfXtqroH+Ai977Pf3YYJ2ewuAw5srgDZk94gwgs7jklzaMYYnAlcVVV/3vfShcDUlVavBC7oK39Fc7XWM4DJpmvkUuBnkjy8+Z/dzzRl6lBVnVpV+1XVSnrfyU9X1XHAZ4CXNKtNP79T5/0lzfrVlB/TXMl1AL1B4f+6QNXQLKrqW8D1SQ5qip4NfAW/v6PgG8Azkuzd/J2eOrd+dxt7zL3KeKqqbUlOovclXgKcVVVXdhyW5vaTwC8Blyf5t6bsjcDbgPOSnAB8HXhp89olwPPoDQz9LvAqgKq6Nckf0kvMAf6gqm5dkBpoV7wBWJ/kj4Av0wwKb/796yTXALfSS+KoqiuTnEfvB2Eb8Nqqunfhw9YMfh04p/mP8LX0vpMPwO/vUKuqLyQ5H/gSve/cl+ndHuli/O4C3jpJkiSpc3ZZSpIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZp3SfZKsiHJZJIPdx3PdEkqyaqu49iR+Y4xyRuTvH++tidpfpmQSQssyXVJntPi9q9MsrV53Jvke33Lb2xrv9O8BFgOPKKqfmGB9jkvkmxM8svDtM8kb07yNzOU/yCpq6q3VtWc++ii/pJgj64DkDS/qupJU8+TbAT+pqq2axlJskdVbWspjMcB/29Xtt9yXOqQ51aanS1k0iKR5EFJ3pXkm83jXUke1Pf665Pc2Lz2yzvbpZVkZfOeE5J8A/h0U/7hJN9quhc/m6Q/oTs7yelJLk5yR5IvJPnR5rUkeWeSLUluT3J5kkOS/D7we8DLmla5E5I8IMnvJPl6s/4HkyybLa4kxyf552b730lybZJnNuXXN9t45bRj9/Yk30hyU5L3Jdmr7/Xf7jt2r96Nc/TqJFcluS3JpUke1/daJXlNkv9sYj49SZrXliR5R5Kbk3wtyUnN+nskeQvwP4D3NMfrPX27fM5M29vF2H/QipbkwUn+JsktzbYvS7J8tliaY39Z8xm5LMkz+7Z7QPO5uSPJJ5s4p/azq5+5M5L8QxPDPyd5dHrfh9uSfDXJU3f1OEiLlQmZtHj8H+AZwFOAJwNPB34HIMlRwP8CngOsAlbvxn7+J/BE4LnN8j8ABwKPAr4EnDNt/WOA3wceDlwDvKUp/xngp4AfA5YBLwVuqao3AW8FPlRVS6vqTOD45nEk8HhgKdCfeMwU1+HAfwCPAP4WWA/8BL36v5xe0rC0WfdtTRxPaV5fQS8pnDp2/xv46aaeu9RdnORFwBuBnwMeCfwTcO601V7QxPjjzfGYqsuJwNFNfIcBL556Q1X9n2ZbJzXH66QBtre7XknvnO1P7/i+BrhrpliS/AhwMXBas+6fAxcneUSzrb8F/rV57c3AL82wv539zL2U3md/X+D7wOeb9fYFzm9ikEaKCZm0eBwH/EFVbamqb9NLgqZ+3F4KfKCqrqyq79L74dtVb66qO6vqLoCqOquq7qiq7zfbffJU61Xjo1X1r01X0zn0kgqAe4CHAE8AUlVXVdWNO6jbn1fVtVW1FTgVOCZJ/7CJ+8UFfK2qPlBV9wIfopc8/EFVfb+qPg7cDaxqWo3WAr9VVbdW1R30EsJjmu1MHbsrqupOdv3YvQb446ae25p9PKW/lQx4W1V9p6q+AXyGHx6rlwJ/UVWbq+o2egnkIGbb3kxe2rR2/eCxg3XvoZdAraqqe6tqU1XdPsu6zwf+s6r+uqq2VdW5wFeBNUkeSy9h/L2quruqPgdcOMM2duUzt6mqvgd8FPheVX2w77NgC5lGjgmZtHg8Bvh63/LXm7Kp167ve63/+c76wXubrrS3JfmvJLcD1zUv7du3/rf6nn+XXusWVfVpeq1cpwNbkqxL8tBZ9jlT3fagN/B/u7gaN/U9n/ohn162lF5r1d7Apr5E5GNN+dS++7fdH8fOeBzwF337uBUIvda4KTMeqxliGPT8zba9mZxXVQ/rf+xg3b8GLgXWN924f5rkgbOsO/3c0SyvaF67tflPwpSZ6razn7np53mm8y6NFBMyafH4Jr0f/SmPbcoAbgT263tt/93YT/U9/0XgRfS68ZYBK5vygcYqVdVpVfU04GB6XYa/PcuqM9VtG/f/oS12zc30fqSf1JeMLKuqqR/tG7n/8XrsLu7neuBXpiU9e1XV/zfAe+c6f7ta911SVfdU1e9X1cHAM+l1jb5illimnzvoHcMb6NXrR5Ls3ffaTJ/NefvMSaPKhEzqxgObgdVTjz3ojUf6nSSPTLIvvTFQU1MZnAe8KskTmx+/352nOB5Cb4zOLfRamd466BuT/ESSw5uWlTuB7wH3zbL6ucBvNQPAl/LDMWa7fcVdVd0H/CXwziSPamJbkWRqvNJ5wPFJDm6O3ZsG2Owe087PA4H3AadODUBPsizJoFN6nAe8ronrYcAbpr1+E72xdQsiyZFJDk2yBLidXhfm1LmbHsslwI8l+cXmIoSX0UvAL6qqrwNfBN6cZM8kRwBr5tj9Ln/mpFFmQiZ14xJ6rTpTjzcDf0Tvx+0/gMvpDWL+I4Cq+gd6g6o/Q29g/b802/n+bsbxQXrdTzcAX+nb7iAeSi8Ruq3Zxi3An82y7ln0usk+C3yNXvL267sW8ozeQHNcmm6wTwIHwQ+O3bvoXeF3TfPvXN7L/c/PB6rqo8Cf0Ovmux24gt5A/UH8JfBxeuf2y/TO/zbg3ub1vwBe0lxFeNqA29wdj6Y3OP524CrgH+mdn+1iqapb6LWgnUzvHL8eeEFV3dysfxxwRPPaH9Eb47Wjz+XufOakkZWqBW0plzQPkjyRXkLwIOd1Gj5JjgbeV1XTuwKHXpIPAV9trraVNCBbyKQhkeRn05tv6+H0Wmo2mIwNh/RuJfW8pstvBb1u0492Hdd8aLqufzS9ueaOojc+7O87DksaOiZk0vD4FWAL8F/0urp+tdtwtBNCbxqT2+h1WV5FM0/aCHg0sBHYSq9b/Ver6sudRiQNIbssJUmSOmYLmSRJUseG+ubi++67b61cubLVfdx5553ss88+re5D7fIcDjfP33Dz/A03z9/82rRp081V9ciZXhvqhGzlypV88YtfbHUfGzduZPXq1a3uQ+3yHA43z99w8/wNN8/f/Eoy651C7LKUJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdG8qELMmaJOsmJye7DkWSJGm3DeVM/VW1AdgwMTFxYtexSNKoWnnKxduVXfe253cQiTT6hjIhkyTNr5mSr0HXM0mTdp8JmSRpt5ikSbvPhEySNO+mkrSTD93G8c3zmZI0kzmpx4RMkrQg7BaVZmdCJkljZtDEaDGZLWYTNY0KEzJJ0tCyNU2jYijnIZMkSRoltpBJ0ggbxu7J3TVonW1J02JiC5kkSVLHbCGTpBExjq1h0qgwIZMkqeFFAuqKCZkkaSzZoqjFxDFkkiRJHbOFbA6X3zD5g9t+TLH5WlLXbN2RRsuiSciSPBF4HbAv8Kmqem/HIUmS5LgyLYhWuyyTnJVkS5IrppUfleTqJNckOQWgqq6qqtcALwV+ss24JEmSFpO2x5CdDRzVX5BkCXA6cDRwMHBskoOb114IXAxc0nJckiRJi0arCVlVfRa4dVrx04FrquraqrobWA+8qFn/wqo6GjiuzbgkSZIWk1RVuztIVgIXVdUhzfJLgKOq6peb5V8CDgfOB34OeBDwH1V1+izbWwusBVi+fPnT1q9f32r8W26d5Ka77l926Iplre5T82vr1q0sXbq06zC0izx/vYuLhtXyvdjub+goGJffAb9/8+vII4/cVFUTM722aAb1V9VGYOMA660D1gFMTEzU6tWrW43r3edcwDsuv/9huu64dvep+bVx40ba/pyoPZ4/trvSe5icfOi27f6GjoJx+R3w+7dwuviW3ADs37e8X1MmSdJQ8MpLzbcuJoa9DDgwyQFJ9gSOAS7cmQ0kWZNk3eTk8DbjS5IkTWm1hSzJucBqYN8km4E3VdWZSU4CLgWWAGdV1ZU7s92q2gBsmJiYOHG+Y5akrjjZqzS+Wk3IqurYWcovwaktJEmSgCG9l6VdlpIkaZQMZUJWVRuqau2yZeNx2bEkSRpto3ctsiRJHfDKS+0OEzJJ6oAD+CX1G8ouS8eQSZKkUTKUCZljyCRJ0igZyoRMkiRplJiQSZIkdWwoEzLHkEmSpFEylFdZeuskSdIwcCoMDWooEzJJGiZOcSFpLkPZZSlJkjRKTMgkSZI6ZkImSZLUsaFMyLzKUpIkjZKhHNTvVZaSpGHllZeayVC2kEmSJI0SEzJJkqSODWWXpSQtVs45JmlX2EImSZLUsaFMyLzKUpIkjZKh7LL0KktJi4Hdk5Lmy1AmZJIkjRKnwtBQdllKkiSNEhMySZKkjpmQSZIkdcyETJIkqWMO6pekAXhFpRaaA/3Hy1C2kDkPmSRJGiVDmZBV1YaqWrts2bKuQ5EkSdptQ5mQSZIkjRITMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdcx4ySZKGxGzz4Tk/2fCzhUySJKljJmSSJEkdMyGTJEnqmAmZJElSx4ZyUH+SNcCaVatWdR2KpBHkjcQlLbShbCHzXpaSJGmUDGVCJkmSNEqGsstSkiT90Ezd7M5NNlxsIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMQf1S5I0ghzoP1xsIZMkSeqYLWSSxpqz8ktaDGwhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1bFFNe5HkxcDzgYcCZ1bVx7uNSNIocYoLSYvVQC1k6Xl5kt9rlh+b5OkDvvesJFuSXDGt/KgkVye5JskpAFX191V1IvAa4GU7VxVJkqThNGgL2RnAfcCzgD8A7gD+DviJAd57NvAe4INTBUmWAKcDPw1sBi5LcmFVfaVZ5Xea1yVJ0jzxdkqLV6pq7pWSL1XVYUm+XFVPbcr+vaqePNBOkpXARVV1SLN8BPDmqnpus3xqs+rbmscnquqTs2xrLbAWYPny5U9bv379ICHssi23TnLTXfcvO3TFslb3qfm1detWli5d2nUY2kXzef4uv2FyXrajwS3fi+3+hmpx2dFvmn8/59eRRx65qaomZnpt0Baye5pWrQJI8kh6LWa7agVwfd/yZuBw4NeB5wDLkqyqqvdNf2NVrQPWAUxMTNTq1at3I4y5vfucC3jH5fc/TNcd1+4+Nb82btxI258TtWc+z9/xjiFbcCcfum27v6FaXHb0m+bfz4Uz6LfkNOCjwKOSvAV4Cb1uxXlVVac1+5IkSRobAyVkVXVOkk3As4EAL66qq3ZjvzcA+/ct79eUDSTJGmDNqlWrdiMESZKkxWHQqyyfAdxQVadX1XuAG5Icvhv7vQw4MMkBSfYEjgEuHPTNVbWhqtYuW+ZYLkmSNPwGnRj2vcDWvuWtTdmckpwLfB44KMnmJCdU1TbgJOBS4CrgvKq6cvCwJUmSRsegY8hSfZdjVtV9SQbt7jx2lvJLgEsG3L8kSdLIGrSF7Nokv5Hkgc3jdcC1bQa2I0nWJFk3Oekl7JIkafgNmpC9BngmvYH3U1NUrG0rqLk4hkySJI2SQbsdt9AbeC9JkkaIs/cvDgMlZM1EsCcCK/vfU1WvbiesOeNx2gtJkjQyBu2yvABYBnwSuLjv0Qm7LCVJ0igZ9CrLvavqDa1GIkmSNKYGbSG7KMnzWo1EkiRpTA2akL2OXlL2vSS3J7kjye1tBiZJkjQuBr3K8iFtB7IzHNQvSZJGyaD3skySlyf53WZ5/yRPbze02TmoX5IkjZJBuyzPAI4AfrFZ3gqc3kpEkiRJY2bQqywPr6rDknwZoKpuS7Jni3FJkiSNjUFbyO5JsgQo+MFEsfe1FpUkSdIYGTQhOw34KPCoJG8BPge8tbWo5uDNxSVJ0iiZMyFL8gDga8DrgT8GbgReXFUfbjm2WTmoX5IkjZI5x5BV1X1JTq+qpwJfXYCYJEmSxsqgg/o/leTngY9UVbUZkCTNh5WndHa7XWnoTX1/Tj50G8c3z6972/O7DGnkDTqG7FeADwPfd6Z+SZKk+TVnC1kzhuyoqvrnBYhHkiRp7MzZQlZV9wHvWYBYJEmSxtKgXZafSvLzSdJqNANy2gtJkjRKhnIMmdNeSJKkUTLQVZZV9ZC2A5GkXeUVlZKG3UAJWZKfmqm8qj47v+FIkiSNn0HnIfvtvucPBp4ObAKeNe8RSZIkjZlBuyzX9C8n2R94VxsBSZIkjZtBB/VPtxl44nwGIkmSNK4GHUP2bmDqlkkPAJ4CfKmlmCRJksbKoGPIvtj3fBtwbpcz9ydZA6xZtWpVVyFIkiTNm0ETsvOB71XVvQBJliTZu6q+215os6uqDcCGiYmJE7vYvyRJ0nwaNCH7FPAcYGuzvBfwceCZbQQlSZIWl5nm+7vubc/vIJLRNGhC9uCqmkrGqKqtSfZuKSZJAno/ACcfuo3j+34I/AGQNIoGvcryziSHTS0keRpwVzshSZIkjZdBW8h+E/hwkm8CAR4NvKytoCRJksbJoBPDXpbkCcBBTdHVVXVPe2FJ0sy8b6WkUTRQl2WS1wL7VNUVVXUFsDTJr7UbmiRJ0ngYtMvyxKo6fWqhqm5LciJwRjthSZKkxc4rL+fPoIP6lyTJ1EKSJcCe7YQkSZI0XgZtIbsU+FCS/9ssvwb4WDshSZIkjZdBE7LfBU4EpsaNXQqc2UpEkiRJY2aHCVmSPYC3Aq8Crm+KHwtcS6+7895Wo5s9Lu9lKUmSRsZcY8j+DPgR4PFVdVhVHQYcACwD3t52cLOpqg1VtXbZsmVdhSBJkjRv5krIXkDvCss7pgqa578KPK/NwCRJksbFXAlZVVXNUHgvsF25JEmSdt5cCdlXkrxiemGSlwNfbSckSZKk8TLXVZavBT6S5NXApqZsAtgL+Nk2A5MkSRoXO0zIquoG4PAkzwKe1BRfUlWfaj0ySSNptntROru3pHE26M3FPw18uuVYJEmSxtKgt06SJElSS0zIJEmSOmZCJkmS1DETMkmSpI4NenNxSZKkXTLT1dVeWX1/tpBJkiR1zBYySZI0b2aba1A7ZguZJElSxxZNQpbk8UnOTHJ+17FIkiQtpFa7LJOcBbwA2FJVh/SVHwX8BbAEeH9Vva2qrgVOMCGTRsfOdF3YzSFpnLXdQnY2cFR/QZIlwOnA0cDBwLFJDm45DkmSpEWr1YSsqj4L3Dqt+OnANVV1bVXdDawHXtRmHJIkSYtZF1dZrgCu71veDBye5BHAW4CnJjm1qv54pjcnWQusBVi+fDkbN25sNdjle8HJh267X1nb+9T82rp1q+esI9O/O7tipu+ghofnb7i1ef78u3x/i2bai6q6BXjNAOutA9YBTExM1OrVq1uN693nXMA7Lr//YbruuHb3qfm1ceNG2v6caGbHz8O4sJMP3bbdd1DDw/M33No8f/6W3l8XV1neAOzft7xfUyZJkjSWukjILgMOTHJAkj2BY4ALd2YDSdYkWTc5OdlKgJIkSQup1YQsybnA54GDkmxOckJVbQNOAi4FrgLOq6ord2a7VbWhqtYuW7Zs/oOWJElaYK127FfVsbOUXwJc0ua+JUmShsWimal/Z9hlKUmSRslQJmR2WUqSpFEylAmZJEnSKDEhkyRJ6thQztaXZA2wZtWqVV2HIqnhzcEl7YyZ/mZc97bndxDJ4jCULWSOIZMkSaNkKBMySZKkUWJCJkmS1DHHkEnaIceGSVL7hrKFzDFkkiRplAxlQiZJkjRKTMgkSZI6ZkImSZLUMRMySZKkjg1lQpZkTZJ1k5OTXYciSZK024YyIfMqS0mSNEqGMiGTJEkaJSZkkiRJHTMhkyRJ6pgJmSRJUse8l6UkSRp6s91397q3PX+BI9k1Q9lC5lWWkiRplAxlQiZJkjRKTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWPOQyaNqdnm7JGkxWSmv1U7M7fY7r5/oQxlC5nzkEmSpFEylAmZJEnSKDEhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6pj3spRGzLDct02Sphv0Hru7ey/exfh3cihbyLyXpSRJGiVDmZBJkiSNEhMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLH9ug6gClJ9gHOAO4GNlbVOR2HJEmStCBabSFLclaSLUmumFZ+VJKrk1yT5JSm+OeA86vqROCFbcYlSZK0mLTdZXk2cFR/QZIlwOnA0cDBwLFJDgb2A65vVru35bgkSZIWjVRVuztIVgIXVdUhzfIRwJur6rnN8qnNqpuB26rqoiTrq+qYWba3FlgLsHz58qetX7++1fi33DrJTXfdv+zQFcta3afm19atW1m6dGnXYey2y2+Y3K5sps/iTOsNs+V7sd13UMPD8zfcxun8LcRv+5FHHrmpqiZmeq2LMWQr+GFLGPQSscOB04D3JHk+sGG2N1fVOmAdwMTERK1evbq9SIF3n3MB77j8/ofpuuPa3afm18aNG2n7c7IQjj/l4u3KZvoszrTeMDv50G3bfQc1PDx/w22czl/Xv+2L5ihX1Z3Aq7qOQ5IkaaF1Me3FDcD+fcv7NWUDS7ImybrJydHqmpEkSeOpi4TsMuDAJAck2RM4BrhwZzZQVRuqau2yZY7lkiRJw6/taS/OBT4PHJRkc5ITqmobcBJwKXAVcF5VXdlmHJIkSYtZq2PIqurYWcovAS5pc9+SJEnDYihvneQYMkmSNEqGMiFzDJkkSRolQ5mQSZIkjZKhTMjsspQkSaOk9VsntSnJt4Gvt7ybfYGbW96H2uU5HG6ev+Hm+Rtunr/59biqeuRMLwx1QrYQknxxtvtOaTh4Doeb52+4ef6Gm+dv4Qxll6UkSdIoMSGTJEnqmAnZ3NZ1HYB2m+dwuHn+hpvnb7h5/haIY8gkSZI6ZguZJElSx0zIJEmSOmZCtgNJjkpydZJrkpzSdTwaXJL9k3wmyVeSXJnkdV3HpJ2XZEmSLye5qOtYtHOSPCzJ+Um+muSqJEd0HZN2TpLfav5+XpHk3CQP7jqmUWZCNoskS4DTgaOBg4FjkxzcbVTaCduAk6vqYOAZwGs9f0PpdcBVXQehXfIXwMeq6gnAk/E8DpUkK4DfACaq6hBgCXBMt1GNNhOy2T0duKaqrq2qu4H1wIs6jkkDqqobq+pLzfM76P0YrOg2Ku2MJPsBzwfe33Us2jlJlgE/BZwJUFV3V9V3Og1Ku2IPYK8kewB7A9/sOJ6RZkI2uxXA9X3Lm/EHfSglWQk8FfhCx6Fo57wLeD1wX8dxaOcdAHwb+EDT5fz+JPt0HZQGV1U3AG8HvgHcCExW1ce7jWq0mZBppCVZCvwd8JtVdXvX8WgwSV4AbKmqTV3Hol2yB3AY8N6qeipwJ+A43CGS5OH0eoUOAB4D7JPk5d1GNdpMyGZ3A7B/3/J+TZmGRJIH0kvGzqmqj3Qdj3bKTwIvTHIdveECz0ryN92GpJ2wGdhcVVOt0ufTS9A0PJ4DfK2qvl1V9wAfAZ7ZcUwjzYRsdpcBByY5IMme9AYzXthxTBpQktAbv3JVVf151/Fo51TVqVW1X1WtpPfd+3RV+b/zIVFV3wKuT3JQU/Rs4CsdhqSd9w3gGUn2bv6ePhsvzGjVHl0HsFhV1bYkJwGX0ru65KyqurLjsDS4nwR+Cbg8yb81ZW+sqku6C0kaK78OnNP8h/Za4FUdx6OdUFVfSHI+8CV6V61/GW+j1CpvnSRJktQxuywlSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLHTMgkLRpJDkryb0nuSPIbXcfTL8nqJJu7jmNH2ogxyT8keeV8blPS9kzIpF2U5LokdyXZmuS2JBcn2b+lfT00ybuSfKPZ3381y/vO0/Yf22x36lFJ7uxb/h/zsZ8BvB74TFU9pKpOW6B9zovmmK0apn0m2Zjkl6eV3S+pq6qjq+qv2o5FGncmZNLuWVNVS4H/BtwEvHu+d5BkT+BTwJOAo4CHAkcAtwBPn499VNU3qmrp1KMpfnJf2T/1xbPHfOxzFo8DrtyVN7YclzrkudU4MCGT5kFVfQ84Hzh4qizJsiQfTPLtJF9P8jtJHpDkR5JsTrKmWW9pkmuSvGKWzb8CeCzws1X1laq6r6q2VNUfVtUlzTae2LR2fCfJlUle2JQ/M8nNUy13SZ7ctOY9YdC6JTk+yT8neWeSW4A3J/nRJJ9Ockuz/XOSPKzvPdcl+d9J/iPJZJIPJXlw89q+SS5qYr01yT81x+XTwJHAe5pWuR+b7RjuIK6zk5zRdLNtbV5/dNOaeFuSryZ5al+cj0nyd832v9bfTZpkr2Z7tyX5CvATgx6zacfvQUne3rRu3pTkfUn2al5b3XwWTk6yJcmNSV7V995HJNmQ5PYklyX5oySfa177bLPavzd1fVnf+2bc3i7G/4NWtCSrkvxjc05vTvKhHcWS5MTms31rkguTPKZvuz+T5OpmW2c0253az65+5n67+czdmeTMJMubz8IdST6Z5OG7cyykNpmQSfMgyd7Ay4B/6St+N7AMeDzwP+klVq+qqluBVwN/meRRwDuBf6uqD86y+ecAH6uqrbPs+4HABuDjwKOAXwfOSXJQVf1/wP8F/qpJAv4G+N2q+upOVvFw4FpgOfAWIMAfA48BngjsD7x52nteSq9F7wDgx4Hjm/KTgc3AI5vtvRGoqnoW8E/ASU2r3P9jlmO4g7im9vs7wL7A94HPA19qls8H/rw5bg9ojtu/AyuAZwO/meS5zXbeBPxo83gusKvjqN4G/BjwFGBVs6/f63v90U0dVwAnAKf3JQ6nA3c267yyP4aq+qnm6VRL5ocG2N7u+kN6n7OHA/vRtAjPFEuSZ9H7jLyUXgvy14H10EvK6Z2LU4FHAFcDz5y2r135zP088NP0jvca4B/ofb4eSe/3blGNS5Tup6p8+PCxCw/gOmAr8B3gHuCbwKHNa0uAu4GD+9b/FWBj3/K7gcuBG4BH7GA/nwDetoPX/wfwLeABfWXnAm9unj8Q2NTs62NABqhbAaua58cD35hj/RcDX552bF7et/ynwPua538AXDC1/Wnb2Qj88iDHcKa4gLOBv+xb/nXgqr7lQ4HvNM8Pn+H9pwIfaJ5fCxzV99paYPMgx6yvLPQSqh/tKzsC+FrzfDVwF7BH3+tbgGc09b8HOKjvtT8CPjfbPne0vVli3gh8l95neOqxtb+e087JB4F1wH5z1R84E/jTvuWlTX1W0kusPz/tOF3ft59d/cwd17f8d8B7p30W/n6uz74PH109bCGTds+Lq+phwIOBk4B/TPJoeq0xD6TXKjDl6/RaLaasAw4Bzq6qW2D7wfXNerfQa2GYzWOA66vqvpn2VVX30EtUDgHeUVW1C/W8vn+h6Qpan+SGJLfTa3mbfoHBt/qef5feDzLAnwHXAB9Pcm2SU2bZ5yDH8H5xNW7qe37XDMtTcTwOeEx6XaffSfIdeq0py5vXHzNt+/1xDOqRwN7Apr59fKwpn3JLVW3rW546Vo8E9pgWw0z1nW627c3mN6rqYVMP4AU7WPf19JKnf02va/zVO1j3MfQds+q18N5C7/zd79g2n8npV4fuymdu0HMvLTomZNI8qKp7q+ojwL3Afwduptca8Li+1R5LrzWMJEvoJWQfBH4tzdVpNfPg+k8Cz02yzyy7/yawf9MFN9O+VtDrfvsA8I4kD9qVKk5bfmtTdmhVPRR4Ob0f6rk3VHVHVZ1cVY8HXgj8ryTPnmHVHR7DWeLaGdfTa6l6WN/jIVX1vOb1G+l1i/Xve2fdTC8ReFLfPpb1ndsd+TawjV7X4JRWruIdVFV9q6pOrKrH0GutPCOzX1n5TfrOXfP5fQS983cjffVKEu5fT5jHz5w0DEzIpHmQnhfRG1tzVVXdC5wHvCXJQ5I8Dvhf9P5XD824KXpjyf4M+GCTpM3kr+klD3+X5AnpDYB/RJI3Jnke8AV6rSCvT/LAJKvpjZ9Z3/zQnU2v++gEej+EfzgPVX4Iva6tySbh++1B35jkBc3g8ACT9JLY+6avN8Ax3F3/CtyR5A3pDeBfkuSQJFOD988DTk3y8CT70evymsueSR489aCXMPwl8M5mvCBJVvSNU5tVU/+P0BvQvnd6F2JMv/DjJnrj6xZEkl9ojgXAbfQ+w1Pnbnos5wKvSvKU5j8BbwW+UFXXARcDhyZ5cXpXUL6W3ti3Hdnlz5w0DEzIpN2zoelavJ3ewONXVtXUtA2/Tm/80LXA54C/Bc5K8jR6icUrmh/dP6H3wzZj111VfZ/ewP6v0htPdju9ZGJfej9wd9NLwI6m1yJzRrPtr9IbxPwoegP5i96A+Fdl9+cV+33gMHoJ1cX0EodBHUiv1W8rvQH3Z1TVZ2ZZd8ZjuIsx309z7F9Ab7D91+gdu/fTGxAPvTp+vXnt4/QS47lcSa9FbOrxKuAN9Lpo/6XpavskcNCAYZ7UxPOtZv/n0rtQYcqb6V2w8Z0kLx1wm7vjJ4AvNJ/5C4HXVdW1M8VSVZ8EfpfeWK4b6V0ccQxAVd0M/AK9sYW30Ls6+Yvcv27T7c5nTlr0smvDSSRJCy3JnwCPrqqRmjm/6W7fTG9Q/mzJuTTSbCGTpEWq6aL+8aZL/On0up0/2nVc8yHJc5M8rOnOfCO97t1/meNt0shy9mNJWrweQq+b8jH0xmi9g96UIaPgCHpd0HsCX6F3xfJd3YYkdccuS0mSpI7ZZSlJktSxoe6y3HfffWvlypWt7uPOO+9kn31mm/5p9Fl/62/9rf+4Guf6j3Pdob36b9q06eaqeuRMrw11QrZy5Uq++MUvtrqPjRs3snr16lb3sZhZf+tv/Vd3HUZnrP/41n+c6w7t1T/JrHf8sMtSkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSODWVClmRNknWTk5NdhyJJkrTbhjIhq6oNVbV22bJlXYciSZK024Z6pn6pTStPuZiTD93G8adcvMP1rnvb8xcoIknSqDIhk+glX5IkdWUouywlSZJGiS1k0m6aqXXNbkxJ0s4wIdPYWYjuydn2YaImSZqJXZaSJEkdMyGTJEnqmF2WGmmL7erJQeOxa1OSxosJmbQIeaGAJI0XEzKNjMXWGjbfTNIkaXSZkElDzCRNkkaDg/olSZI6ZkImSZLUMbss53D5DZPb3VzaLqHujfp4sd3hlZySNHxsIZMkSerYoknIkjwxyfuSnJ/kV7uOR5IkaaG0mpAlOSvJliRXTCs/KsnVSa5JcgpAVV1VVa8BXgr8ZJtxSZIkLSZtjyE7G3gP8MGpgiRLgNOBnwY2A5clubCqvpLkhcCvAn/dclwaIo4Xa8egU2Y4tYYktS9V1e4OkpXARVV1SLN8BPDmqnpus3wqQFX9cd97Lq6qGf/iJ1kLrAVYvnz509avX99q/FtuneSmu+5fduiKZa3uczHZunUrS5cu7TSGy2+Y7Gzfy/diu/M/Tmar/7h8BxbD579L1n986z/OdYf26n/kkUduqqqJmV7r4irLFcD1fcubgcOTrAZ+DngQcMlsb66qdcA6gImJiVq9enVbcQLw7nMu4B2X3/8wXXdcu/tcTDZu3Ejbx3gu069yXUgnH7ptu/M/Tmar/7h8BxbD579L1n986z/OdYdu6r9ofmmqaiOwseMwJEmSFlwXV1neAOzft7xfUzawJGuSrJuc7K4rS5Ikab50kZBdBhyY5IAkewLHABfuzAaqakNVrV22bDzGsUiSpNHWapdlknOB1cC+STYDb6qqM5OcBFwKLAHOqqor24xD0vzanStfvUJTkrbXakJWVcfOUn4JOxi4P5cka4A1q1at2tVNaJFyigtJ0jhaNDP17wy7LCVJ0igZyoRMkiRplJiQSZIkdWzRzEO2MxxDJg2v2cYJOthf0jgbyoSsqjYAGyYmJk7sOhZJ88N7ZkoaZ3ZZSpIkdcyETJIkqWND2WWp0eCcY5Ik9QxlC5n3spQkSaNkKBMyJ4aVJEmjxC5LSYuWV15KGhdD2UImSZI0SkzIJEmSOjaUXZbO1D98vKJSkqTZDWULmYP6JUnSKBnKhEySJGmUDGWXpaTx5ZWXkkaRLWSSJEkdMyGTJEnq2FB2WXqVpaR+dmNKGnZD2ULmVZaSJGmUDGVCJkmSNEpMyCRJkjo2lGPItHg5I78WC8eVSRomtpBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6thQJmRJ1iRZNzk52XUokiRJu20or7Ksqg3AhomJiRO7jkXS8PDKS0mL1VC2kEmSJI2SoWwhk6T5YquZpMXAFjJJkqSOmZBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6phXWUrSNFNXXp586DaOb5575aWkNtlCJkmS1DETMkmSpI4NZZdlkjXAmlWrVnUdiqQx4QSykto0lC1kVbWhqtYuW7as61AkSZJ221AmZJIkSaNkKLssJWkxsBtT0nwxIdMum+nHSJIk7Ty7LCVJkjpmQiZJktQxuywlaR45rkzSrrCFTJIkqWMmZJIkSR0zIZMkSeqYY8gkqWWOK5M0F1vIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHVsUQ3qT/Ji4PnAQ4Ezq+rj3UYkSe1woL+kfq23kCU5K8mWJFdMKz8qydVJrklyCkBV/X1VnQi8BnhZ27FJkiQtBgMlZOl5eZLfa5Yfm+TpA+7jbOCoadtbApwOHA0cDByb5OC+VX6neV2SJGnkDdpCdgZwBHBss3wHAyZMVfVZ4NZpxU8Hrqmqa6vqbmA98KIm8fsT4B+q6ksDxiZJkjTUUlVzr5R8qaoOS/LlqnpqU/bvVfXkgXaSrAQuqqpDmuWXAEdV1S83y78EHA78P+CVwGXAv1XV+2bY1lpgLcDy5cuftn79+kFC2GVbbp3kprvuX3boimWt7nMx2bp1K0uXLp3xtctvmFzgaBbe8r3Y7vyPE+u/sPVfbH9bdvT9HwfjXP9xrju0V/8jjzxyU1VNzPTaoIP672m6GQsgySOB++Ypvh+oqtOA0+ZYZx2wDmBiYqJWr14932Hcz7vPuYB3XH7/w3Tdce3uczHZuHEjsx3j42cYlDxqTj5023bnf5xY/4Wt/2x/W2a6AGDG98/zRQE7+v6Pg3Gu/zjXHbqp/6BdlqcBHwUeleQtwOeAt+7Gfm8A9u9b3q8pkyRJGjsD/devqs5Jsgl4NhDgxVV11W7s9zLgwCQH0EvEjgF+cdA3J1kDrFm1atVuhKCdMej/0CVJ0s4bKCFL8gzgyqo6vVl+aJLDq+oLA7z3XGA1sG+SzcCbqurMJCcBlwJLgLOq6spBg66qDcCGiYmJEwd9jyQtdv7HRxpfgw6OeC9wWN/y1hnKZlRVx85SfglwyYD7lyRJGlmDjiFL9V2OWVX30eEs/0nWJFk3OTn6V/lJkqTRN2hCdm2S30jywObxOuDaNgPbkaraUFVrly1bXJeIS5Ik7YpBE7LXAM+kNwB/M705w9a2FZQkSdI4GfQqyy30roSUJEnSPBv0KstHAicCK/vfU1WvbiesOeNx2gtJkjQyBu2yvABYBnwSuLjv0QnHkEmSpFEy6JWSe1fVG1qNRJIkaUwN2kJ2UZLntRqJJEnSmBq0hex1wBuT3A3cTe/2SVVVD20tsh1wDJkkDWam2f/n+ybkknbfQC1kVfWQqnpAVT24qh7aLHeSjDXxOIZMkiSNjIESsvS8PMnvNsv7J3l6u6FJkiSNh0HHkJ0BHAH8YrO8FTi9lYgkSZLGzKBjyA6vqsOSfBmgqm5LsmeLcUmSdtJM48UkDYdBW8juSbIEKPjBRLH3tRaVJEnSGBk0ITsN+CjwqCRvAT4HvLW1qOaQZE2SdZOTk12FIEmSNG/m7LJM8gDga8DrgWfTm/LixVV1VcuxzaqqNgAbJiYmTuwqhlHW3+1x8qHbON5uEGmkOBWGtPjMmZBV1X1JTq+qpwJfXYCYJEmSxsqgXZafSvLzSdJqNJIkSWNo0ITsV4APA99PcnuSO5Lc3mJckiRJY2PQMWRHVdU/L0A8kiRJY2fOFrKqug94zwLEMjCvspQkSaNkKMeQeS9LSZI0Sgadqf9XgP8FbEvyPXpTX1SXNxjX/HBmb0kw89+Cmaa9cXoMqR0DJWRV9ZC2A5EkSRpXAyVkSX5qpvKq+uz8hiNJkjR+Bu2y/O2+5w8Gng5sAp417xFJkiSNmUG7LNf0LyfZH3hXGwFJkiSNm0GvspxuM/DE+QxEkiRpXA06huzdQDWLDwCeAnyppZgGiWcNsGbVqlVdhSBJkjRvBh1D9sW+59uAc7ucub+qNgAbJiYmTuwqBkmSpPkyaEJ2PvC9qroXIMmSJHtX1XfbC02SJGk8DJqQfQp4DrC1Wd4L+DjwzDaCkiQtTjNNIOtksdLuG3RQ/4OraioZo3m+dzshSZIkjZdBE7I7kxw2tZDkacBd7YQkSZI0XgbtsvxN4MNJvknvPpaPBl7WVlCSJEnjZNCJYS9L8gTgoKbo6qq6p72wJEmSxseg85C9Fjinqq5olh+e5NiqOqPV6CRJi54D/aXdN+gYshOr6jtTC1V1G+AcYJIkSfNg0IRsSZJMLSRZAuzZTkiSJEnjZdBB/ZcCH0ryf5vl1wAfayckSZKk8TJoQva79Loof61ZvhQ4s5WIBuC9LCVJ0ijZYZdlkj2S/CnwDeB44ADgfwIHzvXeNlXVhqpau2zZsq5CkCRJmjdztZD9GfAQ4PFVdQdAkocA7wDeDryu3fA0n2a6EkqSJHVvrlauF9C7wvKOqYLm+a8Cz2szMEmSpHExV0JWVVUzFN4LbFcuSZKknTdXl+VXkryiqj7YX5jk5cBX2wtLkjTMnCxW2jlzJWSvBT6S5NXApqZsAtgL+Nk2A5MkSRoXO0zIquoG4PAkzwKe1BRfUlWfaj0ySZKkMTHozcU/DXy65VgkSZLG0qATw2rIOMWFJEnDo7PJXSVJktRjQiZJktQxEzJJkqSOOYZMkrQgnJtMmp0tZJIkSR0zIZMkSeqYCZkkSVLHFs0YsiSPB/4PsKyqXtJ1PJKkbjjWTOOo1RayJGcl2ZLkimnlRyW5Osk1SU4BqKprq+qENuORJElajNrusjwbOKq/IMkS4HTgaOBg4NgkB7cchyRJ0qKVqmp3B8lK4KKqOqRZPgJ4c1U9t1k+FaCq/rhZPn9HXZZJ1gJrAZYvX/609evXtxr/llsnuemu+5cdumJZq/ucD5ffMDkv21m+F9vVf5xYf+tv/dvdx0x/T3fm79eg79+Vv9tbt25l6dKlO/2+UTDOdYf26n/kkUduqqqJmV7rYgzZCuD6vuXNwOFJHgG8BXhqklOnErTpqmodsA5gYmKiVq9e3Wqw7z7nAt5x+f0P03XHtbvP+XD8PN3L8uRDt21X/3Fi/a2/9W+3/jP9Pd2Zv1+Dvn9X/m5v3LiRtn9jFqtxrjt0U/9F85emqm4BXtN1HJIkSQuti4TsBmD/vuX9mrKBJVkDrFm1atV8xiVJWmAzXVEpjaMu5iG7DDgwyQFJ9gSOAS7cmQ1U1YaqWrts2eIfyyVJkjSXtqe9OBf4PHBQks1JTqiqbcBJwKXAVcB5VXVlm3FIkiQtZq12WVbVsbOUXwJc0ua+JUmShsWiGdS/MxxDJklqQ/+YtpMP3cbxp1zsXQK0IIbyXpaOIZMkSaNkKBMySZKkUWJCJkmS1LGhTMiSrEmybnJyfm4PJEmS1KWhTMgcQyZJkkbJUCZkkiRJo8SETJIkqWPOQzYCvBecpHHl3z+NiqFsIXMMmSRJGiVDmZBJkiSNEhMySZKkjpmQSZIkdcxB/ZKkseQFAVpMhrKFzEH9kiRplAxlQiZJkjRKTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcxpLyRJI2++p7iYaXvXve3587oPjZehbCFz2gtJkjRKhjIhkyRJGiUmZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMechGzLzPZeOJGl+dDk32WKfF22xx7cYDGULmfOQSZKkUTKUCZkkSdIoMSGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmPeylCRpB+b7HsLe17HH43B/Q9lC5r0sJUnSKBnKhEySJGmUmJBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjq2R9cBTEmyD3AGcDewsarO6TgkSZKkBdFqC1mSs5JsSXLFtPKjklyd5JokpzTFPwecX1UnAi9sMy5JkqTFpO0uy7OBo/oLkiwBTgeOBg4Gjk1yMLAfcH2z2r0txyVJkrRopKra3UGyErioqg5plo8A3lxVz22WT21W3QzcVlUXJVlfVcfMsr21wFqA5cuXP239+vWtxr/l1kluuuv+ZYeuWNbqPqdcfsPkguxnR5bvxXb1HyfW3/pb/66j6M5irP9svz8z/V7szm/VTL99g5ppv7vzezbo9namvnO9f+vWrSxdunTg7Q3qyCOP3FRVEzO91sUYshX8sCUMeonY4cBpwHuSPB/YMNubq2odsA5gYmKiVq9e3V6kwLvPuYB3XH7/w3Tdce3uc8rxp1y8IPvZkZMP3bZd/ceJ9bf+1t/6Lyaz/f7M9HuxO79VM/32DWqm/e7O79mg29uZ+s71/o0bN9J2fjHdovmkVdWdwKu6jkOSJGmhdTHtxQ3A/n3L+zVlA0uyJsm6ycnuu/QkSZJ2VxcJ2WXAgUkOSLIncAxw4c5soKo2VNXaZcsWZiyXJElSm9qe9uJc4PPAQUk2JzmhqrYBJwGXAlcB51XVlW3GIUmStJi1Ooasqo6dpfwS4JJd3W6SNcCaVatW7eomJEmSFo2hvHWSXZaSJGmUDGVCJkmSNEpMyCRJkjo2lAmZ015IkqRR0vqtk9qU5NvA11vezb7AzS3vYzGz/tbf+o8v6z++9R/nukN79X9cVT1ypheGOiFbCEm+ONt9p8aB9bf+1t/6dx1HV8a5/uNcd+im/kPZZSlJkjRKTMgkSZI6ZkI2t3VdB9Ax6z/erP94s/7ja5zrDh3U3zFkkiRJHbOFTJIkqWMmZJIkSR0zIduBJEcluTrJNUlO6TqetiU5K8mWJFf0lf1Ikk8k+c/m34d3GWObkuyf5DNJvpLkyiSva8pH/hgkeXCSf03y703df78pPyDJF5rvwIeS7Nl1rG1KsiTJl5Nc1CyPTf2TXJfk8iT/luSLTdnIf/anJHlYkvOTfDXJVUmOGJf6JzmoOe9Tj9uT/Oa41B8gyW81f/uuSHJu8zdxQb//JmSzSLIEOB04GjgYODbJwd1G1bqzgaOmlZ0CfKqqDgQ+1SyPqm3AyVV1MPAM4LXNOR+HY/B94FlV9WTgKcBRSZ4B/AnwzqpaBdwGnNBdiAvidcBVfcvjVv8jq+opffMvjcNnf8pfAB+rqicAT6b3ORiL+lfV1c15fwrwNOC7wEcZk/onWQH8BjBRVYcAS4BjWODvvwnZ7J4OXFNV11bV3cB64EUdx9SqqvoscOu04hcBf9U8/yvgxQsZ00Kqqhur6kvN8zvo/UFewRgcg+rZ2iw+sHkU8Czg/KZ8JOs+Jcl+wPOB9zfLYYzqP4uR/+wDJFkG/BRwJkBV3V1V32FM6j/Ns4H/qqqvM1713wPYK8kewN7AjSzw99+EbHYrgOv7ljc3ZeNmeVXd2Dz/FrC8y2AWSpKVwFOBLzAmx6Dprvs3YAvwCeC/gO9U1bZmlVH/DrwLeD1wX7P8CMar/gV8PMmmJGubsrH47AMHAN8GPtB0Wb8/yT6MT/37HQOc2zwfi/pX1Q3A24Fv0EvEJoFNLPD334RMA6veHCkjP09KkqXA3wG/WVW39782ysegqu5tuiz2o9dC/IRuI1o4SV4AbKmqTV3H0qH/XlWH0Rum8dokP9X/4ih/9um1jhwGvLeqngrcybTuuRGvPwDNGKkXAh+e/too178ZG/cieon5Y4B92H74TutMyGZ3A7B/3/J+Tdm4uSnJfwNo/t3ScTytSvJAesnYOVX1kaZ4rI5B01XzGeAI4GFNEz6M9nfgJ4EXJrmO3vCEZ9EbUzQu9Z9qJaCqttAbP/R0xuezvxnYXFVfaJbPp5egjUv9pxwNfKmqbmqWx6X+zwG+VlXfrqp7gI/Q+5uwoN9/E7LZXQYc2FxlsSe9ZtwLO46pCxcCr2yevxK4oMNYWtWMGToTuKqq/rzvpZE/BkkemeRhzfO9gJ+mN4buM8BLmtVGsu4AVXVqVe1XVSvpfdc/XVXHMSb1T7JPkodMPQd+BriCMfjsA1TVt4DrkxzUFD0b+ApjUv8+x/LD7koYn/p/A3hGkr2b34Gp87+g339n6t+BJM+jN65kCXBWVb2l24jaleRcYDWwL3AT8Cbg74HzgMcCXwdeWlXTB/6PhCT/Hfgn4HJ+OI7ojfTGkY30MUjy4/QGrS6h9x+186rqD5I8nl6L0Y8AXwZeXlXf7y7S9iVZDfzvqnrBuNS/qedHm8U9gL+tqrckeQQj/tmfkuQp9C7o2BO4FngVzXeB8aj/PvQSk8dX1WRTNk7n//eBl9G72v7LwC/TGzO2YN9/EzJJkqSO2WUpSZLUMRMySZKkjpmQSZIkdcyETJIkqWMmZJIkSR0zIZMkSeqYCZkkSVLH/n8jP9q/61BISwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 15))\n",
    "\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_title(\"Article Length Histogram\")\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"Occurrence\")\n",
    "\n",
    "news[\"n_tokens_content_lmbda0\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_title(\"Log Transformed Length Histogram\")\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"Occurrence\")\n",
    "\n",
    "news[\"n_tokens_content_opt\"].hist(ax=ax3, bins=100)\n",
    "ax3.set_yscale(\"log\")\n",
    "ax3.set_title(\"Box-Cox Transformed Length Histogram\")\n",
    "ax3.set_xlabel(\"\")\n",
    "ax3.set_ylabel(\"Occurrence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d978ee33-f2e4-4941-a369-f053466316de",
   "metadata": {},
   "source": [
    "### Discretization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be77ad3-8608-4577-a57c-6bf757a9e75a",
   "metadata": {},
   "source": [
    "In a first step, we will look at synthetic count data: we create uniformly distributed random counts (once small values, once with a wide range)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a72e595e-dbf2-4247-b9cc-4a258ea6e3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44,  9, 38, 70, 69, 33, 56, 74, 18,  5, 41, 17, 25, 88, 83, 44, 80,\n",
       "       38, 76,  5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uniformly distributed, small values\n",
    "small_counts = np.random.randint(0, 100, 20)\n",
    "small_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "82b66c99-f869-4330-a93e-522ec78ea7b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 3, 7, 6, 3, 5, 7, 1, 0, 4, 1, 2, 8, 8, 4, 8, 3, 7, 0])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed width binning by division\n",
    "np.floor_divide(small_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "70968305-2047-46fe-a71c-ea4e87d2fe1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counts spanning a wide value range\n",
    "large_counts = [\n",
    "    296,\n",
    "    8286,\n",
    "    64011,\n",
    "    80,\n",
    "    3,\n",
    "    725,\n",
    "    867,\n",
    "    2215,\n",
    "    7689,\n",
    "    11495,\n",
    "    91897,\n",
    "    44,\n",
    "    28,\n",
    "    7971,\n",
    "    926,\n",
    "    122,\n",
    "    22222,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03396b9f-c3bb-4f18-8930-30253bf4470b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 3., 4., 1., 0., 2., 2., 3., 3., 4., 4., 1., 1., 3., 2., 2., 4.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed width binning via powers of 10 (0-9, 10-99, 100-999, 1000-999, etc.)\n",
    "np.floor(np.log10(large_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6e9522-b474-4d1e-8a90-3952acceae56",
   "metadata": {},
   "source": [
    "In the next step, we look at quantile binning to avoid empty bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09f2515a-439a-40f9-a350-dffeda1b27a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1      2.0\n",
       "0.2      5.0\n",
       "0.3      8.0\n",
       "0.4     13.0\n",
       "0.5     21.0\n",
       "0.6     34.0\n",
       "0.7     56.0\n",
       "0.8    104.0\n",
       "0.9    230.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute 10 deciles\n",
    "deciles = rating_counts.quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
    "deciles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c6a23d2-0fb2-4e87-b158-9833176856cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieID\n",
       "1        (230.0, 1670.0]\n",
       "2        (230.0, 1670.0]\n",
       "3        (230.0, 1670.0]\n",
       "4           (34.0, 56.0]\n",
       "5         (104.0, 230.0]\n",
       "              ...       \n",
       "65088       (0.999, 2.0]\n",
       "65091       (0.999, 2.0]\n",
       "65126       (0.999, 2.0]\n",
       "65130       (0.999, 2.0]\n",
       "65133         (2.0, 5.0]\n",
       "Name: rating, Length: 10109, dtype: category\n",
       "Categories (10, interval[float64, right]): [(0.999, 2.0] < (2.0, 5.0] < (5.0, 8.0] < (8.0, 13.0] ... (34.0, 56.0] < (56.0, 104.0] < (104.0, 230.0] < (230.0, 1670.0]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actually use deciles to bin data\n",
    "pd.qcut(rating_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fe53b730-6fe8-41b4-b70d-0eec41627b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92de59a00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
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     "execution_count": 23,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the deciles on the histogram\n",
    "sns.set_style(\"white\")\n",
    "fig, ax = plt.subplots()\n",
    "rating_counts.hist(ax=ax, bins=100)\n",
    "for pos in deciles:\n",
    "    handle = plt.axvline(pos, color=\"r\")\n",
    "    ax.legend([handle], [\"deciles\"])\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xlabel(\"Review Count (log)\")\n",
    "    ax.set_ylabel(\"Occurrence (log)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "752c20ea-849a-4c99-886f-4c202214f0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92dee9e20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb933b9b7f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92dede280>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92deb92e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92dede2e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92e200df0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92dee9190>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92e26afa0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb92dee9190>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the deciles on the histogram - now with x-axis not on log scale\n",
    "sns.set_style(\"white\")\n",
    "fig, ax = plt.subplots()\n",
    "rating_counts.hist(ax=ax, bins=100)\n",
    "for pos in deciles:\n",
    "    handle = plt.axvline(pos, color=\"r\")\n",
    "    ax.legend([handle], [\"deciles\"])\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_xlabel(\"Review Count (log)\")\n",
    "    ax.set_ylabel(\"Occurrence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2154d441-f801-4276-ae79-d9b69b247ffa",
   "metadata": {},
   "source": [
    "### Discretization: Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aaed8c0-d909-4bc4-992e-053d8af6cf65",
   "metadata": {},
   "source": [
    "In the following, we will dive into clustering algorithms based on a synthetic data set randomly created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "efc4e87c-3afb-40b5-bc6c-399b934c87c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y_true = sklearn.datasets.make_blobs(\n",
    "    n_samples=5000, centers=20, cluster_std=0.6, random_state=0\n",
    ")\n",
    "plt.scatter(X[:, 0], X[:, 1], s=50)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e4f7eb-8915-4dc5-a88c-a2b139938c50",
   "metadata": {},
   "source": [
    "Now we can compute k-means and also experiment with the number of clusters to be chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd875714-cc16-4f7b-8955-3cec153fbfb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(max_iter=200, n_clusters=5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute kmeans, repeat 10 times\n",
    "# init could also be 'kmeans++'\n",
    "kmeans = KMeans(n_clusters=5, init=\"k-means++\", n_init=10, max_iter=200)\n",
    "kmeans.fit(X)\n",
    "y_kmeans = kmeans.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d0caecc-89cb-4541-9d8c-ed636943cdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize results\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap=\"Set1\")\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c=\"black\", s=200, alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f30522f-a728-4278-bc9f-291c0d269ac8",
   "metadata": {},
   "source": [
    "For determining the number of clusters k, we can compute the sum of squared errors of each point to their closest cluster center for different k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e19fa6c-f455-4055-87af-9d2a4b5c37ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate sum of squared errors for different k\n",
    "k_candidates = range(1, 20)\n",
    "sse = []\n",
    "for i in k_candidates:\n",
    "    km = KMeans(n_clusters=i, init=\"random\", n_init=20, max_iter=200)\n",
    "    km.fit(X)\n",
    "    sse.append(km.inertia_)\n",
    "\n",
    "# plot\n",
    "plt.plot(k_candidates, sse, marker=\"o\")\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Distortion\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bf2b4f-cf67-493d-8ae3-c3e34825f172",
   "metadata": {},
   "source": [
    "The following example decomposes k-means into the expectation and the maximization steps (taken from DSHandbook):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6a3414b0-a14d-4279-bc21-06ffe74382d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.98, 0.98, 'Random Initialization')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.4967141530112327, 3.861735698828815, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.6476885381006925, 5.523029856408026, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-0.23415337472333597, 3.7658630430508193, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.5792128155073915, 4.7674347291529084, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'E-Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'M-Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.617884036481574, 1.7302164268618159, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.05544831815144, 7.31289000117005, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.5304909178700825, 2.8944167383856816, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.5802860667196599, 4.421012702817745, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'E-Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'M-Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.9825828101483296, 0.8677131436145118, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.3732439791827424, 7.75368870941779, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.570847026766382, 2.8553540230789376, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.9700766645513808, 4.415327315087125, '')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'E-Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'M-Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.98, 0.98, 'Final Clustering')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y_true = sklearn.datasets.make_blobs(\n",
    "    n_samples=300, centers=4, cluster_std=0.60, random_state=0\n",
    ")\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "centers = [0, 4] + rng.randn(4, 2)\n",
    "\n",
    "\n",
    "def draw_points(ax, c, factor=1):\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=c, cmap=\"inferno\", s=50 * factor, alpha=1)\n",
    "\n",
    "\n",
    "def draw_centers(ax, centers, factor=1, alpha=1.0):\n",
    "    ax.scatter(\n",
    "        centers[:, 0],\n",
    "        centers[:, 1],\n",
    "        c=np.arange(4),\n",
    "        cmap=\"inferno\",\n",
    "        s=200 * factor,\n",
    "        alpha=alpha,\n",
    "    )\n",
    "    ax.scatter(\n",
    "        centers[:, 0], centers[:, 1], c=\"black\", s=50 * factor, alpha=alpha\n",
    "    )\n",
    "\n",
    "\n",
    "def make_ax(fig, gs):\n",
    "    ax = fig.add_subplot(gs)\n",
    "    ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "    ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "    return ax\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(15, 4))\n",
    "gs = plt.GridSpec(\n",
    "    4, 15, left=0.02, right=0.98, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2\n",
    ")\n",
    "ax0 = make_ax(fig, gs[:4,:4])\n",
    "ax0.text(\n",
    "    0.98,\n",
    "    0.98,\n",
    "    \"Random Initialization\",\n",
    "    transform=ax0.transAxes,\n",
    "    ha=\"right\",\n",
    "    va=\"top\",\n",
    "    size=16,\n",
    ")\n",
    "draw_points(ax0, \"gray\", factor=2)\n",
    "draw_centers(ax0, centers, factor=2)\n",
    "\n",
    "for i in range(3):\n",
    "    ax1 = make_ax(fig, gs[:2, 4 + 2 * i:6 + 2 * i])\n",
    "    ax2 = make_ax(fig, gs[2:, 5 + 2 * i:7 + 2 * i])\n",
    "\n",
    "    # E-step\n",
    "    y_pred = pairwise_distances_argmin(X, centers)\n",
    "    draw_points(ax1, y_pred)\n",
    "    draw_centers(ax1, centers)\n",
    "\n",
    "    # M-step\n",
    "    new_centers = np.array([X[y_pred == i].mean(0) for i in range(4)])\n",
    "    draw_points(ax2, y_pred)\n",
    "    draw_centers(ax2, centers, alpha=0.3)\n",
    "    draw_centers(ax2, new_centers)\n",
    "    for i in range(4):\n",
    "        ax2.annotate(\n",
    "            \"\",\n",
    "            new_centers[i],\n",
    "            centers[i],\n",
    "            arrowprops=dict(arrowstyle=\"->\", linewidth=1),\n",
    "        )\n",
    "\n",
    "    # Finish iteration\n",
    "    centers = new_centers\n",
    "    ax1.text(\n",
    "        0.95,\n",
    "        0.95,\n",
    "        \"E-Step\",\n",
    "        transform=ax1.transAxes,\n",
    "        ha=\"right\",\n",
    "        va=\"top\",\n",
    "        size=14,\n",
    "    )\n",
    "    ax2.text(\n",
    "        0.95,\n",
    "        0.95,\n",
    "        \"M-Step\",\n",
    "        transform=ax2.transAxes,\n",
    "        ha=\"right\",\n",
    "        va=\"top\",\n",
    "        size=14,\n",
    "    )\n",
    "\n",
    "\n",
    "# Final E-step\n",
    "y_pred = pairwise_distances_argmin(X, centers)\n",
    "axf = make_ax(fig, gs[:4, -4:])\n",
    "draw_points(axf, y_pred, factor=2)\n",
    "draw_centers(axf, centers, factor=2)\n",
    "axf.text(\n",
    "    0.98,\n",
    "    0.98,\n",
    "    \"Final Clustering\",\n",
    "    transform=axf.transAxes,\n",
    "    ha=\"right\",\n",
    "    va=\"top\",\n",
    "    size=16,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eecbf7a6-5cb8-4373-a6f2-ce9f744b38c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute kmeans, repeat 10 times\n",
    "# init could also be 'kmeans++'\n",
    "dbscan = DBSCAN(eps=0.5, min_samples=5)\n",
    "dbscan.fit(X)\n",
    "y_dbscan = dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4b6bf319-81c4-40b1-b9a8-ebefd7bcc9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb92e8b0790>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize results\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_dbscan, s=50, cmap=\"inferno\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c118aedd-677f-43f5-87f4-5dca871843d9",
   "metadata": {},
   "source": [
    "### Text Data Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5183ed93-0ca5-44ee-8928-8ce8d1abf5aa",
   "metadata": {},
   "source": [
    "In the following, we will look at different representations of text and feature extraction methods for text. We will make use of the Yelp Dataset (https://www.kaggle.com/yelp-dataset/yelp-dataset): \"This dataset is a subset of Yelp's businesses, reviews, and user data. It was originally put together for the Yelp Dataset Challenge which is a chance for students to conduct research or analysis on Yelp's data and share their discoveries. In the most recent dataset you'll find information about businesses across 8 metropolitan areas in the USA and Canada.\" This example is adapted from the FeatEng book."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78930164-4e89-426f-b153-efdc4a1cf7ef",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> As the dataset is simply too large to store it on gitlab, please download the dataset directly using the link above, unzip it and store it in the data directory..</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0218975-9c55-4953-bcc5-816ed530c252",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in review data\n",
    "# restrict to first 5000 reviews\n",
    "with open(os.path.join(data_dir, \"yelp_academic_dataset_review.json\")) as fh:\n",
    "    lines_read = 0\n",
    "    data = []\n",
    "    for line in fh:\n",
    "        data.append(json.loads(line))\n",
    "        lines_read += 1\n",
    "        if lines_read == 5000:\n",
    "            break\n",
    "reviews = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7e6dede3-d6a1-4ad8-ab55-2f6dabae4456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   review_id    5000 non-null   object \n",
      " 1   user_id      5000 non-null   object \n",
      " 2   business_id  5000 non-null   object \n",
      " 3   stars        5000 non-null   float64\n",
      " 4   useful       5000 non-null   int64  \n",
      " 5   funny        5000 non-null   int64  \n",
      " 6   cool         5000 non-null   int64  \n",
      " 7   text         5000 non-null   object \n",
      " 8   date         5000 non-null   object \n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 351.7+ KB\n"
     ]
    }
   ],
   "source": [
    "reviews.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5cb54db-4e22-42d5-822e-12a78cbdb53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "529    I enjoy living at Avana! The complex is in a great location convenient to shopping, restaurants, and the interstate. The office staff (Brittni, Dereck, and Antwoin) are all super nice and professional. They do a great job with planning resident appreciation events and they respond promptly to inquiries. The maintenance crew is also very nice and responsive. I would recommend this property to anyone looking to make Atlanta their home!\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect single review\n",
    "pd.set_option(\"display.max_colwidth\", None)\n",
    "reviews.sample(1)[\"text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "48fb9b9c-9281-4d23-950d-e7a006c5d3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5000x19525 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 371018 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract bag-of-words\n",
    "bow_converter = text.CountVectorizer(token_pattern=\"(?u)\\\\b\\\\w+\\\\b\")\n",
    "bow = bow_converter.fit_transform(reviews[\"text\"])\n",
    "bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3e2f80ad-3cee-4b79-91af-a9556eb71456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '00', '000', '00am', '00pm', '01', '02', '05', '07', '08'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['heels', 'comicon', 'benny', 'granted', 'brazos', 'colour',\n",
       "       'ignorance', 'disregard', 'marky', 'heartburn', 'purr', '915',\n",
       "       'baffling', 'brighton', 'sharp', 'soups', 'say', 'equal', 'floor',\n",
       "       'spoiler'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bow returns a sparse matrix, extract feature name mapping\n",
    "words = bow_converter.get_feature_names_out()\n",
    "words[:10]\n",
    "np.random.choice(words, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "133ff6b7-6dbc-4872-8aa9-c5da2b248d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19525"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of vocabulary\n",
    "len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ab802f5-0881-4725-8c14-280d18369358",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract bag-of-bigrams\n",
    "bigram_converter = text.CountVectorizer(\n",
    "    ngram_range=(2, 2), token_pattern=\"(?u)\\\\b\\\\w+\\\\b\"\n",
    ")\n",
    "bob = bigram_converter.fit_transform(reviews[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b032c40-dde6-43eb-ae7c-06026f154dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0 1', '0 2', '0 25', '0 30', '0 4', '0 40', '0 50', '0 9mi',\n",
       "       '0 books', '0 environment'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['lobster was', 'service improve', 'inexpensive 2', 'was king',\n",
       "       'quickly let', 'are exceptionally', 'arises my', 'a newly',\n",
       "       'entry way', 'man tried', 'soup six', 'of her', 'things mixed',\n",
       "       'margarita our', 'we learned', 'the greasy', 'we uncovered',\n",
       "       'providers and', 'broken tiles', 'closed can'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract feature name mapping\n",
    "bigrams = bigram_converter.get_feature_names_out()\n",
    "bigrams[:10]\n",
    "np.random.choice(bigrams, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c28ea5e6-a868-45ac-a655-89972a8e0570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193577"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of bigram vocabulary\n",
    "len(bigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5ac88c2a-293c-46fb-b774-3b9bb8881bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract bag-of-trirams\n",
    "trigram_converter = text.CountVectorizer(\n",
    "    ngram_range=(3, 3), token_pattern=\"(?u)\\\\b\\\\w+\\\\b\"\n",
    ")\n",
    "bot = trigram_converter.fit_transform(reviews[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b1f59230-c2f2-4db6-a53e-44949f27fbbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0 1 stars', '0 25 oysters', '0 30 possible', '0 4 miles',\n",
       "       '0 40 oz', '0 50 for', '0 9mi 22min', '0 books on',\n",
       "       '0 environment 5', '0 i would'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['are lemongrass chicken', 'indicated that to', 'ones but the',\n",
       "       'other donuts and', 'town it s', 'she got pissy', '3 people can',\n",
       "       'got the bombay', 'lil too fried', 'we finally got',\n",
       "       'with my negative', 'microwave sink fridge', 'staff member marina',\n",
       "       'heart of gastown', 'is there both', 'office so i',\n",
       "       'least it wasn', 'when it calls', 'haircut but it', 'bill came at'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract feature name mapping\n",
    "trigrams = trigram_converter.get_feature_names_out()\n",
    "trigrams[:10]\n",
    "np.random.choice(trigrams, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ac01cd6-cd4f-4059-b5ed-77ebf93fa4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409449"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of trigram vocabulary\n",
    "len(trigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a1b15405-3461-4002-b7b8-443707e19739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gx44de2C6si67pOe+VatWRb7LzM7OJjY2lpYtW2JlZYW1tTXnzp2ja9euAJw7d46WLVuWOm9Z1tPe3p7Bgwczb968Yqd9/vnnSUlJISoqiu3bt/Puu+8WO93d1+Lvf/97seM9PT355ZdfuHbtGpMmTcLS0pJt27YRHh7OqFGj9NONHTuWsWPHkpycTFBQECtXrizyPQQ8+XvtUV+/J902PM4y0tPTyc7O1m8D4+Pjiz3Rxtra+oFpr1+/rl/H0rYD9z8X165d44MPPuCbb77B1dUVIyMjBg8eXKS9u33lxIkTjBs3Dg8Pj1LX71Gec01/R+To6Ej//v35/vvv9cMaNGiAra0tW7duRafTsWHDBuLi4p6onQMHDnDixAny8vL4/PPPcXZ2xt7enhdeeIGYmBi2bNlCfn4++fn5REZGlvil7/3s7e1xdXVl0aJF5Obmcu7cOTZs2FAuG8kmTZrw3HPPsWzZMvLy8ggPDy/yCbY4WVlZmJiYUL9+fW7fvs2iRYseu/0///yTI0eOkJeXh4mJCaampvpPVuWpUaNGj/T6jhgxguDgYP2XrxkZGcUevnhcW7du1X+KtLS0BMDQ0JAGDRpgaGj40FoPHDjAzZs3gTtf4n7xxRf06tVLP37MmDEsW7YMuHOI9OLFi+zevZvc3FxWrFhBmzZt9EcHhgwZwr///W/S0tK4dOkS69evZ+jQoWWatzSDBg1i3759HDx4EJ1OR25uLseOHdN/YKtVqxZ9+/bl008/JS0tTR+G9wsICGDNmjWcPHkSpRTZ2dns379fv7fm4eHBsWPHyMnJwc7ODnd3dw4ePMitW7do164dAJGRkZw8eZL8/HzMzMwwMTEptp896XutYcOGJCYmkpeXV6bpn3Tb8LjLuPt+P3HiBPv376dv374PTHP/tuHEiRNFtg2lbQcaNmzI1atX9Y9v376NgYEBDRo0AO6caHLvSU47d+7U9w0rKysMDAwwNDQsdf3ub+dhNP9B6+TJkx84bDN37lxWrVqFl5cX0dHRuLq6PlEbAwYMYMWKFXh5eXHmzBn9p3pzc3NWrVrFjh076NatG97e3ixcuLDMnRVg0aJFXLt2jW7dujFlyhTeeOMNnn/++Seq966FCxcSERGBl5cXS5YsoX///piYmJQ4/ZAhQ2jcuDHdunXD39//kb7fuF9eXh6fffYZXl5eeHt7k5KSwtSpUx97eSUZO3Ysu3fvxsPDo8RP6Pfy9fVlwoQJTJ06FTc3NwYMGKA/rbk8HDx4EH9/f1xdXZk/fz6LFy+mdu3amJmZMWnSJEaOHIm7uzsREREPzHv06FEGDRqEi4sLEydOxNfXt8hhufj4eNzc3IA7H7iWLVvG4sWL8fDwIDIyssgGIzAwEAcHB3r27MmYMWMYP3483bt3L9O8pbG3t+eLL77gyy+/pEuXLvTo0YNVq1ZRWFion2bgwIEcPnyYvn37FjnMeq8OHTowd+5c5syZg4eHB3369GHTpk368c8++yx169bVH7oxNzenadOmuLm5YWRkBNzZaH7wwQd4enrSs2dP6tWrx/jx44tt70nea507d6Zly5Z4e3uX6Xdk5bFteNRlNGrUCEtLS7p168Y777zDrFmzSvxw8dlnn3Hy5Em8vLxYsWIFQ4YM0Y8rbTswfPhwoqOjcXd35/XXX6dly5a8+uqrjBgxgueff54LFy7o+ynAqVOnCAgIwNXVlb///e+8//77ODg4lLp+97fzMAbqcY/biAoXFBRE8+bNi5wGK6qGhIQEgoKCWLNmjdaliEro2LFjTJs2rVw/VFUlmu8RiZJFRkYSGxtLYWEhYWFhhIaG6n8PI6oWOzs7CSEhSqDpyQri4W7evMkbb7zBrVu3sLOzY9asWfpj60IIUV3IoTkhhBCakkNzQgghNFXtDs1FREQ8cAWEpyk3N7dC2xNVl/QV8Sgqur/k5uY+0Zm2T6LaBdHdy39UlKioqAptT1Rd0lfEo6jo/nL38kZakENzQgghNCVBJIQQQlMSREIIITQlQSSEEEJTEkRCCCE0JUEkhBBCUxJEQgghNCVBJIQQQlMSREIIITQlQSSEEEJTEkRCCCE0JUEkhBBCUxJEQgghNCVBJIQQlcjq1fDMM9C+fVueeebO4+qu2t0GQgghqqrVq2HiRMjOBjDgypU7jwFGjdKysqdL9oiEEKKSeP/9uyH0P9nZd4ZXZxJEQghRScTGqhKGV3AhFUyCSAghKoGLiRmY1sstdlyzZhVcTAUrcxDpdDqGDBnC3/72NwDi4uIICAjA19eXoKAg8vLyAMjLyyMoKAhfX18CAgK4evWqfhlffvklvr6++Pn5cfDgQf3wsLAw/Pz88PX1JTg4WD+8pDaEEKK6UErxw9ErDFh2CFufi5jWLrpXVKcOzJ+vUXEVpMxB9N1339GiRQv944ULF/LKK6+wd+9eLC0t2bBhAwDr16/H0tKSvXv38sorr7Bw4UIAoqOjCQkJISQkhJUrVzJ79mx0Oh06nY45c+awcuVKQkJC2L59O9HR0Q9tQwghqoOUrDwmfv87H2w5jeezDTj+dStWrTTA0REMDBSOjhAcXL1PVIAyBlFCQgL79+9n+PDhwJ0EP3r0KH5+fgAMHTqU0NBQAH755ReGDh0KgJ+fH0eOHEEpRWhoKP7+/piYmODg4ICjoyORkZFERkbi6OiIg4MDJiYm+Pv7Exoa+tA2hBCiqjscfZN+n4ex/3wSH/g78e04T2wsajNqFMTEwJkz54iJqf4hBGU8fXvBggVMmzaNrKwsAFJTU7G0tMTY+M7sdnZ2JCYmApCYmIi9vf2dhRsbY2FhQWpqKomJiTg7O+uXaWtrq5/Hzs6uyPDIyMiHtvEwubm5REVFlWW1ykVOTk6FtieqLukrAiBfp/g+IoUNp9NoYlmLRf0a07JhLufPnysyXU3qL6UG0b59+2jQoAHPPfccx44dq4ianoipqSlOTk4V1l5UVFSFtieqLukr4vLNLKavCSfyahojPZvxzwFO1DEpfjNc0f1Fy9ArNYj++OMPfvnlF8LCwsjNzSUzM5P58+eTnp5OQUEBxsbGJCQkYGtrC9zZo4mPj8fOzo6CggIyMjKoX78+tra2JCQk6JebmJion6e44fXr1y+xDSGEqEqUUmz84xozt56mlpEh/x7lRr8O9lqXVWmU+h3R22+/TVhYGL/88guLFi2ic+fOfPbZZ3h5ebF7924ANm/ejI+PDwA+Pj5s3rwZgN27d9O5c2cMDAzw8fEhJCSEvLw84uLiiImJoWPHjnTo0IGYmBji4uLIy8sjJCQEHx8fDAwMSmxDCCGqirTb+QSuieCd9Sfp0MSKnW92kxC6z2Nf4mfatGm89dZbLFmyBCcnJwICAgAYPnw406ZNw9fXFysrKxYvXgxAq1at6NevH/3798fIyIiZM2diZGQEwMyZM5kwYQI6nY5hw4bRqlWrh7YhhBBVwYmYFN5cE0FCeg7T/NowqUcLjAwNtC6r0jFQShX/U94qSovjqnLcX5SF9JWao0BXyPJ90SwNvUjT+nX4fIQLrs3qP9IyatK2TC56KoQQ5ehqajZBayI4cSWVF12bMHtweyxq19K6rEpNgkgIIcrJtpPXeW/zKZSCJS+7MMS1idYlVQkSREII8YQycwuY9dMZNvx+Fddm9fj8ZVeaNayjdVlVhgSREEI8gZNxt3hzTTixKdkE+rTkjV6tqGUk15N+FBJEQgjxGAoLFcEH/2Th7vNYW5jy42ud8WreUOuyqiQJIiGEeEQJaTlMXRfB4UvJ9O9gx0dDO2JVR05IeFwSREII8Qj2nElg+sZIcvIL+WRYB15yd8DAQH4b9CQkiIQQogxu5+mYv+MsPxyNpX1jS5aOdKWFtbnWZVULEkRCCFGKqPh0An8M52JSJhO7N+ftPq0xNTbSuqxqQ4JICCFKoJTim8MxfLTzHFZmtfh+vCfdWllrXVa1I0EkhBDFuJmZy7T1J9l3/ga92trw6fCONDQ31bqsakmCSAgh7nPgwg3eXneS9Jx85gxuz5jOjnJCwlMkQSSEEP9fboGOf+06z8pDl2lta84PEzxpa2epdVnVngSREEIA0UmZBP4Yztn4dMZ2ceS9/k7UriUnJFQECSIhRI2mlGLNb3HM3nYGs1pGrBzrTu92cjfoiiRBJISosW5l5zFj4yl2nUnAu2UjPnvJGVvL2lqXVeNIEAkhaqQjl5J5a20EyVm5vNe/LRO8m2Mod0/VhASREKJGydcVsuTnC3yx/xLPNKzLprFd6dDUSuuyajQJIiFEjXElOYvANRGcjLvFy+4OzBzYjrqmshnUmrwCQogaYdMfV/nnltMYGRqw4i9u+He017ok8f9JEAkhqrX0nHxmbjnNlojreD7TgMUjXGhSz0zrssQ9JIiEENXW71dSCVobzvVbOUz1bc3kni0xkhMSKh0JIiFEtaMrVHyxL5oloRext6rNur91ppNjA63LEiWQIBJCVCvXbt3mrTURHI9JYZBzY+YNfQ7L2nL31MpMgkgIUW3sOBXPjI2R6AoVi15yZqhrE7lYaRUgQSSEqPKy8wqY/dNZ1p6Iw9mhHktHuODYsK7WZYkykiASQlRpp6+lEfhjOJeTs5jcswVBvVtTy8hQ67LEI5AgEkJUSYWFipWH/uRfu8/TsK4p/53QmS4tGmpdlngMEkRCiConKT2Ht9ef5ODFm/i1t+XjFztSv66J1mWJxyRBJISoUkKjEpm2IZLsvAIWDO3ASE8HOSGhipMgEkJUCTn5Oj7aEcW3R67gZG/JspEutLSx0LosUQ4kiIQQld75hAwCfwznfGIG472f5R9922BqLHdPrS4kiIQQlZZSiu+PXmFeSBSWtY35ZpwHL7Sx0bosUc4kiIQQlVJyZi7/2BBJ6LkkXmhjzb+GO2NtYap1WeIpkCASQlQ6By/eYOq6k6Rl5/PhwHa88vwzckJCNSZBJISoNPIKClm45zzBYX/S0sacb8d50q6xpdZliadMgkgIUSn8eSOTwDXhnL6WziivZnzg3w4zEzkhoSYo9ToYubm5DB8+nEGDBuHv78/SpUsBiIuLIyAgAF9fX4KCgsjLywMgLy+PoKAgfH19CQgI4OrVq/plffnll/j6+uLn58fBgwf1w8PCwvDz88PX15fg4GD98JLaEEJUH0op1v0Wh//SQ1xNvc2XYzoxf2gHCaEapNQgMjEx4dtvv+Wnn35iy5YtHDx4kIiICBYuXMgrr7zC3r17sbS0ZMOGDQCsX78eS0tL9u7dyyuvvMLChQsBiI6OJiQkhJCQEFauXMns2bPR6XTodDrmzJnDypUrCQkJYfv27URHRwOU2IYQonpIy85nyn/D+cfGSFwc6rHrze74tbfTuixRwUoNIgMDA+rWvXMV24KCAgoKCjAwMODo0aP4+fkBMHToUEJDQwH45ZdfGDp0KAB+fn4cOXIEpRShoaH4+/tjYmKCg4MDjo6OREZGEhkZiaOjIw4ODpiYmODv709oaChKqRLbEEJUfccvp9Dv8zB2n0lget+2/DDBCzur2lqXJTRQpu+IdDodL774IrGxsfzlL3/BwcEBS0tLjI3vzG5nZ0diYiIAiYmJ2Nvb31m4sTEWFhakpqaSmJiIs7Ozfpm2trb6eezs7IoMj4yMJDU1tcQ2HiY3N5eoqKiyrFa5yMnJqdD2RNUlfeUOXaFi9clU1p66hZ25MQv72dOmUR4Xzp/TurRKpSb1lzIFkZGREVu3biU9PZ3Jkyfz559/Pu26HpupqSlOTk4V1l5UVFSFtieqLukrEJeSzZtrwvkj9hbDOzVl1qD2mJvKOVPFqej+omXoPVIPsLS0xMvLi4iICNLT0ykoKMDY2JiEhARsbW2BO3s08fHx2NnZUVBQQEZGBvXr18fW1paEhAT9shITE/XzFDe8fv36JbYhhKh6tkZc44PNpwFYOtKVQc6NNa5IVBalfkeUkpJCeno6cGdX8fDhw7Ro0QIvLy92794NwObNm/Hx8QHAx8eHzZs3A7B79246d+6MgYEBPj4+hISEkJeXR1xcHDExMXTs2JEOHToQExNDXFwceXl5hISE4OPjg4GBQYltCCGqjoycfKaujeDNNRG0trNgx5vdJIREEaXuESUlJTFjxgx0Oh1KKfr27UvPnj1p2bIlb731FkuWLMHJyYmAgAAAhg8fzrRp0/D19cXKyorFixcD0KpVK/r160f//v0xMjJi5syZGBndOT1z5syZTJgwAZ1Ox7Bhw2jVqhUA06ZNK7YNIUTVEBF3i8Afw7mams2bvVrxhk9LjOXuqeI+BkoppXUR5UmL46o1/bi/KJua1Fd0hYr/HLjE4r0XsLWszZIRLng800DrsqqUmrQtk28JhRDlKj7tNm+tjeDonyn4d7RnwdAOWJnV0rosUYlJEAkhys2u0/FM33iKfF0h/xrekeGdmsrFSkWpJIiEEE8sO6+Auduj+PF4LB2bWvH5CFeebVRX67JEFSFBJIR4ImeupxH4Yzh/3sxiUo8WTPVtjYmxnJAgyk6CSAjxWAoLFV/9eplPd52nXp1a/DDei64tG2ldlqiCJIiEEI8sKSOHd9ZHEnbhBr2dbPl0eEca1DXRuixRRUkQCSEeyb5zSUzbcJKMnALmDnmO0V7N5IQE8UQkiIQQZZKTr+OTXef4+tcY2tpZ8N/XOtPa1kLrskQ1IEEkhCjVxcQM3vgxnHMJGbzy/DPM6NeW2rXkxnWifEgQCSFKpJRi9bFY5m4/i7mpMV+94o5PW7n4sChfEkRCiGKlZOUxfWMke88m0q1VIz57yRkbC7lxnSh/EkRCiAccjr7JW+siSMnK4wN/J17t+iyGhnJCgng6JIiEEHp5BYUs2nuBL8Mu8Wyjuqz6qwfPNbHSuixRzUkQCSEAuHwzizfXhBN5NY2Rns345wAn6pjIJkI8fdLLhKjhlFJs/OMaM7eeppaRIf8e5Ua/DvZalyVqEAkiIWqwtNv5fLDlNNtOXsfr2QYsftmFxvXMtC5L1DASRELUUCdiUnhzTQQJ6TlM82vDpB4tMJITEoQGJIiEqGEKdIUs3xfN0tCLNK1fhw2TuuDarL7WZYkaTIJIiBrkamo2QWsiOHEllRddmzB7cHssasvdU4W2JIiEqCG2nbzOe5tPoRQsedmFIa5NtC5JCECCSIhqLzO3gFk/nWHD71dxbVaPz192pVnDOlqXJYSeBJEQ1djJuFu8uSac2JRsAn1a8kavVtQykrunispFgkiIaqiwUBF88E8W7j6PtYUpP77WGa/mDbUuS4hiSRAJUc0kpOUwdV0Ehy8l07+DHR8N7YhVHTkhQVReEkRCVCN7ziQwfWMkOfmFfDKsAy+5O8jdU0WlJ0EkRDVwO0/H/B1n+eFoLO0bW7J0pCstrM21LkuIMpEgEqKKi4pPJ/DHcC4mZTKxe3Pe7tMaU2O5e6qoOiSIhKiilFJ8cziGj3aew8qsFt+P96RbK2utyxLikUkQCVEF3czMZdr6k+w7f4NebW34dHhHGpqbal2WEI9FgkiIKubAhRu8ve4k6Tn5zBncnjGdHeWEBFGlSRAJUUXkFuj4167zrDx0mda25vwwwZO2dpZalyXEE5MgEqIKiE7KJPDHcM7GpzO2iyPv9Xeidi05IUFUDxJEQlRiSinW/BbH7G1nMKtlxMqx7vRuZ6t1WUKUKwkiISqpW9l5zNh4il1nEvBu2YjPXnLG1rK21mUJUe4kiISohI5cSuattREkZ+XyXv+2TPBujqHcPVVUUxJEQlQi+bpClvx8gS/2X+KZhnXZNLYrHZpaaV2WEE+VBJEQlcSV5CwC10RwMu4WL7s7MHNgO+qayltUVH/Sy4WoBDb9cZV/bjmNkaEBK/7ihn9He61LEqLClHqHrPj4eMaMGUP//v3x9/fn22+/BeDWrVuMGzeOPn36MG7cONLS0oA7Z/nMmzcPX19fBg4cyJkzZ/TL2rx5M3369KFPnz5s3rxZP/z06dMMHDgQX19f5s2bh1LqoW0IUV2k5+QTtCacqetO0r6xFTuDuksIiRqn1CAyMjJixowZ7Nixg7Vr1/Lf//6X6OhogoOD6dKlC3v27KFLly4EBwcDEBYWRkxMDHv27GHu3LnMmjULuBMqy5cvZ926daxfv57ly5frg2XWrFnMnTuXPXv2EBMTQ1hYGECJbQhRHfx+JRX/pQfZFhnPVN/W/DixM03qmWldlhAVrtQgsrGxoX379gCYm5vTvHlzEhMTCQ0NZciQIQAMGTKEn3/+GUA/3MDAABcXF9LT00lKSuLQoUN07dqVevXqYWVlRdeuXTl48CBJSUlkZmbi4uKCgYEBQ4YMITQ0tMiy7m9DiKpMV6hYFnqRl748glKw7m+dCezVCiM5K07UUI/0HdHVq1eJiorC2dmZ5ORkbGxsALC2tiY5ORmAxMRE7Ozs9PPY2dmRmJj4wHBbW9tih9+dHiixjYfJzc0lKirqUVbrieTk5FRoe6Lq2b7dksWLrUlIaIuNbR72PhdIdbjCC8/WZUpna+pkJxIVlah1maKSqUnbljIHUVZWFoGBgbz33nuYmxe94ZaBgcFTv+hiWdswNTXFycnpqdZyr6ioqAptT1Qtq1fDrFmQnX3ncWKCCUnr2xL4oT2LJzaQi5WKElX0tkXL0Cv10BxAfn4+gYGBDBw4kD59+gDQsGFDkpKSAEhKSqJBgwbAnT2dhIQE/bwJCQnY2to+MDwxMbHY4Xenf1gbQlQV77//vxC6S+Ubs+X/GkoICfH/lRpESinef/99mjdvzrhx4/TDfXx82LJlCwBbtmyhV69eRYYrpYiIiMDCwgIbGxu8vb05dOgQaWlppKWlcejQIby9vbGxscHc3JyIiAiUUsUu6/42hKgKcvJ1XIlVxY6Lja3gYoSoxEo9NPf777+zdetWWrduzeDBgwGYOnUqEydOJCgoiA0bNtC4cWOWLFkCQI8ePThw4AC+vr6YmZmxYMECAOrVq8frr7/O8OHDAZg8eTL16tUD4MMPP+Tdd98lJyeH7t270717d4AS2xCiMlNKseNUAgt2RGFk0Rldep0HpmnWTIPChKikDNTdH+1UE1ocV5XviMRdZ6+nM3vbGY5dTsHJ3pLOhS58+oFFkcNzdepAcDCMGqVdnaLyq0nbMrmyghDlIDkzl8/2XmDN8ViszGoxf+hzjPBohpGhAS1t7nxXFBuraNbMgPnzJYSEuJcEkRBPIF9XyPdHrrDk5wtk5+l45flnebNXK6zq1NJPM2rUnb+oqHOy9yxEMSSIhHhMYRduMGf7WaKTMunWqhEfDmxHSxsLrcsSosqRIBLiEV2+mcX8kLP8HJXEMw3rsHKsO72cbOR0bCEekwSREGWUkZPP8n3RfHXoMiZGhszo15ZxXZ/B1NhI69KEqNIkiIQoRWGhYuMfV/l093luZOQS0Kkp0/q2wcZCbtstRHmQIBLiIf6ITWX2T2c4eTUN12b1WDnWHWeHelqXJUS1IkEkRDES0nL4ZNc5Nodfw9bSlMUvOzPYuQmGcoVsIcqdBJEQ98jJ17Hq0GVW7IumoFAxuWcLXn+hpdyyW4inSN5dQnDnsjy7zyQyf8dZ4lJu07e9He/1d6JZwwcvzyOEKF8SRKLGO5+QwextZzh8KZk2thasnuBF15aNtC5LiBpDgkjUWLey81i09wI/HL2CRe1azBncnr94NsPYqEx3RxFClBMJIlHjFOgK+e/xWBbtvUD67XxGd3bkrd6tqV/XROvShKiRJIhEjXI4+iazt53lfGIGz7doyMyB7WhrZ6l1WULUaBJEokaITc5m/o6z7D6TiEMDM/4zuhN+7W3lsjxCVAISRKJay8ot4Iv90fzfwcsYGxowza8N472fpXYtuSyPEJWFBJGolgoLFVtPXuPjnedITM9lqGsTpvdti52VXJZHiMpGgkhUOyfjbjFr2xnCY2/h3NSKL0Z1opNjfa3LEkKUQIJIVBtJGTl8uus8G36/SiNzU/41vCPD3JrKZXmEqOQkiESVl1ug4+tfY1gWepE8XSF/69GcKT1bYlG7VukzCyE0J0EkqiylFKFRScwLOUtMcja9nWx539+JZxvV1bo0IcQjkCASVVJ0Ugazt53l4MWbtLQx59tXPenR2lrrsoQQj0GCSFQpadn5LAm9wHdHrlDHxIiZA9oxposjteSyPEJUWRJEokrQFSrW/BbLZ3sukJqdx0jPZrzt25qG5qZalyaEeEISRKLSO/pnMrO3nSUqPh3PZxvw4cB2tG9spXVZQohyIkEkKq2rqdl8tOMcIafiaVLPjBV/caN/Bzu5LI8Q1YwEkah0bufp+PeBS3x54BIGBvBW79ZM7N4cMxO5LI8Q1ZEEkag0lFJsi4znox1RxKflMNC5Me/2a0vjemZalyaEeIokiESlcPpaGrO3neG3mFTaN7bk8xGueD7bQOuyhBAVQIJIaOpmZi4Ld59n7Yk4GtQx4eMXOxDg7oCRXJZHiBpDgkhoIq+gkO+OxPD5zxe5na9jfNdneaNXK6zM5LI8QtQ0EkSiwu07n8Tc7Wf580YWL7Sx5p8D2tHC2lzrsoQQGpEgEhXmzxuZzN1+ln3nb9C8UV2+fsWDnm1ttC5LCKExCSLx1KXn5LMs9CLfHI7B1NiI9/s78dfnn8HEWC7LI4SQIBJPUWGhYv3vcfxr93mSs/J4qZMD7/i1wdpCLssjhPgfCSLxVJyISWH2trOcupZGJ8f6fP2KJx2aymV5hBAPkiAS5So+7TYf7TjHTyevY2dZm89HuDDIubFclkcIUaJSD9K/++67dOnShQEDBuiH3bp1i3HjxtGnTx/GjRtHWloacOeX8fPmzcPX15eBAwdy5swZ/TybN2+mT58+9OnTh82bN+uHnz59moEDB+Lr68u8efNQSj20DVE55eTrWBp6EZ+FB9h9JoFAn5b88k4PBrs0kRASQjxUqUH04osvsnLlyiLDgoOD6dKlC3v27KFLly4EBwcDEBYWRkxMDHv27GHu3LnMmjULuBMqy5cvZ926daxfv57ly5frg2XWrFnMnTuXPXv2EBMTQ1hY2EPbEJWLUoodp+Lp9dkBFu29QM+21vw8tQdT+7ShjonscAshSldqEHl4eGBlVfTYfmhoKEOGDAFgyJAh/Pzzz0WGGxgY4OLiQnp6OklJSRw6dIiuXbtSr149rKys6Nq1KwcPHiQpKYnMzExcXFwwMDBgyJAhhIaGPrQNUXlExacz8v+O8vrqP7CobcyPr3Xmi1GdcGhQR+vShBBVyGN9ZE1OTsbG5s7vP6ytrUlOTgYgMTEROzs7/XR2dnYkJiY+MNzW1rbY4Xenf1gbpcnNzSUqKupxVuux5OTkVGh7lUFajo7vwlPYdTEDcxNDpnRuRN9WFhjlJREVlaR1eZVWTewr4vHVpP7yxMdODAwMnvp3AI/ShqmpKU5OTk+1nntFRUVVaHtaytcV8sPRKyzee4GsPB1juzxDUO9W1KtjonVpVUJN6iviyVV0f9Ey9B4riBo2bEhSUhI2NjYkJSXRoMGdqyTb2tqSkJCgny4hIQFbW1tsbW05fvy4fnhiYiKenp4lTv+wNoQ2Dl68wZxtZ7mYlIl3y0bMHNiO1rYWWpclhKgGHuun7T4+PmzZsgWALVu20KtXryLDlVJERERgYWGBjY0N3t7eHDp0iLS0NNLS0jh06BDe3t7Y2Nhgbm5OREQESqlil3V/G6JiXUnO4rXvTjBm1XHydIX831h3vh/vKSEkhCg3pe4RTZ06lePHj5Oamkr37t154403mDhxIkFBQWzYsIHGjRuzZMkSAHr06MGBAwfw9fXFzMyMBQsWAFCvXj1ef/11hg8fDsDkyZOpV68eAB9++CHvvvsuOTk5dO/ene7duwOU2IaoGJm5BSz/JZqvDl2mlpEB0/u25VXvZzA1lrukCiHKl4G6+8OdakKL46rV6bh/YaFiU/g1Ptl1jhsZuQxza8o/+rbB1rK21qVVedWtr4inqyZty+SHHkIvPDaVWdvOcjLuFs4O9Qge0wnXZvW1LksIUc1JEAkS03P4ZOc5NoVfw8bClEUvOTPEpQmGcpdUIUQFkCCqwXLydaw6dJkV+6Ip0Clef6EFr/dsibmpdAshRMWRLU4NpJRiz9lE5odEEZuSTZ92trzv74Rjw7palyaEqIEkiGqYC4kZzNl2lkPRN2llY84P473wbtVI67KEEDWYBFENcSs7j8V7L/DDsVjqmhgxa2A7Rnd2xNhI7pIqhNCWBFE1V6Ar5Mff4li05zxpt/P5i1czpvq2oUFduSyPEKJykCCqxg5fusmcbWc5l5BB5+YN+HBge5zsLbUuSwghipAgqobiUrJZsCOKnacTaFLPjH+PcqPvc3ZygzohRKUkQVSNZOcV8O/9l/gy7E+MDAx427c1r3VvTu1aclkeIUTlJUFUDSil+OnkdT7acY6E9ByGuDRmer+22FuZaV2aEEKUSoKoiou8eovZ287y+5VUOjSxYsUoVzo5yi0zhBBVhwRRFZWUkcO/dp1nwx9XaVjXhE+HdWR4p6ZyWR4hRJUjQVTF5BUU8s3hyywNjSa3QMdr3Zrzhk9LLGrX0ro0IYR4LBJEVYRSil/OJTEvJIrLN7Po1daG9/2daG5trnVpQgjxRCSIqoDopEzmbj/LgQs3aG5dl2/GefBCGxutyxJCiHIhQVSJpd3OZ2noRb49HIOZiREf+Dvx1+efoZZclkcIUY1IEFVCukLFuhNxLNx9npTsPEZ4OPB2nzY0MjfVujQhhCh3EkSVzPHLKcz66Qxn49PxfKYB3w5sx3NNrLQuSwghnhoJokri2q3bfLQjiu2R8TS2qs2yka4M6Ggvl+URQlR7EkQau52n48uwS/znwCWUgjd7tWJSjxaYmchleYQQNYMEkUaUUoSciuejHee4dus2/h3tea+/E03qyWV5hBA1iwSRBk5fS2POtrMcj0mhnb0li15yxqt5Q63LEkIITUgQVaDkzFwW7rnAmt9iqV/HhAVDO/CyhwNGclkeIUQNJkFUAfJ1hXx35ApLfr7A7Twd455/ljd7t8LKTC7LI4QQEkRP2YELN5iz7QyXbmTRvbU1Mwc40dLGQuuyhBCi0pAgekou38xi3vazhJ5L4pmGdVj1V3d82trI6dhCCHEfCaJylpGTz/Jfovnq18uYGhvxbr+2vNL1GUyN5XRsIYQojgRROSksVGz44yqf7jrPzcxcAjo1ZVrfNthY1Na6NCGEqNQkiB7T6tXw/vsQG9sW28Y67Hwuktr4Em7N6rHqr+44O9TTukQhhKgSJIgew+rVMHEiZGcDGJBwzYikNa0I/NCGRX+vL98DCSHEI5D7CTyG99+/G0L/U5hvxOb/ayAhJIQQj0iC6DHExj7acCGEECWTIHoMzZo92nAhhBAlkyB6DPPnQ506RYfVqXNnuBBCiEcjQfQYRo2C4GBwdAQDA4Wj453Ho0ZpXZkQQlQ9ctbcYxo16s5fVNQ5nJyctC5HCCGqLNkjEkIIoalKH0RhYWH4+fnh6+tLcHCw1uUIIYQoZ5U6iHQ6HXPmzGHlypWEhISwfft2oqOjtS5LCCFEOarUQRQZGYmjoyMODg6YmJjg7+9PaGio1mUJIYQoR5X6ZIXExETs7Oz0j21tbYmMjHzoPLm5uURFRT3t0vRycnIqtD1RdUlfEY+iJvWXSh1Ej8PU1LRCz2KLioqSs+ZEmUhfEY+iovuLlqFXqQ/N2drakpCQoH+cmJiIra2thhUJIYQob5U6iDp06EBMTAxxcXHk5eUREhKCj4+P1mUJIYQoR5X60JyxsTEzZ85kwoQJ6HQ6hg0bRqtWrbQuSwghRDmq1EEE0KNHD3r06KF1GUIIIZ6SSn1oTgghRPUnQSSEEEJTEkRCCCE0ZaCUUloXUZ4iIiIwNTXVugwhhKhScnNzcXFx0aTtahdEQgghqhY5NCeEEEJTEkRCCCE0JUEkhBBCUxJEQgghNCVBJIQQQlMSREIIITQlQVSKU6dOMW/ePK3LEBq7evUqAwYMeGD4+++/L7evF3rp6emsXr26xPEjRoyowGqqDvkdUTlTSqGUwtBQMr46uXr1KpMmTWL79u1PtJyCggKMjSv9tYbFYyqpnzzu667T6TAyMiqv8iqtGveOuL+jrFq1iuzsbI4fP07Hjh05duwYGRkZzJ8/H3d3d44dO8ZXX33Fl19+SUpKCm+//TZJSUm4uLhw+PBhNm7cSHZ2NuPHj8fZ2ZkzZ84QHBxMcHAwp06dIjc3Fz8/PwIDAwHw8fHB39+fsLAwjIyMmDt3LosWLeLKlSuMHz+ekSNHavn0iIcoKCjg7bff5uzZs7Rq1YpPPvmEiRMn8o9//IMOHTqwfv16Vq5ciYWFBW3btsXExISZM2cyY8YMTExMiIqKws3NDX9/f+bPn09ubi61a9dmwYIFNG/enE2bNvHzzz9z+/Ztrly5wquvvkp+fj5bt27FxMSE4OBg6tWrp/XTIB7is88+IzY2lsGDB2NsbIypqSmWlpZcvnyZ3bt34+rqSnh4OIWFhcyZM4ejR49ib2+PsbExw4YNo2/fvvj4+NCvXz8OHz7MhAkTyMrKYu3ateTn5+Po6Minn36KmZkZM2bMwNTUlKioKJKTk1mwYAFbtmwhIiICZ2dnPv74Y62fjrJTNUxcXJzy9/fXP165cqVaunSpGj16tProo4+UUkrt379f/fWvf1VKKXX06FE1ceJEpZRSs2fPVv/5z3+UUkodOHBAtW7dWiUnJ6u4uDjVpk0bFR4erl9uamqqUkqpgoICNXr0aBUVFaWUUqpnz55q9erVSiml5s+frwYMGKAyMjJUcnKy6tKly9NcdfEE4uLiVOvWrdWJEyeUUkrNmDFDrVy5Uo0ePVpFRkaqhIQE1bNnT5Wamqry8vLUyJEj1ezZs5VSSk2fPl1NnDhRFRQUKKWUysjIUPn5+UoppX799Vc1ZcoUpZRSGzduVL1799b3Bzc3N/Xf//5XKXWnr3z99dcVvNbiUd27fTl69KhydnZWsbGx+vEuLi5KKaV27typJkyYoHQ6nUpKSlLu7u5q586dSqk724jg4GD9PCkpKfr/Fy1apL777jul1J1+FRQUpAoLC9XevXuVq6urOnfunNLpdGro0KHq7NmzT319y0uN2yN6GF9fXwDat2/PtWvXHhj/+++/s3z5cgC6d++OlZWVflzjxo2LXKdp586drFu3joKCAm7cuMGlS5do27YtAL169QKgdevWZGdnY25uDoCJiQnp6elYWlo+lfUTT8be3p5OnToBMGjQIL7//nv9uFOnTuHh4aHfY+nbty8xMTH68X379tUfYsnIyGD69OlcuXIFAwMD8vPz9dN5eXnp+4OFhYX+jsStW7fm/PnzT3P1xFPQoUMHHBwcHhj++++/07dvXwwNDbG2tsbLy6vI+P79++v/v3jxIkuWLCEjI4OsrCy8vb3143r27ImBgQFt2rShUaNGtGnTBoCWLVty7do1nJycntKala8aF0TGxsYUFhbqH+fm5ur/NzExAcDQ0BCdTvdIy61Tp47+/7i4OL766is2bNiAlZUVM2bMKNJOrVq19O3cbfPu44KCgkdbIVFhDAwMHvr4YczMzPT/f/7553h5ebFixQquXr3K2LFj9ePu7w/39pVH7ZNCe/duFx7Fvf1lxowZfPHFF7Rt25ZNmzZx/Phx/bi7/cXAwKBKb0tq3DfqDRs2JDk5mdTUVPLy8ti/f3+Z53Vzc2Pnzp0AHDp0iLS0tGKny8rKwszMDAsLC27evElYWFh5lC40dv36dcLDwwHYvn27fu8I7nzy/e2330hLS6OgoIA9e/aUuJyMjAxsbW0B2Lx589MtWlSounXrkpWVVep0bm5u7Nmzh8LCQm7evFkkXO6XlZWFtbU1+fn5bNu2rTzLrTRq3B5RrVq1mDx5MgEBAdja2tK8efMyzztlyhSmTp3KTz/9hIuLC9bW1pibm5OdnV1kurZt29KuXTv69euHnZ0dbm5u5b0aQgPPPvssq1ev5r333qNly5aMHDmSffv2AWBra8vf/vY3AgICsLKyonnz5lhYWBS7nAkTJjBjxgz+/e9/06NHj4pcBfGU1a9fHzc3NwYMGICpqSmNGjUqdjo/Pz+OHDlC//79sbe3p127diX2lzfffJOAgAAaNGiAs7NzmYKuqpHTtx9BXl4ehoaGGBsbEx4ezqxZs9i6davWZYlKIisri7p161JQUMCUKVMYNmyY/ntHIe53t7+kpqYSEBDAjz/+iLW1tdZlaaLG7RE9ievXrxMUFERhYSG1atVi7ty5WpckKpHly5dz+PBhcnNz8fb2pnfv3lqXJCqxSZMmkZ6eTn5+Pq+//nqNDSGQPSIhhBAaq3EnKwghhKhcJIiEEEJoSoJICCGEpiSIhBBCaEqCSAghhKb+HzCj4PC1Xr2tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare vocabulary size of uni, bi, and trigrams\n",
    "sns.set_style(\"whitegrid\")\n",
    "counts = [len(words), len(bigrams), len(trigrams)]\n",
    "plt.plot(counts)\n",
    "plt.plot(counts, \"bo\")\n",
    "plt.margins(0.1)\n",
    "plt.xticks(range(3), [\"unigram\", \"bigram\", \"trigram\"])\n",
    "plt.title(\"Number of ngrams in the first 5,000 reviews of the Yelp dataset\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6c4e24b0-b2b7-4401-a3f3-bc22d7bc0c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0', 'x1', 'x2', ..., 'x193574', 'x193575', 'x193576'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 193577)\n"
     ]
    }
   ],
   "source": [
    "# Create the tf-idf representation using the bag-of-words matrix\n",
    "tfidf_transformer = text.TfidfTransformer(norm=None, use_idf=True)\n",
    "tf_idf = tfidf_transformer.fit_transform(bob)\n",
    "tfidf_transformer.get_feature_names_out()\n",
    "print(tf_idf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5dcf0a-c279-4295-b138-73b3857d4f95",
   "metadata": {},
   "source": [
    "### Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff4d805-993f-483a-a211-4befd6b2e1b3",
   "metadata": {},
   "source": [
    "The following example showcases encoding of categorical variables (dataset and code adapted from FeatEng)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bdf41196-1783-4f87-bb3c-551c4cb8369a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renting data per city\n",
    "rent_data = pd.DataFrame(\n",
    "    {\n",
    "        \"City\": [\n",
    "            \"SF\",\n",
    "            \"SF\",\n",
    "            \"SF\",\n",
    "            \"NYC\",\n",
    "            \"NYC\",\n",
    "            \"NYC\",\n",
    "            \"Seattle\",\n",
    "            \"Seattle\",\n",
    "            \"Seattle\",\n",
    "        ],\n",
    "        \"Rent\": [3999, 4000, 4001, 3499, 3500, 3501, 2499, 2500, 2501],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "50eb31c9-3d47-439f-b07e-1e37b0b4ddf0",
   "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>City</th>\n",
       "      <th>Rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SF</td>\n",
       "      <td>3999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SF</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SF</td>\n",
       "      <td>4001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NYC</td>\n",
       "      <td>3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NYC</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NYC</td>\n",
       "      <td>3501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      City  Rent\n",
       "0       SF  3999\n",
       "1       SF  4000\n",
       "2       SF  4001\n",
       "3      NYC  3499\n",
       "4      NYC  3500\n",
       "5      NYC  3501\n",
       "6  Seattle  2499\n",
       "7  Seattle  2500\n",
       "8  Seattle  2501"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3333.3333333333335"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect data\n",
    "rent_data\n",
    "rent_data[\"Rent\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7ca30c5f-8169-4fbc-a5bd-3c62569428a8",
   "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>Rent</th>\n",
       "      <th>city_NYC</th>\n",
       "      <th>city_SF</th>\n",
       "      <th>city_Seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3499</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3501</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rent  city_NYC  city_SF  city_Seattle\n",
       "0  3999         0        1             0\n",
       "1  4000         0        1             0\n",
       "2  4001         0        1             0\n",
       "3  3499         1        0             0\n",
       "4  3500         1        0             0\n",
       "5  3501         1        0             0\n",
       "6  2499         0        0             1\n",
       "7  2500         0        0             1\n",
       "8  2501         0        0             1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical variable (city) to one-hot-encoding\n",
    "one_hot_rent = pd.get_dummies(rent_data, prefix=[\"city\"])\n",
    "one_hot_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fc9043a5-24bd-453c-8914-5222729175b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 166.66666667,  666.66666667, -833.33333333])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3333.3333333333335"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit a linear regression model\n",
    "# y = w1x1 + ... + wnxn + b\n",
    "lin_reg = linear_model.LinearRegression()\n",
    "lin_reg.fit(\n",
    "    one_hot_rent[[\"city_NYC\", \"city_SF\", \"city_Seattle\"]], one_hot_rent[\"Rent\"]\n",
    ")\n",
    "\n",
    "# inspect regression coefficients (y = w1x1 + ... + wnxn + b)\n",
    "# for one-hot encoding\n",
    "# coefficients: difference from global mean\n",
    "# intercept: global mean of rent\n",
    "lin_reg.coef_\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "010243bb-4220-4fc3-b1cc-7e389fd749b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Rent</th>\n",
       "      <th>city_SF</th>\n",
       "      <th>city_Seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2499</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2501</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rent  city_SF  city_Seattle\n",
       "0  3999        1             0\n",
       "1  4000        1             0\n",
       "2  4001        1             0\n",
       "3  3499        0             0\n",
       "4  3500        0             0\n",
       "5  3501        0             0\n",
       "6  2499        0             1\n",
       "7  2500        0             1\n",
       "8  2501        0             1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical variable (city) to dummy coding\n",
    "# reference level not shown anymore\n",
    "dummy_rent = pd.get_dummies(rent_data, prefix=[\"city\"], drop_first=True)\n",
    "dummy_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b9cca547-09ea-4471-9757-65b37063d350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([  500., -1000.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3500.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect regression coefficients (y = w1x1 + ... + wnxn + b)\n",
    "# for dummy coding:\n",
    "# coefficients: difference per category to mean of reference category\n",
    "# intercept: mean of reference category\n",
    "lin_reg.fit(dummy_rent[[\"city_SF\", \"city_Seattle\"]], dummy_rent[\"Rent\"])\n",
    "lin_reg.coef_\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b470bd89-05b7-4bb7-878f-ed3b572f276b",
   "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",
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       "    }\n",
       "\n",
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       "        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>Rent</th>\n",
       "      <th>city_SF</th>\n",
       "      <th>city_Seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3499</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3500</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3501</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2499</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2501</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rent  city_SF  city_Seattle\n",
       "0  3999        1             0\n",
       "1  4000        1             0\n",
       "2  4001        1             0\n",
       "3  3499       -1            -1\n",
       "4  3500       -1            -1\n",
       "5  3501       -1            -1\n",
       "6  2499        0             1\n",
       "7  2500        0             1\n",
       "8  2501        0             1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical variable (city) to effect coding\n",
    "effect_rent = dummy_rent.copy()\n",
    "effect_rent[\"city_Seattle\"] = effect_rent[\"city_Seattle\"].astype(\"int32\")\n",
    "effect_rent[\"city_SF\"] = effect_rent[\"city_SF\"].astype(\"int32\")\n",
    "effect_rent.loc[3:5, [\"city_SF\", \"city_Seattle\"]] = -1.0\n",
    "effect_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7b8e650d-4ae4-4f0b-8afb-45b1b0094318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 666.66666667, -833.33333333])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3333.3333333333335"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect regression coefficients (y = w1x1 + ... + wnxn + b)\n",
    "# for dummy coding:\n",
    "# coefficients: difference per category to mean of reference category\n",
    "# intercept: mean of reference category\n",
    "lin_reg.fit(effect_rent[[\"city_SF\", \"city_Seattle\"]], effect_rent[\"Rent\"])\n",
    "lin_reg.coef_\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba0c1ce-3524-40a2-b783-c32185bb326a",
   "metadata": {},
   "source": [
    "In the following, we will briefly dive into feature hashing based on the Yelp review dataset used previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "80f35142-c195-4b67-89ff-faae20e6f55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2795\n"
     ]
    }
   ],
   "source": [
    "# we will define m as equal to the unique number of business_id\n",
    "m = len(reviews.business_id.unique())\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "640ab4fc-d38a-4208-ae7a-ca16c87886ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_hasher = FeatureHasher(n_features=m, input_type=\"string\")\n",
    "hashed = feature_hasher.transform(reviews[\"business_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ea541357-d52e-48c1-a9a4-cbc9be12ae1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our pandas Series, in bytes:  395144\n",
      "Our hashed numpy array, in bytes:  48\n"
     ]
    }
   ],
   "source": [
    "# We can see how this will make a difference in the future by\n",
    "# looking at the size of each\n",
    "print(\"Our pandas Series, in bytes: \", getsizeof(reviews[\"business_id\"]))\n",
    "print(\"Our hashed numpy array, in bytes: \", getsizeof(hashed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4c8d67cb-a29f-4136-855c-c4bd02a97010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['buF9druCkbuXLX526sGELQ',\n",
       " 'RA4V8pr014UyUbDvI-LW2A',\n",
       " '_sS2LBIGNT5NQb6PD1Vtjw',\n",
       " '0AzLzHfOJgL7ROwhdww2ew',\n",
       " '8zehGz9jnxPqXtOc7KaJxA']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare oriignal data to hashed data\n",
    "reviews[\"business_id\"].unique().tolist()[0:5]\n",
    "hashed.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef7c751-7be0-45e4-84cc-27e2bebb4d44",
   "metadata": {},
   "source": [
    "## Expanding Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb42d44-a081-46e1-97e7-beb4dd674586",
   "metadata": {},
   "source": [
    "### Value Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c497c77-a87a-44f8-89cf-46f326a35d52",
   "metadata": {},
   "source": [
    "Different imputation methods showcased, adapted from CleanData."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8df460fc-829c-4592-ad72-80b5f43d4dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_derm_data():\n",
    "    np.random.seed(1)\n",
    "\n",
    "    # Histopathological Attributes: (values 0, 1, 2, 3)\n",
    "    # Clinical Attributes: (values 0, 1, 2, 3, unless indicated)\n",
    "    features = [\n",
    "        \"erythema\",\n",
    "        \"scaling\",\n",
    "        \"definite borders\",\n",
    "        \"itching\",\n",
    "        \"koebner phenomenon\",\n",
    "        \"polygonal papules\",\n",
    "        \"follicular papules\",\n",
    "        \"oral mucosal involvement\",\n",
    "        \"knee and elbow involvement\",\n",
    "        \"scalp involvement\",\n",
    "        \"family history\",  # 0 or 1\n",
    "        \"melanin incontinence\",\n",
    "        \"eosinophils in the infiltrate\",\n",
    "        \"PNL infiltrate\",\n",
    "        \"fibrosis of the papillary dermis\",\n",
    "        \"exocytosis\",\n",
    "        \"acanthosis\",\n",
    "        \"hyperkeratosis\",\n",
    "        \"parakeratosis\",\n",
    "        \"clubbing of the rete ridges\",\n",
    "        \"elongation of the rete ridges\",\n",
    "        \"thinning of the suprapapillary epidermis\",\n",
    "        \"spongiform pustule\",\n",
    "        \"munro microabcess\",\n",
    "        \"focal hypergranulosis\",\n",
    "        \"disappearance of the granular layer\",\n",
    "        \"vacuolisation and damage of basal layer\",\n",
    "        \"spongiosis\",\n",
    "        \"saw-tooth appearance of retes\",\n",
    "        \"follicular horn plug\",\n",
    "        \"perifollicular parakeratosis\",\n",
    "        \"inflammatory monoluclear inflitrate\",\n",
    "        \"band-like infiltrate\",\n",
    "        \"Age\",  # linear; missing marked '?'\n",
    "        \"TARGET\",  # See mapping\n",
    "    ]\n",
    "\n",
    "    targets = {\n",
    "        1: \"psoriasis\",  # 112 instances\n",
    "        2: \"seboreic dermatitis\",  # 61\n",
    "        3: \"lichen planus\",  # 72\n",
    "        4: \"pityriasis rosea\",  # 49\n",
    "        5: \"cronic dermatitis\",  # 52\n",
    "        6: \"pityriasis rubra pilaris\",  # 20\n",
    "    }\n",
    "\n",
    "    data = os.path.join(data_dir, \"dermatology.data\")\n",
    "    df = pd.read_csv(data, header=None, names=features, na_values=[\"?\"])\n",
    "    df[\"TARGET\"] = df.TARGET.map(targets)\n",
    "\n",
    "    derm = df.copy()\n",
    "    derm.loc[derm.Age == \"?\", \"Age\"] = None\n",
    "    derm[\"Age\"] = derm.Age.astype(float)\n",
    "    return derm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ef10fdef-711f-4546-a9bd-d54a31366cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "derm = read_derm_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "90fcb5e3-4e4c-466a-8f76-c9481ed04593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>erythema</th>\n",
       "      <th>scaling</th>\n",
       "      <th>definite borders</th>\n",
       "      <th>itching</th>\n",
       "      <th>Age</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>psoriasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>lichen planus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>lichen planus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     erythema  scaling  definite borders  itching   Age               TARGET\n",
       "247         2        2                 2        0  62.0            psoriasis\n",
       "127         2        2                 2        2  44.0        lichen planus\n",
       "230         3        2                 0        1  30.0  seboreic dermatitis\n",
       "162         3        2                 2        2  22.0        lichen planus\n",
       "159         3        2                 2        1  47.0  seboreic dermatitis\n",
       "296         2        1                 1        3  19.0    cronic dermatitis"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use iloc on both axes and sample\n",
    "derm.iloc[:, [0, 1, 2, 3, -2, -1]].sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "43734f3f-a997-436c-93c9-04785c6a5c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean, suspicious = [], {}\n",
    "for col in derm.columns:\n",
    "    values = derm[col].unique()\n",
    "    if set(values) <= {0, 1, 2, 3}:\n",
    "        clean.append(col)\n",
    "    else:\n",
    "        suspicious[col] = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "46b6f7d4-77a0-4e7d-b70e-039caa83568d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No problem detected:\n",
      "['erythema',\n",
      " 'scaling',\n",
      " 'definite borders',\n",
      " 'itching',\n",
      " 'koebner phenomenon',\n",
      " 'polygonal papules',\n",
      " 'follicular papules',\n",
      " 'oral mucosal involvement']\n",
      "... 25 other fields\n"
     ]
    }
   ],
   "source": [
    "print(\"No problem detected:\")\n",
    "pprint(clean[:8])\n",
    "print(f\"... {len(clean) - 8} other fields\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d1180b53-e9b0-407d-bd1a-22474288e195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suspicious:\n",
      "{'Age': array([55.,  8., 26., 40., 45., 41., 18., 57., 22., 30., 20., 21., 10.,\n",
      "       65., 38., 23., 17., 51., 42., 44., 33., 43., 50., 34., nan, 15.,\n",
      "       46., 62., 35., 48., 12., 52., 60., 32., 19., 29., 25., 36., 13.,\n",
      "       27., 31., 28., 64., 39., 47., 16.,  0.,  7., 70., 37., 61., 67.,\n",
      "       56., 53., 24., 58., 49., 63., 68.,  9., 75.]),\n",
      " 'TARGET': array(['seboreic dermatitis', 'psoriasis', 'lichen planus',\n",
      "       'cronic dermatitis', 'pityriasis rosea',\n",
      "       'pityriasis rubra pilaris'], dtype=object)}\n"
     ]
    }
   ],
   "source": [
    "# Notice age has some expected ages and also a '?'\n",
    "print(\"Suspicious:\")\n",
    "pprint(suspicious)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "afb090dc-1a70-4aa3-945f-ac8331cc84db",
   "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>inflammatory monoluclear inflitrate</th>\n",
       "      <th>band-like infiltrate</th>\n",
       "      <th>Age</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>psoriasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pityriasis rosea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lichen planus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     inflammatory monoluclear inflitrate  band-like infiltrate  Age  \\\n",
       "33                                     0                     0  NaN   \n",
       "34                                     0                     0  NaN   \n",
       "35                                     0                     0  NaN   \n",
       "36                                     0                     3  NaN   \n",
       "262                                    3                     0  NaN   \n",
       "263                                    2                     0  NaN   \n",
       "264                                    3                     0  NaN   \n",
       "265                                    3                     0  NaN   \n",
       "\n",
       "                  TARGET  \n",
       "33             psoriasis  \n",
       "34      pityriasis rosea  \n",
       "35   seboreic dermatitis  \n",
       "36         lichen planus  \n",
       "262    cronic dermatitis  \n",
       "263    cronic dermatitis  \n",
       "264    cronic dermatitis  \n",
       "265    cronic dermatitis  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset encodes missing values as ?\n",
    "# --> added ? to na_values list to be recognized when reading\n",
    "# file in panda's read_csv\n",
    "derm.loc[derm.Age.isnull()].iloc[:, -4:]\n",
    "# alternatively:\n",
    "# Assign missing ages marked with '?' as None\n",
    "# derm.loc[derm.Age == '?', 'Age'] = None  # or NaN\n",
    "# Convert string/None ages to floating-point\n",
    "# derm['Age'] = derm.Age.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f8b344-39d7-4593-be81-ad90ad57830f",
   "metadata": {},
   "source": [
    "#### Typical-Value Imputation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2e990956-1d39-4894-ab5e-a3ac8712cfea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# detailed plot\n",
    "# count values\n",
    "age_stats = pd.DataFrame(\n",
    "    [\n",
    "        derm[derm.Age == x][\"Age\"].count()\n",
    "        for x in np.arange(derm[\"Age\"].min(), derm[\"Age\"].max())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "295def47-3520-42d2-b6df-3093585e4900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot\n",
    "plot = age_stats.plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Age\",\n",
    "    ylabel=\"Count\",\n",
    "    legend=False,\n",
    ")\n",
    "\n",
    "# adapt ticks on x-axis\n",
    "for ind, label in enumerate(plot.get_xticklabels()):\n",
    "    if ind % 10 == 0:  # every 10th label is kept\n",
    "        label.set_visible(True)\n",
    "    else:\n",
    "        label.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "86677c98-7415-4126-840c-a5bc5fffec5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36.29608938547486, 35.0)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean and median seem like best solution\n",
    "derm.Age.mean(), derm.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f153af7d-d05e-4902-82be-45be358be237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40.0\n",
       "1    50.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "derm.Age.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9888b03-63b3-466e-b8ac-8e862a8c5bb6",
   "metadata": {},
   "source": [
    "Alternatively, this dataset was created in Turkey - we might also want to use domain knowledge and use the mean/median age in Turkey at the time of dataset creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c3aa2477-b374-411a-bc44-fb84d48e9d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use sklearn's SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"mean\")\n",
    "# expects 2d array or dataframe\n",
    "derm[\"Age_imputed\"] = imputer.fit_transform(pd.DataFrame(derm[\"Age\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b9cbaf00-26a8-4763-af9f-d6146408ee51",
   "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>band-like infiltrate</th>\n",
       "      <th>Age</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>Age_imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>psoriasis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pityriasis rosea</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lichen planus</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     band-like infiltrate  Age               TARGET  Age_imputed\n",
       "33                      0  NaN            psoriasis    36.296089\n",
       "34                      0  NaN     pityriasis rosea    36.296089\n",
       "35                      0  NaN  seboreic dermatitis    36.296089\n",
       "36                      3  NaN        lichen planus    36.296089\n",
       "262                     0  NaN    cronic dermatitis    36.296089\n",
       "263                     0  NaN    cronic dermatitis    36.296089\n",
       "264                     0  NaN    cronic dermatitis    36.296089\n",
       "265                     0  NaN    cronic dermatitis    36.296089"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "derm.loc[derm.Age.isnull()].iloc[:, -4:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be55127-f078-4a6f-a4d8-80e7c9c9d4ee",
   "metadata": {},
   "source": [
    "#### Locality Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c289112b-0a6b-438a-98f2-9c911f1dc9ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array shape: (50, 8, 8)\n"
     ]
    }
   ],
   "source": [
    "# load digits dataset (adopted, now missing a few pixels)\n",
    "digits = np.load(os.path.join(data_dir, \"digits.npy\"))\n",
    "print(\"Array shape:\", digits.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e1526d7e-8c7b-48dd-953a-1db09d8d050d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# display digits\n",
    "def show_digits(digits=digits, x=3, y=3, title=\"Digits\"):\n",
    "    \"Display of 'corrupted numerals'\"\n",
    "    if digits.min() >= 0:\n",
    "        newcm = cm.get_cmap(\"Greys\", 17)\n",
    "    else:\n",
    "        gray = cm.get_cmap(\"Greys\", 18)\n",
    "        newcolors = gray(np.linspace(0, 1, 18))\n",
    "        newcolors[:1,\n",
    "                 ] = np.array([1.0, 0.9, 0.9, 1])\n",
    "        newcm = ListedColormap(newcolors)\n",
    "\n",
    "    fig, axes = plt.subplots(\n",
    "        x,\n",
    "        y,\n",
    "        figsize=(x * 2.5, y * 2.5),\n",
    "        subplot_kw={\"xticks\": (), \"yticks\": ()},\n",
    "    )\n",
    "\n",
    "    for ax, img in zip(axes.ravel(), digits):\n",
    "        ax.imshow(img, cmap=newcm)\n",
    "        for i in range(8):\n",
    "            for j in range(8):\n",
    "                if img[i, j] == -1:\n",
    "                    s = \"╳\"\n",
    "                    c = \"k\"\n",
    "                else:\n",
    "                    s = str(img[i, j])\n",
    "                    c = \"k\" if img[i, j] < 8 else \"w\"\n",
    "                _ = ax.text(j, i, s, color=c, ha=\"center\", va=\"center\")\n",
    "    fig.suptitle(title, y=0)\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6b4cf573-8f4d-4f8e-9c38-1528a875dce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_digits(digits, title=\"Digits with missing pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b81a57ad-a81f-445b-83b2-d2352e614167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coded for clarity, not for best vectorized speed\n",
    "# Function definition only; used in later cell\n",
    "def fill_missing(digit):\n",
    "    digit = digit.copy()\n",
    "    missing = np.where(digit == -1)\n",
    "    for y, x in zip(*missing):  # Pull off x/y position of pixel\n",
    "        # Do not want negative indices in slice\n",
    "        x_start = max(0, x - 1)\n",
    "        y_start = max(0, y - 1)\n",
    "        # No harm in index larger than size\n",
    "        x_end = x + 2\n",
    "        y_end = y + 2\n",
    "        # What if another -1 is in region? Remove all the -1s\n",
    "        region = digit[y_start:y_end, x_start:x_end].flatten()\n",
    "        region = region[region >= 0]\n",
    "        total = np.sum(region)\n",
    "        avg = total // region.size\n",
    "        digit[y, x] = avg\n",
    "    return digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a92431e8-ede0-4e84-ab13-3669b2f91d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new = np.empty_like(digits)\n",
    "for n in range(new.shape[0]):\n",
    "    new[n] = fill_missing(digits[n])\n",
    "\n",
    "show_digits(digits, title=\"Digits with missing pixels\"), show_digits(\n",
    "    new, title=\"Digits with imputed pixels\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41904e77-0f98-43c3-afd1-dcfd0c98200b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Reducing Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731dd15c-6901-4a23-96cf-a42d103fbd16",
   "metadata": {},
   "source": [
    "### Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0aa854-2346-468c-b869-3cb615e5d6d0",
   "metadata": {},
   "source": [
    "The following initial examples are based on the iris datasets (directly loaded via scikit-learn). The dataset consists of 50 samples from three species of the iris flower and describes its sepals (Kelchblatt) and petals (Blütenblatt) (length and width). More information on the dataset can be found here: https://en.wikipedia.org/wiki/Iris_flower_data_set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a0a3fa-6e2d-47d1-8baf-eea4b8ffcbc6",
   "metadata": {},
   "source": [
    "#### Example 2D -> 1D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e30aed5e-b026-44ce-9da4-ae698e02dfa9",
   "metadata": {},
   "source": [
    "Note that we could also load the iris dataset from scikit learn via the `load_iris` method and then convert it to a dataframe and reset the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "67acad73-88ee-445f-b303-af99f37af6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load iris dataset via sklearn\n",
    "def load_iris():\n",
    "    iris = sklearn.datasets.load_iris(as_frame=True)\n",
    "    return iris.frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a7a7fbb4-744d-478c-b628-bba92964cce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "935e8acd-f27c-4cec-829f-36c0240da5d4",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'species'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'species'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_17572/353937712.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# first look at data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miris\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'species'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"viridis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib/python3.9/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2094\u001b[0m     \u001b[0;31m# Set up the PairGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m     \u001b[0mgrid_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"diag_sharey\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2096\u001b[0;31m     grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,\n\u001b[0m\u001b[1;32m   2097\u001b[0m                     \u001b[0mhue_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2098\u001b[0m                     height=height, aspect=aspect, dropna=dropna, **grid_kws)\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib/python3.9/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, hue, hue_order, palette, hue_kws, vars, x_vars, y_vars, corner, diag_sharey, height, aspect, layout_pad, despine, dropna, size)\u001b[0m\n\u001b[1;32m   1287\u001b[0m             \u001b[0;31m# to the axes-level functions, while always handling legend creation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1288\u001b[0m             \u001b[0;31m# See GH2307\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1289\u001b[0;31m             \u001b[0mhue_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhue_order\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcategorical_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1290\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1291\u001b[0m                 \u001b[0;31m# Filter NA from the list of unique hue names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'species'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first look at data\n",
    "sns.pairplot(iris, hue='species', palette=\"viridis\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c6e7a68-b433-4399-9c76-0507730015b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6ba2df8-f27c-40eb-909b-9d27c628f3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a6df280-a126-41ee-8c5b-2f299471109f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iris_preprocessing(iris_data):\n",
    "    # encode species label as numeric value\n",
    "    label_encoder = preprocessing.LabelEncoder()\n",
    "    iris_data[\"species_id\"] = label_encoder.fit_transform(iris.species)\n",
    "\n",
    "    # scale values\n",
    "    scaler = preprocessing.MinMaxScaler()\n",
    "    iris_data[\n",
    "        [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n",
    "    ] = scaler.fit_transform(\n",
    "        iris_data[\n",
    "            [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n",
    "        ]\n",
    "    )\n",
    "    return iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3222fe3-9f71-4fb9-a8a3-ef9dad329859",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "iris = iris_preprocessing(iris)\n",
    "iris[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50e46151-194a-4ec9-b2cb-355e8c668b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = iris.plot.scatter(\n",
    "    x=\"petal_length\", y=\"petal_width\", c=\"species_id\", colormap=\"viridis\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc13815a-80e4-48bd-813b-8a6129bd7dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply PCA, reduce to a single dimension\n",
    "petals = iris[[\"petal_length\", \"petal_width\"]]\n",
    "pca = PCA(n_components=1)\n",
    "pca.fit(petals)\n",
    "petals_1d = pca.transform(petals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9892b191-3eee-4d22-9d59-0be19aee77aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "petals_1d[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "567712a3-2554-4822-af66-01c2e0578d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# actual components of the PCA (principal axes)\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe92f4e0-35ec-4970-bd29-92df0cb81616",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56c720f9-a073-4eb1-ac33-6b29e0e4a918",
   "metadata": {},
   "outputs": [],
   "source": [
    "petals_inverse = pca.inverse_transform(petals_1d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07377fe7-7eb8-45b5-8d4a-33e44b9d3309",
   "metadata": {},
   "outputs": [],
   "source": [
    "petals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b386e633-ccc3-43fd-bc81-98a4a5c83388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize sepals\n",
    "plt.scatter(\n",
    "    petals[\"petal_length\"], petals[\"petal_width\"], s=50, label=\"2d data\"\n",
    ")\n",
    "plt.scatter(petals_inverse[:, 0], petals_inverse[:, 1], label=\"1d data\")\n",
    "plt.xlabel(\"Petal length\")\n",
    "plt.ylabel(\"Petal width\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c13bd66e-d7cc-442d-8163-c30645f1bfb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(petals_1d, y=np.zeros(len(petals_1d)))\n",
    "plt.xlabel(\"PC1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d9d8408-cba1-40de-9e23-47b9917bd721",
   "metadata": {},
   "source": [
    "#### Example 4D -> 2D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c18f82-a823-413d-8a5f-53365abc8672",
   "metadata": {},
   "source": [
    "We will continue to use the iris dataset, but now use all four features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff7ae65-f278-4d9d-8916-95c432eb1471",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> The follow code is based on plot.ly. To show plot.ly charts in this notebook in jupyer lab, we ned to install an extension: `jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyterlab-plotly`</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eb30d87-b7d4-404d-8f05-95f1a4b8020d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load iris dataset from seaborne\n",
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbc52ea1-a06d-41d1-a772-7b39f6cdbac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"sepal_width\", \"sepal_length\", \"petal_width\", \"petal_length\"]\n",
    "fig = px.scatter_matrix(\n",
    "    iris, dimensions=features, color=\"species\", width=1000, height=800\n",
    ")\n",
    "fig.update_traces(diagonal_visible=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e37cb24-87df-42e4-a05b-b52c7ffeb6e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing\n",
    "iris = iris_preprocessing(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a53b42e1-7250-44dc-92e8-da68b009b71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=3)\n",
    "pcs_3d = pca.fit_transform(iris[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5387887c-cf8e-4dbd-9766-edf0b082b8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# code adapted from https://plotly.com/python/pca-visualization/\n",
    "fig = px.scatter_3d(\n",
    "    pcs_3d,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    z=2,\n",
    "    color=iris[\"species\"],\n",
    "    labels={\"0\": \"PC 1\", \"1\": \"PC 2\", \"2\": \"PC 3\"},\n",
    "    width=900,\n",
    "    height=900,\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61e2cde0-c177-466b-a1db-310d56b5b2b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at explained variance contribution of each PC\n",
    "explained_variance_stats = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "px.area(\n",
    "    x=range(1, explained_variance_stats.shape[0] + 1),\n",
    "    y=explained_variance_stats,\n",
    "    labels={\"x\": \"# Components\", \"y\": \"Explained Variance\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88d683cd-762d-4c06-9064-7e8d317f6a72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot as elbow curve\n",
    "number_pcs = np.arange(pca.n_components_) + 1\n",
    "plt.plot(number_pcs, pca.explained_variance_ratio_)\n",
    "plt.title(\"Elbow Plot\")\n",
    "plt.xlabel(\"PC\")\n",
    "plt.ylabel(\"Variance Explained (%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bdc113-6342-433a-bcfb-a5f84c70df7d",
   "metadata": {},
   "source": [
    "Using PCA, we reduce the number of dimensions to 2, essentially projecting the points onto a 2d plane/surface. This is easily observable when printing it in 3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1100ee6e-950b-411b-a496-5ff728ac131f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pcs_2d = pca.fit_transform(iris[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e078204-145c-45a2-b1f3-0bf2b09a0a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter_3d(\n",
    "    pcs_2d,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    z=np.zeros(len(iris)),\n",
    "    color=iris[\"species\"],\n",
    "    labels={\"0\": \"PC 1\", \"1\": \"PC 2\", \"2\": \"PC 3\"},\n",
    "    width=900,\n",
    "    height=900,\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e203fc7-c89b-43ef-ab43-21aafffe00b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot 2d plot\n",
    "# include principal components\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "components = pca.fit_transform(iris[features])\n",
    "# loadings = actual weights for the linear combination for the projection \n",
    "loadings = pca.components_.T * np.sqrt(pca.explained_variance_)\n",
    "\n",
    "fig = px.scatter(\n",
    "    components,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    color=iris[\"species\"],\n",
    "    labels={\"0\": \"PC1\", \"1\": \"PC2\"},\n",
    ")\n",
    "\n",
    "for i, feature in enumerate(features):\n",
    "    fig.add_shape(\n",
    "        type=\"line\", x0=0, y0=0, x1=loadings[i, 0], y1=loadings[i, 1]\n",
    "    )\n",
    "    fig.add_annotation(\n",
    "        x=loadings[i, 0],\n",
    "        y=loadings[i, 1],\n",
    "        ax=0,\n",
    "        ay=0,\n",
    "        xanchor=\"center\",\n",
    "        yanchor=\"bottom\",\n",
    "        text=feature,\n",
    "    )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6b6801f-54e1-4453-9bf0-7755f8476d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0df46b2-52a3-4e67-9665-93391f64ef57",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c0d7e0-9edf-4cd4-b360-d0f33d34486f",
   "metadata": {},
   "source": [
    "#### Computation of PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aeca1cd-aa62-4e41-a48e-c7200d21e2fb",
   "metadata": {},
   "source": [
    "In the following, we will perform PCA step-by-step, again based on the iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ed46dc-2140-4e84-a764-c9c2276c9f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load iris dataset from seaborne, preprocessing\n",
    "# preprocessing includes centering\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "features = [\"sepal_width\", \"sepal_length\", \"petal_width\", \"petal_length\"]\n",
    "# for reasons of simplicity, reduce pandas df to simple numerical matrix\n",
    "iris=iris[features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b51565-4e68-48da-ad86-9d11a1c24fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute covariance matrix on centered data\n",
    "mean = np.mean(iris, axis=0)\n",
    "iris_centered = iris - mean\n",
    "covariance_matrix = np.cov(iris_centered.T)\n",
    "covariance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a7c0f1b-998c-44ab-bd98-e736aa0beb55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform eigendecomposition on covariance matrix\n",
    "eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd2013cb-78ca-4d39-a686-1873900ee04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigenvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a10a7db0-6061-4650-b79a-f460b7539e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c10dbf3f-8025-433f-a832-77a62f4f7200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.linalg.eig does not necessarily return eigenvalues and vectors sorted\n",
    "# by Eigenvalue --> sort both vectors analoguously\n",
    "sorted_index = np.argsort(eigenvalues)[::-1]\n",
    "eigenvalues_sorted = eigenvalues[sorted_index]\n",
    "eigenvectors_sorted = eigenvectors[:,sorted_index]\n",
    "eigenvalues_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3f3d67-71d4-4ac9-b241-2a10c47df74c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the eigenvalues, we can already compute the contribution to \n",
    "# explained variance for each PC\n",
    "sum_eigenvalues = np.sum(eigenvalues_sorted)\n",
    "for i in range(len(eigenvalues_sorted)):\n",
    "    print(f\"PC{i}: {(eigenvalues_sorted[i] / sum_eigenvalues).round(2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7e557a9-50d7-45ca-9f48-f061c4295b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we choose to use 2 components\n",
    "eigenvectors = eigenvectors_sorted[:,0:2]\n",
    "eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64bed45-4a2c-4c29-9bf6-c09be1c54ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply eigenvectors to original data to reduce dimensions\n",
    "iris_2d = np.dot(eigenvectors.transpose(),iris_centered.transpose()).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90303ea2-a2d7-4e1a-b520-7af82f923419",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize results\n",
    "fig = px.scatter(\n",
    "    iris_2d,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    #color=iris[\"species\"],\n",
    "    labels={\"0\": \"PC1\", \"1\": \"PC2\"},\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1db78571-2cd2-4e45-998a-3f2e9bcc1928",
   "metadata": {},
   "outputs": [],
   "source": [
    "# principal components are orthogonal\n",
    "np.matrix.round(np.cov(eigenvectors[0], eigenvectors[1]))"
   ]
  }
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