lectures/mehrdad/05_preprocessing-pipelines.ipynb

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    "![](../img/330-banner.png)"
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   "source": [
    "# Lecture 5: Preprocessing and `sklearn` pipelines\n",
    "\n",
    "UBC 2024 Summer\n",
    "\n",
    "Instructor: Mehrdad Oveisi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports, LOs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Imports"
   ]
  },
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   "source": [
    "import sys\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "\n",
    "sys.path.append(\"../code/.\")\n",
    "\n",
    "from IPython.display import display\n",
    "from plotting_functions import *\n",
    "\n",
    "# Classifiers and regressors\n",
    "from sklearn.dummy import DummyClassifier, DummyRegressor\n",
    "\n",
    "# Preprocessing and pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# train test split and cross validation\n",
    "from sklearn.model_selection import cross_val_score, cross_validate, train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    MinMaxScaler,\n",
    "    OneHotEncoder,\n",
    "    OrdinalEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from utils import *\n",
    "import mglearn_utils\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   "source": [
    "### Learning outcomes\n",
    "\n",
    "From this lecture, you will be able to \n",
    "\n",
    "- explain motivation for preprocessing in supervised machine learning;\n",
    "- identify when to implement feature transformations such as imputation, scaling, and one-hot encoding in a machine learning model development pipeline; \n",
    "- use `sklearn` transformers for applying feature transformations on your dataset;\n",
    "- discuss golden rule in the context of feature transformations;\n",
    "- use `sklearn.pipeline.Pipeline` and `sklearn.pipeline.make_pipeline` to build a preliminary machine learning pipeline. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Quick recap\n",
    "\n",
    "- Types of data for our purposes\n",
    "  - Categorical:\n",
    "    - Nominal (sometimes just called *categorical*!), Ordinal\n",
    "  - Numerical:\n",
    "    - Discrete, Continuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (iClicker) Exercise 5.1\n",
    "\n",
    "\n",
    "Take a guess: In your machine learning project, how much time will you typically spend on data preparation and transformation?\n",
    "\n",
    "- (A) ~80% of the project time \n",
    "- (B) ~20% of the project time\n",
    "- (C) ~50% of the project time \n",
    "- (D) None. Most of the time will be spent on model building\n",
    "    \n",
    "The question is adapted from [here](https://developers.google.com/machine-learning/data-prep/process). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivation and big picture [[video](https://youtu.be/xx9HlmzORRk)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "source": [
    "- So far we have seen\n",
    "    - Three ML models (decision trees, $k$-NNs, SVMs with RBF kernel)\n",
    "    - ML fundamentals (train-validation-test split, cross-validation, the fundamental tradeoff, the golden rule)\n",
    "- Are we ready to do machine learning on real-world datasets?\n",
    "    - Very often real-world datasets need preprocessing before we use them to build ML models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example: $k$-nearest neighbours on the Spotify dataset\n",
    "\n",
    "- In lab2 you used `DecisionTreeClassifier` to predict whether the user would like a particular song or not. \n",
    "- Can we use $k$-NN classifier for this task? \n",
    "- Intuition: To predict whether the user likes a particular song or not (query point) \n",
    "   - find the songs that are closest to the query point\n",
    "   - let them vote on the target\n",
    "   - take the majority vote as the target for the query point   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to run the code below, you need to download the dataset from [Kaggle](https://www.kaggle.com/geomack/spotifyclassification/download)."
   ]
  },
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   "source": [
    "spotify_df = pd.read_csv(\"../data/spotify.csv\", index_col=0)\n",
    "train_df, test_df = train_test_split(spotify_df, test_size=0.20, random_state=123)\n",
    "X_train, y_train = (\n",
    "    train_df.drop(columns=[\"song_title\", \"artist\", \"target\"]),\n",
    "    train_df[\"target\"],\n",
    ")\n",
    "X_test, y_test = (\n",
    "    test_df.drop(columns=[\"song_title\", \"artist\", \"target\"]),\n",
    "    test_df[\"target\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "name": "stdout",
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     "text": [
      "Mean validation score 0.508\n"
     ]
    },
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       "      <th>0</th>\n",
       "      <td>0.001185</td>\n",
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       "      <td>0.507752</td>\n",
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       "      <td>0.000821</td>\n",
       "      <td>0.001378</td>\n",
       "      <td>0.507740</td>\n",
       "      <td>0.507752</td>\n",
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       "      <td>0.000698</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.507740</td>\n",
       "      <td>0.507752</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.000764</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.508133</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.000906</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.507359</td>\n",
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       "  </tbody>\n",
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       "   fit_time  score_time  test_score  train_score\n",
       "0  0.001185    0.000855    0.507740     0.507752\n",
       "1  0.000821    0.001378    0.507740     0.507752\n",
       "2  0.000698    0.000539    0.507740     0.507752\n",
       "3  0.000764    0.000683    0.506211     0.508133\n",
       "4  0.000906    0.001153    0.509317     0.507359"
      ]
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     "execution_count": 3,
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   "source": [
    "dummy = DummyClassifier(strategy=\"most_frequent\")\n",
    "scores = cross_validate(dummy, X_train, y_train, return_train_score=True)\n",
    "print(\"Mean validation score %0.3f\" % (np.mean(scores[\"test_score\"])))\n",
    "pd.DataFrame(scores)"
   ]
  },
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     "text": [
      "Mean validation score 0.546\n"
     ]
    },
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       "      <td>0.006943</td>\n",
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       "      <td>0.717829</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.005517</td>\n",
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       "      <td>0.721705</td>\n",
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       "      <td>0.002398</td>\n",
       "      <td>0.009951</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.708527</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.003554</td>\n",
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       "      <td>0.537267</td>\n",
       "      <td>0.721921</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002963</td>\n",
       "      <td>0.009315</td>\n",
       "      <td>0.562112</td>\n",
       "      <td>0.711077</td>\n",
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       "   fit_time  score_time  test_score  train_score\n",
       "0  0.006943    0.015189    0.563467     0.717829\n",
       "1  0.005517    0.014411    0.535604     0.721705\n",
       "2  0.002398    0.009951    0.529412     0.708527\n",
       "3  0.003554    0.009013    0.537267     0.721921\n",
       "4  0.002963    0.009315    0.562112     0.711077"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "scores = cross_validate(knn, X_train, y_train, return_train_score=True)\n",
    "print(\"Mean validation score %0.3f\" % (np.mean(scores[\"test_score\"])))\n",
    "pd.DataFrame(scores)"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>842</th>\n",
       "      <td>0.229000</td>\n",
       "      <td>0.494</td>\n",
       "      <td>147893</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0469</td>\n",
       "      <td>-9.743</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>140.832</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.704</td>\n",
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       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.771</td>\n",
       "      <td>227143</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>-4.712</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>111.959</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "842      0.229000         0.494       147893   0.666          0.000057    9   \n",
       "654      0.000289         0.771       227143   0.949          0.602000    8   \n",
       "\n",
       "     liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "842    0.0469    -9.743     0       0.0351  140.832             4.0    0.704  \n",
       "654    0.5950    -4.712     1       0.1750  111.959             4.0    0.372  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_songs = X_train.sample(2, random_state=42)\n",
    "two_songs"
   ]
  },
  {
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     "execution_count": 6,
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   ],
   "source": [
    "euclidean_distances(two_songs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider only two features: `duration_ms` and `tempo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "      <th>duration_ms</th>\n",
       "      <th>tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>147893</td>\n",
       "      <td>140.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>227143</td>\n",
       "      <td>111.959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration_ms    tempo\n",
       "842       147893  140.832\n",
       "654       227143  111.959"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_songs_subset = two_songs[[\"duration_ms\", \"tempo\"]]\n",
    "two_songs_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.134998Z",
     "iopub.status.busy": "2024-05-17T15:07:37.134466Z",
     "iopub.status.idle": "2024-05-17T15:07:37.143278Z",
     "shell.execute_reply": "2024-05-17T15:07:37.142230Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.134974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.        , 79250.00525962],\n",
       "       [79250.00525962,     0.        ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euclidean_distances(two_songs_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you see any problem? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The distance is completely **dominated by the the features with larger values**\n",
    "- The features with smaller values are being ignored. \n",
    "- Does it matter? \n",
    "    - Yes! Scale is based on how data was collected. \n",
    "    - Features on a smaller scale can be highly informative and there is no good reason to ignore them.\n",
    "    - We want our model to be robust and not sensitive to the scale. \n",
    "- Was this a problem for decision trees?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Scaling using `scikit-learn`'s [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
    "\n",
    "- We'll use `scikit-learn`'s [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html), which is a `transformer`.   \n",
    "- Only focus on the syntax for now. We'll talk about scaling in a bit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.144893Z",
     "iopub.status.busy": "2024-05-17T15:07:37.144534Z",
     "iopub.status.idle": "2024-05-17T15:07:37.159194Z",
     "shell.execute_reply": "2024-05-17T15:07:37.158103Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.144860Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()  # create feature trasformer object\n",
    "scaler.fit(X_train)  # fitting the transformer on the train split\n",
    "X_train_scaled = scaler.transform(X_train)  # transforming the train split\n",
    "X_test_scaled = scaler.transform(X_test)  # transforming the test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.161966Z",
     "iopub.status.busy": "2024-05-17T15:07:37.160739Z",
     "iopub.status.idle": "2024-05-17T15:07:37.189654Z",
     "shell.execute_reply": "2024-05-17T15:07:37.187578Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.161920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>0.004770</td>\n",
       "      <td>0.585</td>\n",
       "      <td>214740</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>0.2730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>0.114000</td>\n",
       "      <td>0.665</td>\n",
       "      <td>216728</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.303000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-7.314</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3310</td>\n",
       "      <td>100.344</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0373</td>\n",
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       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>0.030200</td>\n",
       "      <td>0.798</td>\n",
       "      <td>216585</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>-10.488</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>127.136</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.912</td>\n",
       "      <td>194040</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0723</td>\n",
       "      <td>-12.719</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>99.346</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.021100</td>\n",
       "      <td>0.697</td>\n",
       "      <td>236456</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.893000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-7.787</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>119.977</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.584</td>\n",
       "      <td>274404</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.002690</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>-3.501</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>74.976</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.535</td>\n",
       "      <td>203500</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>10</td>\n",
       "      <td>0.2630</td>\n",
       "      <td>-3.566</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>116.956</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>256333</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2190</td>\n",
       "      <td>-4.469</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>60.391</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>0.705000</td>\n",
       "      <td>0.894</td>\n",
       "      <td>222307</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3120</td>\n",
       "      <td>-14.311</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0880</td>\n",
       "      <td>104.968</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.8180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>0.623000</td>\n",
       "      <td>0.470</td>\n",
       "      <td>394920</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.187000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>-17.036</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0399</td>\n",
       "      <td>118.176</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1613 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "1505      0.004770         0.585       214740   0.614          0.000155   10   \n",
       "813       0.114000         0.665       216728   0.513          0.303000    0   \n",
       "615       0.030200         0.798       216585   0.481          0.000000    7   \n",
       "319       0.106000         0.912       194040   0.317          0.000208    6   \n",
       "320       0.021100         0.697       236456   0.905          0.893000    6   \n",
       "...            ...           ...          ...     ...               ...  ...   \n",
       "2012      0.001060         0.584       274404   0.932          0.002690    1   \n",
       "1346      0.000021         0.535       203500   0.974          0.000149   10   \n",
       "1406      0.503000         0.410       256333   0.648          0.000000    7   \n",
       "1389      0.705000         0.894       222307   0.161          0.003300    4   \n",
       "1534      0.623000         0.470       394920   0.156          0.187000    2   \n",
       "\n",
       "      liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "1505    0.0762    -5.594     0       0.0370  114.059             4.0   0.2730  \n",
       "813     0.1220    -7.314     1       0.3310  100.344             3.0   0.0373  \n",
       "615     0.1280   -10.488     1       0.3140  127.136             4.0   0.6400  \n",
       "319     0.0723   -12.719     0       0.0378   99.346             4.0   0.9490  \n",
       "320     0.1190    -7.787     0       0.0339  119.977             4.0   0.3110  \n",
       "...        ...       ...   ...          ...      ...             ...      ...  \n",
       "2012    0.1290    -3.501     1       0.3330   74.976             4.0   0.2110  \n",
       "1346    0.2630    -3.566     0       0.1720  116.956             4.0   0.4310  \n",
       "1406    0.2190    -4.469     1       0.0362   60.391             4.0   0.3420  \n",
       "1389    0.3120   -14.311     1       0.0880  104.968             4.0   0.8180  \n",
       "1534    0.1040   -17.036     1       0.0399  118.176             4.0   0.0591  \n",
       "\n",
       "[1613 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train # original X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine transformed value of the energy feature in the first row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.191964Z",
     "iopub.status.busy": "2024-05-17T15:07:37.191509Z",
     "iopub.status.idle": "2024-05-17T15:07:37.200974Z",
     "shell.execute_reply": "2024-05-17T15:07:37.199185Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.191932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.614"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['energy'].iloc[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.204294Z",
     "iopub.status.busy": "2024-05-17T15:07:37.203214Z",
     "iopub.status.idle": "2024-05-17T15:07:37.216229Z",
     "shell.execute_reply": "2024-05-17T15:07:37.215006Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.204241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3180174485124284"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train['energy'].iloc[0] - np.mean(X_train['energy']))/ X_train['energy'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.218705Z",
     "iopub.status.busy": "2024-05-17T15:07:37.217910Z",
     "iopub.status.idle": "2024-05-17T15:07:37.233879Z",
     "shell.execute_reply": "2024-05-17T15:07:37.232738Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.218662Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
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       "      <td>0.296</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>-0.796</td>\n",
       "      <td>0.598</td>\n",
       "      <td>-1.487</td>\n",
       "      <td>-0.439</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.781</td>\n",
       "      <td>2.728</td>\n",
       "      <td>-0.803</td>\n",
       "      <td>-3.781</td>\n",
       "      <td>-1.861</td>\n",
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       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>-0.600</td>\n",
       "      <td>1.111</td>\n",
       "      <td>-0.376</td>\n",
       "      <td>-0.947</td>\n",
       "      <td>-0.493</td>\n",
       "      <td>0.447</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-0.879</td>\n",
       "      <td>0.781</td>\n",
       "      <td>2.535</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.576</td>\n",
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       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.809</td>\n",
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       "      <td>-0.492</td>\n",
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       "      <td>-0.609</td>\n",
       "      <td>-0.840</td>\n",
       "      <td>0.139</td>\n",
       "      <td>1.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>-0.635</td>\n",
       "      <td>0.492</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>1.057</td>\n",
       "      <td>2.723</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-0.458</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-1.281</td>\n",
       "      <td>-0.653</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.139</td>\n",
       "      <td>-0.754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      acousticness  danceability  duration_ms  energy  instrumentalness  \\\n",
       "1505        -0.698        -0.195       -0.399  -0.318            -0.492   \n",
       "813         -0.276         0.296       -0.374  -0.796             0.598   \n",
       "615         -0.600         1.111       -0.376  -0.947            -0.493   \n",
       "319         -0.307         1.809       -0.654  -1.722            -0.492   \n",
       "320         -0.635         0.492       -0.131   1.057             2.723   \n",
       "\n",
       "        key  liveness  loudness   mode  speechiness  tempo  time_signature  \\\n",
       "1505  1.276    -0.738     0.396 -1.281       -0.618 -0.294           0.139   \n",
       "813  -1.487    -0.439    -0.052  0.781        2.728 -0.803          -3.781   \n",
       "615   0.447    -0.400    -0.879  0.781        2.535  0.191           0.139   \n",
       "319   0.170    -0.763    -1.461 -1.281       -0.609 -0.840           0.139   \n",
       "320   0.170    -0.458    -0.176 -1.281       -0.653 -0.074           0.139   \n",
       "\n",
       "      valence  \n",
       "1505   -0.908  \n",
       "813    -1.861  \n",
       "615     0.576  \n",
       "319     1.825  \n",
       "320    -0.754  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train_scaled, columns=X_train.columns, index=X_train.index).head().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `fit` and `transform` paradigm for transformers\n",
    "- `sklearn` uses `fit` and `transform` paradigms for feature transformations. \n",
    "- We `fit` the transformer on the train split and then transform the train split as well as the test split. \n",
    "- We apply the same transformations on the test split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `sklearn` API summary: estimators\n",
    "\n",
    "Suppose `model` is a classification or regression model. \n",
    "\n",
    "```\n",
    "model.fit(X_train, y_train)\n",
    "X_train_predictions = model.predict(X_train)\n",
    "X_test_predictions = model.predict(X_test)\n",
    "```    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `sklearn` API summary: transformers\n",
    "\n",
    "Suppose `transformer` is a transformer used to change the input representation, for example, to tackle missing values or to scales numeric features.\n",
    "\n",
    "```\n",
    "transformer.fit(X_train, [y_train])\n",
    "X_train_transformed = transformer.transform(X_train)\n",
    "X_test_transformed = transformer.transform(X_test)\n",
    "```  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- You can pass `y_train` in `fit` but it's usually ignored. It allows you to pass it just to be consistent with usual usage of `sklearn`'s `fit` method.   \n",
    "- You can also carry out fitting and transforming in one call using `fit_transform`. But be mindful to use it **only** on the **train split** and **not** on the **test split**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Do you expect `DummyClassifier` results to change after scaling the data? \n",
    "- Let's check whether scaling makes any difference for $k$-NNs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.235425Z",
     "iopub.status.busy": "2024-05-17T15:07:37.235120Z",
     "iopub.status.idle": "2024-05-17T15:07:37.306853Z",
     "shell.execute_reply": "2024-05-17T15:07:37.305965Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.235395Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.726\n",
      "Test score: 0.552\n"
     ]
    }
   ],
   "source": [
    "knn_unscaled = KNeighborsClassifier()\n",
    "knn_unscaled.fit(X_train, y_train)\n",
    "print(\"Train score: %0.3f\" % (knn_unscaled.score(X_train, y_train)))\n",
    "print(\"Test score: %0.3f\" % (knn_unscaled.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.308917Z",
     "iopub.status.busy": "2024-05-17T15:07:37.307951Z",
     "iopub.status.idle": "2024-05-17T15:07:37.413242Z",
     "shell.execute_reply": "2024-05-17T15:07:37.411992Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.308888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.798\n",
      "Test score: 0.686\n"
     ]
    }
   ],
   "source": [
    "knn_scaled = KNeighborsClassifier()\n",
    "knn_scaled.fit(X_train_scaled, y_train)\n",
    "print(\"Train score: %0.3f\" % (knn_scaled.score(X_train_scaled, y_train)))\n",
    "print(\"Test score: %0.3f\" % (knn_scaled.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The scores with scaled data are better compared to the unscaled data in case of $k$-NNs.\n",
    "- I am not carrying out cross-validation here for a reason that we'll look into soon. \n",
    "- Note that I am a bit sloppy here and using the test set several times for teaching purposes. But when you build an ML pipeline, please do assessment on the test set only once.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Common preprocessing techniques\n",
    "\n",
    "Some commonly performed feature transformation include:  \n",
    "- **Imputation**: Tackling missing values\n",
    "- **Scaling**: Scaling of numeric features\n",
    "- **One-hot encoding**: Tackling categorical variables      \n",
    "    \n",
    "\n",
    "We can have one lecture on each of them! In this lesson our goal is to getting familiar with them so that we can use them to build ML pipelines. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In the next part of this lecture, we'll build an ML pipeline using [California housing prices regression dataset](https://www.kaggle.com/harrywang/housing). In the process, we will talk about different feature transformations and how can we apply them so that we do not violate the golden rule. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Imputation and scaling [[video](https://youtu.be/G2IXbVzKlt8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dataset, splitting, and baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "We'll be working on [California housing prices regression dataset](https://www.kaggle.com/harrywang/housing) to demonstrate these feature transformation techniques. The task is to predict median house values in Californian districts, given a number of features from these districts. If you are running the notebook on your own, you'll have to download the data and put it in the data directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.414679Z",
     "iopub.status.busy": "2024-05-17T15:07:37.414349Z",
     "iopub.status.idle": "2024-05-17T15:07:37.473390Z",
     "shell.execute_reply": "2024-05-17T15:07:37.472208Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.414644Z"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "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",
       "    .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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2948.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>170100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>129300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5566.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>6303.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>205000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "6051     -117.75     34.04                22.0       2948.0           636.0   \n",
       "20113    -119.57     37.94                17.0        346.0           130.0   \n",
       "14289    -117.13     32.74                46.0       3355.0           768.0   \n",
       "13665    -117.31     34.02                18.0       1634.0           274.0   \n",
       "14471    -117.23     32.88                18.0       5566.0          1465.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "6051       2600.0       602.0         3.1250            113600.0   \n",
       "20113        51.0        20.0         3.4861            137500.0   \n",
       "14289      1457.0       708.0         2.6604            170100.0   \n",
       "13665       899.0       285.0         5.2139            129300.0   \n",
       "14471      6303.0      1458.0         1.8580            205000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "6051           INLAND  \n",
       "20113          INLAND  \n",
       "14289      NEAR OCEAN  \n",
       "13665          INLAND  \n",
       "14471      NEAR OCEAN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df = pd.read_csv(\"../data/housing.csv\")\n",
    "train_df, test_df = train_test_split(housing_df, test_size=0.1, random_state=123)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Some column values are mean/median but some are not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's add some new features to the dataset which could help predicting the target: `median_house_value`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.476096Z",
     "iopub.status.busy": "2024-05-17T15:07:37.475017Z",
     "iopub.status.idle": "2024-05-17T15:07:37.494757Z",
     "shell.execute_reply": "2024-05-17T15:07:37.493903Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.476051Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "train_df = train_df.assign(\n",
    "    rooms_per_household=train_df[\"total_rooms\"] / train_df[\"households\"]\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    rooms_per_household=test_df[\"total_rooms\"] / test_df[\"households\"]\n",
    ")\n",
    "\n",
    "train_df = train_df.assign(\n",
    "    bedrooms_per_household=train_df[\"total_bedrooms\"] / train_df[\"households\"]\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    bedrooms_per_household=test_df[\"total_bedrooms\"] / test_df[\"households\"]\n",
    ")\n",
    "\n",
    "train_df = train_df.assign(\n",
    "    population_per_household=train_df[\"population\"] / train_df[\"households\"]\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    population_per_household=test_df[\"population\"] / test_df[\"households\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.496079Z",
     "iopub.status.busy": "2024-05-17T15:07:37.495681Z",
     "iopub.status.idle": "2024-05-17T15:07:37.515558Z",
     "shell.execute_reply": "2024-05-17T15:07:37.513737Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.496058Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2948.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4.897010</td>\n",
       "      <td>1.056478</td>\n",
       "      <td>4.318937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>170100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
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       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>129300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>0.961404</td>\n",
       "      <td>3.154386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5566.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>6303.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>205000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.817558</td>\n",
       "      <td>1.004801</td>\n",
       "      <td>4.323045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "6051     -117.75     34.04                22.0       2948.0           636.0   \n",
       "20113    -119.57     37.94                17.0        346.0           130.0   \n",
       "14289    -117.13     32.74                46.0       3355.0           768.0   \n",
       "13665    -117.31     34.02                18.0       1634.0           274.0   \n",
       "14471    -117.23     32.88                18.0       5566.0          1465.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "6051       2600.0       602.0         3.1250            113600.0   \n",
       "20113        51.0        20.0         3.4861            137500.0   \n",
       "14289      1457.0       708.0         2.6604            170100.0   \n",
       "13665       899.0       285.0         5.2139            129300.0   \n",
       "14471      6303.0      1458.0         1.8580            205000.0   \n",
       "\n",
       "      ocean_proximity  rooms_per_household  bedrooms_per_household  \\\n",
       "6051           INLAND             4.897010                1.056478   \n",
       "20113          INLAND            17.300000                6.500000   \n",
       "14289      NEAR OCEAN             4.738701                1.084746   \n",
       "13665          INLAND             5.733333                0.961404   \n",
       "14471      NEAR OCEAN             3.817558                1.004801   \n",
       "\n",
       "       population_per_household  \n",
       "6051                   4.318937  \n",
       "20113                  2.550000  \n",
       "14289                  2.057910  \n",
       "13665                  3.154386  \n",
       "14471                  4.323045  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.518423Z",
     "iopub.status.busy": "2024-05-17T15:07:37.517996Z",
     "iopub.status.idle": "2024-05-17T15:07:37.525468Z",
     "shell.execute_reply": "2024-05-17T15:07:37.524051Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.518388Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 3 columns not needed any more and optionally can be deleted\n",
    "\n",
    "train_df = train_df.drop(columns = ['population', 'total_rooms', 'total_bedrooms'])\n",
    "test_df =  test_df.drop(columns = ['population', 'total_rooms', 'total_bedrooms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### When is it OK to do things before splitting? \n",
    "\n",
    "- Here it would have been OK to add new features before splitting because we are not using any **global information** in the data but only looking at **one row at a time**.\n",
    "- But just to be safe and to avoid accidentally breaking the golden rule, it's **better to do it after splitting**.\n",
    "\n",
    "- Question: Should we remove `total_rooms`, `total_bedrooms`, and `population` columns? \n",
    "    - Probably. You could experiment with keeping or removing them and examine whether results change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.527187Z",
     "iopub.status.busy": "2024-05-17T15:07:37.526753Z",
     "iopub.status.idle": "2024-05-17T15:07:37.547926Z",
     "shell.execute_reply": "2024-05-17T15:07:37.546937Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.527129Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4.897010</td>\n",
       "      <td>1.056478</td>\n",
       "      <td>4.318937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>170100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>129300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>0.961404</td>\n",
       "      <td>3.154386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>205000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.817558</td>\n",
       "      <td>1.004801</td>\n",
       "      <td>4.323045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "6051     -117.75     34.04                22.0       602.0         3.1250   \n",
       "20113    -119.57     37.94                17.0        20.0         3.4861   \n",
       "14289    -117.13     32.74                46.0       708.0         2.6604   \n",
       "13665    -117.31     34.02                18.0       285.0         5.2139   \n",
       "14471    -117.23     32.88                18.0      1458.0         1.8580   \n",
       "\n",
       "       median_house_value ocean_proximity  rooms_per_household  \\\n",
       "6051             113600.0          INLAND             4.897010   \n",
       "20113            137500.0          INLAND            17.300000   \n",
       "14289            170100.0      NEAR OCEAN             4.738701   \n",
       "13665            129300.0          INLAND             5.733333   \n",
       "14471            205000.0      NEAR OCEAN             3.817558   \n",
       "\n",
       "       bedrooms_per_household  population_per_household  \n",
       "6051                 1.056478                  4.318937  \n",
       "20113                6.500000                  2.550000  \n",
       "14289                1.084746                  2.057910  \n",
       "13665                0.961404                  3.154386  \n",
       "14471                1.004801                  4.323045  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the feature **scales** are quite different. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.549206Z",
     "iopub.status.busy": "2024-05-17T15:07:37.548921Z",
     "iopub.status.idle": "2024-05-17T15:07:37.566558Z",
     "shell.execute_reply": "2024-05-17T15:07:37.565042Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.549186Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 18576 entries, 6051 to 19966\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   longitude                 18576 non-null  float64\n",
      " 1   latitude                  18576 non-null  float64\n",
      " 2   housing_median_age        18576 non-null  float64\n",
      " 3   households                18576 non-null  float64\n",
      " 4   median_income             18576 non-null  float64\n",
      " 5   median_house_value        18576 non-null  float64\n",
      " 6   ocean_proximity           18576 non-null  object \n",
      " 7   rooms_per_household       18576 non-null  float64\n",
      " 8   bedrooms_per_household    18391 non-null  float64\n",
      " 9   population_per_household  18576 non-null  float64\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have one **categorical** feature and all other features are **numeric** features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.574632Z",
     "iopub.status.busy": "2024-05-17T15:07:37.574020Z",
     "iopub.status.idle": "2024-05-17T15:07:37.619446Z",
     "shell.execute_reply": "2024-05-17T15:07:37.618146Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.574611Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18391.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.565888</td>\n",
       "      <td>35.627966</td>\n",
       "      <td>28.622255</td>\n",
       "      <td>500.061100</td>\n",
       "      <td>3.862552</td>\n",
       "      <td>206292.067991</td>\n",
       "      <td>5.426067</td>\n",
       "      <td>1.097516</td>\n",
       "      <td>3.052349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.999622</td>\n",
       "      <td>2.134658</td>\n",
       "      <td>12.588307</td>\n",
       "      <td>383.044313</td>\n",
       "      <td>1.892491</td>\n",
       "      <td>115083.856175</td>\n",
       "      <td>2.512319</td>\n",
       "      <td>0.486266</td>\n",
       "      <td>10.020873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.790000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.560225</td>\n",
       "      <td>119400.000000</td>\n",
       "      <td>4.439360</td>\n",
       "      <td>1.005888</td>\n",
       "      <td>2.430323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>3.527500</td>\n",
       "      <td>179300.000000</td>\n",
       "      <td>5.226415</td>\n",
       "      <td>1.048860</td>\n",
       "      <td>2.818868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>4.736900</td>\n",
       "      <td>263600.000000</td>\n",
       "      <td>6.051620</td>\n",
       "      <td>1.099723</td>\n",
       "      <td>3.283921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age    households  \\\n",
       "count  18576.000000  18576.000000        18576.000000  18576.000000   \n",
       "mean    -119.565888     35.627966           28.622255    500.061100   \n",
       "std        1.999622      2.134658           12.588307    383.044313   \n",
       "min     -124.350000     32.540000            1.000000      1.000000   \n",
       "25%     -121.790000     33.930000           18.000000    280.000000   \n",
       "50%     -118.490000     34.250000           29.000000    410.000000   \n",
       "75%     -118.010000     37.710000           37.000000    606.000000   \n",
       "max     -114.310000     41.950000           52.000000   6082.000000   \n",
       "\n",
       "       median_income  median_house_value  rooms_per_household  \\\n",
       "count   18576.000000        18576.000000         18576.000000   \n",
       "mean        3.862552       206292.067991             5.426067   \n",
       "std         1.892491       115083.856175             2.512319   \n",
       "min         0.499900        14999.000000             0.846154   \n",
       "25%         2.560225       119400.000000             4.439360   \n",
       "50%         3.527500       179300.000000             5.226415   \n",
       "75%         4.736900       263600.000000             6.051620   \n",
       "max        15.000100       500001.000000           141.909091   \n",
       "\n",
       "       bedrooms_per_household  population_per_household  \n",
       "count            18391.000000              18576.000000  \n",
       "mean                 1.097516                  3.052349  \n",
       "std                  0.486266                 10.020873  \n",
       "min                  0.333333                  0.692308  \n",
       "25%                  1.005888                  2.430323  \n",
       "50%                  1.048860                  2.818868  \n",
       "75%                  1.099723                  3.283921  \n",
       "max                 34.066667               1243.333333  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.620755Z",
     "iopub.status.busy": "2024-05-17T15:07:37.620480Z",
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     "shell.execute_reply.started": "2024-05-17T15:07:37.620733Z"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude                   18576.0\n",
       "latitude                    18576.0\n",
       "housing_median_age          18576.0\n",
       "households                  18576.0\n",
       "median_income               18576.0\n",
       "median_house_value          18576.0\n",
       "rooms_per_household         18576.0\n",
       "bedrooms_per_household      18391.0\n",
       "population_per_household    18576.0\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe().loc['count']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Seems like `total_bedrooms` column has some missing values. \n",
    "- This must have affected our new feature `bedrooms_per_household` as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.651999Z",
     "iopub.status.busy": "2024-05-17T15:07:37.651199Z",
     "iopub.status.idle": "2024-05-17T15:07:37.677538Z",
     "shell.execute_reply": "2024-05-17T15:07:37.676263Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.651949Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>-118.45</td>\n",
       "      <td>34.30</td>\n",
       "      <td>27.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.3047</td>\n",
       "      <td>152100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.295345</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>-121.89</td>\n",
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       "      <td>7.8532</td>\n",
       "      <td>422800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>6.961538</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>-122.08</td>\n",
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       "      <td>626.0</td>\n",
       "      <td>2.9330</td>\n",
       "      <td>85000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.707668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.317891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5751</th>\n",
       "      <td>-118.27</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>6.9135</td>\n",
       "      <td>374000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>6.165644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.785276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13932</th>\n",
       "      <td>-117.28</td>\n",
       "      <td>34.26</td>\n",
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       "      <td>3.3672</td>\n",
       "      <td>133600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>10.386667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.896000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4591</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.06</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>1.2254</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2.096692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.218830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19485</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.9336</td>\n",
       "      <td>127100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.662745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.572549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6962</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>33.99</td>\n",
       "      <td>38.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>3.7328</td>\n",
       "      <td>182400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.535014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.481793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>-117.01</td>\n",
       "      <td>32.74</td>\n",
       "      <td>31.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>2.6973</td>\n",
       "      <td>135200.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5.129985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.098966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.6389</td>\n",
       "      <td>167600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5.584615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.769231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "3482     -118.45     34.30                27.0       623.0         4.3047   \n",
       "17840    -121.89     37.44                 8.0       364.0         7.8532   \n",
       "1606     -122.08     37.88                26.0       626.0         2.9330   \n",
       "5751     -118.27     34.17                52.0       326.0         6.9135   \n",
       "13932    -117.28     34.26                18.0       375.0         3.3672   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "4591     -118.28     34.06                42.0      1179.0         1.2254   \n",
       "19485    -120.98     37.66                10.0       255.0         0.9336   \n",
       "6962     -118.05     33.99                38.0       357.0         3.7328   \n",
       "14970    -117.01     32.74                31.0       677.0         2.6973   \n",
       "7763     -118.10     33.91                36.0       130.0         3.6389   \n",
       "\n",
       "       median_house_value ocean_proximity  rooms_per_household  \\\n",
       "3482             152100.0       <1H OCEAN             4.295345   \n",
       "17840            422800.0       <1H OCEAN             6.961538   \n",
       "1606              85000.0        NEAR BAY             4.707668   \n",
       "5751             374000.0       <1H OCEAN             6.165644   \n",
       "13932            133600.0          INLAND            10.386667   \n",
       "...                   ...             ...                  ...   \n",
       "4591             162500.0       <1H OCEAN             2.096692   \n",
       "19485            127100.0          INLAND             3.662745   \n",
       "6962             182400.0       <1H OCEAN             4.535014   \n",
       "14970            135200.0       <1H OCEAN             5.129985   \n",
       "7763             167600.0       <1H OCEAN             5.584615   \n",
       "\n",
       "       bedrooms_per_household  population_per_household  \n",
       "3482                      NaN                  4.271268  \n",
       "17840                     NaN                  4.195055  \n",
       "1606                      NaN                  1.317891  \n",
       "5751                      NaN                  2.785276  \n",
       "13932                     NaN                  2.896000  \n",
       "...                       ...                       ...  \n",
       "4591                      NaN                  3.218830  \n",
       "19485                     NaN                  1.572549  \n",
       "6962                      NaN                  2.481793  \n",
       "14970                     NaN                  3.098966  \n",
       "7763                      NaN                  3.769231  \n",
       "\n",
       "[185 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df[\"bedrooms_per_household\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.679362Z",
     "iopub.status.busy": "2024-05-17T15:07:37.678865Z",
     "iopub.status.idle": "2024-05-17T15:07:37.687827Z",
     "shell.execute_reply": "2024-05-17T15:07:37.686605Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.679324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df[\"total_bedrooms\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:37.689560Z",
     "iopub.status.busy": "2024-05-17T15:07:37.689039Z",
     "iopub.status.idle": "2024-05-17T15:07:39.348273Z",
     "shell.execute_reply": "2024-05-17T15:07:39.346872Z",
     "shell.execute_reply.started": "2024-05-17T15:07:37.689525Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
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EV2W51HJULlu1aiVJ2rRpU440Z8+eVXR0tLy8vHTNNdcUex5Lo2eeeUZnz57Ve++9J29vb4fbxcbG6vjx45KkTp062d3Gutxd66v5jWVe3P1vJhTdb7/9ptTUVPn5+dm9V3t7e6tdu3aS3Pf7ml9c+3Jn73pFzHJnr75CzC6jTlJwX375pfr06aObb75ZAwcO1Lvvvqvz58/n2I6Y/cNxtzfc1smTJ/XDDz9IcvwFmTt3rn766SeNGzdOjRs3LsnslVr5iZs9ycnJWrZsWYHTlRe5xc3awWHvCVer8PBwHThwwG5niLuqWLGixo4dq/Hjx6tPnz56++231aFDB6WlpWnDhg16+umn5e3trRkzZrg6q6XKtm3bJF0eaXXrrbfmeHrw448/1n/+8x8tX75cQUFBrshiqcZ9Ae4qIyNDx48f17Jly/T888+rYsWKmjx5co7trCPtOnfurMGDB7sgp6VfXrHcsWOH0tPTFRISojfeeENTpkzJln7JkiWaNm2aVqxY4bYdG1b5KZcTJkzQ7bffrtGjR8vLy0t33HGHAgIC9Pvvv2v06NG6ePGixo0bp9DQUBedheusW7dOn376qe6//3516dIl122tdVBfX1/VqVPH7jbWuqw71lcLEsu8fPHFF5Lc828mOIf1OxgWFuZwFEh4eLjWrVvnlt/XguDa55gxRosWLZKU/XpFzHLKq75CzKiTFNaKFSuy/b5w4UK9/PLLWrBggW6//XbbcmL2D0YSIYdRo0YpJSVFTZo0Ue/evXOstw5zbNCggV544QUX5LB0yitujrz66qs6ceKEqlatandoY3mXW9zi4+MlKdcGees667a47KWXXtJ///tfpaamqlu3bqpcubKqVaum/v37q1GjRvrxxx91xx13uDqbpYr16ZFly5Zpw4YNevHFF3XixAklJSVp8eLFqlatmqKiotzye5oX7gtwR2+//bYsFou8vLwUGhqqxx9/XLfccot+/vlntW/fPtu21pF2mZmZeu+991yU49Irv7G0XqdPnTqlKVOm6M4771R0dLRSUlL0yy+/KCIiQseOHVPv3r114cIFV52OSxWkXN58881as2aNWrVqpYceeki1atVSQECArr/+eh0/flyffPKJJk6c6KIzcZ3k5GSNHDlSlStX1ptvvpnn9tY6aJUqVRxOL+2u9dWCxjI3q1ev1ldffSVJGj16tBNyB3fE35fOw7XPsTlz5ui3336Tj4+P/vWvf9mWE7N/5Le+4u4xo05ScA0bNtTrr7+uHTt2KCEhQYmJiVq9erU6dOig+Ph49enTR1u3brVtT8z+QScRsnn//fe1YMECeXp6av78+XafrrEOc3z33XcZ6v//5Sdu9qxYscL2FOysWbPcbuqZvOJmHaLt4+PjcB++vr6S/plXH5elpqbq4MGDOnfunHx8fNS8eXM1atRInp6e+vnnn/XBBx8QsytcvHhRkpSWlqYHHnhAkyZNUs2aNeXv76+77rpLc+fOlSQtXbpUf/zxhyuzWupwX4A7CgkJUadOndS+fXvblKgbNmzQZ599poyMjGzbWkfa/etf/1LLli1dkd1SLb+xzHqdDg8P1+LFi3XVVVfJx8dH7dq104oVK1ShQgXFxsbqww8/dMm5uFpByqUkHTx4UKdOnZLFYlG9evV09dVXy9/fX4cOHdIHH3ygQ4cOlfAZuJ51WsjXXnvN7nTHV6K+6lhBY+lIbGys7rvvPknSY489phtuuMFZWYSb4fvqPMTSvu3bt+vpp5+WdPkaaJ0eXyJmWeW3vuLuMaNOUnAvvfSSxo4dq1atWqlSpUoKCAhQ165d9eOPP6p9+/ZKSUnRc889Z9uemP2D6eZcaMyYMbZpxgriww8/1HXXXef0/HzzzTd66qmnJEn/93//Z/cY1mGOvXv3Vo8ePZyeh/woi3GzZ+vWrRo4cKCMMRo7dqz69+/v9LxlVRbj5ufnJ+lyh4cjKSkpkopvbvLSFrf86tWrl7777jv17t1bc+bMUfXq1SVdbgy69957NXfuXB0/fjzHEFxnKYtxs5Y3SbaKfVa9evVSw4YNdeDAAX333Xe2dzk4U1mMW2m4LwCu0L9//2z37i1btmjEiBF6/fXXdfbsWb3//vuS/hlpV7duXb388suuym6plt9YZr1OP/bYYznmZK9Vq5YGDhyoefPmadWqVXryySdL5gRKkfzGUpImT56sF154QU2bNtXvv/9uu69duHBB//rXvzR37lx16tRJe/bsUeXKlUv8XFzBOi1kRESEHn300XylKQ311dKoMLG05+zZs+revbvOnDmjG2+8kemSUSR8X52HWOZ08OBB9ezZU8nJybr33nv17LPPZltPzP5R0LqfO8aMOolz+fj4aOLEibrtttv0/fffKz4+XkFBQcQsCzqJXOjYsWP6888/C5zO+hSlM/34448aMGCA0tPT9frrr2vEiBE5tklPT9fIkSNVoUIFzZw50+l5yK+yFjd79u7dq+7du+vChQsaPny4Xn/9dafn7UplMW75GdKZnykDiqI0xS2/li1bpu+++07Vq1fXxx9/rEqVKtnWNWjQQJ9//rkaN26slStXavPmzcXSuVAW45a1DDVt2tTuNk2bNtWBAweK7cnqsha30nJfAEqDDh06aOXKlQoPD9fs2bP1/PPPq169ehozZozOnj2r//73vwoICHB1NssER7HMz3W6WbNmkuSWI2DscRTLU6dO6dVXX5UkzZ8/P9uDDwEBAZo1a5Y2b96sPXv26L333tPYsWNddQol6rHHHlN6erref/99eXjkb9INa7k8d+6cjDF2pyop7vpqaVSYWF7pwoULuuOOO7Rnzx5FRkZq2bJltqd5gcIoDX9flhdc+7I7ceKEunbtquPHj6tHjx6aP39+jpgQM8fyqvu5Y8yokzifte0rMzNTMTExioyMJGZZMN2cC33yyScyxhT459Zbb3VqPrZt26Y777xTly5d0pgxYxz+EXjhwgXt379f6enp6tChg2rVqpXtZ9OmTZKkJ554QrVq1dJdd93l1HxalbW4XenQoUPq2rWrzpw5o4EDB2Z7orM4lcW4NW7cWJIUExPjcBvrOuu2zlZa4lYQGzdulCS1b98+WweRVb169WzxyjoXqzOVxbhdddVVkiSLxZLj6XQra8OEvSl7nKGsxa203BeA0qJOnTq65pprlJmZqR07dkiSfvvtN0n/fA+y/ljnFl+wYIFtGS6zF0vrdVqSw4bi4r5Ol0X2Yrl161YlJycrICAgx7uKJMnLy0s33nijbVt38dtvv8lisahXr145vq8LFy6UJL3xxhuqVauW2rVrJ+mfOmhKSoqOHTtmd7/FXV8tjQoTy6xSUlLUu3dvbdmyRc2bN9eqVavs1muBgrB+B2NjY5Wenm53G3f8vhYG175/nD17Vl27dtWBAwfUpUsXLVq0yO7fk8Qsd/bqK+4cM+okzpf1e2m9BxCzf9BJ5Ob27t2r22+/XQkJCRoxYoTeeOONPNOkpqbq5MmTOX7S0tIkSefPn9fJkyd19uzZ4s6+yxQmbtLlp0tuvfVWHT16VD179tRHH31U6CfryqKCxq1Dhw6SpP379+vkyZM51p84cUIHDhzIti2kxMTEPLcxxkj6Z/5V/PNUiTHG4RPo1spBSEhISWWrTOC+APzD+gfHlY1P9r4j1lGAly5dsi3DP66MZd26dRUaGirJ8QMkXKftuzKW1BUcy8jIsPt9tcbhwoULOnnypE6fPi1JCgsLs3XwRkVF2d2ndbm71VcLGkur9PR0DRgwQOvXr1d4eLjWrFmjatWqueIUUM60adNG3t7eSk5O1vbt23OsT0tL06+//irJ/b6vBcW17zLriMddu3apXbt2Wr58ucMpqYhZ3q6sr7h7zKiTONfu3btt/69bt64kYpaV+7ROI4esI1ruvfdevffee7luX6VKlVyfZO/SpYsk6eOPP5YxRt9//30JnEXJK2jcrLI+XXLTTTc5fLqkvCpM3Jo2bWqbNmbevHk51luXXX311WrSpIlzM1yGWZ9u+OWXX+w2Ah0+fFj79u2TJOKWRcOGDRURESFJ+t///pdj/e+//257ounmm28u0byVVtwXgOwOHTpku060bt1a0uVrh6PviPUdRcOGDbMtw2X2YinJNn/9Rx99lCNNcnKy7clKrtP/sBdLa13hwoUL+uWXX3KkSU9P1w8//CDJveoK1qlG7P08+OCDkqSJEydme6DEYrGob9++kqS5c+fm2OemTZsUHR0tb29v9erVq8TOxdUKE0vpcufkkCFDtGzZMtWpU0dr165VnTp1XHQWKG8CAwNtI/DtfV8XLVqkhIQEBQcH20ZTwj6ufdlHPLZo0SLPEY/ELHf26ivuHDPqJM43ffp0SZfbGq0PlBGzLAzKHUlGkjly5IjDbU6cOGEaNWpkJJlevXqZtLS0Ih+3S5cuRpL5+OOPi7wvVyjOuF24cMFce+21RpLp0KGDSUxMdFa2Xa64y9unn35qJJmKFSuadevW2ZavW7fOVKxY0UgyCxcuLNI5uEJ+4mZPvXr1jCTz008/Odzm8OHDxsfHx0gyvXv3NqdOnbKti4mJsZXFmjVrmosXLxb6HFyhOONmjDHLly83kkyFChXMqlWrbMv//vtvExERYSSZ66+/vlB5d6XijpsjZf2+AFxp69atZvz48ebAgQM51n377bemadOmRpK544478rW/l19+2Ugyw4YNc3ZWS73CxvL48eMmICDASDKTJk0yGRkZxhhjkpKSzIMPPmgkmaCgoGz3vvKuMLHMzMw0zZs3N5JM06ZNzY4dO2zrEhISzLBhw2z3jq1bt5bIeZR21vI1ceLEHOtiYmJsda+pU6eazMxMY4wxhw4dMldddZWRZB599NGSznKplVssn3zySSPJVKtWzezZs8cFuUN5t3HjRmOxWIyHh4dZsGCBbfnvv/9uatasaSSZN954w4U5LB1y+55aufO1Lz093fTp08dIMg0bNjTHjh3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Xmxo1aridR0xMjDl8+LBJT083t99+u8dl/f3vf3e7nNWrV5vq1at7Xc/q1auXuN6NMWbVqlXWPFetWmWaNWvmcZnDhw/3OJ+MjAzz4IMPei1zgwYNTGJiott4xzQffvihufnmm11iX3755RKtp3Mb+/TTT01oaKjbMoaGhpqVK1d6nM+cOXM8xkoygYGB5r333nMb69x+ClvW/ErSNg4fPmzi4+O9xrZv396cPHnSJda5naxfv95cccUVHufxxBNPeFy3o0ePmiuvvNJrGbp3727S0tLcxjvap6f28Pnnn5uQkBCP3+1FixYVOA8AlYPzvnTKlCkej/3h4eFm7dq1buexfft207x5c6/7rNtuu82cP3/ebfyePXtM06ZNPca+9NJLXvf5zseNBQsWmLCwMJd5OGRmZpoHHnjAa1ljYmJMUlKSxzoryTFu2rRp1nQ//fSTadiwodt51K1b1/z0008mNzfXPP744x6X9cgjj7hdzo4dO0yDBg28rqckc+rUKY/rWVj79++35rdgwQKvx69+/fqZ7Oxst/PJzc01I0eONDabzeuxe+HChW7jHcetl156ye02fuihh0q0ns7t7NtvvzW1a9d2W8aAgADzySefeJzPt99+a+rUqeN1u4waNcptrHP7KWxZ8ytJ2zhz5ozp3r2717i4uDizZ88el1jndvLVV1+Zzp07e5xHjx49TE5Ojtt1K+l5zk033eS1Paxfv97UqlXL43d7ypQpBc4DgG/5oq/iq2P3xo0bTd26dT3u/999990C18Ebb/uXgs6TPR2XHB+bzWZee+01j2Xz9nE+JyzoWFTSvpZzHfzjH/8wAQEBbucRERFhtm/f7rU+i8K5fhctWuS2PynJ2O12M2/ePI/z8dX1hOXLl7vtH69atapE6+l8nj9s2DCv28jTsTQ3N9cMHz7c6zpGRER4/D4W9jjq7ZrEpEmTCmy3PXv2dDvfb7/91tSrV89r7ODBg932Wf2hP+jcp/LUHp5//nmP823WrJlZvXq1z9pUVUOSCH7BcfBu2LChCQoKMtdff71ZsmSJOXnypDly5Ij56quvjDHG9OjRw/qy9+/f3/z444/m9OnTZtu2beaZZ56xTr4TEhLMxYsXXZbz6aefWvEtWrQwc+bMMSdOnDBHjhwx06ZNM40bNzaSTFBQkPnhhx9c4h07zVq1apk6deqY+Ph489VXX5nffvvNHD582Lz11lsmKCjISDIDBgwwf/3rX01wcLAZM2aM2blzp0lOTjbr1683119/vdVhy9+ByMnJMdHR0UaSqVevnvnggw/M7t27TXJysvnll1/MypUrzejRo02bNm18UvfOB+vmzZubGjVqmDfffNPs27fP/Pbbb2bx4sXm6quvtqb56KOP3M7nvvvusw4YTzzxhFm3bp05deqUOX78uJk7d66VVKhfv77bA7Jj/pGRkSYgIMAMHTrUbN261SQnJ5sdO3aYH3/8sUTr6WhjkZGRJjQ01HTs2NEsXrzYnDx50hw7dsx8/PHHJjw83DqwuDtorl271uq4N27c2EydOtUcPnzYnDhxwsybN89cfvnl1nq461yVNElUkraRlpZmWrRoYbXf119/3Wzbts2kpKSYffv2mXfffdfqBNx4440uFzCc20lcXJypW7eueeedd8yvv/5qkpOTzZo1a0zHjh3zdPzyy8zMNFdddZWRLnXkn376afPzzz+b5ORks2HDBtO/f38r/vbbb3dbN946U0lJSVaCqF69euajjz6yts+nn35qmjdvburUqWOtJ0kioHJz3u/b7XbTrl07s2TJEvPbb7+Zffv2mTfffNNKutetW9ecOHEiT/zhw4etk6zGjRub9957z+zevdukpKSYXbt2mXHjxlkXZQYMGOCy/PT0dNOyZUurX/HCCy+YXbt2mdOnT5vVq1dbF4NjYmLc7vON+e9xIzw83NSsWdO0bt3azJkzxxw7dsycOHHCLFiwwJp2yJAh1j701ltvNd9++605ffq02b17txk7dqyx2+1GkomKijKpqakuyyrpMc75wkrz5s1NXFycmT17tlXWqVOnWj+uueGGG8xbb71lJJknn3zSbNmyxaSkpJiffvrJ9OrVy+ux5IYbbjDSpcT/xIkTzY4dO8zp06fNvn37zLfffmteffVV07p1a58niZo3b2716dxtR0nmhRdecDsf55PZe++913zzzTfm5MmT5rfffjNLliyx+oVhYWFm27ZtLvGOY19kZKR14SExMdGcPn3a/Prrr+bbb78t0Xo62lnt2rVNRESEadOmjfnss8/M0aNHzW+//WbmzJljmjRpYiSZmjVrmuTkZJd5/Prrr9b2DQ8PN//4xz+svuTXX39t2rdvb9XBW2+95RLviyRRcdtGVlaWFRsSEmJGjRplNm3aZJKTk83hw4fNtGnTrItZLVu2dEkKO7eTuLg4ExYWZv7+97+bpKQkk5KSYjZv3mzuuOMOa5opU6a4XbeSnud4uzD122+/WfuzatWqmYkTJ7r09e12e542BqD0lbSv4utjd4MGDcy//vUvl3MoxzTLli3zuA6lmSSKj483Q4cONYsWLTI///yzOXXqlNm/f7/5+uuvTd++fa3yLVmyJE9cRkaGOXfunHUOPXr0aHPu3Lk8n8zMTLf14U5J+1qOOmjevLmx2WymV69eZvXq1ebUqVPm4MGD5q233jLBwcFGkunUqZPX+iwK5+N8nTp1TGxsrPnss8/MiRMnzOHDh82UKVNM/fr1jXQpUeQuQeXL6wmRkZGmTp065u233zZ79+41p0+fNmvXrjWHDh0q0Xo6+ktxcXFGkhk0aJD54YcfTHJysvn111/N6NGjrWNp//793c5jwoQJVjnbt29vli5dan777Tezf/9+8/bbb1vXjMLDw82+fftc4kuaJNq9e7f1nW7Xrp2ZP3++2b9/v9Uf+PLLL80jjzxiHnjgAZd5btmyxTo3ufzyy820adPMvn37TEpKivn555/NsGHDrMTkmDFjXOL9oT9YUJJo6tSp1vgrr7zSLFq0yGV/6bzPIklUNCSJ4Becf+Fxww035DlQO3z++efWNH/961/dzsdxwUGS+ec//5lnXGZmpvWr1ubNm5vTp0+7xO/du9c6sCUkJLiMd+w0JZn4+Hhz4cIFl2n+9re/GelSssRms5nZs2e7TJOcnGxq1qxpdVSc/fzzz9YyPv/8c7fr6UvOB2ubzeZ2J5qWlmYleerUqeOy3gsWLLDm8emnn7pdztmzZ61O6lNPPeUy3hEvybz//vs+WTdnzm2sW7dubk+uP/vsM2uapUuXuoxv166dddDcu3evy/jTp09bB6TGjRu7LKOkSaKStI1nnnnG2n6eftm0detWK8kyZ86cPOOc20n16tXNjh07XOLT0tKs71i/fv1cxr/99tteOwTGGPPXv/7VmsbdL6q9JYkcd+2FhISYn376yWX80aNH8/yynSQRULk57/fbtm1rzp075zLN8uXLrZPFIUOG5Bl35513Wn2G/BdlHJYsWWItY8OGDXnGOfdJ/vd//9clNisry/zxj3+0pvGWJHKc7J05c8ZtObZu3WpNd9ddd7m9U2HevHnWNM8995zL+JIe45wvrERFRblN0kyZMsWaJjAw0EyYMMFlmszMTGs5999/f55xZ8+eteLffvttt3XhS84nqpLM9OnTXaZx3o5BQUHm8OHDecZv3rzZamNvvPGG2+VcvHjRuvvE3S9Dne/yHjlypG9WzolzO/vDH/7g9rvy448/WtN8+OGHLuPvuusuI8kEBwe7/aHVhQsXTIcOHYx06a7t/O2jpEmikrSNf/zjH1bZv//+e7fTOCeNJ02alGecczsJDAw033zzjUt8dna2dSfadddd5zK+pOc5xni/MOXcv3LXx3Xu6xfm4hYA3yhpX8WXx+5q1aqZnTt3uszD+RyqVatWHtehNJNEBRk5cqSRLiUn3CnskyS8HYt80ddy1IEk8/DDD7stw8SJE61pdu3a5bW8heV8nG/cuLE5fvy4yzTbt2+3Egx/+tOfXMb78nqC3W43W7Zs8cm6OXPuL40dO9btNE8++aRVhvxPLzl58qRV/g4dOri93vfDDz9Yiby+ffu6jC9pkujdd9+1+hPurll64/hxd/v27T0+5eDDDz+01v/o0aN5xvlDf9BbkigjI8Pqi7Vs2dKcPXvWZf7O+0t384B3JIngF5w7R+52JMYY65eldevWNb///rvbaXJzc60TnCuvvDLPuLlz51rLcJe4cXD+5cDmzZvzjHPeabr7dasxxmzbts2axtuvPxy/eOnSpUue4Zs3b7biN27c6DHeV5wP1vfcc4/H6b788ktruhkzZuQZ5zgQ3nbbbV6XNX36dGsb5ubm5hnnmHfr1q2LvzJeOLcxT52a7OxsK0mY/1GAGzdutOLdXdRymD17tjVd/scWljRJVNy2cf78eesXaBMnTvQ67aBBg6yOrzPnduLp1mBjjPnLX/5iJJnY2FiXcW3btrW2cf7t73DhwgXrMQd33HGHy3hPnamTJ09av4rxdHHFmP92ugpzkgCgYnPe7y9atMjjdH369DGSTI0aNawfqezbt886wfjss8+8LsfxiNRnn302z/A2bdq47Y84c07+F5Qk8tZ3cex7AwMDzZEjRzxO96c//ck6wXe+uOGLY5zzhRVPd0qkpaVZ9RodHe3x8WzPPfec22NJSkqKtYy5c+d6LKevOJ+otm/f3uN0ztvx1VdfzTPO0Q7btGnj8dhnjLEejWGz2UxKSkqecY5jX506dUx6enrJVsoN53bmLoHg4DiO538UoPMxOP8FTGc//PCDtZzJkyfnGVfSJFFJ2objbr4nn3zS63Rjx441kszVV1+dZ7hzO3H3IxmHN99807owk5WVlWdcSc9zjPF8YSo7O9t6zJynO7WNydvXJ0kElI2S9FV8feweMWKEx3k4n0Plf8yWPySJduzYYfWD3O1DfZEkKmlfy5j/1kFYWJjLsd7h9OnTVhn+7//+z2t5C8v5OP8///M/HqdzPKLNZrPl+dGLr68neOsrlIRjOzdt2tTlOOvg/L357rvv8oxzHKclz9cljfnvHWWBgYHmt99+yzOupEmiyZMnG+nSXTieHk/rjnP9ensKT25uromNjTWS649q/KE/6C1JNGfOHGuct8ciOvaX7uYB7wIE+JGIiAhde+21LsONMVq7dq0kqWfPnqpWrZrbeJvNpnvuuUeStG3bNp05c8Ya53i5WnBwsO68806PZbjvvvtcYvILCQnRTTfd5HZcXFyc9f9bb73V43Ic0x0/fjzP8JYtWyo0NFSS9PTTT2v79u0e5+Frffr08TiuR48eVr2vWbPGGn7hwgXrZbfdunXT+fPnPX7atGkjSUpJSdG+ffvcLue2227z1eq41bx5c7Vq1crtuMDAQLVo0UKSdOLEiTzjnNuCo425c9ddd8lut7vE+EJx28a6deusly136dLF6za68sorJUkbN270OL8ePXp4HNe6dWtJrvWXmpqqHTt2SJLuvvtujy8DDQsL0+233y4pbzsryLp165SbmyvJezvu27dvoecJoHKoXr261+OxY79w/vx5bd26VZK0YsUKGWNks9l04403et1vXnXVVZLy7jdTU1O1c+dOSVLv3r09LvsPf/iDddzxxmazed33Oo43HTt2VGRkpMfpHH2c1NTUPMcQXx/jPNV3zZo1Vb9+fUnSLbfcosDAQLfTeeoj1alTR9HR0ZKkMWPGeH2Rtq95O7Y4b8f8x67ly5dLutRH+v333z22I8fx0xijzZs3u11Oly5drH5AaQgJCVGXLl08jvd0jHc+BntrP9dee62aNWsmyfd9pOK2jT179lgvCO/atavX73rbtm0lST///LMuXrzodn6F6SNdvHhRqamp1nBfnOd4s23bNp09e1ZS4fv6AMpWcfoqvj52F/YcqijnaL60ceNGDRkyRFdeeaVq1aqlwMBA2Ww22Ww26zpDTk6O9u7dWyrLL2lfy9l1112nOnXquB0XERFh9ZXyH299oTDb2RhjXeORfH89oWfPnr5YFY+6deumoKAgt+Mcx2LJ8zWfqKgot9clHRzbOCcnx+d90auvvlqSlJaWpiFDhujkyZOFinP0N+vXr6/WrVt73D6///6723MXZ/7aH3Tse+x2u3r16uVxOq75FB9JIviV2NhYt8PT0tKskylHB8ATx3hjjA4dOmQNP3jwoKRLFx68nWDHxMSoevXqkmSdNOZXv359BQcHux0XFhZm/b9JkyYel+OYLj09Pc/watWqafz48ZIu7WT/8Ic/6PLLL9ejjz6qqVOn6siRIx7nWVLOB8z8goKCrAsgzvWyb98+ZWVlSZKGDh2qmjVrevy0b9/eijt16pTb5XhqA77ibZtIsk6OL1y4kGe4o/2EhoZ6LWNISIguu+wySZ7bT3EVt23s3r3b+v8111zjdRsNGzZMkuftI3mvQ0/1d+jQIRljJBX+O5ySkqK0tDSv0zo417W3dtykSRPVqlWrUPMEUDm0aNHCYzJCkq644grr/459iWO/aYxRo0aNvO4333nnHUl595sHDx609nne9kn5l+9JvXr1FB4e7nG84xhV2P2rlHe/6etjXGH6P8XpI0nSW2+9JZvNpl27dqljx46Kjo7WgAED9P7772vPnj0e51lShd2OzvVy/vx5HTt2TJL09ttve21HDRs2tOLKq49Ur149j/1bqeA+klT4NujrPpJUvLbh3Efq27ev12109913S5Jyc3OVkpLidn6F6SNJeevQF+c53hS2j+Tc1wdQtorTV/H1sbuw51Clsf8uyJgxY9ShQwf97//+r7Zt26a0tDTrYnR+jqS4r5W0r+WsuNckSqp27dpq1KiRx/Hu2pnk++sJ5XnNx9OxWPLtNi6uP/7xj9aPZqdMmaImTZro2muv1dChQ7VgwQKdO3fObZxjG506dcrr9qlZs6YWLFhgTeuOv/YHHdPGxsZaiW93CnNuBfdIEsGvePr1mvOOsEaNGl7nUbNmTbdxjv8XFO88jacdsLcOXFGnc1xEcvbcc89p7ty51q8X9uzZo48//liDBw9WdHS0brvttjwHal8pqG7c1UtxO2EZGRluh5f2Lxg9/aIkv/zbpSjtx9EGPbWfkihO2yjONsrMzPQ4rrB16MwX32Fvzp8/X+j5F2YbAqg8irJPcOxzirPfdD6u+XqfVNCxsbDHKF/0kQpzjCtM/6ewfan87r77bn3zzTfq2rWrAgICdPjwYc2YMUNPPfWULr/8ct1www368ccfizVvb+gj/ZenPpJU+DZYGn2k4rQNX2+j4tQhfSQAxemr+PrYXZzjXFn49NNP9eqrr8oYo86dO2vGjBnasWOHTp06pbS0NJ07d07btm2zps/Ozi6VcpS0r+WsuMfbkipOO5N8fz3B3/szpXEsLoq5c+fqjTfeUPPmzZWbm6sff/xRb7/9tvr06aMGDRroqaeecvkxbUnPXZz5a3/Q0Z+hL1N6SBKhQnDeCTuf6LjjPN45zvH/guKdp3GOL2t9+/bVDz/8oJMnT+rzzz/X8OHD1bp1axljtGTJEl1//fXav3+/T5fpuIXYE3f14rwDXrBggcyld50V+Ln55pt9WvbS5ov24+kRa/kV1LEtattw3kapqamF3ka+5IvvsDfO61jYdgygaijKPsGxz3HsU2rVqlXofabzL+HKep9U2GNUZekj/fGPf9SKFSuUnJysxYsXa8yYMUpISJAkrV27Vp07d1ZiYqJPl1nSPtI//vGPQrelQYMG+bTspa04x/jS6iMVtW04b6MtW7YUehvFxMQUqryFQR8JQHH6Kr4+dhfnOCf5bv/tyf/8z/9IuvSYt9WrV+uBBx7QFVdcoXr16qlmzZqqUaOG9WST0lTSvpY/KE47k/zjekJZ8MU29sX3wW63a+TIkdq3b59++eUX/fvf/9bgwYPVsGFDZWRk6P3331eXLl3yzMOxja666qpCb5/Vq1cXqqyF5Yv+oDeOdaQvU3pIEqFCCA8Pt57Z6niviSeO577abDbr2eSSrJO5vXv3esyYS5duYXTsdHx5AlhcDRo00J133qlJkyZp586d+uSTT2Sz2ZSammo94sZXkpKSPI7LycmxHtXhXC8xMTEKCLi0K/H0DP3KwLHOGRkZHt+nJF36xYy7epJkPebQ3eNznB09erRQZSps23B+T1Z5baPo6Girw1TY73DdunW9Pl7JmXNde2vHx48fL7VHEADwT7/88otycnI8jnfeZzj2JY795tmzZ73u8z1p1qyZtc/ztk8qzPjCcJS7sPtX5xjn/5fkGFceateurR49emjcuHHauHGjvvnmG4WGhurixYt6/fXXfbqswm5H53qpVauWIiIiJFWNPpJU+DboqY8kee8nFbaPVNi24Q99JF+c53hT2D6Sc18fQNkqTl/F18fuwp5DlfY5bn6OdzDde++91nWH/JzvJCotJe1r+YPU1FSv7zly184k/zhWlgVfbOPCfB+ysrK8Po7PWYsWLTRw4ED961//0uHDh/XUU09JkjZt2qSvvvrKms6xjXbv3u3zxxQWli/6g4WZ/969ez2+G1LyzblVVUWSCBWCzWbTDTfcIElatGiRxx2uMUZz5syRdOklwrVr17bGde7cWdKlHfKXX37pcVmffvqpS4w/6d+/v/W8YF/v/BzPJnVn6dKl1sHGsS2kSxdAHI8+++STT8rkVzzlwbktzJ071+N0n3/+uXXAyt9+GjduLEn67bffPD7LPiUlpdi/fvbUNm688UaFhIRIkqZOnVqseZdUnTp1rBc+z5s3z+Mvi9LT07Vw4UJJedtZQTp27GidNHhrx/Pnzy/0PAFUDr///rv1Mld3HPuFGjVqWC+L7d69uzW+OPvNOnXqWM/D9rZP2rFjh3755Zcizz8/x/Fm3bp11jtw3HH0cZz3yc7xUvGPcf6gS5cu1ot2y7KP5Lwd8x+7HG3piy++8Hjsr+g6duxoPT7QW/tJTEy0nlfvqY8kSbt27XIbn52drRUrVhSrjJ7aRps2bax3F0ybNq1Y8y4pX5znePOHP/zBepdIYfv6AMpWcfoqvj52F/YcKv9xzrH/3rNnj8f3BO3atavY725xPLbMWxLtP//5j9d5ON6v4m0eBSlpX8tffP755x7HObazzWZTx44dreH+cD2hLDi28eHDh70+utixjQMDA/PUk/Tf74OnvowkffPNN16THJ4EBwfr5Zdftv527s84+psZGRmaOXNmkeftC77oD3rj2PdcvHhRixcv9jgd13yKjyQRKoxHH31U0qWL6H/729/cTvOPf/xDO3fulCQ9/vjjecb16tXLeinwCy+8YL0g1tm+ffs0YcIESVJCQoLatWvns/IX1tGjR73eHnnhwgUdP35ckqxfp/rKnDlz9N1337kMP3/+vEaNGiXpUmend+/eecY/99xzkqT9+/fr6aef9tg5dCiN9ymVtvj4eKs9TJgwwW0nNzk5WaNHj5Z0qXPQq1evPOOvu+466//Tp093u5zhw4d7vDhQ3LYRHh6uxx57TJI0c+bMAjsNGRkZeV466CuO7/DOnTv1z3/+0+00o0aNsi6i5f8Oe9OgQQP17NlTkvS///u/bn+5cuLECY0fP76oxQZQCYwaNcrtxc9Vq1Zp3rx5kqQBAwZYL0G9/PLLrX34m2++WeDjGNLS0qz9r8MjjzwiSfrpp5/08ccfu8RkZ2fr2WefLeqquDV48GBJly5+PP30024T8fPnz9fSpUut6Z1/jeuLY1xZOH36tJKTkz2Oz8nJscru6z5SYmKiZsyY4TLceTsGBQVp4MCBecY7XuCclpamQYMGeX1Gv1Qx+0j169fXHXfcIUn617/+pY0bN7pMk5GRoaefflrSpV/ZPvjgg3nGt2vXzvr+eUrWvPrqqzpy5IjbccVtGzabzdpGa9asKfAOtJycHP36669epymOkp7neBMYGGi1yy+//NJtos25rw+gfBS1r+LrY/d7773n9hjkfA7VqlUrl4vijnPc1NRUtwmIrKwsa/9fHLGxsZIu7b/c9W/+/e9/F/gDgnr16kmS1+ROQUra1/IX48aN02+//eYyPCkpyXq036233qqmTZta4/zlekJpGzBggJUMe/bZZ9322RITE61+fe/eva225eD4Pvz8889u77o6f/68Ro4c6bEM3pKt0qW7aByc+zPdunXTlVdeKUl6/vnnC7yT57fffnN7TbQkfNEf9Ob222+36vuFF15w+z4j5/0lisEAfuChhx4yksxNN93kdboePXoYSUaSGThwoNm4caNJTk4227dvN88884yx2WxGkomPjzcXL150iZ89e7YV36pVKzN//nxz4sQJc/ToUTN9+nTTuHFjI8kEBQWZH374wSX+5ZdfNpJMs2bNvJbTsYxp06Z5nMbTvKZNm2Zq1aplBg8ebObNm2f27NljUlJSzMGDB83ChQvN9ddfb83/yy+/9FqOwli1apU1v5iYGFOzZk0zefJkc+DAAXPq1CmzZMkSEx8fb03z0UcfuZ1P//79rWk6depkPvvsM3PgwAGTmppqDh8+bFauXGnGjh1rWrVqZXr27OkSX5g6K4nCtrGbbrrJSDIPPfSQy7i1a9eawMBAI8lERkaa6dOnm6NHj5qTJ0+a+fPnm8svv9xaj3nz5rmdf+fOnY0kExISYv7xj3+Yw4cPm9OnT5tvv/3W9OrVy9hsNtO8eXO3ZS1J2zh79qxp3bq1kWRsNpsZMGCAWbFihTl+/LhJSUkxe/fuNV9++aX5y1/+YurXr28mTZqUJ965nezfv99j/U2bNs2aLr/MzExz1VVXGUkmICDADB061Gzbts0kJyebjRs3mgEDBlixt99+u9v5N2vWzEgyL7/8ssu4pKQkExISYiSZ+vXrm3/961/myJEj5sSJE2bOnDkmLi7O1K5d29SuXdvjPABUHo79fmRkpAkODjYJCQlm6dKl5tSpU+bAgQNm8uTJpkaNGkaSqVu3rjlx4kSe+MOHD5uGDRsaSSY4ONj85S9/MWvWrDEnT540ycnJZvfu3WbOnDnm4YcfNjVr1jRz5szJE5+enm5atmxp9SvGjBljdu/ebU6fPm2+++4707VrV+vY6+n4VNh+hzHGDBkyxNqH3nbbbWbNmjXm9OnTZs+ePeaVV16x9o9RUVEmNTXVJb6kxzhv+39n3vbjBc1r1apVJiwszNx///1m5syZJikpySQnJ5sjR46Yb775xtx2221W3LvvvltgnRVk//79efpIwcHB5qWXXjK//PKLSU5OzrMdJZkXXnjB7XxeeOEFa5q2bduaadOmmV9//dWkpqaaY8eOmTVr1piJEyeaa665xrRp08YlvjB1VhKFbWfe+lK//vqr9X2qXbu2effdd62+5Ndff206dOhg1cFbb73ldv4PPvig1U8ZM2aM2bt3r0lOTjaJiYlm0KBBRpKJi4tzW9aStI2LFy+am2++2Rrfq1cvs3DhQnPkyBGTmppqDhw4YJYuXWpGjhxpoqOjzVNPPZUn3rmdrFq1ymP9FdSXKul5jrc+7G+//Wbq1atnJJnq1aubN9980+zfvz9PX99ut5vIyEiP8wDgeyXtq/jy2B0TE2MaNmxoPv74Y3P06NE851COaZYtW+Yyj+zsbKsvU7t2bTNt2jRz/Phxc/LkSbN48WLTqVMnExoa6nX/4u045BgnyfTr189s3LjRnD592vz0009m6NChJjAw0FxxxRVe98N/+ctfrDpctWqVOX/+vMnKyjJZWVkmJyfHbX24U9K+lrf9tDNfH/cddeg4F77sssvMnDlzzIkTJ8yRI0fMxx9/bBo0aGAkGbvdbrZv3+4yj7K6nlASha03b9eeJkyYYI2/7rrrzNdff219H9955x1Tq1YtI8mEh4e7XY+UlBQTHh5utYP58+ebU6dOmWPHjpk5c+aYNm3amIiICI/XJB566CETExNjRo0aZZYtW2YOHTpkUlJSzJ49e8zHH39sfY+qV69ujh07lid269atpnr16kaSqVGjhvnb3/5mNmzYYE6fPm1OnTpltm/fbv7zn/+Ye++914SEhJgNGzbkifeH/mBBfaqpU6da46+++mqzZMkSl/2l43paQf0yuCJJBL9Q2Av4Z86cMV26dLG+8O4+7dq1c9lZOvvHP/5hdaTcfcLCwsz8+fPdxpZVksjb+jk+o0aN8lqGwnI+WK9cudJERUV5XOZzzz3ncT4XL140f/nLX6wTWG+fPn36uMQXps5KwhdJImOM+eyzz0xoaKjHdQsMDDTvvfeex/nv3LnTOknP/wkICDBvv/22x7KWtG2cOHHCWr+CPvkvoPgiSWSMMUeOHDFXXnml12V369bNpKWluY0vqOO3YMECq3Oe/xMaGmoWLlxY6hfbAPgH533phx9+aAICAtzuG8LDw83atWvdzuOXX34xf/jDHwq133T3w41ffvnFNG3a1GPM3/72N6/Hp6IkiTIzM80DDzzgtYwxMTEmKSnJ4zxKcowrqyRRYbbFgAEDTHZ2ttdyFIbzier8+fNN27ZtPS7zvvvu87jM3NxcM378eK/9T8cnPj7eJb4iJImMMebbb781derUKVYfxZhL/ZTY2FiPscOGDfNY1pK2jbS0NHP33XcXah7Dhg3LE+urJFFJz3MK6sOuX7/euriV/xMQEGD+93//t9AXMAH4hi/6Kr46dv/4448e9+EBAQFef3zx7bffmrCwMLexoaGhZs6cOV73L96OQ7///rtp3769x/Vr06aNSUxM9Lof3rlzp8c6cj62FtSXKWlfq7yTRM2aNTMLFy70WBd2u93jj12NKZvrCSXhiyRRbm6uGT58uNd1i4iI8Ph9NMaYWbNmeezz1a5d23z33Xcey+rYJ3j7hIWFmQULFrhddmJioomOji5wHjabzWzdujVPrD/0BwvTp3r++ec9zjcqKsqsXLmywHnAPf+79xHwolatWlqxYoVmzpypHj16qEGDBgoODlZERIT++Mc/6sMPP9SPP/6Y57nm+T377LP6+eef9dhjjykuLk5hYWGqXr26rrjiCg0dOlS//PKLy+PUytK9996rr776Ss8++6yuu+46RUVFKSQkRGFhYbr88ss1aNAgrV+/3ucvZJak5s2ba/PmzXruuefUokULhYaGqk6dOurevbsWLVqkN99802NscHCw/vnPf2rr1q166qmn1LZtW4WHhyswMFC1a9dWu3bt9Je//EXffPNNnvc+VTT33HOPdu/erWeffVatW7dW9erVFRYWpssuu0yPP/64tm3bZr1M0J3WrVtr48aNevTRRxUVFaXg4GA1bNhQvXv31nfffadnnnnGY2xJ20bDhg21evVqffXVV7r//vsVExOjsLAwBQcHq0GDBrrhhhs0duxY/fTTT/rrX/9a4rpyJzIyUhs3btQHH3ygm2++WREREVYd3HbbbZo1a5a+/vpr1axZs1jzv+uuu7R161Y9+OCDaty4sex2uyIjI9W/f38lJiaWy+ORAJS/J554QqtWrdJdd92lRo0ayW63q1mzZhoyZIh27Njh8ugUhxYtWmjLli2aMWOGevfuraZNmyokJER2u11NmjRR165dNXHiRP3666+6/fbb3cZv27ZNI0eOVIsWLRQSEqJ69eqpW7du+uKLL3z6CEy73a4ZM2Zo8eLF6tOnj5o0aaLg4GDVrl1b119/vSZOnKgdO3aoVatWHudR0mNcaevYsaNWrFih0aNHq3PnztZxLCQkRDExMbrvvvu0dOlS/ec//7Geie4rderUUWJiosaOHas2bdqoevXqCg8P1w033KD//Oc/mj17tsdl2mw2/e1vf9OePXs0YsQIxcfHq3bt2goMDFR4eLjatm2rwYMHa+HChVq3bp1Py12WbrzxRv3yyy8aM2aMrr76aoWHhyskJETNmjXTgAED9MMPP3jtvzZs2FCJiYkaOnSo4uLiZLfbFRERoe7du2vhwoV66623PMaWtG3UrFlTc+bM0ffff69HHnlEl19+uWrUqKGgoCBFRETo2muv1ciRI7V+/Xqv/eGS8MV5jjfXXXedduzYoT//+c9q1qyZ7Ha7GjVqpLvuukurV68u0iPsAPhecfsqvjp2t2/fXps3b9YTTzyhZs2aKSQkRPXr11fv3r21Zs0ar+eHN954o3788Uf169dPDRs2VHBwsCIjIzVgwABt2LBBd999d7HrpVq1alq9erVefvlltWrVSiEhIapVq5batWunV199VT/++KMaNGjgdR6tW7fW2rVrde+996pp06bWO4qKyhd9rfLWq1cvJSYmqn///oqMjJTdblfjxo01YMAAbd26VX369PEY6w/XE0qbzWbTpEmTtG7dOvXv31/R0dEKCQlReHi42rVrpxdffFG//PKLx++jJPXr10/ffvutbr/9dkVERMhutysmJkZDhgzR1q1bvb6H54033tB//vMfDRo0SFdffbUaNmyooKAg1axZU+3atdOIESO0a9cu3XXXXW7jO3TooN27d+vDDz9Ujx49rGsioaGhio6O1m233ab33ntPR44c0VVXXVXS6nKrpP3BgkyYMEFLlizRrbfeqjp16ig0NFQtWrTQc889p82bN6t58+Y+XJuqxWaMh7eHA6gSVq9erT/+8Y+SLr1TKCYmpnwLBAAA4AcOHDhgnWiuWrVKN998c/kWCABQqQwaNEj//ve/ddNNNxX4/sPSMH36dD388MOS5PYdO6gcxo4dq1deeUXNmjVz++4qAJAk7iQCAAAAAAAAAACogkgSAQAAAAAAAAAAVEFB5V0AACWTk5Oj9PT0IscFBwcrJCSkFEpUOjIzM5WVlVXkuLCwMJ+/lwAAAPg/Y4x+//33IscFBgYqLCysFEpUOi5evKiLFy8WOS40NFRBQZwOAgDgz6rKcf7ChQvKzc0tclyNGjVKoTRA1VNx9hYA3Pr++++tdwoVxUMPPaTp06f7vkCl5IknntC///3vIsfxDgEAAKqmgwcPFuvlteX1bojieu211/T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",
      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## (optional)\n",
    "train_df.hist(bins=50, figsize=(20, 15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:39.349383Z",
     "iopub.status.busy": "2024-05-17T15:07:39.349156Z",
     "iopub.status.idle": "2024-05-17T15:07:39.931500Z",
     "shell.execute_reply": "2024-05-17T15:07:39.929876Z",
     "shell.execute_reply.started": "2024-05-17T15:07:39.349350Z"
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## (optional)\n",
    "train_df.plot(\n",
    "    kind=\"scatter\",\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    alpha=0.4,\n",
    "    s=train_df[\"population_per_household\"],\n",
    "    figsize=(10, 7),\n",
    "    c=\"median_house_value\",\n",
    "    cmap=plt.get_cmap(\"jet\"),\n",
    "    colorbar=True,\n",
    "    sharex=False,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What are the transformations we need to apply on the dataset? \n",
    "\n",
    "Here is what we see from the **EDA**:\n",
    "- Some **missing values** in `total_bedrooms` column\n",
    "- **Scales** are quite different across columns. \n",
    "- **Categorical** variable `ocean_proximity`\n",
    "\n",
    "Read about [preprocessing techniques implemented in `scikit-learn`](https://scikit-learn.org/stable/modules/preprocessing.html).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are dropping the categorical variable `ocean_proximity` for now. We'll come back to it in a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:39.932837Z",
     "iopub.status.busy": "2024-05-17T15:07:39.932566Z",
     "iopub.status.idle": "2024-05-17T15:07:39.941939Z",
     "shell.execute_reply": "2024-05-17T15:07:39.940615Z",
     "shell.execute_reply.started": "2024-05-17T15:07:39.932805Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns=[\"median_house_value\", \"ocean_proximity\"])\n",
    "y_train = train_df[\"median_house_value\"]\n",
    "\n",
    "X_test = test_df.drop(columns=[\"median_house_value\", \"ocean_proximity\"])\n",
    "y_test = test_df[\"median_house_value\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's first run our baseline model `DummyRegressor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:39.943443Z",
     "iopub.status.busy": "2024-05-17T15:07:39.942995Z",
     "iopub.status.idle": "2024-05-17T15:07:39.950356Z",
     "shell.execute_reply": "2024-05-17T15:07:39.948884Z",
     "shell.execute_reply.started": "2024-05-17T15:07:39.943404Z"
    }
   },
   "outputs": [],
   "source": [
    "results_dict = {}  # dictionary to store our results for different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:39.952985Z",
     "iopub.status.busy": "2024-05-17T15:07:39.951746Z",
     "iopub.status.idle": "2024-05-17T15:07:39.961029Z",
     "shell.execute_reply": "2024-05-17T15:07:39.959859Z",
     "shell.execute_reply.started": "2024-05-17T15:07:39.952922Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def mean_std_cross_val_scores(model, X_train, y_train, **kwargs):\n",
    "    \"\"\"\n",
    "    Returns mean and std of cross validation\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model :\n",
    "        scikit-learn model\n",
    "    X_train : numpy array or pandas DataFrame\n",
    "        X in the training data\n",
    "    y_train :\n",
    "        y in the training data\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "        pandas Series with mean scores from cross_validation\n",
    "    \"\"\"\n",
    "\n",
    "    scores = cross_validate(model, X_train, y_train, **kwargs)\n",
    "\n",
    "    mean_scores = pd.DataFrame(scores).mean()\n",
    "    std_scores = pd.DataFrame(scores).std()\n",
    "    out_col = []\n",
    "\n",
    "    for i in range(len(mean_scores)):\n",
    "        out_col.append((f\"%0.3f (+/- %0.3f)\" % (mean_scores.iloc[i], std_scores.iloc[i])))\n",
    "\n",
    "    return pd.Series(data=out_col, index=mean_scores.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:39.962154Z",
     "iopub.status.busy": "2024-05-17T15:07:39.961881Z",
     "iopub.status.idle": "2024-05-17T15:07:39.992471Z",
     "shell.execute_reply": "2024-05-17T15:07:39.990563Z",
     "shell.execute_reply.started": "2024-05-17T15:07:39.962126Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dummy = DummyRegressor(strategy=\"median\")\n",
    "results_dict[\"dummy\"] = mean_std_cross_val_scores(\n",
    "    dummy, X_train, y_train, return_train_score=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:39.996562Z",
     "iopub.status.busy": "2024-05-17T15:07:39.994687Z",
     "iopub.status.idle": "2024-05-17T15:07:40.006447Z",
     "shell.execute_reply": "2024-05-17T15:07:40.004875Z",
     "shell.execute_reply.started": "2024-05-17T15:07:39.996426Z"
    }
   },
   "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>dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fit_time</th>\n",
       "      <td>0.002 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_time</th>\n",
       "      <td>0.001 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_score</th>\n",
       "      <td>-0.055 (+/- 0.012)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_score</th>\n",
       "      <td>-0.055 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          dummy\n",
       "fit_time      0.002 (+/- 0.001)\n",
       "score_time    0.001 (+/- 0.000)\n",
       "test_score   -0.055 (+/- 0.012)\n",
       "train_score  -0.055 (+/- 0.001)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Imputation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-05-17T15:07:40.030523Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.008731Z"
    }
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
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       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>3.1250</td>\n",
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       "      <td>1.056478</td>\n",
       "      <td>4.318937</td>\n",
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       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
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       "      <td>6.500000</td>\n",
       "      <td>2.550000</td>\n",
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       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
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       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>0.961404</td>\n",
       "      <td>3.154386</td>\n",
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       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>3.817558</td>\n",
       "      <td>1.004801</td>\n",
       "      <td>4.323045</td>\n",
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       "      <th>7763</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.6389</td>\n",
       "      <td>5.584615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15377</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>33.37</td>\n",
       "      <td>14.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>4.5391</td>\n",
       "      <td>6.016688</td>\n",
       "      <td>1.017972</td>\n",
       "      <td>3.127086</td>\n",
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       "    <tr>\n",
       "      <th>17730</th>\n",
       "      <td>-121.76</td>\n",
       "      <td>37.33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>5.6306</td>\n",
       "      <td>5.958393</td>\n",
       "      <td>1.031564</td>\n",
       "      <td>3.493544</td>\n",
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       "    <tr>\n",
       "      <th>15725</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.78</td>\n",
       "      <td>44.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>4.739264</td>\n",
       "      <td>1.024540</td>\n",
       "      <td>1.720859</td>\n",
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       "    <tr>\n",
       "      <th>19966</th>\n",
       "      <td>-119.08</td>\n",
       "      <td>36.21</td>\n",
       "      <td>20.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.5156</td>\n",
       "      <td>5.491379</td>\n",
       "      <td>1.117816</td>\n",
       "      <td>3.566092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>18576 rows × 8 columns</p>\n",
       "</div>"
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       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "6051     -117.75     34.04                22.0       602.0         3.1250   \n",
       "20113    -119.57     37.94                17.0        20.0         3.4861   \n",
       "14289    -117.13     32.74                46.0       708.0         2.6604   \n",
       "13665    -117.31     34.02                18.0       285.0         5.2139   \n",
       "14471    -117.23     32.88                18.0      1458.0         1.8580   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "7763     -118.10     33.91                36.0       130.0         3.6389   \n",
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       "17730    -121.76     37.33                 5.0       697.0         5.6306   \n",
       "15725    -122.44     37.78                44.0       326.0         3.8750   \n",
       "19966    -119.08     36.21                20.0       348.0         2.5156   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "6051              4.897010                1.056478                  4.318937  \n",
       "20113            17.300000                6.500000                  2.550000  \n",
       "14289             4.738701                1.084746                  2.057910  \n",
       "13665             5.733333                0.961404                  3.154386  \n",
       "14471             3.817558                1.004801                  4.323045  \n",
       "...                    ...                     ...                       ...  \n",
       "7763              5.584615                     NaN                  3.769231  \n",
       "15377             6.016688                1.017972                  3.127086  \n",
       "17730             5.958393                1.031564                  3.493544  \n",
       "15725             4.739264                1.024540                  1.720859  \n",
       "19966             5.491379                1.117816                  3.566092  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.033239Z",
     "iopub.status.busy": "2024-05-17T15:07:40.032944Z",
     "iopub.status.idle": "2024-05-17T15:07:40.039447Z",
     "shell.execute_reply": "2024-05-17T15:07:40.037880Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.033219Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "# knn.fit(X_train, y_train)  # This call gives ValueError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What's the problem? \n",
    "\n",
    "```\n",
    "ValueError: Input contains NaN, infinity or a value too large for dtype('float64').\n",
    "```\n",
    "\n",
    "- The classifier is not able to deal with **missing values** (NaN).\n",
    "- What are possible ways to deal with the problem?\n",
    "    - **Delete** the rows? \n",
    "    - **Replace** them with some reasonable values?    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- `SimpleImputer` is a transformer in `sklearn` to deal with this problem. For example, \n",
    "    - You can impute missing values in **categorical** columns with the **most frequent** value.\n",
    "    - You can impute the missing values in **numeric** columns with the **mean** or **median** of the column.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-05-17T15:07:40.041146Z"
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    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>20248</th>\n",
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       "    <tr>\n",
       "      <th>3125</th>\n",
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       "      <td>3.000000</td>\n",
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       "      <td>1.333333</td>\n",
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       "    <tr>\n",
       "      <th>12138</th>\n",
       "      <td>-117.22</td>\n",
       "      <td>33.87</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.785714</td>\n",
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       "    <tr>\n",
       "      <th>8219</th>\n",
       "      <td>-118.21</td>\n",
       "      <td>33.79</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.5938</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.666667</td>\n",
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       "    <tr>\n",
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       "      <th>4591</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.06</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>1.2254</td>\n",
       "      <td>2.096692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.218830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19485</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.9336</td>\n",
       "      <td>3.662745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.572549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6962</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>33.99</td>\n",
       "      <td>38.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>3.7328</td>\n",
       "      <td>4.535014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.481793</td>\n",
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       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>-117.01</td>\n",
       "      <td>32.74</td>\n",
       "      <td>31.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>2.6973</td>\n",
       "      <td>5.129985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.098966</td>\n",
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       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.6389</td>\n",
       "      <td>5.584615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.769231</td>\n",
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      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "20248    -119.23     34.25                28.0         9.0         8.0000   \n",
       "12649    -121.47     38.51                52.0         9.0         3.6250   \n",
       "3125     -117.76     35.22                 4.0         6.0         1.6250   \n",
       "12138    -117.22     33.87                16.0        14.0         2.6250   \n",
       "8219     -118.21     33.79                33.0        36.0         4.5938   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "4591     -118.28     34.06                42.0      1179.0         1.2254   \n",
       "19485    -120.98     37.66                10.0       255.0         0.9336   \n",
       "6962     -118.05     33.99                38.0       357.0         3.7328   \n",
       "14970    -117.01     32.74                31.0       677.0         2.6973   \n",
       "7763     -118.10     33.91                36.0       130.0         3.6389   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "20248             2.888889                0.333333                  3.222222  \n",
       "12649             2.222222                0.444444                  8.222222  \n",
       "3125              3.000000                0.500000                  1.333333  \n",
       "12138             4.000000                0.500000                  2.785714  \n",
       "8219              0.888889                0.500000                  2.666667  \n",
       "...                    ...                     ...                       ...  \n",
       "4591              2.096692                     NaN                  3.218830  \n",
       "19485             3.662745                     NaN                  1.572549  \n",
       "6962              4.535014                     NaN                  2.481793  \n",
       "14970             5.129985                     NaN                  3.098966  \n",
       "7763              5.584615                     NaN                  3.769231  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.sort_values(\"bedrooms_per_household\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.065291Z",
     "iopub.status.busy": "2024-05-17T15:07:40.064637Z",
     "iopub.status.idle": "2024-05-17T15:07:40.073620Z",
     "shell.execute_reply": "2024-05-17T15:07:40.072089Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.065265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((18576, 8), (2064, 8))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.076575Z",
     "iopub.status.busy": "2024-05-17T15:07:40.075528Z",
     "iopub.status.idle": "2024-05-17T15:07:40.106593Z",
     "shell.execute_reply": "2024-05-17T15:07:40.105436Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.076528Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(X_train)\n",
    "X_train_imp = imputer.transform(X_train)\n",
    "X_test_imp = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's check whether the NaN values have been replaced or not\n",
    "- Note that `imputer.transform` returns an `numpy` array and not a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.107803Z",
     "iopub.status.busy": "2024-05-17T15:07:40.107549Z",
     "iopub.status.idle": "2024-05-17T15:07:40.134748Z",
     "shell.execute_reply": "2024-05-17T15:07:40.133499Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.107785Z"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude                   18576.0\n",
       "latitude                    18576.0\n",
       "housing_median_age          18576.0\n",
       "households                  18576.0\n",
       "median_income               18576.0\n",
       "rooms_per_household         18576.0\n",
       "bedrooms_per_household      18576.0\n",
       "population_per_household    18576.0\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train_imp, columns=X_train.columns).describe().loc['count']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- This problem affects a large number of ML methods.\n",
    "- A number of approaches to this problem. We are going to look into one of the most popular ones.\n",
    "\n",
    "| Approach | What it does | How to update $X$ (but see below!) | sklearn implementation | \n",
    "|---------|------------|-----------------------|----------------|\n",
    "| standardization | sets sample mean to $0$, s.d. to $1$   | `X -= np.mean(X,axis=0)`<br>`X /=  np.std(X,axis=0)` | [`StandardScaler()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "There are all sorts of articles on this; see, e.g. [here](http://www.dataminingblog.com/standardization-vs-normalization/) and [here](https://medium.com/@rrfd/standardize-or-normalize-examples-in-python-e3f174b65dfc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.135906Z",
     "iopub.status.busy": "2024-05-17T15:07:40.135632Z",
     "iopub.status.idle": "2024-05-17T15:07:40.684961Z",
     "shell.execute_reply": "2024-05-17T15:07:40.683785Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.135885Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yt2+fQ/9XGR2TUkR2JL1TunLlSoe2XblypXnb+fPnJ3vNlJQydcbx8fFW92Or41u1apV5P1OmTLG4fdIRP2lJSgEQixcvtniMDh06CEDe8UiNefPmmeMzGo0pXk+PpNTRo0fN73vChAkOH/ONN94QAET//v0tvm7tJMdRUVFR5qTjiycFRERqJP28SnqB/uqrr4qpU6eKffv2WV3gQwjnFjofO3asACCaN29u8fW0fiadOHHCvH3v3r0tbp/0ppK1pJQz38e8efOs7uf99983t9u2bVuK1yMjI5ONwrD0OZnWn68QQrRr104AcvTE3r17rbZLelNFrbScA8yePdsc14EDByzu/+7du+bE3syZM5O9lvTCH4DYvHmzw/FTxvZiUkqI54npF3+nTH1Zv379hBDCKUkpX19fi4nNyMhIUbBgQavJD0tJqd9//10ActGCpH8rpveY9G8orYXO7Z3HGo1G0b59e/N7vHLlihBCiH379plngAwdOtTitleuXBF+fn7J/vb8/f1F69atxSeffCLWr18vwsPDbcY3bdo087bTpk2z2s5oNDqc8Dp79qzQarVCURRx7dq1FK/b+tkHBQWZR0jNmDHD4v4TEhLMM2o6duyY4nVTUgqAGDt2rEOxZ0ZMShHZMX36dJsng7Zs3brVvO2LnVLSpNTRo0dt7sdWx/fKK68IQI64snZC+fTpU5EjR440J6UaNmxoNcY1a9aY2925c8fm+7Hk2bNn5u1NH2pJpUdS6vLly+aYbE0rsGbz5s02E3XOSkoJ8Xy1FWsXWERE9qxevVoUKVIkRfLC9MiXL5+YOHGixRVJnZmUunDhggDktDRL0+/S+pn03nvvmZMYDx48sLh9fHy8KFSoUJqSUmrfR+XKla3uQ6/Xm6ft2Zp6t2HDBptJKSHS9vNN+nnoiguktJwDmKYTvvvuuzaPMWXKFAFA1KpVK9nzSS/827dvn6b3QRmTpaTUzp07BQCRM2dO899n0hFUppFLzkhKjRs3zmpsI0aMEABEmTJlUrxmKSkVGRlpPqdPOlrGNIIq6ejQtCal7J3HCiHEgwcPzIm1OnXqiAcPHojixYub+zZrU6iFEOLMmTOibt26VvskLy8v0a9fP3Hr1q0U2xqNRlG4cGEBQDRo0CBV788e0wqMv/76a4rXbP3sTZ+HVatWtZhkN9m7d68A5FTvF1dFNSWlAgICssUsCBY6J7JDqCw+mFp58+ZFgwYNUrWtEAJHjhwBAHTo0MHqioC5c+dGixYtUh2jySuvvGL1tcqVK5u/fvDggcU2V65cwejRo1G3bl0EBARAp9OZi0D6+voma5cRJP3ZWytuv3v3bgwYMACVKlVCrly5oNFozO+pY8eOAICQkBBERUWlKRa9Xo8lS5agS5cuKFGiBHx8fJIVEjYV088o/3dElPn06NEDN27cwOrVqzFw4EBUqFAhWd/3+PFjTJ06FQ0aNEhWPDs1Tpw4gWHDhqFGjRrw9/eHVqs192emVZYMBgOuX79udR+p/Uw6ePAgAKBx48YoWLCgxe09PT3RuXNnt7yPDh06WH3t3Llz5iLCr732mtV2r7zyCnx8fGzGmpaf765du8xfp7bArivOAZKuLNiqVStER0dbfVSrVg0AcPbsWasFjE2f25T1mYqYR0dH4++//wYA/P3334iOjkbRokXRsmVLpx1LTV9l7dz5Rbly5UK3bt0APC94fvfuXfNK36Zi6Gql9Ty2YMGCWLJkCRRFQVBQEKpXr467d+/Cy8sLK1eutNkv1ahRA8ePH8f+/fvxwQcfIDAwEJ6enubX4+PjsWzZMtSqVSvFgg6XLl1CSEgIAGDAgAEOvWeTJ0+eYMaMGXjppZdQsGBBeHp6Jju3Pn78OADHz61NKwu2adMGz549s9onmX72QgiLC0QAQMuWLeHt7Z2q95eZ6NI7AKKMLk+ePOavnz596tC24eHhFveTVJkyZVIVFwBERESYj1GpUiWbbStVqoRt27al+liAXLHEmqQfOjExMSle//nnnzFq1CgkJibaPY61VTzcLWkcL/78DAYDhgwZonpFnoiIiFSvMPLo0SO0b9/e6gfWi8chIkotLy8v9OjRAz169AAAREZG4siRI1izZg2WLl2KhIQEXLhwAUOHDsXatWtTdYyJEyfi66+/VnXTx1afltrPJFMSI2niypIqVarYfN1Z78PWeUDSFXFtxavT6VC+fHmcOXPGZhyp/fmakmpeXl52zzcscdU5QNKLxe7du6vaxmg04smTJyhUqFCK19JyTkaZi0ajwZtvvokZM2Zg6dKl6Nu3rznJ8+abb0Kjcd7YDTV9laVzZ2v69++PlStXYvXq1Zg7dy6WLVsGo9GI+vXro2LFiqr24czz2Hbt2uHDDz/ErFmzzAntb775BjVr1lS172bNmqFZs2YAgISEBJw6dQqbN2/GL7/8gkePHiE8PBw9e/bEtWvXkCNHDgBIluivVauWquMkdfToUXTp0kXVDRZH+qTo6Gjcv38fAPD999/j+++/V7WdtTiyS5/EkVJEdpQuXdr89cWLFx3a9sKFCxb3k5S9O5u2JF3uOuldRkty5syZ6uOY6HTq8tgvnqAfPXoU7733HhITE1GjRg0sWLAAp0+fRmhoKCIjIxEVFYXIyEhze71en+ZYnSHpye6LJxTffvut+YO8c+fO+Ouvv3D58mU8fvwYUVFRiIqKwubNm83t0/Ke+vfvj6CgIOh0OowcORL//PMPbt68ibCwMPOxxo0bl+bjEBG9yM/PD+3atcP8+fNx+PBh8x3bdevW2VzO2ppVq1bhq6++ghACzZo1wx9//IELFy7g0aNH5s+Dc+fOmdvb6tNS+5lk+uy097lo63Vnvg9b5wFJP+fTEq81an++ps/onDlzWh05bI0rzwFSeyMmLi7O4vNpOSejzKd///4A5EjA48ePm0cEmp53FrV9lVpt2rRBoUKFEBkZifXr12PZsmUAHIvb2eex9erVM3/t6emJ119/3YF39JynpycaNGiAL774ApcuXTIn2e7fv4+//vrL3C5pn+HoTd/IyEh069YNjx49Qv78+TFt2jQcOXIE//33H8LDw83vv0mTJgDYJ7kDR0oR2dGoUSNotVoYDAbs3bvXoW1N7XU6Xaqn6NmS9AT02bNnNtsmPbF1t3nz5kEIgdKlS+PIkSMWO1hHR6G5w6FDh8xfN27cONlrP/30EwDgjTfewJ9//mlx+/j4+DTHcOPGDfMItx9//BHDhg2z2M6RO2xERKkRGBiIwYMHm/u/oKAgFC9e3KF9mLZt3Lgx9u7da3E0gprRNGmRM2dOREREpOlz013vw52f87Z+vn5+fuZjCCEcSky58hwg6f/PqVOnUjVigrKvKlWqoE6dOggKCkLv3r1hNBpRp04d89TbjEqr1aJPnz6YNWsWPv30U1y/fh2enp7o1auX6n048zz27t27ePfdd83fJyQkYODAgdi6davDSeyk8ubNi6lTp6Jnz54AgJMnT6Jv374AnvdJABwukbFmzRqEhoZCo9Fgz549Vn/eqSm9kbRPmj17Nj744AOH95EdcaQUkR05c+ZEmzZtAAAHDhzA5cuXVW136dIlc92Ktm3bOmWk0ov8/f2RO3duAPbnO6dnraHTp08DALp27Wo145/0jnJGEB0djVWrVgEAChUqhBo1aphfe/LkifkOcu/eva3uwxnvyfR/545jERHZk/TkPTXJcFOf9vrrr1udHuPq/qxUqVIA5Oe0LbZed9f7MMVqLx6DwYDg4OA0H8/az7dcuXIA5EWq2vMgE1eeA5QtW9b8tZop7kQvMo0uMk0Hc/YoKVd5Me4OHTogb968qrZ15nms0WhE37598fTpUwQEBGDKlCkAgO3bt6ueumaLvT4JSH6urIapfY0aNawmpBISEnD16lWH9gvIazPTz4F9knpMShGp8NFHH5m/HjFihN36EUIIjBgxwvz9xx9/7JK4FEUxj+DZsmWL1buykZGRDo/ycibTnRaDwWC1jWnocUbx8ccfm4fgjh49OtlFR9I7R9bek9FoxIoVK2wewzSc29b/i5pj3blzB/v377d5LCIiZ0g6pSvptGbTQhu2+jMgY3weNG3aFIAcDfvw4UOLbRITE7Fx40ar+3DX+6hevTr8/f0BwGYNr23btjllxKy1n2/r1q3NXy9ZssShfbry/6pq1armOBctWpSqfVD21rt3b/P5mE6ns5mkyUhq1qyZ7IapI8k0Z53HAsBXX31lPgf97bffMHnyZHPNunHjxjmcMHqRtT6pcuXKKFasGIDnBd/VUtMnrV692uqUOnvatm0LAFi/fj2ePHmSqn1kN0xKEanQunXrZPPO3377basJoMTERAwdOhS7d+8GIFeEePnll10Wm2nFiQcPHmDGjBkW24wfPx6xsbEui8EeU5G+7du3WxwKvHv37gxzMpmQkIAPP/wQv/76KwD5of/ee+8la1OgQAHzyLf169db3M8XX3xhd3Ravnz5AMBcENGSpAUOLR0rMTERb7/9tt0LQSIia65fv44JEyYgLCzMZrs7d+5g/vz5AOTUiYYNG5pfU9OfAc/7tA0bNli8wbNkyRLs3LnTofgd9dZbbwGQ/ae1m0Zff/21eWUnS9z1PrRarfn8Y8OGDRb3GR0dba4raIkzfr7ly5c3r8Q1e/Zs80hwS16sv+LKcwBFUTB69GgAclXFadOm2WxvMBhw7dq1VB2LsqYCBQrgypUruHTpEq5cuYICBQqkd0iqbd++HZcuXcKlS5dUrRZq4qzz2CNHjuCLL74AAAwePNicjJo/fz6KFy+OhIQE9O7d22LCfMSIEXZHq8bHx2Pq1Knm703JHpNRo0YBkHXrTKtQWyKESHaebOqTLl26ZHE01H///YdPPvnEZmy2mPqkyMhIDBw40O40SK6czaQUkWo//fQTmjdvDgBYuHAhateujQULFiA4OBhPnjxBcHAwfv/9dwQGBmLBggUAgObNm5vnbLtKjx49zIX4Jk2ahFGjRuHChQt4+vQpgoKCMGDAAMybNy/ZEHd3e+ONNwAAV69eRadOnXDo0CE8fvwYV65cwRdffIFOnTqpXi0krYQQyZZjffLkiblu04QJE1CyZEnzcOMKFSpg48aNKaYbaLVa8yo/S5cuxahRo3D+/HmEhYXh+PHjGDhwID7//HO7KzfVrVsXgLx4OXHiBGJjY6HX66HX680XOXXr1jV/eI4aNQo//PADrl+/jkePHmHbtm1o0aIFduzYYfdYRETWxMbGYtq0aShatChef/11LF68GOfPn8fjx4/x5MkTnD59GjNmzEBgYCAeP34MAJg8eTK8vLzM+zD1Zzdu3MBvv/2Gp0+fmvuzpBcDps+Dffv2oU+fPjh58iTCwsJw9uxZjB49GoMHD3Z5fxYYGGhO9Cxbtgyvv/46jh8/jidPnuDcuXMYMWIEpkyZYnWBEne/j88++wz58uWDEALdunXDd999h1u3buHx48fmz4GrV6+iaNGiFrd3xs8XkOdBefLkQUJCAtq0aYNx48bh1KlTePLkCUJDQ7F//36MHz/efE7y4v+Vq84BRo4ciZdeegkAMGHCBHTu3BmbNm0yFy2+ffs2tm/fjk8++QRlypRxypQiylrKlCmDSpUqZbqVzgoVKoRKlSqhUqVKDhVTd8Z5bEREBPr06QO9Xo8KFSpgzpw55tdy586NP/74A1qtFpcvX7ZYV+nPP/9E1apV0bx5c8yZMwf//vsvQkJCEB4ejuDgYCxevBj16tXDgQMHAMhi7I0aNUq2j1GjRpmfGzt2LHr16oU9e/bg4cOHCAsLQ1BQEGbNmoXq1asnG3HVvXt3aLVa6PV6dOzYEWvXrkVISAju3buHxYsXo2HDhnj69ClKliyp+v80qbp162LChAkAgI0bN6Ju3bpYvHgxrl+/jvDwcISEhODQoUOYOXMm6tWrp3rl0CxNEJFqcXFxYvjw4UKr1QoAVh9arVYMGzZMxMXFWd3XgAEDBADRokULu8edPHmyACBKlixp8fX79++LSpUqWY1n4MCBYtKkSQKAKFu2bIrt9+zZY2578+bNFK+bXlu0aJHVGG/evGlut2fPnmSvGQwG0blzZ6vxFStWTFy+fNnmcVq0aCEAiAEDBlj/j7LBtL2ah4+Pjxg5cqSIjo62ur8HDx6I0qVLW91H8+bNxZYtW2z+v+7atUsoimJx+6T/B/v27RM5cuSweqyPP/7Y7u8IEZE1V69eFZ6enqr6R51OJyZPnpxiH3FxcaJixYoWt0n6Offs2TNRr149q/uvWrWqOHbsmNXPEyHS/plkiuPll1+2GkfLli3Fr7/+av7e0vbueB8mR44cEf7+/haPpdFoxK+//mr1c9IZP1+TM2fOiFKlStncx4ufQ+44B4iMjBQ9evRQ9R5Hjx6dbFt7vyuU+S1atMj8M7506ZJD2wYHB1v93bR17mXv3NpSbC9K+rv5888/OxS3EEIULVrU6t9NWs9je/fuLQAIT09PcfLkSYvHN117ABBr1qxJ9lqZMmVU/b0CEF27drV6Tv7kyRPRqlUru/t4Mf5vvvnGaltvb2+xZs0am/2OvfNuo9Eopk6daveaEYCoU6dOiu1LliwpANjsj7MSjpQicoCXlxfmzZuH8+fPY/z48ahbty7y588PDw8P5MuXD4GBgRg3bhzOnTuHn3/+OcVdRlcpXLgwTp48ab6r4e3tjYCAADRr1gzLli3DokWLzKvyJF2twl00Gg3Wrl2L2bNno1atWvD29kbOnDlRpUoVjB8/HqdPn3bbSKkX+fr6onDhwqhduzYGDx6MBQsW4P79+5gzZw58fX2tblewYEEcP34cH374IUqXLg0PDw/kyZMHjRo1wo8//ojdu3cjR44cNo/dsmVL7Ny5E506dUKhQoWs3uVq3rw5/v33X/Tq1QsFChSAh4cHChUqhE6dOmHTpk02hywTEdlTvnx5PHr0CP/73//w3nvvoXHjxua+xtPTE/nz50fTpk0xYcIEXLx40VzINikvLy/s27cP77//PipWrAhvb2+Lx/Lx8cHevXsxefJkVKpUCV5eXvD390ft2rXx1Vdf4d9//3XL9BkfHx/8888/mDt3LurWrQtfX1/kypULgYGBmD17NrZv3w5PT0+b27vzfTRs2BAXLlzA8OHDUbJkSXh6eqJQoULo1q0b9u7di6FDh1rd1hk/X5MaNWrg0qVL+OGHH/Dyyy8jX7585s+kunXrYty4cSmmA7njHCBXrlxYvXo1Dhw4gEGDBqFChQrImTMndDod8ubNiwYNGmDs2LE4cuQIvv322zQdiygrSMt57OLFi7Fy5UoAsqZUnTp1LLabNGmSeeTk22+/nWy00pUrV7Bz505MmDABrVu3RokSJeDt7Q2dToeAgADUqlULQ4cOxb59+7Bu3Tqr5+QBAQHYuXMn/v77b7z22msoWrQoPD09kSdPHlSrVg2DBw/G9u3bUaJEiWTbjRkzBhs3bkTLli3h5+cHLy8vlCpVCoMGDcLx48fTPHpJURR8+umnCA4OxpgxY1CnTh3kzp0bWq0Wfn5+5tg2btyIw4cPp+lYWYEihJ2KzUSUJXTt2hUbNmxA586dsWHDhvQOh4iIKFMTQuDQoUNYv369eXXemJgY5MuXD40aNcKIESNcWlOSiCg12HdRRsOkFFE2EBUVhSJFiiA6OhpTpkzB5MmT0zskIiKiTG3Xrl3mVek0Gg3KlSsHX19fBAcHm0cnT5w4EV9++WV6hklElAz7LspoOH2PKAsIDw+H0Wi0+vrHH39s/pAxFRwlIiKi1BNCoFy5cpg3b565cHdQUBDCwsIwfvx4AMDUqVOxadOmdI6UiOg59l2U0XCkFFEWsGbNGkyePBlDhw7Fyy+/jGLFiiEhIQFnzpzBnDlzsHXrVgBA3759sWzZsnSOloiIKPOLjIyEj4+P1XqAHTp0wNatW9GlSxery64TEbkb+y7KaNSvHUlEGdrFixctLrlq0qpVK8ybN899AREREWVh9hYOadOmDbZu3YqrV6+6KSIiIvvYd1FGw6QUURbQokULfP/99/jnn39w5coVPHz4EHFxccibNy8CAwPRp08fvPHGG9BoOGOXiIjIHeLi4gDA7kqsREQZCfsucjcmpYiygPz582PUqFEYNWpUeodCRESU7QkhsHr1agAwL4lORJTRse+i9MCkFBGlCyEEoqKikCtXLiiKkt7hEBEROc38+fNx6tQpeHp62pxaHx8fj/j4ePP3QggkJCQgX758/GwkIrdj30XpgXN5iChdREVFwd/fH1FRUekdChERkdMEBQWZRy5PnToVZcuWtdp22rRp8Pf3Nz9y586NAgUK8LORiNyOfRelF66+R0TpIjIyEv7+/oiIiLBbcJGIiLKwy5eBHTuAqCggd26gfXvAxsVQRnbz5k00adIEISEh6NOnD5YvX25z1MCLow0iIyNRvHhxfjYSkVux76L0xOl7REREROR+N24AY8cCJ08CWi2gKIAQwLffAo0bAzNmAMWKpXeUqj148ABt2rRBSEgIOnbsiMWLF9udxuLl5QUvLy/1B3n4EPj3XyAuDihQAGjUCPDwSGPkRJSduaXvIrKB0/eIMqibN29i/vz5ePvtt1GzZk3odDooioKpU6fa3fbIkSPo2rUr8ufPjxw5cqBKlSr48ssvzatppMalS5fw5ptvonDhwvD29kbZsmXx8ccfIzw8PNX7JCKibOrGDeDVV4HTp+X3BgOg18t/AeDYMaBbN+DevfSK0CFPnjxBmzZtcP36dbRo0QKrV6+GhzOTRbdvA8OHyyTU++8DY8YAAwbI73/++fn/GxGRA1zedxGpwKQUUQY1Z84cDB06FAsWLMDZs2dhUHnC+ccff6BZs2bYsGEDvLy8ULlyZVy7dg2TJk1C8+bNERMT43Ase/bsQWBgIFasWAGDwYCqVaviwYMH+O677xAYGIjQ0FCH90lERNnY6NHAs2fWkykGAxAeDowb59awUiM6OhodOnTA+fPnUa9ePWzcuNG5S6lfvQp06QL88w9gNCZ/LSwM+OYbYMQIJqaIyCEu77uIVGJSiiiDypcvHzp16oQvvvgCW7duRffu3e1uc+vWLQwePBgGgwHffPMN7t69i6CgIAQHB6NixYo4fvw4xo4d61AcUVFReOONNxAbG4uRI0fiv//+w8mTJ3Hnzh00adIEN27cwODBg1P7NomIKLs5dw44c8Z+EsVgAA4dkqOqMqj4+Hh07doVx44dQ9WqVbFt2zbkypXLeQcwGIDBg20n8ABg2zZgwQLnHZeIsjSX911EDmBSiiiDmjhxIjZu3IjPPvsM7du3R86cOe1uM3PmTMTHx6Nt27YYM2aMeT54yZIl8fvvvwMAfvvtN4dGNv3yyy949OgRKleujFmzZpmH9ObNmxcrVqyATqfD5s2bERQUlIp3SURE2c62bbKGlBoaDbB9u2vjSSWDwYBevXph9+7dKFu2LP755x/kyZPHuQfZvVtOYVQzCur33zlaiojsckvfReQAFjonyiKEEFi7di0AWBy51LhxY1SqVAmXL1/G+vXrMXToUFX7/fvvvwEAAwcOhPaFi4gSJUqgdevW2LZtG9asWYM6deqk8V0QEVGWFxkpi5qrodEAERGujSeV/ve//2HdunUAAI1Gg549e1psV7hwYaxevTp1B1m3Tibw1CSbTEXQGzVK3bGIKFtwS99F5AAmpYiyiDt37iAkJAQA0KRJE4ttmjRpgsuXL+PYsWOqklJ6vR4nT560u89t27bh2LFjqYyciIiyFUeWCzcaHWvvRkmXQw8ODkZwcLDFdiVLlkz9QUJCHBv99PBh6o9FRNmCW/ouIgdw+h5RFmH6QPHy8kKRIkUstilTpkyytvbcunULiYmJybZN6z6JiCiba9tWrrSnhtEItGvn2nhSaeDAgRBC2H3cunUr9Qfx8XGsPYsUE5Edbum7iBzApBRRFvH06VMAQO7cuc21pF4UEBCQrK3afSbdNrX7jI+PR2RkZLIHERFlQzVrAtWq2a8rpdUCDRsCZcu6J66MqHlz9VMddTqgXj3XxkNERORkTEoRZRFxcXEAAE9PT6ttvLy8AACxsbEO7dPWftXuc9q0afD39zc/ihcvrioGIiLKgmbPlqN6rCWmtFo5bW/GDPfGldH07CmTTfZotUDnzoCVG0hEREQZFZNSRFmEt7c3ACAhIcFqG9Mc8hwqh/eb9mlrv2r3OX78eERERJgfd+/eVRUDERFlQeXKAWvXAlWryu+1Wpl8MSWpatWSRb5LlEivCDOGgABg4kTbbbRaIHdu4OOP3RISERGRM7HQOVEWYZpGFx4eDiGExSl8pil21qbiWdunadvChQunep9eXl7mUVVEREQoVw5Yvx44fx7Yvl2uypc7N/DKK0ClSukdXcbRv79chfDLL4HEREAI+bxpVb5SpYAFCwAr9SSJiIgyMialiLKI8uXLA5Ajl+7fv4+iRYumaHPjxo1kbe0pVaoUPDw8kJiYiBs3blhMSjm6TyIiomSqVZMPsq5vX6BLF2DNGuDQISAmBihcGHjtNaBJE/V1p4iIiDIYTt8jyiJKlCiBQoUKAQAOHTpksY3p+QYNGqjap06nQ506dZy6TyIiIkoFPz9g0CBg4UJg5Upg1iygaVMmpIiIKFNjUoooi1AUBa+++ioAYOHChSleP3z4MC5fvgwPDw906dJF9X5fe+01AMDixYthMBiSvXbnzh3s3LkTANC9e/fUhk5ERERERETZEJNSRFnImDFj4OnpiR07dmDmzJkQ/1934vbt2xg0aBAAYMiQIeYRVSZr1qxBqVKl0LRp0xT7HDZsGPLly4dLly5h9OjRSExMBACEhYWhT58+0Ov1eOWVVxAYGOjid0dERERERERZiSJMV61ElKEcOnQIXbt2NX8fHR2N+Ph4+Pj4JFvp7tSpUyhevLj5+6VLl+Ktt96C0WhE0aJFUaBAAZw/fx6JiYkIDAzEvn374Ovrm+xYixcvxltvvYWSJUvi1q1bKWLZtWsXOnXqhLi4OOTPnx8lSpTApUuXEBMTg1KlSuHIkSMpEl32REZGwt/fHxEREfDz83NoWyIioqyIn41ElBmx76K04EgpogwqMTERYWFh5kd8fDwAICYmJtnzL06p69+/Pw4cOIBOnTohNjYWFy9eRJkyZTBlyhQcPHgwRUJKjVatWuHEiRPo1asXFEXBuXPnULBgQYwePRpBQUEOJ6SIiIiIiIiIOFKKiNIF76gQERElx89GIsqM2HdRWnCkFBERERERERERuR2TUkRERERERERE5HZMShERERERERERkdsxKUVERERERERERG7HpBQREREREREREbkdk1JERETpQQjAaEzvKIiIiIiI0g2TUkRERO6i1wPbtgG9ewPlywNlywL16wOzZgEPHqR3dEREREREbsWkFBERkTs8ewYMGAAMHw78+y9gMMjnHz0CfvoJeOklYP/+1O9fCODgQeCdd4BatYCqVYF27YBFi4DISGe8AyIiIiIip2JSioiIyNWEAN5/Hzh6VH7/4rQ9oxFISADefhu4eNHx/cfFyW379QN27QIiIoCYGCA4GPjyS6BFC+DMmbS/DyIiIiIiJ2JSioiIyNVOnwb27LFdQ0oIOXrqxx8d27cQwKhRcv/A8xFYpteEkCOl+vYFbt1yNHIiIiIiIpdhUoqIiMjVli8HtFr77QwGYMcO4OFD9fs+fVpuYyvhZTQCsbHAzz+r3y8RERERkYsxKUVERORqZ84kH8Fki9Eop92ptXSp+oTXunWsL0VEREREGQaTUkRERK4mhGPtbY16elFQkPqEV0ICcPWqY7EQEREREbkIk1JERESuVqmSutFMAKAoQOnS6vetNiFlotc71p6IiIiIyEWYlCIiInK1Pn3UJY+0WqBZM6BYMfX7LlMG0Kj8OFcUoEQJ9fsmIiIiInIhJqWIiIhcrXFjoFYt+6OlhADee8+xfffpo266nynhVaSIY/snIiIiInIRJqWIiIhcTVGABQuA8uXl14qS/HWtVo52+u47oH59x/bdurUcLWUv4WU0AsOHO7ZvIiIiIiIXYlKKiIjIHfLmBf7+G5g8GShZ8vnznp5A9+7Apk1At26O71enA5YsAQoVsjyNT6uVSbBp04CGDVMdPhERERGRsylCOLokEBFR2kVGRsLf3x8RERHw8/NL73CI3EsI4OlTuRpeQADg5ZX2fT59KpNTy5YBT57I5zQaoF07YMgQoE6dtB+DiFyKn41ElBmx76K00KV3AERERNmOogB58jh3nwEBwAcfyJpUd+8CiYlAwYJA7tzOPQ4RERERkZMwKUVERJSVeHjIGlNERERERBkca0oREREREREREZHbMSlFRERERERERERux6QUERERERERERG5HZNSRERERERERETkdkxKERERERERERGR2zEpRUREREREREREbsekFBERERERERERuR2TUkRERERERERE5HZMShERERERkeOMRiA2FhAivSMhIqJMikkpIiIiIiJSRwjgyBFg2DCgQgWgShWgYkXgww+BU6fSOzoiIspkmJQiIiIiIiL7hAA+/xzo0wfYuRMwGOTziYnAxo3Aa68BP/yQvjESEVGmwqQUERERERHZ9+OPwJIl8mtTQsrE9P3s2cCKFe6Ni4iIMi1degdARERERBmY0QgcPAjs2gU8ewYEBACdOwM1aqR3ZOROUVHAzz+ra/vdd0DPnoCHh2tjIiKiTI9JKSIiIiKy7MQJYPRo4O5dQKeT07cUBViwQCalfvgBKFkyvaMkd9iwAYiPV9f2yROZxGzfPvnzkZHy+adPAV9foEULoFAh58dKRESZBpNSRERERJTS8ePAm28+n5al1yd//cIFWUNo/XqgWDH3x0fudfkyoNWm/D2wRKeT7U1JqagoYPp0YM0aICEB0GjkCDyNBmjTBpg0CShSxLXxExFRhsSaUkRERESUnNEIfPCBTEgZjZbbGAxARATw2WduDY0yCSHkv1FRwOuvA6tWyYQU8Px3ymiUBdO7dpWj8YiIKNthUoqIiIiIktu/H7h/33pCysRgAPbtA+7dc09cer2cGhYd7Z7j0XPly6csbm6NXg9UqCC/njoVCA62vq3BIKfzjRjhnDiJiChTYVKKiIiIiJLbuVNOwVJr1y7XxQIA16/LEVk1agCBgUD16kC7dsDKlerrHFHadOumvnB57txyWt7Tp8Dff9tPZhkMwNmzwJkzaY2SiIgyGSaliIiIiCi56Gj7o6RMNBrXjlzaskXWJlq5EoiNff58cDAwYYJc5S083HXHJ8nPD3j7bXVtR44EPD2B7dvVj67S6YCNG1MfHxERZUpMShERERFRcrlzy2STGkYjEBDgmjiCgoD335eJjReTG6aaRRcvymSJ6XtyndGjZX0oQBY9T8r0/bBhwMCB8uuwMPW/R0IAjx87JUwiIso8mJQiIiIiouQ6dVK3yhogkw5t27omjh9+ABTFdsLJYABOnACOHXNNDFbcvHkT8+fPx9tvv42aNWtCp9NBURRMnTrVrXG4lUYjV9FbuBBo2lT+bEzPt24NrFgBfPLJ8+d9fNSPuFMU2Z6IXC5b9l+UYTlQLICIiIiIsoXAQKBiReDaNdvTr7RamcDKl8/5Mdy/LwuuqxkBpdUCy5cDDRs6Pw4r5syZgzlz5rjteC4TGwscOCBHNfn6Ak2aAHnzWm+vKEDLlvIRHy+nbubMCXh5pWzbogXwxRfq4tDrgZdfTt17ICKHZMj+Ky5O9gO+vs8T25QtMClFRERERMkpCvDTT8BrrwHPnllOTGm1QIkSwJQpronh+nX1U/IMBuDSJdfEYUW+fPnQqVMn1K9fH/Xq1cOCBQvw119/uTWGNImLA2bPBv74Q/6MTXQ6mWgcNw4oWND2Pry8LCejTMqUARo3lqPYbCU3FQUoUEAmuojI5TJM//XsGbBmDbBkCXDzpnwuTx6gd2+gb1+gUCH7+7h3T9YcPHkSSEgASpeW04zr12dyK5NgUoqIiIiIUipbFli3Dpg4ETh8WE7R0mhkckFRgA4dgM8/l/WnXOHsWcfau/niY+LEicm+//PPP916/DSJjZUXfKdPp5xep9fLguNHjgB//QUULZq2Y33xBfDqq0BMjOXElKLIx7ffpqxTBQCRkXJVvrg4mSSrXp0XmkRplCH6r5AQ4M03gVu3kj//5Anw88/A4sXAokVAvXqWtzcYnk8nVpTnfdnZs3LVz5o1gfnzgfz5XfkuyAmYlCIiIiIiy0qXliNpbtwA9u6VU7Xy5AHatXPtif7+/cCsWerba7VA1aquiyermTHDckLKxGCQRcffe08mJtOibFk5EmLYMDkSQquVxzUlOPPkkSO2mjZNvl1oKDBnjry4jI9//nypUsDQoUCvXkxOEWVW8fFAv37AnTuWR8QajTJ5PnCgXIG1ZMmUbb78Eli6VG6fdB+m5Pf583LE1Pr1cvVQyrCYlCIiIiIi28qUkQ930OuBMWMcW03PYJAXOELI6Ruenpk7YXH4sLwg8/UFGjQA/P2dt++oKODPP+0XIDcY5AilM2fkiIO0qFAB2LVLvq9Nm+RIiJw5gVatgDZtAA+P5O1v3wZ69ACePk05uur2bWDCBDka4uuvM/fPmSi72rpVTtG2xWiUyavff5ejcpO6eFFO+bPFYJBJrwUL5MqhlGExKUVEREREGcfu3cDDh+rbazRylNTixUCfPkBioqxz1KmTTFSlNaHiLomJwC+/yK/feUe+L0Am2Lp2BT7+WNZdSqt//kk+8sgWnU6OMnDG/6GiyCLqTZrYbmc0AoMGWU5IAc+TlX/+KX/uffumPTYicq9ly2QfpyY5vnq1rHGXI8fz55cvl6MubdWqA+T+//gDGDFC9qWUIWnSOwAiIiIiIrO9e2UyRC1fX+DcOWDbNpnYAWTSZd06oFs3YO5cFwTpHPHx8YiMjETk48eIHDAAkZZiTUiQU9i6dgX++y/tB330yHLtJkuMRscShM5w4ICcLmrvYlNRgF9/tX9RS0ROZ+67kjwccuWK+r/d2Fi5GmtS+/bZ7yNMnjyRK8lShsWkFBERERFlHDEx6i9WFEVORwNSXqCYvv/uO7kyUwY0bdo0+Pv7wz9/fvivXInid+9abmgwyGTSsGGOTWu0xMfHsf9fH5+0Hc9Rq1erS5oJIVfdCgpyfUxElIy57/r/R/Hixd0bQFycY+3Vjg6ldMGkFBERERFlHHnzPp+6Zo/aBM133z0fRZWBjB8/HhGhoYioUgURJUrgbrFi1hsbDLJw76lTaTtokybq/98MBqB587Qdz1H37qkfAQHIFbyIyK3Gjx+PiIgI8+OutYS6NRUrqu/nc+QAihRJ/lzhwo7Vk3PG1GdyGSaliIiIsqPQUODHH4H335ePuXPdP02HyJIuXWSxc2cKC5OFtjMYLy8v+AUFwS8mBn4aDfzsXaTpdHIqX1qUKQM0bmx/NJKiAAEBQNu2aTueo5LWjVHDy8s1cRCRVV5eXvDz80v2cEi/fupGbGq1QM+eKfuFHj3UHUejAerVA4oWdSw+cismpYiIiLKTxERg0iR5Ufr993Kp5S1b5JLsjRrJFW6cnRAgckTNmkCNGuqSJmrvlOt0wOXLaY/NFUJD1b8PvV62T6spUwBvb+v/x6Z4pk9XXxz40SPgxAk5nc7R+jJJNWqkfgSFVgvUrp36YxFR+njlFaBsWdv9vEYjk86DBqV87bXX5Aqe9voKoxEYMiRtsZLLMSlFRESUXRiNwIcfylVrjEbLjyVLgI8+SnvdGqK0+OknOUrH2gWLRgPkzq2+YDeQcX+nvb3Vx6bRyPZpVb488L//AYUKye9N/4+mZJSvLzBvnrpRUkFBwNtvAw0ayBEN3bvLkQmffALcvOl4bL16qWun1QIdOgD58zt+DCJKX15ecgW+EiUs32DQaOToqMWLgZIlU27v5wcsWAB4eFj+HDDtb8QI94/2JIcxKUVERORser1cdn3mTGDaNGDFirSNHHCWnTuBzZttXwALAWzYAOze7b64iF5UrJj8PWzX7vmdcNO/Xl5Anz7A8OHqaw/p9TIRkxE5MtLHaASaNnXOcatUkStYLVwIdOok99uuney3jh8H2re3v48NG2Qias+e5P1KQgLw119A586O18AqUAD44APbbbRamTj76CPH9k1EGUfhwsDGjXLkZunSz5/Pkwd49115zlKvnvXt69eX05lfeillUqtMGWDWLPYRmYQD6+0SERGRXRs3AlOnyvpMOp08UdLrgS++kDUUxo6Vd/bSw5Il8mJOzVLr338vT/QcGYlC5EyFC8sRUw8fAvv3y1X28uYFXn4ZyJULiIgAvv1WJkDs8fcH2rRxfcypcfSo+raenrLmlrNotUDLlvLhqAsX5MhLa3VhDAa5lPvAgcDevXLkm1ojRsgk1/ffy/7IdAyNRn5doIBMplkaQSEEcOyYPOazZ0C+fDI5VqaMY++PiFzP1xfo318+4uPl+ZKPj/opzVWqyBFT//0HnD0rty9eXE4DN+3j8mVg3TrgwQM50rRpUzl6Su3UZHI5RYiMOpaZiLKyyMhI+Pv7IyIiwvHiiEQZ1YoVwKefWn9dUeSF8bx57k/2xMcDlSo5tk2hQvIuo9qCokTuNnOm/HuyZ+JEYPBgpx760KFD6Nq1q/n76OhoxMfHw8fHBzmSFOU9deqU9eXShQBatQJu3QKEQKTRCP87dxBRooTlouelSslRSRnB6NFypJSaJPf48XKKn6NCQmS/euCATHAVKSL7o7ZtLSf3T54ExoyR0wZ1/3/vXQgZY7NmchVGTvcjck7/lYTq8/o7d4A//pD15xITZZ/2xhuyzqUjq+mp8egRMHKkTPwnPecyGGRyqnNn+bqtVU/JLZiUIqJ0waQUZTn37gEtWqhbTebrr4HevV0fU1IREUCtWqnb9qOP5MgFoozGYJC1i/76K+UoQNP3b78tkyJOvuDZu3cvXn75Zbvtbt68iVKlSll+8YW/S7tJKUUBrl1TXwjcVZ49k3GrXRTBHcm0f/8F+vaVP3NL/bBWKxPt69bJ0VNE2ZhT+q8k7J7XGwxyFPmSJbL/MvXVpn66ShXgl1+AM2eAgweBuDiZQH71Vfmao54+Bbp1kyOo7CXOW7YEvvxSJr0pXTApRUTpgkkpynJmzpQnVPaSUooClCsHbN8uv37yBDh8WF7k5c0r7+a7YolzvV6e2CUmpm77NWuAwEDnxkTkDELI2iNLl8qLGUD+bbVsKaeOOasGkys8eiTrovw/u0kpALh6Nf2mAJvcuiWnUarl4SHjdhW9Xv6cHz2y3QdrtXJ0xOzZrouFKBuye14/caIc9Wgt9WDq74xG+bUQz6fu1q0LzJ0LFCyoPqBx44DVq9XdKNRo5PnX2rVA0aLqj3HqlCzWvnfv8yRaz55y5BdHZDqESSkiShdMSlGW07w5cPeu+varV8sh7Js2JR9tkCuXrD01cqTzk1MffihrXqktDm1iWuXqhx+cGw+Rs8XGAtHR8u8oNavUJSQAR44AYWGy1knDhrIelaskJAA1asjptVCRlMqTR05RS2/37skEulpeXsClS86fnmOyYwfwzjvq2up0cjpP3ryuiYUoG7J5Xn/xItCxY+p3rihylOP69SmTPTEx8qZESIjsZ+rWlTfRlixx/DjFi8uRlHny2G5nMACffQasXJlyhK5GI5Pw8+alrlZfNsXV94iIiJwhIsKx9u+8IxNEL05/iYqSI65MRT+dacAAxxNSgNxmyxZ1BaWJ0lOOHPKixdGEVEKCLKrdoIEcXfXRR8CwYXIU0yefyBE4ruDpCXTvrq7GnEbj/mm/1hQuLIuNqxUfL0dWLVmifsqfI7ZvV1+nT6+XIxuIyD2WLUtbHU0hZNLp44+fP5eQAMyYIVfnGzUK+OYb4PPP5UjI1CSkAHljsWlT+4tPTJsmE1JAynMqo1HG9s47QFBQ6uLIhpiUIiIicgZHR/yFh1tPEBmNsgjozJlpDiuZWrVkbZ3UMBhkwsxZHj2Sw/Ffe00WLe7XTy7t7OxEHJE98fHAoEFyJGB4ePLXEhJkvaquXeVFkSsMHChHAtgaRaQo8u77m2+6JgZHabXyb9aR2lZ37siLxrffdn6COypKfcJdowEiI517fCKybv/+1N0Qs7SfFStkGYKhQ+UNvJgY+ZqaaXpqxMbKG3iXL1t+/f594Pffbe9DCBmPs8/hsjAmpYiIiJyhY0d1dwJNF572TqCMRnny9exZ2mMzSUiQRUOnTUvdajO+vs6JY9EioFEjWdfl1CkgOFjW1froI7kCz6lTzjkOkRqzZ8spe9YqWhgMwMOHwLvvuub45cvLUVqKYrkP0WplQuq33+QIpYyif39Zf0XtCAgh5GPfPrnYgzP5+6uPw2gEcud27vGJyDpn3mz67DN5DrNvn/P2+aKEBJmYsnQu8uef6qYhG41yxNX1686PLwtiUoqIiMgZ3nxT3Z06IdSPLoiNlbUS0urSJTkFqXp1OR1p/HjAx0fWT1BzcqXRyNo6qanR86Jly4Avvki5Qpbp6/BwoE8fWYOCyNViYuTvpL2/XYMBOH1argzlCh07yjooTZokf16jAdq1kwV4mzd3zbFTy89PTmExrcyltl6UEDLh/uKotLR45RX1IzE8PBwr0k5EaVO4sPPqyRmN8saWqz18KIuWb96c/PnTpx0blXXunFPDyqqYlCLKYm7dugVFUVQ99qm8yzBlyhS7+7psbZgrUXZRvDgwZYrtNooi692oPaHRaNJey+avv4BOneS/SafMBAfL+glq1jsxGp1TyyYqCvjqK/vHSkyUd0KJXG337ufTP+zRamVyyFVq1wZ++kl+vWCBTNwcPSqfS82S6O5QtCiwbZucRlO6tPrt9HpgwwbnxdGihVzO3V7CX6uVy8RzpBSR+7z+enpHkDoGA/DBB8lXDnV0GqIzpi1mA7r0DoCInMvb2xtNXrzTmkRISAhu3LgBb29v1KpVy6F9Fy9eHCVKlLD4mo+Pj0P7IsqS+veXU9y+/hp48kSu8qQo8gJMp5MjgMLC5Ip7agghCzen1uHDwJgxlhNPji6+O368HCUyeLC8+EuNtWvV1ZIxGICDB+Wy86ZRGESu8PChTGSoSRQbjUBoqOtjAmTB9cyyMq1OJ0dzHT8uE92Jifa30WhkjSln0WrlaldvvCGPb+nnqdUCJUoAEyY477hEZF+3bsC338qVUZ1V+8ldhAAWL34+5bhMGeDYMfXJJp7DqMKkFFEWU6hQIRw8eNDq63379sWNGzfQpUsX+Du4zPWgQYMwxd5IEKLsrnt3oEsX4J9/ZD0CvR4oWVIWSg4IkAWV1RIi5XQea65ff368EiXkdLs5c2RSTE0CSqt93s7SSWNMjFzR5q+/gD/+AKpWVf8+TI4eVR8PAPz7L0/oyLW8vR0bueiMKazOZFrpycvLedNjUkunU/+3bUo8nz8PVKvmnOPXrCmnQI4bB1y4IPs0jUYeSwigTRt5YclRUkTOFRX1vP5SSEjKhHquXLI4eL9+sr/KTKOHDAa5CMvkybKffeMNYPly+9spijx/qVPH5SFmBUxKEWUj0dHRWLduHQCgX79+6RsMUVbm4QF06CAfScXFyUSLWoUL2y9sfOoUMH16yv0WKCBHgaih1cqaLIUKAQsXWm9nWoGvf3857cnBxDbi49UnABSFK/GR6zVqpL6twSCXC09vpmLhS5bI1aiMRpks69ZNXvSl11S/OnWAX39V3z44WCbwP/sMeOst58RQrZociXr2LLB3r1woIl8+WbMrtSM8iciyu3eBn3+WSZvYWPlc27ZyOu3w4cn718BAYN06ubDEjh2Za8RUfDzw9Kk8R6pWTd4sPHrUdnJNCOD999P/ZkEmwZpSRNnI33//jWfPniF//vxo3759eodDlP3s3+/YanohIfKuXESE5df37ZO1Gk6cSPma2oQUIE+sbt6Uo6Hs1WQxGuXJ2V9/qd+/SZEijq3UlZFWGqOsqXRpueKjvd9LRZF3+zt2dE9c1uj1wIcfyiTOgQPPL+zi4oDVq2X9uCVL0ie2li1lzTy1jEb5d/7FFymLCadVjRrAyJFy2vHbbzMhReRsFy7I/uZ//0t5A+nwYbn4y4oVyZ+vUEEmsQ4flvUlvbzcF29aeXg8//qnn4CKFS0nnEyfJaNGydWOSRUmpYiykeX/P9y0V69e0OkcHyi5Z88e9OzZEy1btkSPHj3wzTff4MGDB84OkyjrevzY8W3On5d3HF+cFvPkiXz+xVXs0uKvv9QPq1czfP1F3bur339AQMZbbYyypkmT5MWRrYSsEMDUqek/fe+rr54XCH/xb8k0TW3KFGDLFreHBp1O/l86SlGA775zvM4dEaWPZ8+AAQPkv5Y+00190cSJlkeHFywoV6XT610fa1opiiyJkCfP8+f8/eVNgHHj5GIPSTVuLGtQffCBO6PM9JiUIsomQkJCsGvXLgCpn7q3f/9+rFmzBnv27MFff/2FTz75BGXKlMHixYudGClRFpYzp+PbGAzAkSNAUFDy51evlqMjnHEhp9UCZcuqny4nhBy276iaNYFatdSNlho0CPD0dPwYRI6qWBH488/no3xMySlFkQ9vb2DWLDnVLD2FhgJLl9r/m1cUWVQ4PZI8nTrJlTPVjogEZJw3b1oe8UlEGc/69XLRFns3mTQaYP78lM9HRcmFTzJDbSkhgIEDU46K8vEBhg6VI+D37gW2bpWLPSxdKqcvkkOYlCLKJv744w8YjUZUrFgR9erVc2jbwoULY8KECTh+/DjCwsIQExODQ4cO4ZVXXkFsbCwGDRqEjRs32txHfHw8IiMjkz2Isp3GjeVoAkdptbK4eFJ//eW8i06DwfFh5o5cdJooilw6vnBh69sriqxvNXy44/snSq3q1eV0uJ9/ljVR6tSRI/WmTJF3+jPCNIz//U9dO1OSx5H6dc7Uq5f8vyxbVv02igJcueK6mIjIeVasUFcryWAAdu2SCaykrl/PPDUj8+WTZRKs0WjkYjaVKsm2lCosdE6UTZim7qVmlNQ777yT4rnGjRtj8+bN6N69O9auXYsPP/wQnTp1gmLlQ2ratGn4/PPPHT42UZZiKri7aZNjdwgNBuDy5eTPOVIzyhZFkSO4vv1WTmFSc6Ko0ciL+NQoWFDeZf3hBznaKybm+WtFiwKDB8tC6qlJelHmIoRcWvv06eerRrZtm35T5Dw8gPbt5SMjunxZfSJaUWT7Bg1cG5M1hQsD5csDN25wWh5RVnP3rvq/ayFkfcy8eZM/l1lUrw74+qZ3FFkek1JE2cC5c+dw5swZKIqCvn37Om2/iqJg+vTpWLt2La5fv46zZ8+iZs2aFtuOHz8eo0ePNn8fGRmJ4sWLOy0Wokxj3DhZ5DMszLFaUJaGjlsrgG6JRmP5eELIofQXLqhfJcZolPUkUitPHjkCZexYuXpgTIxM2NWsab/QOmUNe/YAX34pR/RotfJ3T6+XxcSHDAFGjODvwoscvZBL7wu/ChXkKltq4hBCtieijC9p0e/UtC9e3Po5SUbD1fPcgp/2RNnAsmXLAADNmzdHyZIlnbrvChUqIM//F/+7du2a1XZeXl7w8/NL9iDKlgoVkssnly+vfhutNuXIpBo11G8fECBHIOXObbudmotHU9FPR+K3xsdHLq3cpg1QuzaTENnFxo3y9/HWLfm9wfC84G1UlFwy/OOP0z+pktFUqKD+AikjJHneeEN9n1K6NOBgaQEiSicNGqgfzeznB5Qqlfy5fPmA1q3V76NrV6BMGbkvR1b4TCutVibQyOV49keUxRmNRqxcuRJA6guc2+Px/3dA9JlhFQ2ijKBYMVkUs04d9XUZXhzl6Eg9hvLlgQkT5KikefPUb2cpNiGAO3fkFKc33ki/ujWUOYWGAh99JL+2lbBYu1Ymb+m5119Xn6grXhxo2NC18dhTpIisL2WvjxNC/k5wRAJR5tCvn7oSBFot8OabsjTAi4YNs9+fabVyyn9cnJxCV7Ei0Ls3UKBA6uJ2lMFgu54UOQ2TUkRZ3J49e3Dv3j14e3ujR48eTt//48eP8fD/a9sUK1bM6fsnyrIURS6XbJq6ZI1GI+8oVqv2/Lm4OODgQfXHCg5+/vWKFervTnp62l4B78QJeYK4dav6WCh7W7Xq+XLhtigKsGiRe2LKLIoUkYlgNcmb0aMzxsjDKVOAdu3k1y/2O6a+b9IkWWsvNSIigKtXZe2qxMQ0hUpEKjVoIOv/2epjtFo5qmnQIMuv164NfP+93IelcxKNRvYPoaHAzp3AuXNydbu5c4FHj1If+5o1QKtW9ttptXJxmipVUn8sUi0DfFoRkSuZpu516dIF/v7+Tt//rFmzIISAv7+/w6v6EWV7tWvLkUs6neULNkCeFM2Zk/y1p0+fT3dS4+lTWbtBCODoUfVF1uPjgYQE66+b9jlqFHDvnvp4KPtau1ZdHREhZJ2zO3dcH1Nm8vnncrorYL3P+OQToFs3t4Zllacn8NNPwK+/ygtZU0LN01OuaLhhA/DWW47v99Qp4L335GjTdu3kRWbDhnLq59Onzn0PRJScosjzkrZt5fdJ+yLT10WLAn/+aXtFus6d5XTurl2Tr0ycNy8SvHyxMaoifo5siEXhtXEzMUC+ZjrvSE3MJUoAgYHA/PnPR59bSqxpNEC5crLvIrdQhOCEfaKsKjY2FgULFkRUVBQ2btyITp06WW37/fff4/vvv0fDhg3x559/mp+/cOECfvrpJ7z33nuoWrWq+fm4uDjMmjULn332GYxGI77++muMHz9edWyRkZHw9/dHREQE60sR3bsH/PGHPIELD5cnT/Xry5MmD4/nI50qVABefhmIjpYXY2p5eMjRBAaDPNFyhFZrP4ml1QJDh8rC5US21KrlWIH+tWvlNtmEqs9GoxHYtg1YsuT59FmdDujQARg4UCa7M6rERCA2Vq74mdqRXGvWyL5Go0nZN2k0sm7fqlVymjQRuY4QcvXUZcsQefQo/I8cQUTPnvAbMEAmrBwpiP7sGfDwIfSKDtP7/YXZh3PjidEXCgQEFCgQaOt9Fd/m2YRqnqGOx2oanW4auSWETIovXChHYZnkyyenJw4aJPspcgsmpYiysJUrV6JPnz7Inz8/7t+/D53O+oKbU6ZMweeff44WLVpg79695udPnz6N2v9/gps/f36UKFECAHDp0iXE/P9S7oMHD8b8+fOhOFAPgkkpIisSEuQF5urVwKxZwMOHyVcny5sX+OADeUF6/bq6mgxNmwKLF8vv69aVK/85W968cjofkS1NmwL//ae+/c6dQNmyrosng0n22ejpKf9WPT3lhZKlz9jYWLl6Za5ctqfaZhX//ivrVNnq90zFiXfscHyVMCJKFWec1xsMAq+/uh9rN4ZCIGV/p4UB3ooeewr9inpe957fwDt+3PYIXK1W9qE7dshi6S+6fVtOCfTxkTf/bFwvkWtw+h5RFmaauterVy+bCSlbSpUqhS+//BKvvPIKcubMiStXruDcuXPIkycPevTogW3btmHBggUOJaSIyAZPTzmlb9w4mZACkq9OFhYGfPaZvOhSc1/JYAD693/+/RtvuKbWzJMnXC2N7GvfXl1NM0WRv+OlS7s+pozo00/lCptNm8qLrpdeAn7/XY4mSCpHDpkQzg4JKQD4+Wf7/ZfBIFd2/Ocft4RERM4xZ04w1m58aDEhBQAGaBErPNAudAieGrzlOUdsLLB+vVxd2FLfoCgyIfXHH5YTUgBQsqS8YVelChNS6YQjpYgoXXCkFJEVp0/LWitqVKgAXLtm/Q6hqUh60gu5//4DWra0XSsqNTw9gStXnLtPynpu3pT1f9QUOk861SKbiPzjD/j37YuI0qXhl/T/yHTjp3x5eXFlq05LVhUaCjRqpC75rdXKGlPLl7s+LiJK83m9wSBQqtRm3LsXq6q9FxLxTq5j+LTOTRTYu0GOdFq5Eli2DHj8WDYqVAgYMECOrsyd2+GYyH04UoqIiCgjWbJE3UgSrVbe3WvW7Pn3SV8DZPHQH35IfvewaFFg8mTnxWs6XpMmzt0nZU2lS9uvPabRAPXqyaXEs5N//5WJOCBlrSQh5OP6dWDIEHXF4rOa27fVj8Y0GOSKfESUKRw9GqY6IQUA8fDAT1GNUO9ED9y9GyNX+hs5UvajZ87Ix+HDwLBhTEhlAhyfRkRElFEIAWzerG51PIMB2L0buHRJjlBauVL+q9EA1asDffpYr8Xz6JFs56wLW4NB3o0kUmPYMDnt7JtvZD0kUxLVlHjp3BmYNg3w8krfON1t7lzLdaOSMhieX2w1beqeuDIKR+tDqUnuE1GGEBKiPiFlYoAW92NzoEuXQwgKai1LiSiK9Wl6qXTmTDgWLbqFW7eewctLg2bN8qNfv5Lw92fNOmdhUoqIiCijiI2Vq1OpZTDIlfiqVQO++kr9dqGh6pNSpotkayMUFEXWCWreXP3xiQYMAHr2lMuBnzola6aVKAH06AEUKZLe0bnfvXvAgQPq/ia1WjmFL7slpcqVk9OE1Uw91modW6GUiNzKaBTYsiUEc+dew759jxAfn7qbZHojcPp0OPbvf4wWLfI7NcZHj+LRq9dR7N79EDqdAr1eQKMBVq++hzFjzmD69BoYObIc6+o6AZNSREREGYW3t7yYUjNSCpAJIR+f5M/dvAmsWCFXwtPrgTJlZD2Fhg2fJ5hy5HAsrlKl5H6Txmb6unt3mRDjSRk5ysdHFt5/4430jiT93bypvq3BAFy96rpYMqpcuWS9vTVr7PeRBoNc1p2IMpzYWAN69DiMLVseQKtVYDCkvcT10KEncOlSe2g0zjkXiYhIRIsWe3D1ajQAQK+XMZruG8TFGfHBB6cRH2/A2LGVnHLM7Iw1pYiIiDIKjQZ4+WX1NaWaNXs+xUmvl6vytWwJLFokC6afPy+nA/bpI+tLmVbza9r0+Wp+9gght920CejdW64E1rAh8NZbcvrgzJnZZ+UvIldxdEVMV6ygmRkMHy6Tmbbev0Yji+kHBrovLiJSrV+/Y9i27QEAOCUhBQBXr0ZjyJDjTtkXAHzzzWVcvRptN77x48/hzp0Ypx03u8qmn2hEREQZVP/+6mtK9e8vvxZCLiH/xx/PX0vaDgAuXpQjUiIj5VS7woXVx/Tbb7JA9ZdfAqtWyfpVn34qnyOitKtQQX2iSasFatRwbTwZVcmSckU9f385OjPpCE1TMr95c7nAA0dvEmU4QUFP8ddf/7lkrYZFi27j0KHHad5PfLwBv/xyQ1XCTFEU/PYbF1VIKyaliIiIMpKmTeWIJHu6d5ejogBZ+Ph//7O9MpXBANy5I0dRabX2V0BLKiZG1v4hItfInx9o107dKEmDAejbN+Xz8fHAX3/JkZEvvQS88gowfbr8u89KatQA9u6Vq4iWLy9HTvn5yVGmS5cCCxemnNZMRBnCzz9fd+lAz6lTL2Hhwpv4+efr2LHjQapGYgUFhePJExW16yBHeq1f/5/Dx6DkWFOKiIgoI1EUYOpUIG9eOUIpMfH5harBAOh0wKBBwJgxz0cCLF+urhaV0QgsWwa8+676ulWAHMGxbx9r/xC50vvvA//8Y7uNRiMTTrVqJX/+3Dk5pTYsLPkiBsHBsh957z1g9OisM3rIz08Wy+eqn0SZyr59j1wySspk27YH2LbtARRF3qcrUsQb48ZVwogR6guSR0erLG/w/+7ejYEQggXP04BJKSIiIne6cEGOarp3Ty5xXqeOXIUsIOB5G40G+OgjYMgQYO1a4PJleXZVsSLw2mtA7tzJ93nggPokU1iYLKoc68Dyy0ajY+2JyHGVKwNz58rVLF8cSmBKOjdunHJq2vXrcnRlXJz8PukVn6lfmDtXJrRHjXLteyAisuG//9xzLmEaOH7/fhxGjjyNK1ei8OOPtVUljgoW9HLoWBERenz33VV8/HHF1IRKABQhbI31JyJyjcjISPj7+yMiIgJ+fn7pHQ6R6z15AowYARw58vwC03RypNUCH34oi/im5k5brVpARIT69hs2APfvA8OGqWuv08lk2IwZjsdGRKqZPxtnzIDf33/LxQl0OrnAQP/+soD3i1P8hg+XI6zsJaY1Gtn/FCjgujdARNmSmvP6O3diULLkZjdH9lzjxnnx9dfV0bx5PpvJKSEEKlfejqtXo2xWRUjK11eLBw+6IGdOjvlJDdaUIiIiepHRKJM8kZG26zSpFR0tRzL8+6/83nTxKIR86PVyFbs5c1K3/0KFHEtmFSwItGghl1hXQ6+XS7ETkXsMGwYcPSpHQV29Kqfotm2bMiEVGgrs2KF+pOSffzo/ViIiFX7//Wa6Hv/w4TC89NJeNG++B2Fh8VbbKYqC0aMrOHT6FxNjwMqVWax+nxsxKUVERGQSGgp89x1Qt64cfVSzJtCkCfDLL46NRHrRggXAtWv2LxznzJFT6xz1+uvq2mm1spB6gQKAtzfQr5/9ZJZWK4sJN2jgeFxElDb2/j5PnYLqAi1GoxwpRUTkZjt3huLLLy+mdxgAgCNHnqBt2/2IjbV+TjZkSGnUrOmvep86nYKgoKfOCC9bYlKKiIgIkBd3bdoA8+YBT5OcWISEyFFMr7ySuoRRYqIsLq7mwlGrBf74w/FjdO8O+PraX1LeYJB1qkxGjZI1aqxd+Gq1cr+VKski5716AV9/Ddy65XiMROR8CepWiEp1eyKiNHr4MA5dux5yaYFzRxgMAqdOhWPJkltW22g0Crp1K6p6pUA56J1VkVKLSSkiIqL79+UqTs+eWU4eGY2ytkvfvnIqniOuXJH1pNQwGIBduxzbPwD4+8tl0D09LS8pb0o6ffyxnLZn4ukJ/P67XJnrxRoQWi2QJ4+cwrhlC3D8OHDsmGz/8svAJ5/wApcovRUpor6tVgsUL+66WIiILFiw4Cbi4hxY8dcNhADmzAmGrfLalSrlcmAgqkClSipLIlAKTEoREREtXQrExNgezWQwyOTVunWO7fvZM8fax8Q41t6kfn3g779lIeQXb+1VqgT89JNMPr3I01Ou9Pfvv8D8+cC0acC338ptTMm0pNMOTV+vXi2XmOd6KUTpp04doEQJdTXlDAa50icRkRstXHjTaaOkvL016Ny5MHr0KIpvvqmB114ravFenBqXL0chPDzR6uvduhVF7tweqval0Sjo379U6gIhsDw8ERFlb4mJwIoV6goFK4pMYPXtq37/+fM7Fo+j7QEgOFgWQt60SY7k8vOTNbGaNQMaNgSqVLG/Dy8voHVr+fWyZcDFi7YTTkIAmzfLaX3NmjkeMxGlnUYDvPMO8OmntttptUC5cnK6LhGRG92/H+uU/ZQo4YNDh15GsWI+5ucuXozE33//l+p9xsdbz5Z5e2sxfnwlfPLJOZv7UBRg2LCyyJ/fK9VxZHccKUVERNlbWBgQFaWurRDAjRuOjQ4qU0YmhdSMZFAUWR/KEb/9Jlfl+uMPObIpIQEIDwcOHAC+/FL+6wghgMWL1bXVamWSjojST+/eQP/+8mtL/YxWKxc3WLDAsVU6iYicwNMzbSkHf38P7NjRHDdvdkiWkAKAKlX8MH58pVTt19tbgzx5PG22GTOmIoYPLwsA0GqT95+mQelduxbBrFk1UxUDSUxKERERudqQIfYTWYoC+Pg4lpRatUpOtwNSjvQyfT99umPLwD9+rD7xZjAABw+q3zcROZ+iAFOmyGm35csnf83HR66yuWEDUKxYuoRHRNlb69YFodOlPiE+Z04ttGlTEBqN5X189VU19OnjWL08RQH69StpN2GmKAp++qk21q1rjObN8yV7LTAwAMuW1ceaNY3h4cG0SloowlZ1LyIiF4mMjIS/vz8iIiLg92KBZSJ30uuBevXk6CJ7FAWoWBHYutWxYwgBTJwopwlaotHI0Qy//w40bapunwkJQKNG6oqoBwQAR4/K+lH23L0LNG+uLgZAxn79uvr2RGRVmj8bhZCLK4SGAt7eQPXqMjFFRORCtvqu3bsfolWrfQ7vU6tVUKiQN4KDX0GOHLYLR8XE6FGgwAY8e6auoLpGA5w+3RbVq/s7FNOTJwl48iQBfn46FCjg7dC2ZB1TekRElL3pdECfPpZXrXuREM+nyThCUYCpU4FJk+Q0mhfVrStHPalNSAHAP/+oX9Xv6VNgxw51bQMC1P1fmOTNq74tEbmWoshFClq0ABo0YEKKiNLdyy/nx+uvF0uxBostWq2CPHk8sWNHc7sJKQDw8dHhnXfKpphiZ82iRfUsJqQMBoEDBx5h9eq72LIlBBERyQuh58njiXLlcjIh5WQsdE5ERNS/vxzFFBlpfQU+rVZOf+naNXXHUBTgrbfkVJpDh4B79wAPD7l6Vrlyju/v3DmZUNPr7bfV6WT7Tp3st82ZU9ao2rHDfvF3jQZ4/XV18RIREVG2oygKli1rAG9vLZYuvQ2dToFeLydrabUpTzVy5dJhyJDS+OijiihaNIfNfUdH67FixW1s2HAfR46EwWCwPQlMowF+/LF2ipXyDAaB2bOvYvbsq7h/P878vLe3BgMGlMLnn1dFwYJMRLkKk1JEREQFC8rV6/r1AyIikiemTIWBixaVbdI68kCnk6MY0spgcKxosZrVBU0GDbI/RVFR5Nlk797q90tERETZjqenBkuW1Mcnn1TCL79cx9GjYdDrBSpX9sM775RBhQo5cedOLDw8FFSsmAs+PrbTFPHxBkyYcB7z5l1DXJz1FfQAmYgyGoHq1f3x++91UbdunmSv6/VG9Ox5BOvX309RTjMuzoiFC29i8+YQHD7cEsWLc/SpKzApRUREBABVq8opcatWAcuWAQ8eyOdLlwYGDABee02OIsooSpdWN0oKkO3y5LHfzqRuXeCzz+TqfYqSsui5RiOfnztXJuuIiIiI7KhSxQ8//FDb4muFCtkeFWWSkGDEK68cwN69j1StIRMQ4In165ugSZN8FttMm3bZYkLKRK8XePAgDq++ehjHj7eCwlVMnY41pYiIiEzy5gXefRc4cgS4fBm4ehXYtUtO78tICSkA6NxZXeFyk2+/BQYPlu9JjUGDgF9/lfVpXlS/PrBypZzmR0RERORCsbEGREQkwmgUmDbtEvbts5+QAuQ9tSdPEnDhQqTF1+PjDfj22yt296XXC5w8+RRHj6qs5UkO4UgpIiIiS7y80jsC23Llkkmmn39OOZLJEiGAffvkKnzLlwO1Ld+pTKZtW/m4cEGusKfRAFWqAGXKpD1+IiIiIivi4gxYvvw2fvjhGs6diwAA+PnpkJgorJb/tObHH4MxdGjKc5clS24hMlLdqHOdTsGSJbfQqBEXeHE2JqWIiIgyq9Gjgbt3gY0bnxdNsMVgAOLigCFDZLF1b5VFO6tWlQ8iIiIiF3v8OB5t2+7HqVPhyVbtU5tASkoI4OLFSBiNAhpN8ql3K1feVb0fvV7g7t0Yh49P9nH6HhERUWal1QLffy8f1aur28ZoBJ48ATZtcmVkRERERA4zGgU6dz6Is2cj/v97Z+wTaN9+PyZPvoB792RiKS7OgMOHHzu0nxw5tGkPhlJgUoqIiCgz02iArl2Br79Wv42iAGvWuC4moswgJASYPRto1w5o3FjWaVuwAAgPT+/IiIiyrX/+CcXRo09gMKgoTeDQfh/iq68uoWTJzRgz5gzu3IlBQoJjx2jZsoBTYyKJ0/eIiIiygkeP1LcV4vnqgkTZ0dKlwOefy69Nt+EfPJD10777Dpgzh4X8iYjSwbx516HTKdDrnZuUAmBOdH333VU8eBDn8Pb9+pV0dkgEjpQiIiLKGtTWhzLJoW7pZaIsZ9UqYPJkmYxKOi9ECPmIjweGD5d111TasmULWrdujTx58sDX1xd16tTBjz/+CKMz5p0QEblIRuy7Tp8Od0lCKikhgOXL78DXV/10vFKlfJArl4cLo8q+mJQiIiLKCmrUAHx91bXVaoGXXnJpOEQZUlwc8NVXttuYklNffKFqZcvp06ejY8eO2LVrFwICAlCuXDmcOXMGI0eOxKuvvsrEFBFlSNm979LpFJQu7QutVrHfGMAXX1RzcUTZF5NSREREWUGOHMAbb8iEkz1GI/Dmm66PiSij2bwZiIqy304I4OpV4NQpm82OHDmCCRMmQKPRYMWKFbh+/TrOnDmDoKAgFCxYEBs2bMCsWbOcFDwRkXNk5L6rdu3c0OnUJYrSQq8XePgwHrly6WyeOmm1QKVKudCzZzGXx5RdMSlFRESUVbz3HlCokP3E1KhRQDGeXFE2dOIEoFNZUlWjAY4ft9lk6tSpEEJgyJAh6N27t/n5mjVrmi/opk+fjsTExFSHTETkbBm57xo+vGyqpu8piuy2HZGQYMQ//zSHv79Him0VRT7KlcuFf/5pDm9vrrznKkxKERERZRV58gCrVwOVK8vvkyanNBp5Mf7xx8DIkekTH1F6S0xUNSUPgPybsXFBFhkZiZ07dwIABg8enOL1nj17ws/PD2FhYdizZ0+qwiUicraM3ne1aVMQDRvmUT2tzsQ089oRhQt7o27dPLh8+RVMnVoNxYo9r7dZtaoffvklECdPtkaxYj6O7ZgcwqQUERFRVlK4MLBhgyzm/NprQIMGQIsWwEcfAUeOyNFUiuuHxRNlSI6MENTrgaJFrb586tQpJCQkwNvbG3Xq1EnxuoeHB+rVqwcAOHbsmMOhEhG5QkbvuzQaBZs2NUWtWv7//736bR1JSmk0wFtvlQYA5M/vhfHjK+Pu3U6Ij++OxMTuOHeuHYYOLQNfX5WjaynVmJQiIiLKahQFqF8f+OYb4M8/gcWLgXffBfLlS+/IiNJX9+7JV9yzxccHaN/e6svBwcEAgBIlSkBnZUpgmTJlkrUlIkpvmaHvypvXCwcPtsTChXVRrZq/0/ev0QDe3lq89VapFK95emqg0zFN4k5M+xERERFlMEIIRKkpyE0WCSFw4sRTnD8fASEEqlTxR4MGeaD4+wMtWwK7dtlOTikK0Lu3nL5nZQpfSEgIACBXrlyIjIy02MbHR075ePjwocU28fHxiI+PN39v+plb2x9lbbly5YKSyUeysu/K+DJT39WjR1706JEX8fEGvPzyXly4kPb9azSAVqvB8uUN4ekZj8jIePsbkV1p6b8UIRydeUlElHaRkZHw9/dHREQE/Pz80jscIqIM5dGjRyhQoEB6h0FEbvTw4UPkz58/vcNIE/ZdRNlTWvovjpQiIiIiymA8PT0BAHfv3mXi3gGLFt3EBx+cttlm2rTqePetosCSJbL22uPHz18sUQLo1w/o2dPuKpZz5szBpEmTEBgYiN27d1tsM2nSJMyZMwft2rXD//73vxSvvzjaICQkBPXr18fFixdR1EY9K8paIiMjUbx4cfPffWbGvivjy6x91/DhJ7Fq1V0YDOrG1Jw50xYlS/rg2LEnuHEjGjqdBoGBuVG2bC6XxJddOaP/YlKKiIiIKIMxDYH38/PjhZ1K589H4MMPrwKwvUrS+PHX0apVKdQbOxYYPRq4eBGIjgYCAoBKlVQvBFC4cGEActqKtZ9RTEwMAKBAgQIO/Rxz5crFn3s2lNmn7gHsuzKDzNp3jRpVEytWPLbbTqtV8PLL+VGjhnyfbds6vyYVpZSW/osVvIiIiIgo0/vpp2uqlhDX6RT88EOw6RugRg2gcWOgcmWHVqYsX748AODOnTvQ6/UW29y4cSNZWyKi9JZZ+6569QLQrl1Bm/28qQufPLmqm6IiZ2BSioiIKKMKDweCg4E7dwCDIb2jIcqwhBBYuvQ29Hr70zr0eoFVq+4iMVHlKnxW1K5dGx4eHoiLi0NQUFCK1xMTE3H8+HEAQIMGDdJ0LCIiZ8msfZeiKPjf/xqhceO8AGTB8qR0OgVarYIVKxqgaVOuNpyZMClFRESU0Rw9CgwZAtSpA7RtC7RoATRrBvzyi5xmRFmel5cXJk+eDC8vr/QOJVOIjTUgJkZ94jYxUSAiwvKqemr5+fmhdevWAICFCxemeH316tWIjIxE3rx58dJLL6nap+nnzZ979pKV/t6z0nvJqjJz3+Xn54Fdu1pg2bL6CAwMMD+fM6cOw4eXxfnz7fD668VdGgMl54y/ea6+R0TpgqvvEVmxcCEwdaossvzi6CiNBihXDlixAsibN33iI8qAjEYBD481MDow+OnZs1fh45O28qqHDh1Cs2bNoCgKli9fjt69ewMAzpw5g3bt2iE0NBQzZszA2LFjVe2Pn41E5A5Zpe9KSDAiLs6AnDl10Ggyf0227IojpYiIiDKKHTtkQgqwPF3PaASuXweGDgV4T4nITKNR0KaN7VojJlot0KRJ3jQnpACgSZMm+PLLL2E0GtGnTx+ULVsWNWvWRJ06dRAaGoqOHTvio48+SvNxiIicKav0XZ6eGvj5eTAhlckxKUVERJRRzJ2bskjCiwwGICgI+P96D0QkjRhRTtVS4QYD8P77zive++mnn2Ljxo1o2bIlwsLCcO3aNVSvXh3ff/891q9fD61W67RjERE5C/suyig4fY+I0gWnKBC94PJl4JVX1LXVaoHOnYHZs10bE1EmYjQK9O59FKtX37M6kFCjATp0KIx165qoGlXlbvxsJKLMiH0XpQVHShEREWUEN2+qb2swyGl8RGSm0ShYvrwBhg4tA0UBtFrFvDy46et+/UpizZpGGTIhRURElB0xKUVERJQRODpMXpf2ejiUeURFRWH58uXo27cvKlSogBw5csDHxwdVq1bFmDFjEBISkt4hZggeHhr88ksgbt3qiAkTKqFLlyLo0qUwxo6tiOvXO2Dx4vrw8sp4U1K2bNmC1q1bo2TJkgCAZs2a4ccff4TRkcrtlGkIIXDw4EGMGTMGDRs2RO7cueHp6YkiRYqge/fu2LNnT3qH6DTsu7K+LVu2oEuXLgCAQoUKoU6dOuy/sihX9V2cvkdE6YLDfIlecP8+0LSpugLmWi0wYADw2Weuj4syhH79+mH58uUAgFy5cqFs2bJ49uwZbty4AYPBgLx582Lr1q2oV69eOkeatZw5E46ff76ONWvuISpKj4AAD/TuXQLDh5dFhQq5nHKM6dOnY/z48QCAUqVK4datW1AUBUIIdOnSBWvXroXGXq05ylR27dqF1q1bAwA0Gg3KlSsHX19fBAcHIzo6GgAwceJEfPnll+kZplOw78rakvZfAFC1alVcunQJRqOR/VcW5Kq+i78hREREGUGRIohu2hqLn9XDpKdt8UV4a/wTWx5GYWGakcEA9Onj/hgpXXXr1g07d+7EkydPcOrUKVy9ehVXrlxB/fr1ERYWhu7duyM2Nja9w8wShBD4/PMLqFXrHyxceBNhYQlISDAiNDQeP/54DZUrb8O8edfSfJwjR45gwoQJ0Gg0WLFiBc6cOQMAOHDgAAoWLIgNGzZg1qxZaT4OZSxCCJQrVw7z5s3D48ePceXKFQQFBSEsLMx8gT916lRs2rQpnSN1DvZdWVPS/mvBggUAgMOHDyMoKIj9Vxblqr6LI6WIKF1wpBTRc3q9EZMmXcCc2VcQG2eEDgYIKNBDi9K6MMzOsxFdfS7KxooC9OwJzJiRvkGTWz158gR58uSx+Nrdu3dRrlw5JCQk4O+//8arr77q5uiynu+/v4oPPzxjt92KFQ3Qu3eJVB+nY8eO2LJlC4YOHYpff/012Wfjpk2b8OabbyJv3rwICQmBh4dHqo9DGUtkZCR8fHygszINu0OHDti6dSu6dOmC9evXuzk652LflXUl7b9mzpyZ7Lx+xYoV7L+yIFf1XRwpRURElI6MRoFevY5i+vTLiIkTEFCQCB30kHVvbunz4NWHA7A8JlBu0LkzkAWmdJBjrF3UAUDx4sVRqVIlAMDVq1fdFVKW9eyZHp99dkFV27Fjz8JgSN393cjISOzcuRMAMHjw4BSv9+zZE35+fggLC8tSNYYI8PPzs3pRBwBt2rQBkDX+ntl3ZU3sv7InV/VdTEoRERGlo99/v4m//vrPaikpAQUCwKDHPXH/h+XA998Dnp7uDJEygbi4OABAjhw50jmSzG/VqruIjtaranvvXiy2bXuQquOcOnUKCQkJ8Pb2Rp06dVK87uHhYa6zc+zYsVQdgzKn7PT3nJ3ea1bC/ossSe3fM5NSRERE6UQIgdmzg83L1lunwKhoMD8oN1Q0pmzmzJkz5ruSTZo0SedoMr8TJ57Cw0Pd35lOp+DkyaepOk5wcDAAoESJElbvPJcpUyZZW8r6hBBYvXo1gKz/98y+K/Ni/0UvSkvfxaQUERFROgkOjsbFi5GqFtwzGIAVK+64PijKVAwGA95//30AQMuWLREYGJjOEWV+BoNQ9TcJyBxxaqfvPX0qk1kBAQFW25heM7WlrG/+/Pk4deoUPD098cEHH6R3OC7DvitzY/9FL0pL38WkFBERUTp58iTBofZhYY61p6xvwoQJOHDgAHLlyoXffvstvcPJEipUyAmjUV2iKTFRoEKFnKk6jmmag6eN6bheXl4AwJXJsomgoCCMGjUKgFzBqmzZsukckeuw78rc2H9RUmntu6xXqSIiIiKX8vd3bDUaR9tTxjB27Fhs2LDB4e0WLVqERo0aWX39l19+wTfffAOdToeVK1dm6QtYd+rfvxTGjTunKjHl56fDa68VS9VxvL29AQAJCdaTzfHx8QBYbyc7uHnzJjp16oS4uDj06dMHH3/8cXqHxL6LrGL/RSbO6LuYlCIiIkonFSvmQpkyvrh585nd6UJarYKePVN38Uvp6/79+7hy5YrD2z179szqa6tWrcJ7770HRVGwePFidOzYMS0hUhL583vhnXfKYN6863b/LseOrYQcObSpOo6aqS1qpshQ5vfgwQO0adMGISEh6NixIxYvXgwlA9QPZN9F1rD/IsB5fRen7xEREaUTjUbByJHlVbUVQuCdd3g3OTNavnw5hBAOP1q3bm1xf1u2bEG/fv1gNBoxd+5cvPnmm25+R1nfrFm10KVLEQCA5oWzZe3/56CGDCmN8eMrpfoY5cvLv/07d+5Ar7e82t+NGzeStaXMRwiBgwcfY8yYM3jnnZOYMOEcTp16fiH/5MkTtGnTBtevX0eLFi2wevVqeHhkjFGx7LvIGvZf5My+i0kpIiKidDR8eFm89FL+FBe+L5o1qxZKl/Z1T1CUYe3fvx89evRAYmIipk2bhnfffTe9Q8qSPD01+Ouvxli8uB5q1cqd7LXGjfNh9epG+O23QGg0qR/NUrt2bXh4eCAuLg5BQUEpXk9MTMTx48cBAA0aNEj1cSj9nDr1FNWq7UCzZnvw/ffBWLToJmbOvII6dXaiUaNdOHv2ATp06IDz58+jXr162LhxY5ad6sS+K2th/5W9RUdHO7XvYlKKiIgoHXl6arB5czMMHFgKWq0CjQbw8FDMS9Lnzu2BBQvq4q23SuHu3RhERiamc8SUXk6ePInOnTsjNjYW48ePx7hx49I7pCxNq1UwYEApnDzZBvfvd8KlS+0QGtoZ+/e/jB49iqV5epWfn595RMnChQtTvL569WpERkYib968eOmll9J0LHK/oKCnaNJkD65ciQQA6PUCiYkCer2cE3r8+EMEBrbFsWPHULVqVWzbtg25cuVKz5Bdhn1X1sP+K/uKj49H165dndp3KUKoXfSWiMh5IiMj4e/vj4iICPj5+aV3OEQZQkhILJYtu43r15/Bw0NBw4Z54e2twc8/38Du3Q/N7Vq2LICRI8uhS5ciGaLuCLnelStX0LRpUzx+/Bjvvvsufvrpp/QOyS693oiNG0OwcOFN3LgRDS8vLVq1KoBhw8qiXLnUrViXUQghcPToE8yffwOXLkVCp5N/r++849h7O3ToEJo1awZFUbB8+XJ07NgR/v7+OHjwILp3747Q0FDMmDEDY8eOdeG7IWcTQqBKle0IDo6GwWDpUssI4DsAx5EjRxFcv34ChQsXdnOU7pEZ+y5SJ2n/9dtvv2HIkCGIiIjAzZs30a5dO/ZfWZDBYECPHj2wbt06lC1bFgcOHHBK38WkFBGlCyaliGwzGgWGDj2JhQtvQqtVkl3YmL4fPLh0mqcQUebQrl077NixA4qioFGjRlaTkYMGDcKgQYPcHF1KV69G4ZVXDuDGjWfJfn9NX48eXQEzZ9bIVL+7t28/w+nT4YiISMQPPwTj5Mlw6HSKeeSLRgMYjUCtWv7o0KEwGjTIi44dC0Ortf0ev/rqK0ycOBEAUKpUKdy6dQuKokAIgY4dO2L9+vXQalNXTJ3Sx/79j9CixV4bLQ4C+OH/vy6MwMCS8PZO+TMuXLgwVq9e7YII3Sez9V3kmKT9FwBUrVoVly5dgtFoZP+VBa1cuRJ9+vQBIGuFFShQwGI7R/surr5HRESUAX355UUsXHgTAFLcaTd9v3DhTRQvngOTJ1d1e3zkXqaltYUQOHz4sNV21goMu9N//8WiWbM9CAuTS4Un/f01fT179lUIITBrVq30CNEhx48/weTJF7Bt24MUq/GZElKATEgBwOnTEThzJgJCAEWKeOObb2rgzTdLWt3/p59+ipo1a2L27Nk4ceIEAHlhN2TIEIwYMYIXdJnQunX/JUtYppS0MHQITp4MsdiqZEnrvzeZRWbqu8hxpv5r5syZ2L9/P27cuIHq1avjrbfeYv+VBZn+ngEgODgYwcHBFts52ndxpBRRFjRw4EAsWbLEZpvY2Fh4e3s7tN8jR45g+vTpOHz4MKKjo1G6dGn07t0bY8aMcXhfHClFZF10tB4FC25ATIzBbltfXy0ePOiCnDl5n4kyhuHDT2LBgps2Lsifu3KlPSpUyLh1dLZuDUHXrodgNKZMDjvixx9rY8SIcnbb8bMxaxg8+DiWLr2t6m/Aw0PBuHGV8MUX1dwQGZFrsO+itOAZLFEWZmtYpcbeUl8v+OOPPzBgwAAYDAYULVoUxYsXx/nz5zFp0iRs3LgRe/fuhY+PjzPCJsr2Vq26i9hY+wkpAIiJMWDVqrsYPLi0i6Misi8yMhGLF99SdTGu0yn4+efrmD27lusDS4X792PRvfsR6PUixQgpR40ceQpt2xbM0Ak4cp48eTxVtzUYBPLm9XJhNEREGRtX3yPKwiZMmICDBw9afHh6qj9hunXrFgYPHgyDwYBvvvkGd+/eRVBQEIKDg1GxYkUcP36cRQyJnMhUOFkNnU7BpUuRLo6ISJ2goKeIizOqaqvXCyxbdhuDBx/H2LFncfLkUxdH55jffruB+HhDmhNSgKyl9fPP19O+I8oUevYsrioxCwBCAK++WtTFERERZVxMShGRXTNnzkR8fDzatm2LMWPGmItUlixZEr///jsA4LfffkNoaGh6hkmUZTha/NleIWUid4mPV5eQMgkLS8DSpbcxe/ZV1K27E/Xr78T169Euis4xCxfeNNeJSiu9XmDFijvO2RllePXqBaB27dx2+2adTkHnzkVQogRHmhNR9sWkFBHZJITA2rVrAQCDBw9O8Xrjxo1RqVIlJCYmYv369e4OjyhLqlUrNxIT1d1lT0wUqFkzt2sDIlKpeHHHL671emEeVRIUFI6GDXfh1q1nzg7NYSEhcU7dX0REolP3RxmXoihYubIh/Px0VhNTWq2CwoW98euvgW6OjogoY2FSiigLW7NmDbp164aWLVuiV69e+PHHHxEREeHQPu7cuYOQELkqTJMmTSy2MT1/7NixtAVMRACA7t2LInduD1Vtc+f2QPfunPpBGUOVKn6oXTs3HCxbaGYwCDx9mojhw0+mOoa7d2Nw5kw4bt58hrSs5+Pl5dzT5Fy5WMo1O6lYMReOHWuFpk3zAZBJKA8PBRoNoChAhw6FcOxYKxQq5NhCMUREWQ0/HYmysM2bNyf7ftWqVZg8eTJWrFiB9u3bq9qHaalPLy8vFClSxGKbMmXKJGtLRGnj5aXFl19Ww/vvn7Lb9ssvq8HLK2Mtufz0aQKWL7+Ns2dlErxqVT/061eSxXyziXHjKuGNN46menuDQWD79lDcuBGNMmVyqtpGCDk9bs6cYBw//rw2VZUqfhg1qjwGDSoFnc6xJFPLlgWwdeuDNK26Z6LTKejZs3ia90OZS/nyubB370u4dCkSGzbcR0REIvLm9UT37sVQqpRveodHRJQhMClFlAWVLVsWX3/9NTp27IjSpUtDURQcOXIEn332GY4dO4Zu3brh4MGDqFu3rt19PX0qT+5z585triX1ooCAgGRtLYmPj0d8fLz5+8hIFmYmsuW998oiLCweU6ZchE6nJCuaa/p+ypQqeO+9sukYZXJGo8CkSRfw7bdXkJBgNE9bMRoFxo49iw8/rICvv67OGlhZ3OuvF8fp0+GYNu0yNBqkqi6TogAbN4Zg1KjydtsajQIDBx7HsmW3U4zQunQpEsOGncS6df9h7drGDiVwR4woh02bQhwN3SK9XuDddzPO3yq5V+XKfqhc2S+9wyAiypA4fY8oC/rss88wfvx41KhRA7ly5ULOnDnRpk0b7N+/H/Xr10d8fDw++eQTVfuKi5M1NWyt1uflJUc/xMbGWm0zbdo0+Pv7mx/Fi/OOMWU/CQlGPH4cj7g4g922iqJg8uSqOH68Ffr2LQlfX3kx7eurRe/exfHvvy0xeXJVq8lidxNCYPjwIHz11SXExxshxPNaQUajrH01c+YVDBp0PE1Tqihz+Prr6li5sgGqV/dP1fYajaK6BtOXX17E8uW3AaRMgAkhH9u3P8CoUacdiqFt24J49dWiqZ6KmNTUqdVQrVrq/i+IiIiyMialiLIRT09PfPnllwCAvXv32hzZZOLtLWsdJCQkWG1jGgGVI0cOq23Gjx+PiIgI8+Pu3buOhE6Uqe3b9wg9ehyGj8/fyJ9/A3x8/ka7dvuxadN9uwmaunXzYNGieti6tRl69CiKhAQjli27g6ZN9+LNN4/h6NEwN70L23bteojffrths40QwNKlt7F5s3NGn1DGdPLkUwwadBzvvHMSZ89GwM9Phw4dCqFVq/zQqhyoZDAI5M1r/WaISUyMHt9+exX28pxGo1xNLzRUffFyWay6AXr1KgFAjlA0MX1du3Zu/PJLbXTsWAg6nawX5OEhawcBgL+/B+bOrY1PP62s+rhERETZCafvEWUzjRo1AgAYjUbcuHEDgYG2V30xTc0LDw+HEMLiqAxTcsvU1hIvLy/ziCqi7EIIgc8+u4CvvroEnU4x16YRQiZxduwIxcCBpbBgQV2rU9qEEBg69CQWLLiZ7PmEBCNWrLiDFSvuYOjQMvjllzrpOmrqxx+vpZhmaIlWq2Du3Gvo1MlyjTrKvISQ0zenTr2U7HchMlKPHTtC7f5uJKXRKHj1VfsF/P/66z9ER+tV7dNgEFi69DbGjKmoOg4vLy3++KMBJkyohF9+uYF//30Cg0GgShU/DBtWBo0a5YWiKHjnnXIIC4vHH3/cwZUrUdDpFAQGBqBnz+LIkSNj1XwjIiLKSJiUIspmPDyer+il19s/kS9fXtbziI+Px/3791G0aMqLhBs3biRrS0TSvHnX8dVXlwAgxQW5KUG1ZMktFCrkjWnTqqfYPj7egCZNduPkyXCbx/nttxu4ciUSu3a9lC71moxGgc2bQ1QVhDYYBHbsCEV8vCHDFWintJk9OxhTp1r+fU/6vaLA5sgmrVZB9+5FUaSI9dG3JocOPVYdnxBAcHCU6vZJVa3qjx9/rG2zTd68Xhg5kp+DREREjuD0PaJs5sKFC+avixUrZrd9iRIlUKhQIQDAoUOHLLYxPd+gQQMnREiUNSQmGjFlygW77YQAZs26iqdPk0+RNRoFunc/bDchZbJv32N8/PGZ1ISaZrGxBodWKBMCiIpSN7qFModnz/SYPNn+77uJtTpNOp2CEiV8MHduHVX7OX06XPUxAeDxY+tT0YmIiMj9mJQiyma+++47AEClSpUsjnp6kaIoePXVVwEACxcuTPH64cOHcfnyZXh4eKBLly7ODZYoE9u48b7qC+DERCOWLr2d7Lm1a//D5s0PHDrmnDnBuHMnxqFtnMHHRwtPT/WnFFqtAn9/D/sNKdNYtequ6ml0QgBVqsiVyEw1mDQa+XWXLkVw7Fgr5M+vbrp3YqJjS/sFBPD3jjKW+HgD/vjjtv2GRERZFJNSRFnMP//8g/Hjx+PmzeT1ZyIiIjBy5EisXLkSADBp0qRkr3///fcoVaoUevXqlWKfY8aMgaenJ3bs2IGZM2eaCzPfvn0bgwYNAgAMGTLEPKKKiIALFyKTFUa2RVGAU6eSLzzw44/XkJoSUfaKjbuCoih4/fViqt6vTqfgtdeKwsODpyBZSVDQU3Nxb3t0OgXduxfDpUvtMGNGDYwbVwmzZ9fC7dsd8ddfjVUnpACgQAHHahU2bJjXofZErnT8+BOUKLEZffv+m96hEBGlG9aUIspinj17hunTp2P69OkoWrQoihQpgsTERFy8eBEJCQlQFAWTJk1C7969k20XHh6O27dvo1SpUin2Wbp0acyfPx9vvfUWxo4dizlz5qBAgQI4f/48EhMTERgYiJkzZ7rpHRJlPUYjsG7df3j0KB7583shIcGIffseObwfIYADBxzfzhnef788li+/Y7edXi8wYkQ5N0RE7mQ0wu5KkiaKImuLVarkh0qV/NJ03Pr182LHjlAYVQ6YatQo6yelrl6NwqpVd/HoUTxy5tShU6fC5oLslHFcuBCBl1/ei7g4x0b7ERFlNbxNSZTFBAYG4tNPP0XLli2h1Wpx/vx5XL58GUWLFkX//v1x5MgRTJkyxeH99u/fHwcOHECnTp0QGxuLixcvokyZMpgyZQoOHjwIX19f578ZokysRg1/h1Ybi4zUo1evI7h6NQqnTz+1v4EViYnqj+lM9evnwVdfVbPb7rPPKqN58/xuiIjcqUKFnFCxdgYA+TtaqVIupxz37bdLq2qnKEDjxnlRrZq/U46bEYWExKJdu/2oWHEbPv/8In755TpmzryCJk32oHr1HThx4kl6h0hJjBlzFnFxRofq8RERZUWKUHtbi4jIiSIjI+Hv74+IiAj4+aXtTjlRRqTXG1Gs2CaEhsa77Zg6nYI+fUpgyZL6bjvmixYuvInPPjuPkJA483Q+vV6gQAEvfP55VQwbVjbdYiPXmTnzMsaOPaeqba5cWoSGdkWOHM5ZffGDD07hhx+u2V3Rb9euFmjRImMnRFP72RgaGof69Xfh/v1Yi8lwrVaBh4eCffteRv36eZwZMqXCrVvPUKbMlmS/s0L0TL+AiNKI5/WUFpy+R0RE5AI6nQZTp1bD22+fdNsx9XqBIUPUjRxxlcGDS2PAgJLYuvUBzp2LgBBA1ap+6NSpMHQ6DtDOioxGgR9/vKa6fd++JZ2WkAKA776rhWfPDFiw4CZ0OiVZUkarVaDVKvjzz4YZPiGVFqNGnbaakALw/6NxBF5//Qhu3OgAjYZT+dLTwYOPbSZRiYiyEyaliIiIXGTIkDJYs+Yetm8PdfmxdDoFtWvnRtOm+Vx+LPuxaNC5cxF07lwkvUMhN9i37xHu3o1V1VZR4NC0VjW0WgW//RaIgQNLYd6869iyJQSxsQYUKOCFt94qjbffLo1ixXycesyM5MGDOKxZc8/uNDCDAbh9OwY7doSifXsuTJKe4uIM6R0CEVGGwaQUERGRC1WokNPlSSmtVkHp0r5Yv74JixmT2928+Ux1WyGAGzfUt1dLURQ0aZIPTZqkf1LW3TZtuq+6LpFOp+Dvv+8xKZXOsnKSlIjIURxHT0RE5CK//nodc+ded+kx8uXzxPjxlfDvv61QuHAOlx6LyBJPT8dOJ728ePrpTOHhidBq1SWjjUaBp08TXRwR2dO6dQEUKOCV3mEQEWUIHClFRETkAvv2PcLw4UFOrRsybVp15Mvnifz5vVCggBd8fHSoXNnP4aQAkTM1apRXdVuNBhliimlWEhDgqXqklEajICDAw8URkT06nQYffVQBn3yibnEAIqKsjEkpIiIiF5g58zI0GsWpy3336lUcpUr5Om1/7hAenoCtWx/gyZME+Pt7oH37QsiXjyMEspKyZXOiTZuC2L37od3fd41GweDB6VuMP6uRiwgoqmp16fUCPXoUc0NUZM/HH1fE6dPhWLnyLjjrmoiyMyaliIiInOzBgzhs2fLA6asr5ciReUZERUQkYuzYs1i69Bbi4oxQFFlPyMNDQa9eJfDttzVQoIB3eodJTjJjRnU0brwbQggYjdbbffZZFf7cnaxgQW/07FkMq1ffs5mY0moVlCjhg9atC7oxOrJGo1GwfHkDNG6cD7NmXU3vcIiI0o0iBBckJSL3i4yMhL+/PyIiIuDn55fe4RA51fHjT1C//i6n7/fZs1fh46P+ftKlS5E4fTocRqNA5cp+qF07t1sKoYeHJ6BJkz24ciXK4sgZnU5B0aI5cPRoKxQqxARFVrFv3yN07XoIkZGyZpHpDFOrlSMGJ0yohKlTq5l/B41GgZ07Q3HgwGPExxtRsqQPevUqjrx5s+9IutR+Nj56FI/69Xfi3r1Yi4kprRbw9NTiwIGXERgYoHq/165F4/79WPj4aFG9uj+8vLSqtyX1jEYBjYbDpSjz4nk9pQVHShERETmZh4fzRzRVq+anOiG1b98jTJx4DgcPhiV7vmZNf3z+eVV07VrU6fElNXx4kNWEFCCnEP33Xyz69TuGf/5p4dJYyH1atMiPu3c7YvnyO1i06Cbu34+Fr68OHTsWxrBhZVGhQi5z202b7uP990/h1q0Y6HQKFEX+Xnz44WkMGVIGs2bVhLc3EyBq5c/vhaNHW2HIkBPYvDkEGo0CjUYmBvV6gRo1cmPhwrqoXdt+QkoIgdWr7+Hbb6/g+PGn5ufz5PHEsGFl8PHHFREQ4OnKt5PtMCFFRNkZR0oRUbrgHRXKymJjDcibdx1iY23MY3LQokX1MHBgKbvtVq++i969j1mcRqXRAEYj8MMPtfD+++WdFltSISGxKF58s+paWpcutUOlSuwDMrrIyERcuhQJvV6gTBnfNK30uHr1XbzxxlEAsDjFVaMBXnqpALZubZbtivg747Px5s1n+N//7uLhw3jkyiWTgvXq5VG1rRACH310BrNnB5v7i6S0WgWlS/ti//6XuNonEZnxvJ7SInt90hMREbmBt7fGaaM8NBqgceO86NOnhN22N28+w5tvHoPRaLmuj+m5UaNO4+jRsJQNnGD16ntQe79Lp1OwcuVdl8RBznHjRjSGDj2BggU3oGHD3WjadA+KFt2ETp0O4tChxw7vLywsHv36/QvAckIKkL+ne/c+xJw5wWkJPdsqXdoXn3xSCd99VxNTplRVnZACgN9/v4XZs+X/u6U+xGAQuHXrGbp2PaT675yIiMgWJqWIiIic7MCBx3j6NFF1+8qV5bQmne75FA7T161aFcSWLepGjPz883UYjdYv9k20WsVlF/wPHsRBq1U/FSU0NM4lcVDanTjxBHXq7MSiRbJYvYkQwLZtD9C8+R4sXXrLoX0uWnQLiYlGu7+jckRfsFNXryTbhBD4+utLdtvp9QLHjz/FoUOuSWwTEVH2wqQUERGRkwUFPYVG5SesVqugTp0AHD/eCv37l0Tt2rlRq1ZuvPlmCRw92hLbtzeDv7+Hqn0tWnRT1UW8Xi+wZs09xMTo1QXpgJw5dTZXX3uRry/LW2ZE4eEJaNfuAKKjEy0WzjYY5Gi8t946juPHn6je76pVd1X/fty7F4tTp57ab0hpcv9+LJYvv42xY8/ixo1nqrbR6RQsXHjTxZEREVF2wDNBIiIiJzMa8f8rjKkb5WEwCNStmwcLF6qfZpPymAKPHyeobq/XC4SFJTi0mp8a7dsXwqefnlcdwyuvFHLq8ck5li69jadPE+yOaNJoFMyadRUrVzZUtd/Hj+MdiuPJE/W/0+SYmzefYezYM1i79r7DI9L0eoHr16NdFBkREWUnHClFRETkZBUr5nLoIq9SpVz2G9mh0SgOF4XOkcP5q5vVqROAunUDoLWza40GKFPGFy1bFnB6DJR2v/56XVU706i78HB1yaM8eRxbtS13bq7y5gpXr0ahXr2dqUpImXh58TKCiIjSjp8mRERETtauXUEULuytqq0QAoMHl3bKcVu3LpCsLpU1Gg1QrZof8uZ1zQX/b78FwtNTa7W2lEYjk2i//16PS6FnULdvx9gdJWWi1wvcv6+uNtjrrxdXPbW1SBFvBAYGqGtMqgkh0LXrIUREJKY6IaXRAE2b5nNyZERElB0xKUVERORkOp0GkyZVsdtOUYB+/UqiWDEfpxz3/ffLW6z/8yKjERg5svz/TzF0vtq1A7Bv30soUkQm5kyJMlOSKl8+L2zf3hwtWuR3yfEp7XQ6x04R1Y7SGzSotKpC+BoNMGJEOYeK5rvb3r17MW3aNLz66qsoWrQoFEWBoii4d+9eeodm0+7dD3H5cpSqvsKWt98u46SIiMidMmvfRVmXIrieKxGlg8jISPj7+yMiIgJ+fn7pHQ6R0wkhMGHCeUyffhlarWJ1RELu3DoMH14OH3xQHgUKqBtdZeuYr79+BH///Z/VYtKKIqfdeHtrkCuXB7p1K4rhw8uicmXn/x0aDAJbt4Zg9ep7CAuLh7+/J159tSi6di0CDw/eF8vIOnU6gO3bQ1UlLvLn98J//3VS/TNdvvw2+vX71+rrGg3QuHFe7NzZAl5ezp9i6iy5c+dGREREiufv3r2LYsWKpWqf7vhsHDToOJYtu52mpNSnn1bG1KnVnBgVEblLZu27KOtiUoqI0gU/vCirE0IgKkqPnTtDMX/+DWzbFmq1rVaroGBBL+zd+xLKl09bfamEBCOGDz+J33+/lSwZptHAYqJKp1Og1wtMmlQFU6ZUcdnoKcpctm17gFdeOWC3nVarYOLEypgypapD+//rr3t4//1TCAmJg4eH/J0zGAQURUH//iUxd25tpxfhd7YmTZqgQoUKqF+/PurXr4+6desCyPgXdu3b78f27db7I2tMfcXHH1fAjBk1OPWWKJPKrH0XZV1MShFRuuCHF2VVT58mYP78G5g79xru3o0FAOTO7YHw8ESb22m1CooXz4HLl9s7ZXRIcHAUfvvtBoKCwhEWFo//Y+++45q63j+Af24StoCAiqgg7llncdZqFSfOqq2jjmprW1errbaOOqpVq11WrdZdv26ts9W66taqdWC1igsFVFABCXsk9/fH/SWCELiBDAif9+uVFyT33HufhHCSPDnnOcHB2b8Vfdk337yCiRNrFvjcVPRptSK6dDmJw4efGBzlp1IJKFfOCZcuBcLLy8Hoc2RkaPHHH49x8uQzpKVp4efnjHfeqYiyZQs2YtBadAndwv7Brm/fs9ixI8LgaMqXeXjYwcvLAZ07lzXbqEoisp6i0neR7WJSioisgi9eZItu3YpH27bH8fhxsuwPfC/bsKEpBgzwM2lcDRsewtWrz/OMydFRgcjI7nB3tzPp+aloSkzMwNtvn8Uff0TqR8kAgFIJaDRAtWolcPDg6/D3d7FypIVDUflgt3LlPbz//kVZbRUKIDy8K8qVczJLLERkfUWl7yLbxYIOREREJqBWp6Ndu+OIjEzJd0JKoQCWLbtr0rguXIjBlSt5J6QAIDVVi3Xr7pv0/FR0ubiosHfvazhxog369KmAihWdUaGCE9q0KYNt25rj+vWOTEgVQf37+6FEibynRiqVAnr1Ks+EFBERmVXhnqxPRERURKxb9wAPHyajIOOPtVpptJUpHT/+RHZbhQI4dy4GY8aYNAQqwgRBQKtWpdGqFVdKtBUuLir88ktjDBx4zmAblUqAm5sdFiyob8HIiIioOGJSioiIyASWLLljkuOoVKYrHpyYmIFFi+THJYpAeno+h3kRkdFSU1ORmpqqv65Wqy1yXt0U4Q8+uIiEhAz9Qgi6aZpVqpTA7t0tUakSR8IRUXbW6rvINjEpRUREVEBarYhbt+ILNEoKkD4QNm/uZZqgAEybdh0REcmy2wuCgCpVSpjs/ESUu7lz52LmzJlWOfeAAX7o0aMcNm0Kw59/RiIhIQM+Pk545x0/tG1bhitxEpFB1uy7yPaw0DkRWQULIpItEUURKtX2fNeSyuzo0dZo06ZMgY+TmJiBsmX3IiEhw6j9bt3qhGrVXAt8fqLCauLEidizZ4/R+61ZswbNmzc3uD0/xYJzGm3g6+vL10YiyoZ9F9kqjpQiIiIqIEEQUKuWG/77T53v0VJKpYAOHbzRurVpavccOBBpVEJKEIBevcozIUU279GjRwgJCTF6v8TERJPH4uDgAAcHB5Mfl4hsD/suslVcfY+IiMgERo+umq/9dDWkAgPLYOvW5iabMvPsWZpR7V1dVVi7NsAk5yYqzNavXw9RFI2+BAYGWjt0IirG2HeRrWJSioiIyATeeaci/P1dci1UrlAALi5K1KnjBjc3FTw97dGliw8OHGiFfftayVqmXS5XV/nHEgSgXTtvuLramez8RERERER54fQ9IiIiEyhRQoUjR1qjbdtjePAgCQCyTOVTKABPT3scOtQaDRqUNHs87dt7w85OQHp63vMJRVGaukdEREREZEkcKUVERGQilSq54OrVDli4sAGqVn2xil2FCk74+utXcONGJ4skpACgVCkHvP22b64jtwBplFTJknbo21decVMiIiIiIlPh6ntEZBVcfY+Kg7Q0LbRaEY6OSqucPzIyBQEBhxEZmYKMjOwv97ryVbt3t0S3buUsHB1R0TZmzBhs2rRJfz06OhoA4OHhAYVC+t63ZcuW2L17t+xj8rWRiMyNfRcVNpy+R0REZCb29tYdkFy2rCPOnm2Lfv3+xunT0VCpBIiiCEEQkJEhonRpB6xeHYCgIB+rxklUFMXHx+s/zGUWGxur/z0uLs6SIRER5Yl9FxU2HClFRFbBb1SILOvKledYv/4BIiNT4OKiQqdOZdGtmw9UKs7kJyos+NpIREUR+y4qCI6UIiIiKgYaNChpsXpWRERERERy8OtRIiIiIiIiIiKyOCaliIiIiIiIiIjI4piUIiIiIiIiIiIii2NSioiIiIiIiIiILI5JKSIiIiIiIiIisjgmpYiIiIiIiIiIyOKYlCIiIiIiIiIiIotjUoqIiIiIiIiIiCyOSSkiIiIiIiIiIrI4JqWIiIiIiIiIiMjimJQiIiIiIiIiIiKLY1KKiIiIiIiIiIgsjkkpIiIiIiIiIiKyOCaliIiIiIiIiIjI4lTWDoCIiIiIiMhanj1LxYEDkYiLS4enpz06d/aBu7udtcMiIioWmJQiIiIiIiqCkpM1CA9PgiAAvr7OcHRUWjukIuXJkxR89tlVbN4chvR0EYIAiCLg6KjA0KH+mDevHpNTRERmxqQUEREREVERcvduAn744RbWrLmPpCQNAMDVVYXhwyvhk0+qoWJFFytHWPhFRqagWbMjiIhIhkYjApASUgCQkqLFihWhOHnyGU6degMlS9pbMVIiItvGmlJEREREREXEiRNPUb/+Qfzyyz19QgoA4uMzsHjxHdSvfwjnzkVbMcKiYdCgc3j48EVC6mUajYibN+MxatRlC0dGRFS8MClFRERERFQI3L2bAACoUWM/XFx2wMdnLz7++DJu3lQDAMLCkhAUdArJyRpkZGRPpmRkiIiPT0enTicRFZVi0diLkps31Th8+EmOj2FmGo2ILVvCERnJx5KIyFyYlCIiIiIisrIlS+6gceNDAKSpZUlJGkRGpuDnn++idu0DWLAgBD//fAfJyRpotYaPo9UCanU6Vqy4Z6HIi55Nm8KhUgmy2oqiiG3bws0cERFR8cWaUkRERGS0GzfU2LgxDJGRKXBxUaFTp7Lo0MEbCoW8D3pE9MKGDQ8wenTO08R0o3kmTrwKZ2elwelmmWm1wNKldzF1am2TxmkrIiNTIMjsqpRKgSOliIjMiEkpIiIiG3PrVjwOHYpCYmIGfHyc0KNHObi5mWYFqcePkzFo0HkcOfIky0iDhQtvo2JFZ6xeHYC2bcuY5FxExUFGhhYTJlyV1TZzDam8PHqUgtRUDRwcuCLfy1xclPqi5nnRagEXF35kIiIyF/awRERENuL69TiMGXMZR48+hSAACoUAjUaEk5MS779fCfPm1YOTU/4/oD55koLmzf9CREQyAGSrxxIenoSOHU9g375WaN/eu0D3hai42LcvEo8fm2ckjlLJkYs56dSpLH744basthqNiE6dypo5IiKi4os1pYiIiGzAxo0P0LjxYRw//hSAtLS5bppPcrIGixffQfv2x5GSIn+kxcsmTLiaZfn0l2m1gFYrYsCAv5GWlkvRGyLSu3AhBnZ2pk0eCQJQt64bVCq+1c9J27ZlULasY55T+JRK4NVXPdCokYdlAiMiKob4SkVERFSE/fXXEzRqdBADB55HaqrWYAFkrRY4ezYaM2Zcz9d5oqNTsWlTWJ71bLRa4NmzNOzc+TBf5yEqbvJaAS4zhRHv3MeMqZaPaGzfli3hqFHjT0RGpuQ6hU+pBOztlVi+vLHlgiMiKoaYlCIiIiqitm+PQPv2x3HlSpys9lotsGzZXSQnGz9a6sCBKKSny/vwrFQK2LWLSSkiOapVKyE7MSWKgJeXfa4rx6lUAmrUcMU77/iZKkSb8dNPt9Gv39+4dy/RYBtd4s/HxwnHjrVBw4YcJUVEZE5MShERERVBDx8mY8CAvyGKkF2wFwDi4jJw+HCU0eeLi0uX3VajEfH8ufz2RMVZ374VZNd6s7NT4NCh1+Hv7wIg68gp3e/Vq7viyJHWcHZm6djMgoOf45NPruTZTqsF5s17BffvB6FJE0/zB0ZEVMwxKUVERFQErVhxDxqNaFRCSufJk1Sj9/HyspfdVqkUUKqU/PZExZmrqx3GjaueZ30jQQA+/LAyGjb0wL//dsDatQFo3NgDLi5KlCihRNOmXtiwoSkuXQpEuXJOlgm+CFm8+I6swu8qlYBLl2JZJJ6IyEL4FQoREVER9Ouv9w3Wj8qLq6vxL/+dOpWFo6MCKSl5n1SjEfHWW775CY2oWJo5sw7u3UvApk0h2bYpFNLone7dy+Hbb+sDABwdlRgyxB9DhvhbONKiSRRFbNgQJmuaZEaGiN9+e4jUVA0cHPK/WikREcnDkVJERERF0NOnxo92AgA7OwHt2pUxej83NzsMHlxRxmpVAipUcEKXLj75io+oOFIqBaxf3zTHotp167pj9epX8dtvLWBnx7fu+ZGaqjWqlp5GIxo1ZZmIiPKPr2xENkYURZw6dQoTJkxAs2bNULJkSdjb26NcuXLo3bs3jh49avQxZ8yYAUEQcr3cvHnTDPeGiAzJT70YlUrAgAF+8PJyMHrfpKQMXLr0PM/pgiqVgB07WnDqC5GRFAoBb78tFSe/caMTrl7tgLCwIFy50h7vvluJ/1MF4OCgMPrxK1GCE0qIiCyBvS2Rjfnrr78QGBgIAFAoFKhatSpcXFxw+/Zt7NixAzt27MDUqVMxa9Yso4/t6+sLP7+cV/NxdnYuUNxEZJxu3Xywbt0D2at2qVQCvL0dMXfuK/k63+ef/4tLl2LzbFeligsCAlgcmIqWuLh0HDoUhbi4dHh62qNDB2+4uFjvbXK5ck5wc3Oz2vltjSAI6NKlLPbvj8yzz1QqgZYtS7FQPBGRhbC3JbIxoiiiatWqGD9+PPr16wcPD2kp47S0NMyYMQNz587F7Nmz0bRpU3Tt2tWoYw8bNgwzZswwQ9REZKxRo6pi9er7sts3buyBbduaw8fH+ALIcXHpWLnynqwaVv/9F48LF2KYmKIiITo6FZMm/Yv//e9BlnppLi5KvP9+ZcyaVZcjZmzE6NFVsXfv4zzbaTTAmDHVLBAREREBnL5HZHOaNGmCGzdu4KOPPtInpADA3t4ec+bMQefOnQEAK1assFaIRGQCjRp5YPz46rm2EQTAx8cRZ868gb//bgdf3/yNaNy9+6GsAueANCJrw4awfJ2HyJKePElB06ZHsHr1/WzP78REDRYtuoNWrY5CrWZtIVvQvr033n3XP9e6eIIA9O5dHm++Wd5ygRERFXNMShHZGDc3N6hUhr/Vbd++PQDg1q1blgqJiMzk22/rYdq02rCzE6BQSB+oBEFKDAFAr17lcetWZzRvXqpA54mMTJFdj0WrFREVlVKg8xFZwpAh53H/fhI0mpync2k0Iv79Nw5jxly2cGRkDoIgYMWKVzF+fHWoVFKfqVRKqxtKvwv48MMq2LSpGRQK1u8iIrIUjkcmKmZSUqQPi05Oxk/hOXr0KK5fv47o6Gh4enqiSZMmGDx4MMqWLWvqMIno/4miCK0WOSaFBEHAzJl1MGZMVaxeHYp//olFRoaIqlVLYPjwSqhRw9UkMbi4qKDVyqtdpVAIVq3FQyTHrVvx+PPPqDzbaTQiNm4Mw4IF9VCmjKMFIiNzUioFfPttfUycWANr1tzH1atxEEURtWu7YdiwSihXzvj3RkREVDB810hUjIiiiG3btgEAWrZsafT+J06cyHL9t99+w4wZM/Dzzz9j6NChue6bmpqK1NQXS9ir1Wqjz09UXIiiiEOHorB48R38+Wck0tNFlCxph8GDK2LUqKqoXj1rsqlUKQdMnFjTbPG0b++d56p7OhkZIjp29DZbLESmsGFDGJRKweAoqcy0WhFbt0Zg9OiqFoiMLKFMGUd8/rn5+kwiIpKP0/eIipEVK1bg8uXLsLe3xyeffCJ7Px8fH0yePBkXLlxAdHQ0kpKScPr0aXTu3BnJyckYNmwY9u7dm+sx5s6dC3d3d/3F19e3gPeGyDZlZGgxePB5dOx4Evv3SwkpAHj+PB1LltxFrVp/Yu3a+xaNqXp1V7RtWybPKXyCAJQqZY+ePVmPhQq3x4+ToZD5LlipFBAZySmpRERE5sCkFFExcenSJXz88ccAgNmzZ6NKlSqy9/3ggw/w9ddf49VXX4WnpyecnJzQokUL/PHHH+jVqxdEUcS4ceMg5jKUYtKkSYiLi9NfwsPDC3yfiGzRJ59c0RcKf3npco1Gmso3bNgF7NuX9ypSprR4cUM4OysNJqZ0xYNXrw6AnR3fXlDh5uwsf7KAVgs4OyvNGA0REVHxxXeNRMVAaGgounbtipSUFAwYMACfffaZSY4rCALmzZsHALh79y6uXr1qsK2DgwPc3NyyXIgoq4iIJPz88908p8oJAjBp0r+5JoJNrVYtN5w8+QZ8faWaK7pi6rrRJu7udti5swW6dStnsZiI8qtjR2/9KMS8aDQi2rfnlFQiIiJzYE0pIhsXGRmJ9u3b4/HjxwgKCsLatWsh5LYespGqV68OT09PxMTE4M6dO6hfv77Jjk1U3KxcGQqFIu86N1otcPVqHP75JxYBAZ4Wig6oX78k7tzpggMHIrF5cziiolLg7m6Hrl190LevL5ycOJqEioaOHcvCz88ZERFJ0GoNt1MqBdSr527R/7PCLCYmDbGxaShZ0g5eXg7WDoeIiGwAk1JENiwmJgbt27fH3bt30bp1a2zbtg12dnYmP4/umBkZGSY/NlFxcuXKc1mFlzO3t/SHZaVSQJcuPujSxcei5yUyJYVCwKpVr6JTp5NQKMQcE1MKhTQi8JdfGls+wEJEFEXs2vUICxfexvHjT/W3N2/uhbFjq+Ktt3yhUJjuyy4iIipeOH2PyEYlJCSgS5cuuHbtGgICArB37144OZl+qeNnz57hyZMnAIAKFSqY/PhExYlWa9x0PAvO3iOyOYGB3ti7tyXc3aUvVnRTUXU/S5d2wJEjrYv1KCmtVsQHH1zEm2+ewalTz7JsO38+Bv37n8PAgeeQkZHLcDMiIqJccKQUkQ1KTU1Fjx49cO7cOdSpUwd//vknXF1d894xH77//nuIogh3d3cEBASY5RxExUWtWm7Yty9S9mipWrXM839NVFx07uyDR4+6Ydu2COza9RCxsWkoXdoBffv6okePcsW+aP/cuTexYkUoAGTrl3TXt2wJR/nyTvj2W07fJyIi4wmiJaukEpHZaTQa9OnTB7t27UKVKlVw8uRJ+PjkPc3mxx9/xI8//ohmzZph8+bN+tuvX7+OJUuWYNSoUahTp47+9pSUFHz//ff48ssvodVqMWfOHEyaNEl2nGq1Gu7u7oiLi2PRc6L/d+dOAqpV259nO0EAqlYtgZCQTvmqERcdnYotW8Jx/34SHBwUaNWqFAIDvTkFh8jKCtNrY1JSBnx89kKtzntqvoODAo8fd4OHh70FIiOiwqYw9V1U9HCkFJGN2bp1K3bt2gUAUCgU6Nu3b47tfHx8sG3bNv3158+f48GDB/D398/SLj09HUuXLsXSpUtRunRp+Pn5AQBu3LiBpKQkAMDw4cPxxRdfmP7OEBUzVauWQP/+vtiyJTzX4suiCMycWcfohFRKigbjxwdj1apQpKdroVIJEEVg9mwR/v7OWLy4EYKCWCuKiIDffnsoKyEFAGlpWmzYEIbRo6uaOSoiIrI1TEoR2ZjU1FT977dv38bt27dzbFexYkVZx/P398esWbNw5swZ3Lx5EyEhIUhLS0OZMmXQpUsXvPfee+jYsaNJYiciYNWqAERHp+HgwSgoFMiSnFKpBGRkiJg/vx769/cz6rhpaVoEBZ3CsWNP9MdMT38xWPrBgyR063YKW7Y0Q9++vqa4K0RUhIWExMPOTsjSTxiiUgm4eVNtgaiIiMjWcPoeEVkFh/kSGZaRocXGjWFYtOgO/vknFoC06l3v3uUxdmw1tGxZyuhjLlgQgi++uJrrCCxBAOztFXj4sCuXeyeygsL02jht2jXMm3dTVlLKzk7ARx9VwcKFDS0QGREVNoWp76KihyOliIiIChmVSoHBg/0xeLA/nj9PQ2KiBp6e9nByUubreBqNiJ9+up1rQgqQpgWmp2uxdu19fPppjXydi4hsQ6NGHrISUoA06rJhQw8zR0RERLaoeC8pQkREVMiVLGmP8uWd8p2QAoBLl2IREZEsq61WC2zeHJ7vcxGRbeja1Qfe3vJGTLq5qfDWWxXMHBEREdkiJqWIiIhsXGxsmlHtnz1LzbsREdk0lUqBefPqyWo7c2YdODtzAgYRERmPSSkiIiIbV7Kkccu0e3pyWXei3MTHx2P9+vV45513UL16dTg5OcHZ2Rl16tTBhAkT8PjxY2uHaBJDh/rj22+lxJRKlXW1T931adNq4+OPq1k8NiIyXnHpu6hoYaFzIrIKFkQkspyMDC38/P7A48cpebZVKIA5c17B55/XtEBkREXToEGDsH79egCAq6srqlSpgsTERNy7dw8ajQZeXl7Yv38/AgICjDpuYX1tvHYtDkuX3sWmTWGIi0uHq6sdevcuj1GjqqJRI9aSIioqilvfRUUDR0oRERHZOJVKgdGjq0Ih41VfqRQwbFgl8wdFVMT17NkThw8fRkxMDC5fvoxbt24hJCQETZo0QXR0NHr37o3kZHm13Aq7unXdsWRJI8TE9IRG0xfPn/fEqlUBTEgRFUHFqe+iooEjpYjIKviNCpFlpaZq0K7dcZw9G53rKnzr1jXBoEEVLRcYUREUExMDT0/PHLeFh4ejatWqSEtLw44dO9CrVy/Zx+VrIxGZE/suKow4UoqIiKgYcHBQ4uDB1zF4sD+USgEKBWBnJ8DOTqoL4+PjiO3bmzMhRSSDoQ91AODr64uaNaXpr7du3bJUSEREeWLfRYURl8kgIiIqJpydVVizJgBz576C9esfICwsCfb2CrRqVQpBQT5QqfhdFZEppKRI9ducnJysHAkRkXzsu8gamJQiIiIqZsqWdcRnn9WwdhhENik4OFg/yqBly5ZWjoaISB72XWQtTEoREREREZmARqPBmDFjAABt27ZF48aNc22fmpqK1NRU/XW1Wm3W+IiIcsK+i6yJ4/SJiIiIiExg8uTJOHnyJFxdXbF8+fI828+dOxfu7u76i6+vrwWiJCLKin0XWRNX3yMiq+AqHUREZA0TJ07Enj17jN5vzZo1aN68ucHty5Ytw0cffQSVSoVdu3YhKCgoz2O+PNpAFEWkpaWhVKlSEATB6BiJyHax7yJbxel7RERERZQoijhx4hmCg59DFIHatd3Qrl0ZKBR8Q0hkyKNHjxASEmL0fomJiQa3bdmyBaNGjYIgCFi7dq2sD3UA4ODgAAcHB6NjIaLih30X2SqOlCIiq+BIKaKC2b49ApMm/Ys7dxKg+P/J+Fot4OfnjJkz62DoUH+rxkdUXOzbtw89e/ZEeno6lixZgpEjR1o7JCKiPLHvosKCNaWIiIiKmJ9/voO+fc/i7t0EAFIySquVtoWFJeHddy/g669vWDFCouLhxIkT6NOnD9LT0zF37lx+qCOiIoF9FxUmHClFRFbBkVJE+XPtWhzq1TsIOa/eJ060QatWpc0fFFExdPHiRbRt2xZqtRqTJk3CnDlzrB0SEVGe2HdRYcORUkREREXIkiV3oFTmXTNKpRKwaNEdC0REVPyEhISgU6dOUKvVGDlyJD/UEVGRwL6LCiOOlCIiq+BIKaL8cXXdiYSEDFltlUoBCQm94OioNHNURMVLx44dcfDgQQiCgObNmxtcbWrYsGEYNmyYhaMjIsoZ+y4qjLj6HhERURGRlqaVnZACAI1GxPPn6ShblkkpIlPSLYUuiiLOnDljsF1gYKClQiIiyhP7LiqMOFKKiKyCI6WIjCeKIhwcfkN6uvyX7vj4XihRgt9BEREREVHhw5pSRERERYQgCOjc2QcqVd41pZRK4LXXvJiQIiIiIqJCi0kpIiKiImT06KrIyMh7pJRGA4weXc0CERERERER5Q+TUkREREVIYGAZDB3qDwO1SQEACgXQo0c59OlTwXKBEREREREZiUkpIiKiIkQQBKxc+SrGjasGlUqAQgH9RamUrg8fXglbtzaHUpn3ND8iIiIiImthoXMisgoWOicquKioFKxZcx/Bwc8hikDt2m4YNswfFSo4Wzs0IiIiIqI8MSlFRFbBpBQREREREVHxxul7RERERERERERkcUxKERERERERERGRxTEpRUREREREREREFsekFBERERERERERWRyTUkREREREREREZHFMShERERERERERkcUxKUVERERERERERBbHpBQREREREREREVkck1JERERERERERGRxTEoREREREREREZHFMSlFREREREREREQWp5LTSBRFxMfHmzsWIipG1Gp1lp9ERKbi6uoKQRCsHQYRERER5UEQRVHMq5FarYa7u7sl4iEiIiIqkLi4OLi5uVk7jGJh7dq1ePfdd7PdLggCXF1d4efnh5YtW2LEiBFo1KiRWWLQJSDXrFmDoUOHmuUchcmpU6ewevVqnD17FhEREUhLS0OpUqVQunRp1KpVC61bt0abNm1Qs2ZNa4eaRXH7O5HternfW758Od5//32D7Y8dO4Y33ngDALB//3506tTJ7DEWZTNmzMDMmTNRsWJF3L9/P8u2+/fvo1KlSgCAo0ePok2bNpYPkExO1kgpV1dXxMXFmTuWIk2tVsPX1xfh4eF8I2whfMwtz5SPOf9+8vBxsg4+7pZnysfc1dXVRFFRfomiCLVajWvXruHatWtYsWIFZsyYgS+//NLaoRUqmT+shoaGwt/f32Db9PR0vPfee1i3bl22bY8ePcKjR48QHByMzZs3w8XFBQkJCeYKm4gymTVrFgYPHgwHBwdrh0JUJMlKSgmCwDflMrm5ufGxsjA+5pZnysecfz95+DhZBx93y+NjXnTt27cPrVq1AgBkZGQgNDQUv//+O+bMmYOUlBRMmzYNderUwZtvvmnlSIum0aNH6xNSAQEB+Pjjj9GgQQP4+PggMTERN27cwLFjx7B9+3Y8evTIytESFR/h4eFYvnw5xowZY+1QiIokWUkpIiIiIqLcODk5oUSJEvrrDRs2RMOGDVG7dm306dMHALBgwQImpfLh3r17WLFiBQCgV69e2L59OxSKF+sVeXp6wtfXFx06dMCcOXNw7NgxK0VKVLxUqVIFd+/exZw5c/Dee+/BycnJ2iHZNH9/f8ioPkRFDFffIyIiIiKz6d27N6pVqwYAuHDhAtLS0qwcUdFz5MgR/Qexzz77LEtCKiess0JkGdOmTQMAREZGYsmSJVaOhqhoYlLKRBwcHDB9+nTOJbYgPuaWx8fc8viYWwcfd8vjY27batWqBQDQaDSIiYnJtj09PR3Lli1D27ZtUbp0adjb26Ns2bLo2rUrtmzZIvubcY1Gg8WLF6NJkyYoWbIkXF1dERAQgMWLF0Oj0eS4z7FjxyAIAgRByFZUN7O1a9fq2+UkMTER8+fPR4sWLeDp6Qk7OzuULl0atWvXRt++fbFixQokJibq2wuCoK8nBQCVKlXSH//leJ4+fapvZ4qaaVFRUZg2bRqaNm0KLy8vODg4wM/PD23atMG3336L8PDwbPtcv34dM2fORGBgIKpVqwYXFxeUKFECNWvWxIcffogbN24UOK6UlBQsXrwY7dq1Q5kyZWBvb48yZcqgc+fO2Lx5s8Hnwct/m4iICIwbNw41atSAi4tLnn9bovxq0qQJunXrBgD45ptv8l3LLSwsDOPHj0edOnXg6uoKZ2dnVKtWDR9++CFu3rxpcD9jnvv+/v4QBAEzZswAAGzfvh3t2rVD6dKlUaJECTRq1AjLly/P8n/26NEjTJgwATVq1ICzszNKly6Nt956CyEhIQZjSkpKwubNmzFkyBA0btwYZcuW1f8vBwYGYsWKFfn+cuL+/fv6+/vyiNA2bdpk60MNXQwtthAXF4e5c+eiRYsWKFWqFBwcHFCuXDm8+eabOHDggMG4ZsyYAUEQ9HUBb9y4gREjRqBy5cpwdHTkisB5EYmIrCAuLk4EIMbFxVk7FCIiyqc1a9aIAEQA4tGjRw2269q1q75dUlJSlm0PHz4U69Wrp9+e06VDhw6iWq3O8di6NsuXLxcDAwMNHqN169ZiQkJCtv2PHj2qbxMaGirrvr7s0aNHYrVq1XK9DwDECxcuZIs7t4sunp9//ll/23fffWcwRjk2bdokOjs753reIUOGZNnn+fPnecZqZ2cnrlu3zuB5de3WrFmT4/Zr166JlSpVyvUcXbp0yfFvmPlvc/bsWdHDw8PgY0lUUJmfbzdu3BCvXLkiCoIgAhBnz56drX3mPmb//v3Ztm/btk10dHQ0+LxXKpXi4sWL84wlr+d+xYoVRQDi9OnTxQ8//NDg+d577z1RFEXxn3/+Eb29vXNs4+7uLl69ejXHmD755JM8+4tmzZqJsbGxOe4/ffp0EYBYsWLFbNtCQ0MNvua0bt1aVr+aUx8niqJ4/PhxsVSpUrnuN3z4cDEjIyPXmHfu3Ck6OTll25cM40gpIiIiIjIr3Tf9VapUyVJzJS0tDV26dMHVq1chCALGjh2Lq1evIjo6GhcuXMDAgQMBAAcPHtT/bsjcuXNx+PBhjBo1ClevXsWzZ89w7tw59O3bFwBw/PhxjBgxwiz3b+LEibh9+zYUCgUmT56My5cv48mTJ3jw4AFOnz6NH3/8Ea+++mqWb8vj4+Oxb98+/fXr168jPj4+y6VixYoAgHbt2un3nTJlCubNm4eoqCij49y5cyf69++PpKQk+Pj4YOHChbh58yZiYmIQGhqKHTt2YNCgQXB0dMy2b506dTBlyhQcPHgQ169fx7Nnz3D79m3s2rUL7dq1068O+O+//xodV0REBNq0aYPQ0FD4+Phg8eLFCAkJQUxMDG7evIlZs2bB0dER+/btw4cffpjrsfr06QMXFxesXr0aYWFhePLkCQ4dOgQPDw+j4yKSo379+ujduzcA4Ntvv8Xz589l73vmzBn069cPKSkp8PHxwerVqxEeHo7IyEj89ttvqF69OjQaDUaPHo0dO3bkeiy5z/1169Zh2bJlGDVqFK5cuYLo6GhcuXIFXbt2BQCsXLkSO3bswJtvvokSJUpg06ZNePToESIjI7FmzRqUKFECcXFx+Oijj3KMo0SJEujduzd+/fVX/P333wgLC0NUVBQuXryIadOmwcPDA3///bfB/fNr//792frQzJeZM2fq2zZq1CjLvleuXEHHjh3x7NkzVK9eHWvWrMG9e/cQExODq1evYvz48VAoFFi1apV+pFlOYmNjMXjwYPj7+2Pbtm36x23nzp0mva82x9pZMSIqnjhSioio6JMzUmrr1q36Nj/88EOWbT/++GOeI4DGjBmjb7N3795s25Hpm+gZM2bkeIxBgwbp2/z9999ZtplipJSnp6cIQPzkk08M7p8TuecWRTHbyAZBEMRatWqJgwcPFhctWiQGBwfnun9CQoLo5eUlAhArVaokPnz40GDb9PR0o+6HKIri22+/LQIQBw8enON2Xdw5jZTq0aOHPq7IyMgc99+/f7/+GJlHnIli1r+Nl5eXGBYWZnT8RHK9PFJKFEXxv//+ExUKhQhAnDp1apb2uY2UatiwoQhALFmypHj37t1s53r27Jl+BKGPj4+YlpZmMJa8nvu6kVIAxHnz5mXbnpqaqj+XUqkUfX19xadPn2Zrt2LFCv1xbt68afiBMuDq1auiUqkUBUEQ79y5k217fkdK5WbXrl36v09OfVSDBg1EAGJAQECOozFFURSXLVsmAhDt7e2z9Z+6mAGI1atXF58/fy47NuJIKSIiIiIygeTkZCQkJCAhIQFxcXG4cuUKZs2ahcGDBwMA+vXrl23J9JUrVwKQak6NGzcux+N+88038PT0BAD9CnQ5KVeuHCZPnpzjtu+//x729vYAgFWrVhl3x2TIyMgAAFSoUMHkx9ZZvHgxpk+frh9pJooibty4gXXr1mHMmDGoX78+atasibVr1+ZYe2n9+vWIjo4GACxZsgTlypUzeC6VyvgFunV/50OHDhm1X2hoKPbs2QNA+lt7e3vn2K5Tp076Au4bNmwweLwJEybA19fXqBiICqpWrVoYMGAAAGDhwoV49uxZnvtcvHgRly9fBgB88cUXqFy5crY2Xl5emDt3LgDg8ePH+P333w0eT+5z38/PD5999lm22+3t7fWro2o0GkybNg2lSpXK1u6tt97Sj9w8f/58nud72SuvvIJGjRpBFEUcOXLE6P2NdenSJQwcOBBarRatWrXK9jpy7NgxXLlyBYDUN7q4uOR4HF2NqLS0NGzbts3g+b766iu4u7ubLP7igEkpIiIiIiqwLl26wNXVFa6urihZsiQaNmyIadOmQaPRYP369di0aROUSqW+fWxsLK5fvw5AmnZiqBCsk5OTvpDwqVOnDJ6/W7dusLOzy3FbqVKl0Lp16zyPkV8NGjQAIE3dOXDgALRarcnPoVQqMWPGDISFhWHJkiXo2bMnfHx8srQJCQnBu+++iwEDBmSLQffhz9vbG506dcpXDH/99ReGDBmCmjVrwtXVFQqFQl84OCgoCID0wTk+Pl72MQ8fPgxRFCEIAl5//XV9YjOnS/369QEA//zzj8Hj6eIgsrTp06dDpVIhPj4e8+fPz7P9yZMn9b/rphnnpGfPnvqkeuZ9Xib3uR8YGJilL86sSpUq+t87duyYYxs3NzeULl0agPT/npOYmBh88803aNOmDby9vWFvb5+l0PiFCxcAINeC6aYQERGBbt26ITExEVWqVMHOnTv1j6WOLpFeunRp1KpVy2D/k5iYmGcfJAgCOnfubNb7ZIuYlCqgY8eOYe7cuejVqxfKly+v/0eLiIjIc1+tVouffvoJDRs2hIuLCzw9PREYGIj9+/dbIHLblNdKC/369bN2iEXWvn37EBgYCE9PT7i4uKBRo0ZYtGiRWd54F3dDhw7N87mckpJi7TCLnNDQUKxYsQLvv/8+6tevD5VKBUEQMHv27Dz3PXv2LHr06IHSpUvDyckJtWvXxqxZs/h3yEN+HnPdCja5XXJbiYgKn/T0dEycODHbh4+wsDD9iJ46derkegzd9piYGKjV6hzb6Fb4M6R27doAYJZV2ObNmwd7e3tERkaiU6dO8PHxQd++ffHDDz/g6tWrJj1XqVKlMHLkSOzcuROPHj3Cw4cPsXHjxiyJps2bN2PRokVZ9rt79y4Aqf6NsStBaTQavPvuu2jXrh3WrVuHkJAQJCQkGFwNLy4uTvaxdc8LURRRtmxZfWIzp8vChQsBZF2N8GU5jTYhsoSqVatiyJAhAKQRN5GRkbm2f/DgAQDA0dEx1+etg4MDqlatCiD3/kvucz+3UZKZa/7JaZecnJxt299//42aNWviiy++wPHjx/HkyROkp6fneBxj+gpjJSQkoGvXrnj06BFKliyJP/74A15eXtna6fqgp0+f5tr/uLq66mtDGeqDSpUqBTc3N7PdJ1tl/NhcyqJnz575+mfSaDTo0aMH/vjjDygUCtStWxfx8fE4cuQIjhw5ggULFuQ4rJLkadmyZY6316xZ08KR2IZ58+Zh0qRJAKQXvBIlSiA4OBhjx47F4cOHsXPnTigUzHGbWrVq1VCmTJkct/HxNt7ChQv1H2iMsWHDBgwZMgQajQbly5eHr68vrl27hmnTpmHv3r04duwYnJ2dzRBx0ZffxxwAfH194efnl+M2Pt6F09GjR/XTqxITE3Hz5k189913+iK5vXr1wqVLl/RFtDOPpilRokSux3Z1ddX/Hh8fn+Ob/ryOoduenJwMjUZjcKRAfjRv3hx///03vvrqK/zxxx948uQJtm/fju3btwOQpqvMnz8/3yOUclOuXDn0798f/fv3x5IlSzB69GgAwKJFi/Dxxx/r2+mSeZkfS7m+/fZbrF27FoA0Im3o0KGoU6eOfsl0ADhx4oR+pIZuOqMc+XkfndsXAuwfyJqmTZuG//3vf0hKSsKcOXPw008/GWyr6wPz6ruAF/+3uY1ClPvcl9v3yWn3cmJarVajZ8+eePr0KUqXLo3x48ejTZs28PPzg4uLi/6YnTp1wunTp43qK4yh1WrRv39/BAcHQ6VSYfv27ahRo0aObU3ZB7H/yR8mpQqoTp06qF69Opo0aYImTZrg1VdflbXfggUL8Mcff8Db2xsHDhzQDwXcuHEjBg0ahIkTJ6J169YICAgwZ/g2yxxD84urs2fPYvLkyVAoFFi/fj369+8PAAgODkbHjh2xZ88efP/990yimsHkyZMxdOhQa4dhM0qVKoWuXbuiSZMmCAgIwMqVK/Hbb7/lus/9+/cxfPhwaDQazJ8/H5999hkEQcCDBw/QsWNHXLhwARMnTsTixYstdC+Klvw85jrDhg3LdYUbKtxcXFzQuHFjbNy4ES4uLli5ciVu3LiB+fPnY9q0aQCyJkcSEhJyPV7m7YaSKomJibKO4eTklOXDltxRQ3l9eGrYsCF27tyJxMREnDt3DmfPnsXBgwdx8uRJ/Pvvv+jSpQt+++039OrVS9b58mPUqFFYtmwZrl27hrt37yIuLk5f20SXyDNmap3OkiVLAABvv/02Nm/enGOb1NTUfMWs+0Du7u5u1KplRIWRn58f3n//fSxZsgTLly/HxIkTDbbV9WV59X+Z2+QnqWxJ27dvR1RUFBQKBY4ePWpwFGx++iFjjBs3Tl9/a8mSJWjXrp3Btro+qH79+vraUmRZ/Kq9gE6fPo01a9bgo48+QuPGjWXtk5aWpp9n/MMPP+gTUgAwYMAADB8+HKIoyppSQmRus2fPhiiKeO+99/QJKUDquL///nsA0kgqQ8NyiQqLqVOnYu/evfjyyy/RqVMnWd9MLliwAKmpqejQoQMmTJig//BasWJFrF69GgCwfPnyfC3NXhzk5zEn2/Pjjz/qp4F8++23+mLbfn5++v8pXW0pQ65duwYA8PT0NDg14saNG7keQ7fd398/y+26kVtAzlNRdB4+fJjr8XVcXFzQtm1bTJkyBcePH8eVK1fg5eUFURSzLEluLrppigCQlJSk/103/Sc4ONjgtLucxMTEIDw8HACyvA942b///mtsqABe1LCJi4vDvXv38nUMosJkypQpcHJyQmpqKmbNmmWwna4vSklJyfW5n5qaitu3b2fZp7DSJXXq1atnMCGVlpaGW7dumS2GJUuW6EeoffrppxgxYkSu7XV9UEhISJY+kyyHSSkrOHr0KGJjY+Hm5oY+ffpk2z58+HAAwIEDB8yeRSbKjVqtxuHDhwG8eF5m1rdvX7i5uSE6OhpHjx61dHhEZiWKor52QE7P/xYtWqBmzZpIT0/H7t27LR0eUZHh4uKiH/UWHx+PH374AQDg4eGBunXrAgB+++03g4mS5ORk7N27FwDw2muvGTzP3r17DY5mio6OxvHjx3M8RuZi4bnVK8tvzc969erh7bffBpA9cZa5MLtGo8nX8V+mSyDZ2dnpixEDQPv27QEAUVFROHDggOzjZR4BZShGrVaLjRs35idcdOjQQf+7LtlPVJT5+Pjgo48+AgCsWbPGYMKpVatW+t91U31zsmvXLqSlpWXbpzDS9Re59Wfbtm0zW03O/fv366ctd+/eXVbBeV0flJKSku9+jAqGSSkr+PvvvwEATZo0yXGVmMaNG8PR0RGpqakcQphPY8eORYcOHdCpUyeMHDkS+/btM+pbQZJcvnwZaWlpcHR0RKNGjbJtt7Oz008xPXfunKXDs3nbt29Hz5490bZtW/Tr1w+LFi0ya0FIyiosLEy/qoyhOnW62/n8N72jR4+ib9++aNu2Lfr06YP58+fnWTSWCq+hQ4eiUqVKAKRaR7ppWu+99x4A4L///stWmFvniy++QExMDADk+o33w4cP8c033+S47bPPPtN/WBo2bFiWbX5+fvrE1Jo1a3Lc/9dffzX4f56YmKhPBBmiKzL+cpHdzMutP3r0yOD+O3fuxPLly/UfTA35448/9O8z33jjDahULyp1DBgwQJ+kGjVqVK7/T5mTe2XKlNGPcjSUgP/qq6/yvYpW9erV0bVrVwDSSLpjx47l2l6tVhtc8YuosPjiiy9QokQJpKenY86cOTm2adSoERo2bAhAmnWQUxHz6OhofV1XHx8f/f9KYaUrtn7jxo0cR0M9fPgQn3/+uVnO/e+//+Ltt9+GRqNBgwYNsHHjRlk1WNu3b4969eoBAD7//PM8R+4+efIEsbGxJomZJExKWYFu+KWhFRJUKhV8fX2ztCXjLFq0CIcOHcKBAwewdOlSBAUFoU2bNrmu1kLZ6Z5/fn5+Wd7YZqZ7HvO5anp//PEHdu/ejaNHj2LLli0YO3YsKlWqhD///NPaoRULuue0g4ODwRVo+Pw3nxMnTmD79u04evQofvvtN3z++eeoXLmyvtgyFS12dnaYOnUqACmpoJta8eGHH+rLGIwbNw7jx4/HtWvXEBMTg4sXL2LQoEH6tt26dct1yXN/f398+eWXGDt2rP4Y//zzD/r166d/3gwYMADNmjXLtq8uObZ371588MEHuHnzJmJjY3H16lV8+umnGDZsWJal0jN7+vQpKleujB49emDNmjX4999/ER0djcjISJw5cwaDBw/Wj0x6efpb1apVUbJkSQDSdOF79+4hLS0NGRkZWRJDDx8+xAcffIBy5cph9OjR2LlzJ+7cuYPY2FhERUXh5MmTGDNmDHr16gVRFKFSqbJNG3J2dsaqVasgCALu3buHxo0bY/Hixbh9+zaeP3+OsLAw7NmzB++++66+WDogFTvu3bs3AGDdunX4+OOPce3aNURHR+PChQsYOnQoZs6cmWXaoLGWLl0Kb29v/XTpMWPG4PTp03jy5AliYmJw69YtbN++HcOGDUOFChVw+vTpfJ+LyBJKly6NMWPGAHiRlM7J4sWLoVQqERsbi9deew2//vorHj16hCdPnmDnzp1o0aIFQkND9W1zGtBQmPTu3RtKpRIZGRkICgrCzp078fjxY0RERGDt2rVo1qwZYmNjUbFiRZOeV7fSXnx8PEqXLo1NmzZBFEUkJCTkeMk8AlQQBKxbtw4uLi6IiYlBs2bNMHXqVPzzzz+Ijo7Gs2fPcP36daxfvx5vv/02/Pz8cv2bUj6IZFIARABieHi4wTZdunQRAYiff/65wTZNmjQRAYjffvutOcK0WZ06dRK3bt0q3r17V0xJSREjIiLERYsWiW5ubiIAsWnTpmJ6erq1wywy5s+fr3/cDJk4caIIQOzatatRx46LixMBiHFxcQUN0+Z89dVX4pw5c8Tg4GBRrVaL8fHx4sGDB8WmTZuKAEQHBwfxwoUL1g6zyBsyZIgIQJw1a1aO27du3SoCEL29vQ0e4+effxYBiHXr1jVXmDYlr8dcFEVx2bJl4uTJk8ULFy6I0dHRYlJSknj69Gmxc+fOIgBREARxz549FoyacrNmzRr9e5+jR4/m2jY9PV2sWrWqCED08PAQ1Wq1KIqiGBERIdarV09/nJwu7du317d/ma7NL7/8Ir7xxhsGj9G6dWsxISEhx2MkJSWJr776qsF933rrLXHlypX665mFhobmGrvuEhgYmOP5p0+fbnCf0NBQURRFce3ataIgCLLO4+npKe7atcvg32HTpk2is7NzrscYMmRIln0iIyPFSpUqGWz/+uuvi/v27csWd05/pzVr1uQY161bt8RXXnlF1n18uQ/I/DwkMrfMz7cbN24YbBcTEyO6u7tnee7u378/W7utW7eKjo6OBp/vSqVSXLx4cZ6x5KVixYoiAHH69Omy7lt+j6X7/JDTxdHRUdy+fbvYunXrHPsaUXzRJ1asWDHbtsz9bebXHLn9sKE+ThRF8dy5c6Kfn1+e+wqCIF65ckV2zJQ3jpSyAt0cWnt7e4NtdMvr5lZwk7Lbv38/+vbti8qVK8PBwQHly5fH6NGjcfjwYdjZ2eHcuXPYtGmTtcMsMvhctY4vv/wSkyZNQr169eDq6ooSJUqgffv2OHHiBJo0aYLU1FSzDX2mF/j8t44PPvgAX3/9NV599VV4enrCyckJLVq0wB9//KEfBTJu3DhOyS6CVCqVfuW92NhY/aqV5cuXxz///IOlS5eiTZs28PLygp2dHby9vdGlSxds2rQJBw4cyHPVKXt7exw8eBA//PADGjduDFdXV/0qgD/99BOOHDkCFxeXHPd1cnLCsWPHMGPGDNSuXRuOjo4oWbIkXnvtNaxbtw5btmwxuDy6n58fTp8+jZkzZ6Jdu3aoUqUKXFxcYG9vjwoVKqB79+7YsmULDh48mOP5p0+fjl9++QUtW7ZEyZIlc5xuMmTIEISFheGXX37BoEGD0KBBA3h4eECpVMLJyQm+vr7o1KkTfvzxR9y+fRs9evQw+Dj169cPd+7cwaRJk9CwYUO4u7vDwcEBFStWRJs2bfDdd9/h66+/zrKPt7c3Lly4gHHjxqFSpUqws7ODp6cnmjdvjkWLFuGvv/6Ck5NTbn+ePFWrVg2XL1/Ghg0b0KtXL1SoUAEODg6wt7dHuXLl0K5dO8yfPx937txBt27dCnQuIkvw8PDA+PHj82zXt29fhISE4JNPPkGtWrXg4uICJycnVK1aFSNGjMC///6LUaNGWSBi05gwYQL27t2Ltm3bws3NDQ4ODvD398ewYcNw4cIF/cjLwqZJkyYICQnBsmXL0LlzZ/j4+MDe3h6Ojo7w8/NDly5dsHjxYkRERGRZqIwKThCL6bu6iRMnYs+ePUbvt2bNGjRv3tzgdt0qMuHh4ahQoUKObYKCgrBv3z58/vnnmDdvXo5tmjZtivPnz+Pbb7/Fp59+anScRY25/h6ZDRgwAJs2bULPnj31xYspdwsWLMDEiRPRtGlTfY2Kl33++eeYP38+unbtqi9EK4darYa7uzvi4uIMrqRE2R08eBAdO3aEQqHAs2fP4OHhYe2QiqyhQ4fi119/xaxZs/TTijLbtm0b3nrrLXh7exusvbJ06VKMHDkSdevWzffKU8VJXo95Xm7duoUaNWoAkFb44ZtCsiZRFHH69Gns3r0bJ0+exM2bN5GUlIRSpUqhefPmGD16NN544w1rh0lElAX7Lipsci4SUww8evQoXwUZExMTC3xu3YfI3Aqk6bYVlw+clvh7NG/eHJs2bcKdO3eMPk9xxedq4aNLwmq1Wn1NEDIP3XP6+fPnEEVR/6VDZnz+W1b16tXh6emJmJgY3Llzh0kpsqq//voLgYGBAACFQoGqVavCxcUFt2/fxo4dO7Bjxw5MnTo11yXhiYgsjX0XFTbFdvre+vXrIYqi0RfdP3BBVKtWDQAMLg+akZGBsLCwLG1tnSX+HrrCgIaWi6bsdM+/sLAwg4+b7nlcXJ6r1pa5wCWfy+ale06npqYaXBWLz3/LY19OhYUoiqhatSp+/vlnPHv2DCEhIbh06VKW1bJmz56N33//3cqREhG9wL6LCptim5SypqZNmwIAzp8/j/T09GzbL168iNTUVNjb26NBgwYWjs526Zb3NDStkrJr2LAh7OzskJKSgkuXLmXbnp6ejgsXLgB48bwm88q8TC2fy+bl5+eHsmXLAoDBlZ50t/P5bxnPnj3DkydPAPD5T9bXpEkT3LhxAx999FGW0ZL29vaYM2cOOnfuDABYsWKFtUIkIsqGfRcVNkxKWcEbb7wBDw8PqNVqbN++Pdv2VatWAQA6duyYZ1FPkicqKgobNmwAAJOMdisu3Nzc9I+X7nmZ2bZt26BWq+Hl5YU2bdpYOLri6bvvvgMA1KxZE+XLl7dyNLZNEAT06tULQM7P/zNnzuDmzZuws7ND9+7dLR1esfT9999DFEW4u7sjICDA2uFQMefm5gaVynAljPbt2wOQaqERERUW7LuosGFSygocHBzw2WefAQDGjx+P4OBg/baNGzdi1apVEAQBU6ZMsVaIRdKkSZOwYcMGJCUlZbk9ODgY7du3R2xsLMqUKYMPPvjAShFa3r59+xAYGAhPT0+4uLigUaNGWLRoEbRarexjTJkyBYIgYOXKlZg5cyZ69OiB0qVLw9HREYMHDwYAjBs3LtcVyki+Q4cOYdKkSQgNDc1ye1xcHMaOHatfPVK3ghWZ14QJE/Qrei1YsEC/4tuDBw8wbNgwAMB7772nH1FFBXP9+nWMHDkyy4hAQFoJcc6cOfjmm28ASAsssM+hwk63gmdBV6UjIrIk9l1kcSIVyOjRo0UvLy/9BYAIQPTw8NDf1r1792z7paeni506dRIBiAqFQqxXr55YuXJl/f5z5861wr0p2nr06CECEFUqlVizZk2xadOmWR5Tb29v8dy5c9YO02Lmzp2rv++VK1cW69WrJyoUChGA2L17d1Gj0cg+1uzZs/XHAiDa2dlluf7qq6+KiYmJRsUXFxcnAhDj4uKMvWs2befOnfrHtXz58mJAQIDYoEED0d7eXgQgCoIgTp8+3dphFkmnTp3K0l87ODiIAERnZ+cst4eFhWXZ79dff9X/75QvX15s2LCh/n+gcePGYkJCgpXuUeFn7GN++fJl/fO/dOnSYuPGjcXGjRuLzs7O+tuHDx8uarVaK98zotxptVqxYcOGIgBx9OjR1g6HiEgW9l1kDUxKFdCQIUOyfDjP6dK6desc983IyBB//PFHsX79+qKzs7Po7u4utm3bVvz9998teydsxJ9//imOGDFCbNCggVimTBlRpVKJbm5uYkBAgDhjxgzx6dOn1g7RYs6cOSMKgiAqFApx48aN+tuvXLkient7iwDEBQsWyD5eaGio/kO4o6Oj6OTkJNavX1+cNm2aWKNGDRGAOGrUKKNiZFIqZ2FhYeKUKVPEtm3bin5+fqKTk5Po6OgoVqpUSRw8eLD4999/WzvEIuvo0aN59tcAxNDQ0Gz7nj59Wuzatavo6ekpOjg4iDVq1BBnzJghJicnW/6OFCHGPuaxsbHirFmzxM6dO4uVKlUSS5QoIdrb24sVKlQQ+/TpI/7555/WvUNEMv3yyy8iANHe3l68c+eOwXYpKSliXFyc/vL8+XPxyZMnTLwSkVWw7yJrEETx/+ciEJHNCAoKwr59+zBixAj88ssvWbZt3LgRAwcOhJeXFx4/fpxlNTdDRo0ahZ9//hkdOnTAgQMHsmw7c+YMWrZsCTs7O4SHh8Pb21tWjGq1Gu7u7oiLi4Obm5v8O0dERFSIXbp0CS1btkRKSgrmz5+PCRMmGGw7Y8YMzJw5M9vtfG0kIktj30XWwqQUkY1Rq9UoXbo00tLScO7cOTRp0iTL9vT0dJQqVQpqtRoHDhxAhw4dcj2eKIooX748Hj9+jC1btuCtt97K1qZWrVq4efMmfvnlF4wYMUJ2nExKERGRLQkNDUXLli3x+PFjDBgwAOvXr4cgCAbbp6amIjU1VX9drVbD19eXr41EZFHsu8iaWOicyMZcvnwZaWlpcHR0RKNGjbJtt7Oz069ade7cuTyPFxYWhsePHwMAWrZsmWMb3e1yjkdERGSLIiMj0b59ezx+/BhBQUFYu3Ztrh/qAGnxGzc3tywXIiJLYt9F1sakFJGNuX37NgDAz8/P4HKvlStXztJWzvEcHBxQrly5Ah+PiIjI1sTExKB9+/a4e/cuWrdujW3btsmaHk9EZE3su6gwyPkTKxEVWbGxsQAADw8Pg21023Rt5RyvZMmSBr81kXO8nIb5EhERFXUJCQno0qULrl27hoCAAOzdu5dLqRNRoce+iwoLjpQisjEpKSkAAHt7e4NtHBwcAADJyckWO97cuXPh7u6uv/j6+uZ5biIiosIsNTUVPXr0wLlz51CnTh38+eefcHV1tXZYRES5Yt9FhQmTUkQ2xtHREQCQlpZmsI1uxJKcb0NMdbxJkyYhLi5OfwkPD8/z3ERERIWVRqNBv3798Ndff6FKlSo4dOgQPD09rR0WEVGu2HdRYcPpe0Q2Rs5UOjlT/F4+3vPnzyGKYo5T+OQcz8HBQT+iioiIqKjbunUrdu3aBQBQKBTo27dvju18fHywbds2C0ZGRGQY+y4qbJiUIrIx1apVAyCtmpeRkZFjsfN79+5laSvneKmpqXj06BHKly9foOMRERHZgsx1Em/fvm1wsY+KFStaKiQiojyx76LChtP3iGxMw4YNYWdnh5SUFFy6dCnb9vT0dFy4cAEA0LRp0zyP5+fnh7JlywIATp8+nWMb3e1yjkdERGQLhg4dClEU87zcv3/f2qESEemx76LChkkpIhvj5uaGwMBAAMCqVauybd+2bRvUajW8vLzQpk2bPI8nCAJ69epl8HhnzpzBzZs3YWdnh+7duxcseCIiIiIiIio2mJQiskFTpkyBIAhYuXIlNm3apL89ODgY48ePBwBMnDgxy4p6P/74I/z9/dGvX79sx5swYQLs7e1x8OBBLFiwAKIoAgAePHiAYcOGAQDee+89/YgqIiIiIiIiorwwKUVkg1q2bIlZs2ZBq9ViwIABqFKlCurXr49GjRohKioKQUFB+PTTT7Ps8/z5czx48ACRkZHZjlepUiWsWLECCoUCEydOhK+vLxo1aoRq1aohJCQEjRs3xoIFCyx194iIiIiIiMgGMClFZKOmTJmCvXv3om3btoiOjsadO3fwyiuv4Mcff8Tu3buhVCqNOt7gwYNx8uRJdO3aFcnJyfjvv/9QuXJlzJgxA6dOnYKLi4uZ7gkRERERERHZIkHUzcMhIrIgtVoNd3d3xMXFwc3NzdrhEBERWR1fG4moKGLfRQXBkVJERERERERERGRxTEoREREREREREZHFMSlFREREREREREQWx6QUERERERERERFZHJNSRERERERERERkcUxKERERERERERGRxTEpRUREREREREREFsekFBERERERERERWRyTUkREREREREREZHFMShERERERERERkcUxKUVERERERERERBbHpBQREREREREREVkck1JERERERERERGRxTEoREREREREREZHFMSlFREREREREREQWx6QUERERERERERFZHJNSRERERERERERkcUxKERERERERERGRxTEpRUREREREREREFsekFBERERERERERWRyTUkREREREREREZHFMShERERERERERkcUxKUVERERERERERBbHpBQREREREREREVkck1JERERERERERGRxTEoREREREREREZHFMSlFREREREREREQWx6QUERERERERERFZHJNSRERERERERERkcUxKERERERERERGRxTEpRUREREREREREFsekFBERERERERERWRyTUkREREREREREZHEqawdARERERFQgoggEBwM7dgCRkYCjI9C8OdCjB+DsbO3oiIiIyAAmpYiIiIio6IqMBD76CLhyBVAqAY0GUCiAvXuB2bOBGTOAvn2tHaVx1Gpg+3Zg0ybg0SPAzg5o2hQYPBho0QIQBGtHSEREZBJMShERERFR0RQdDfTuDURFSdc1GumnViv9TEoCJk4EMjKA/v2tE2N+dOokxQ5Io8AA4MgR4OBBoEMH4KefAAcH68VHRERkIqwpRURERERF0w8/SAkpXTLKkOnTgdhYy8RUEKGh0s/ERCkZpUtIAS/u4+HDwLhxlo+NiIjIDJiUIiIiIqKiJz5emuKWV0IKkEZKbd9u/pgKavly6adupFdOtFpg/37g6lXLxERERGRGTEoRERERUdHzzz9Aaqq8tqIoTX8rzGJjgT//lNdWqQTWrzdvPERERBbApBQRERERFT2Jica1T0gwTxymEhIijeiSQ6MBLl40bzxEREQWwKQUERERERU9pUrJb6tQAN7e5ovFFOQmpHTkTFskIiIq5JiUIiIiIqKi59VXgTJl5LXVaoGePc0aToH5+8tvq1QClSubLRQiIiJLYVKKiIiIiIoelQp4911AEHJvp1BIo6o6drRMXPlVoQLQtKm8thoNMGCAeeMhIiKyACaliIiIiKhoeu89oG1bw4kppRJwcABWrADs7S0bW368917ebZRKoFo14I03zB8PERGRmTEpRURERERFk0oFLF0KjBkDuLpKt2VOUDVrBuzYATRoYJXwjNasmfRTEKTk08sUCqBcOWDt2py3ExERFTGCKIqitYMgouJHrVbD3d0dcXFxcHNzs3Y4RERU1KWmAseOAY8fS6OjmjUDKlWydlRG0b82Hj0Kt82bgUOHpHpYAODlBQwaBAwZApQsadU4iYgy4/t6KgiVtQMgIiIiIiowB4fCXzdKrkaNgDZtgOfPgagowM4O8POTRoYRERHZEE7fIyIiIiIyUmhoKFasWIH3338f9evXh0qlgiAImD17tulOUrIkUKOGtNIeE1JEZCIW6b+IZOKrGxERERGRkRYuXIiFCxdaOwwiIqOx/6LChCOliIiIiIiMVKpUKXTt2hVfffUV9u/fj969e1s7JCIiWdh/UWHCkVJEREREREaaOnVqluubN2+2UiRERMZh/0WFCUdKERERERERERGRxTEpRUREREREREREFsekFBERERERERERWRxrShERERERWUFqaipSU1P119VqtRWjISKSh30XmRJHShERERERWcHcuXPh7u6uv/j6+lo7JCKiPLHvIlNiUoqIiIiIyAomTZqEuLg4/SU8PNzaIRER5Yl9F5kSp+8REREREVmBg4MDHBwcrB0GEZFR2HeRKXGkFBERERERERERWRyTUkREREREREREZHFMShERERERERERkcUxKUVERERERERERBYniKIoWjsIIip+1Go13N3dERcXBzc3N2uHQ0REZJTTp0+jR48e+usJCQlITU2Fs7MznJyc9LdfvnxZ9nLpfG0kIkswdf/FvosKgqvvEREREREZKT09HdHR0dluT0pKQlJSkv66RqOxZFhERHli/0WFCZNSRERERERGatOmDTjhgIiKIvZfVJiwphSRDYmPj8f69evxzjvvoHr16nBycoKzszPq1KmDCRMm4PHjx/k6rr+/PwRBMHhp1qyZie8JERERmY0oApcvA+PHA02bAg0aAF26AL/+CqjV1o6OiIiKEY6UIrIhI0eOxPr16wEArq6uqFmzJhITExESEoL//vsPa9aswf79+xEQEJCv47/66qtwcHDIdnudOnUKFDcRERFZSHo6MGkS8NtvgFIJ6KbnqNXAzJnA998Dq1cDjRtbN04iIioWmJQisjE9e/bE6NGj0bp1a6hU0r/43bt3MWDAAJw/fx69e/dGSEhIliKGcm3btg3+/v4mjpiIiIo9UQTOnAF27gSiogBnZ6BVK6BnT6BECWtHZ1umTQN27JB+z1wvRjeVJyEBGDwY2LULqFbN4uEREVHxwtX3iGxITEwMPD09c9wWHh6OqlWrIi0tDTt27ECvXr1kH9ff3x8PHjxAaGioyZJSXKWDiIgAAKGhwIgRwJ07L0buCIKUJHFyAr76CujTx9pRWoTZXxvv3AHat8+7nVIJdO4MvP8+8L//AYcOAcnJgKcn0KsXMGAAUKGC6eMjoiKJ7+upIFhTisiGGEpIAYCvry9q1qwJALh165alQiIiIjLs4UMp4RQaKl3XjdzRfWeanAxMmABs326d+GzNxo1SwikvGg3wxx9Ajx7S6LW4OCAtDYiMBJYvB9q0kUZSERERFRCn7xEVIykpKQCQr6l7ADBr1iw8evQIGRkZ8PPzQ4cOHdCnTx8o5bzBJSIietk330gJj7yWHf/yS6BjR8DV1fhziCJw9iywaZM0UkilAho2BN55B6hePX9xF1UXLuT9WOvoEoMvt9ddHz8eKFlSSlARERHlE5NSRMVEcHCwfoRUy5Yt83WM1atXZ7tet25d7Nq1C1WqVClwjEREVIw8fQrs2ycvSZKaKo3MGTTI+HO8/z4QHJy1qPeNG9K0tF69gLlzgRwW8bBJ6emmPd7cuUDr1tJ0S1snisXjfhIRWRin7xEVAxqNBmPGjAEAtG3bFo2NXFGnZcuWWLNmDUJCQpCcnIwnT57g119/Rbly5XDt2jV06NABcXFxuR4jNTUVarU6y4WIiIqxc+fkj9oBgOPHjTt+fLxU++jaNel65nPpft+1C/j00xejgmxd5crypu/JIYrArVvAlSumOV5hdPcuMGMGUL++9NjVrAmMGiU9d4vLc4aIyMyYlCIqBiZPnoyTJ0/C1dUVy5cvN3r/DRs2YOjQoahevTocHR1RunRpDB48GKdPn0bJkiVx7949/PTTT7keY+7cuXB3d9dffH1983t3iIjIFiQny28rikBionHHX78euHcv98SXKEq1k86dM+7YhVlqqrS63pAhUrHyvn2BX34BYmOBfv2MSwTmRRCkUWi2aNMmqSj8+vWA7ou01FTgwAHpcZw6FdBqrRsjEZEN4PQ9okJi4sSJ2LNnj9H7rVmzBs2bNze4fdmyZZg/fz5UKhU2bdpk0ml2/v7++OijjzB37lzs2LEDX375pcG2kyZNwvjx4/XX1Wo1E1NERMVZ6dLy2yqVgLe3/PYaDbB6tbykgVIJ/Por0KyZ/OMXVv/8I61kGBsLKBQv7v/Fi8B330mjfmrWBG7fNk1yShCkc/z3H3D+vFQMvVw5IDAQcHQs+PGt5eBBYPJk6XdDNbU2bpRqak2YYNHQiMjERFEa8XnjhnS9enWgcWNO17UgJqWIColHjx4hJCTE6P0Sc/nmeMuWLRg1ahQEQcDatWsRFBRUkBBzpEuI3blzJ9d2Dg4OcCguNTuIiChvLVoAHh5SAiUvGo1U/0muEyeAZ8/ktdVogNOn5R/bXFJSgJs3pd9jYgBjl1W/dk0q3q6rG5U5ISeK0u1TpkiX//0PiIjInrTT1d0SBHnT07RaaSTRrFnSPgqFtL+rKzB0KDB2rFRYvigRRWDePHmPwfLlwPDhQC6rHxNRIXboEPDtt9JU5MwqV5YWczDDZyfKjtP3iAqJ9evXQxRFoy+BgYE5Hm/fvn0YNGgQtFotFi9ejIEDB5olbjs7OwBARkaGWY5PREQ2yt5eSlzk9W20UglUrAi0aiXvuGlpwMSJxsUSHy+NlFq4EIiKMm7fgoqKAmbPBgICpKl2APDGG8AHHwCXLsk/zldfSYmnvEaHLVwIbN4MjB6dPZnSooX0Ac0Y9+9LP0XxxSii+Hhg8WLpHKacLmgJFy4AoaHyknIaDbBtm/ljIiLT27hRGll6+3b2baGhUv+1Zo3l4yqGmJQiskEnTpxAnz59kJ6ejrlz52LkyJFmO9f169cBABUqVDDbOYiIyEZ99BHw+uuGE1NKJeDiAqxcKY3CkePgQfmjpDKLigJ++glo00YaaWUJd+5I38SvXQskJLy4XasFjhyRklS//Zb3cW7flpIpcqYrJiQAJ08C48YBf/8N/PknsHu3VFdr3Tqgd2+gWzf5j7eh5I0oSn+LdevkHaewuH5d/n0XhBdTfoio6AgJkerCATn3YbrbvvrKduvmFSJMShHZmIsXL6Jbt25ITk7GpEmT8MUXX5jtXElJSVi2bBkAGByxRUREZJCdHbBihTTNy9096zaFQqpNtGcPULWq/GNu3y4/qfAyrVYqZv3++1JywpxSUoDBg4Hnz3MeTaTRSPFMnCjVhMrN33/Lr3+iVAJnz0q/29kBNWoA9eoBZcq8aDNvHtCkifT7y8c1dvU+ubW9CgtjYy1K942IJOvWyX+d+OILrrZpZkxKEdmQkJAQdOrUCWq1GiNHjsScOXNk7/vaa6/B398f27dvz3L7d999h6VLl+L58+dZbr937x6CgoJw584dODs747PPPjPFXSAiouLGzg745BNppM7KlcCcOcAPP0h1npYtk6buGePx44IlCnTT0PJYVbbAfv9dijWv6W2CINUuyk1qqvyklC7xlpkoSsXKjx+XRlwJglT8/euvgZcXSGnWTEoSyjmfKEq1q65dkxdbYVC1qnHPHxMuIENEFiCKwM6d8qcW37wJDBvGxJQZFbHKg0SUm7Fjx+LZs2cQBAFXrlzBa6+9lmO7YcOGYdiwYVlui4iIwIMHD5CQefoAgPDwcCxcuBCjR49G5cqV4eXlhefPn+PWrVsQRRElSpQw+ap+RERUDDk4AO3aFfw4Tk4FP4ZGAxw+LE3pM2bVP2Ns2GBcLNHRgJdXzm18fOQnUhQKqT0gfcjavFlKBt6796KNqyvQrx8wciTQvz8QGQkkJko1qDw9pZpXxnxAi46W39baXnsNKFtWus95EcUXdcCIqGhISQGSk43b59gxqU7emDFmCam4Y1KKyIak/v83n6Io4syZMwbbGTPVrl+/ftBqtTh37hzCw8MRFhYGe3t71K1bFx07dsSYMWPg5+dX4NiJiIhMolUr4N9/Cz6tSquVviE3V1IqNNS4WB4+NJyUatcOKFEia10qQzQaqW6UVgt8/rk03fHlUU/x8dK0u4MHga1bXySxdFxc5McOAM7OwJkzUi2rpCRpqmCPHkBhrEepVErTSSdPzr2dIAB9+gDlylkmLiIyDQcH+SuMZvb991JhdK4mbnJMShHZkGPHjuV73/u61XNe0qxZMzRr1izfxyUiIrKo/v2BJUtMcyxzTtdISTGuvSqXt+2OjlJ9qqVLc49ZqQRefRWoXVsaHaWbsp/TPhqNNPXuww+lYuuZE1cdOkgFvuUk/lxcpLpYYWEv7oNWC3z3nVQz7JtvAA+PvI9jSf37A48eSSMjlMqs03wUCin+1q2BWbOsFyMR5Y9CIY2IPHPG+NVBAwKAq1fNE1cxxppSRERERGQ7ypWTalSZQuXKpjnOy2JjgbQ0+e3t7fOO5ZNPpJUDc1vJsHx5YNEiICMj7zpVgPSB7fLl7KtP9esnr6aUIEjTZCIipOsZGdJFq5USYX/9JY02iovL+1iW9umnwPr12R/TunWlERMrVnDEBFFRNXiw8QkpQBpFunSp6eMp5piUIiIiIiLbMmYMMH689I145hWW5K62pFAAzZsDmaenX7smTXdr0wZo2RIYMADYu9e45JLO2bPGjcLy9pZGQ+XGzk5KNE2cmH3KobMzMGgQsHs3ULq0VET+6VN55xYEYM2arLeVKQNMm5b7fgrFi2SOoRFVGg3w4AGwYIG8WCytZUtpRNnVq9LUw8uXpcewV6/cR64RUeHWti3QuXP+9p0/37SxEKfvEREREZGNEQQpMfXWW8CWLdI0jeRkaaRQSIiUCMntW3JRBEaPln5PS5OSUbt2ZZ3KFRkpJZfKl5dWqjNmwY+kJOPuj6FaUi9TqaTpdu+/D1y8KBUYd3GRpuw5O79o9/Ch/HOLIrBnj1TgfOpU6TEApJEG9vbSFLakJOl2UZQee40GqFZNeqzzotFI0wgnTgTc3OTHZUklSkgXIrINCgWwcCHw99/SyFVjvfYacOqU6eMqppiUIiIiIiLb5O0tFa0eO/bFbdHRwDvvvEiYZB6xpEuszJsHtGgh3TZxopSUAbImsnSjfyIjpelsv/8uvyi6p6f8+6BQSIkvYyiVQJMmhrfnZ9rZr79KCbqvv35xW79+QPfu0oixCxek7eXKSYXUf/gBuH1bXt2p1FTpA16XLsbHRUSUH3Z20ijN994zft+HD6UvOkyx2itx+h4RERERFSNeXlLh7i+/BHx9X9xuZyclWHbtAvr2lW4LDpama+U21U6jkb5pl1OjSadlS/mjgrRaKS5TatzY+H1EEdi4UVrZMDNnZ+Dtt4FvvwV++gn44gtplFRcnHErIKrVxsdERFQQ7dpJ05HzIz/9KOWISSkiIiIiKl6cnYF33wWOHQPOn5fqBQUHSwWsX3nlRbv//e/FdLXcaDTA5s3SN+dyODhINZ7yqnGlVAI+PlJ9q7t3gfv3pVFFBeXvL00/kXPfMhMEYNUqeW1LlpRfwwsA3N2Ni4WIyBQOHcrffsnJQEKCaWMpppiUIiIiIqLiSRCkwt8VKuQ8DePvv+Wv0JSUBNy6Jf/cY8ZIy4sbStwoldLorXr1pHaBgcAbb0i/z5kjTRssiEmTpBpUclbR0xFFaeTYrl15t+3cWf5IKUdHoFUr+XEQEZmKmxtQp07+9g0IMG0sxRSTUkREREREOTF2Zb30dPltHRykOk3Dhr1IiGVOUNWoISXEDh/OOjoqPh5YvVpK+vz3n3HxZVa7NrB+fdYC6HKNGwfs3597mw4dpKmSckaD9e3LQuJEZD1yEu0v0YhAUlKG/C8uyCAmpYiIiIiIcuLnZ9wUtHLljDu+gwMwZYpUJHzhQinZAwC//CJN18sw8IFHo5FqMA0eXLBaTK++KhVoz49p06T4DLG3B5YskZJOuY0Gq1IFmDAhfzEQEZmCSgUEBRm1i1IAnBUZCO082ExBFR9MShERERFR0RYZCSxdCkyeDEyfLiVajB3llJP+/eVNQVMqpeLlxialdFxcpGLmQ4dK148elRI+uRVY12qBmBhg+/b8nVPH3x9o1sy45BsAPHsmjeLKTdOmwKZNUuIJeDElUaGQLp06Adu2Aa6u+QqdiMhkFi/O126rznOUZ0GprB0AEREREVG+JCUBU6dKdY6AF4mVdesADw9pNE/Pnvk/flCQtGT4s2e5T9HQaIARI/J/npft2iV/Ssj//idNASyI99+X6mcZQ6UCzp2TEku5adwYOHAAuHQJOHFCKg5cujTQrRtQtmz+YyYiMrW33wa2bDFqlxMplfDvhSi8EuBtpqBsH5NSRERERMWBRiONwNm5UxpZ5OwMtG4N9OkjrZRW1KSmSivYXbnyYjRT5lFNsbHSdLjkZGnEU344Okp1n/r1k2o5vZwoUiikc375JfD66/k7R05SUuSNXBJFIDxc+mlMwfKXtW0LfPIJ8OOPxu0ndzSaIEjJKS6hTkSF2bx5wJYt0IjS9Ly8JGrtcCmtAnr3OolbEX3MH5+N4vQ9IiIiIlt3+7a0ctv7778YtXLqlLSKW5MmwIYN1o7QeKtXA5cv5z297ssvgSdP8n+eGjWk6YD9+0tJqsyaNAHWri34SKWCUCgKlpDS+fhjaZSAXFot4OOT/fa0NOnxGjJESnZ16gTMmgXcu1fwGImIzK19e1kJqXRRgdUJAUgS7XHnoQbPn6eYPzYbJYhibpPViYjMQ61Ww93dHXFxcXBzc7N2OEREtissTJoqlZiY+5Sw2bOBgQMtF1dBaDRSDaeoqLzbKhRSwmXs2IKfNyEBuHVLSrxUqCBdTEj/2tiwIdxiY/PeQaEA6tWTRr+ZQlyclGiTMwJKEICTJ4Hy5V/cdueOlIx69OjFKDJAqiWl0UjJu8mTpetEZDNs7X39obLtEeh4x2C+P11U4KnGBY0ff4xIjXR/S5ZUITa2lwWjtB0cKUVERERky+bNyzshBUijWQqykpsl/fefvIQUICVG9u0zzXlLlAAaNZIKg5syIaXRSEXDP/pIup6aKm8/rfZFcXRTcHeXpirmNXVQqQQ6d86akHr4EHjrrRd/l8wj2HTPvTVrgLlzTRcvEZEZOHbpgO/Ur0MrAhnii8xUhij1jfczPPB65EeI1LhBCamve/48l9VIKVdMShERERHZqqgoabqenKLZaWnAb7+ZPyZTiI83rn1hTrY9eQJ07SpNrTx7VrotISHv/ZRKoHZtKTlkSpMnS6vmGUpMKRRAzZpSsjOzRYukxzm355ooAqtWAaGhpouXiMjEXlv1GX5Qt0LNhxPwvfp1XEitgKtpZfF7Uk10ixqKmg8n4G5GKQgQocmUUrlyRcYIV8qGSSkiIiIiW3X+fN41lzI7dcp8sZiSsYXZPTzMEkaBJSQAAwZINb8AeclDXbKoZk2pCLu9vWljcnCQ6mR9+ilQpkzWbR4ewMiRwNatgKvri9vVamkKoZz4lcqiWcOMiIoNQRCgLV0aERp3TI7thCaPx6L+o/Ho9XQofk+uDe3/p1FeroP0zjtGrmJKALj6HhEREZHtSjGi8KooAklJ5ovFlGrWBHx9gYgIKe7cCALQvbtl4jLWli1SAXA5JV7t7QE7O6BOHWDwYKBDB+m6OdjbS8mnESOA4GBpJUNXV6Bhw5yTYNeuyV+JT6MBzpwxbbxERCb27bf18M47/0ABLQSIEJFTgamst12/LmOUK2XDkVJEREREturlkS65USpzXk2tMFIogHffzbudIEiJm759zR+TsURRGukkh66g+bVrUiIrKMh8CanMVCqgcWMgMFCa0mdoVJbchJSO3JpZRERWMnBgJQCAFgoDCamcJSQY2R8Sk1JERERENqtFC8DTU15bjQZ4803zxmNKgwYBbdrA4PJIutu//17+Y2BJ0dFAeLi8UVJaLXD5stQ2IgJYtkxaLfH774GLF+Udw5zKlZPfVqGQRrkRERVyjo7yk1E6X311wwyR2DYmpYiIiIhslZ0dMHy44cSNjlIJVKkiJbGKCpVKSs4MHy7VQRIE6TalUtru5wesXi2NKiqM0tONa6/RSNPpXn8dWLAAWLcO+PlnoE8foFMn4MoVs4QpS/XqUtH1vJ5ngJRge/tt88dERFRA5861NXqf1au5kIOxBFG09lcrRFQcqdVquLu7Iy4uDm5ubtYOh4jIdmk0wKhRwMGDOY+oUSqlwuHbtgGVKlk8PJOIjwf27ZNGEdnZAa++CjRvLi9JYi2pqUD9+lmmsqm1WriHhSHOzw9uL69+p0u25VRMXKGQEnIbN0rT7azh99+BMWNyb6NUAmXLAkePWmb6IRFZhC2/rxeEbUa1t7MTkJbWx0zR2CaOlCIiIiKyZUolsGQJMHEiULp01m0qFdCtG7BnT9FNSAFSEe6335ZWjBs7VhrxVZgTUoA0uuvNN18km/Ki1Rpe3U6rBTIygNGj5a2AZw5du0rF0YEXKwRmplQC7u5SHS0mpIioiChTxsHaIdg8JqWIiIiIbJ1SCXz4obTq2fr1Ui2ipUuBc+eAH34wriYQmc7QoVLyLLcEmm5bXpMbtFogMhI4csRk4RltwgRg0SJpKl9mDg7AW28Be/dK00SJiIqIiRNrGNU+PV1EcnKGmaKxTZy+R0RWYcvDfImIiGTbv18/7U2dnp51+p5SKY06klt/SqkEuneXko7Wdvs28OiRtGJfnToAX+uJbJatv69XKrdBq5XffuTIKliypJH5ArIxHClFRERERGQtnTsDv/0GBAZmnfamUkkJphEj5E9F1GiAuDjzxGmsatWA1q2l2l42+CGViIqPwYMrGtV++fJ7ZorENjEpRURERERkTfXrSysJ6qbe/e9/wIUL0oinKlXynrqno6vbREREJtOunbdR7TMyRGRkGDG0qphjUoqIiIiIqDAoVUr62aCBtCIiALRpI78wuEYDdOpkhsCIiIqv/Kyb8ddfUaYPxEYxKUVEREREVFh5eAA9euS9Sp9CAZQpA7Rta5m4iIiKidatS+fd6CVHjz41QyS2iUkpIiIiIqLCbPJkoEIFw4kpXVH0JUukWlRERGQyFSo4G73PnTsJZojENjEpRURERERUmHl4SMXQ27SR5pEoldKUPl0CqnJlYMsW4NVXrRomEZGtsrc3bg7fs2dpZorE9vCrFCIiIiKiws7LC1i5EggPB/buBZ49A5ydpURV48b5K3pCRESyNGvmhRMnnslu//hxshmjsS1MShERERER5UatBlJTpZXt7O2tG4uvLzBypHVjICIqZtq39zYqKRUTk27GaGwLk1JERERERC9LTQV27gTWrgVCQqTb7O2Bnj2BoUOBWrWsGBwREVlSixZeRrV3dGSlJLn4SBERERERZaZWA/37A5MmAbduvbg9LU2q7dS1q/Tz/+3btw+BgYHw9PSEi4sLGjVqhEWLFkGr1VoheCIiedh3yVejhpvstgoFUKuW/PbFHZNSREREREQ6ogiMGgVcvfriemYaDaDVAhMmAGfOYN68eQgKCsKRI0fg4eGBqlWrIjg4GGPHjkWvXr344Y6ICiX2XcYpX94JnTuXhUJGBkWrBT78sIr5g7IRTEoREREREelcuQKcOiUln3IjCDj75ZeYPHkyFAoFNm7ciLt37yI4OBiXLl2Ct7c39uzZg++//94iYRMRyXX27Fn2XfkwaVLNbN9TvEylElCjhiu6dfOxTFA2gEkpIiIiIiKdjRsBpTLvdlotZp89C1EU8d5776F///76TfXr19d/oJs3bx7S01nwlogKj9mzZ7PvyodWrUpjzZoAKBRS8ullCgVQoYITDhxoBZWKqRa5+EgREREREQFIS9NC+9+NvEdJAVBrtTicLC35PXz48Gzb+/btCzc3N0RHR+Po0aMmj5WIKD/UajUOHz4MgH1XfgwZ4o+zZ9uhd+8KWRJTZco4YPr0Orh4sT0qVnSxYoRFD5NSRERERFRs3b2bgE8/DYaHxy44OPwGu32D0DZyBHYm1oFGzP5NuM7ltDSkAXC0t0ejRo2ybbezs0NAQAAA4Ny5c+YKn4jIKJcvX0ZaWhocHR3Zd+VTkyae2Ly5GZ4964H//uuIO3c64+HDrpg2rTY8Pe2tHV6Rw6QUERERERVL27aFo1atP7Fw4W08fy5NU9FCgRMplfDm0yHo9uRdJGtVOe57+/+ntfhVqACVKuc2lStXltrevm2G6ImIjKfrj/z8/Nh3FZC7ux1q1XJDlSolOF2vAHJ+FhIRERGR1YiiiPj4eGuHYdPOnHmGfv1OQavNXrVWN3nvz+QKGPC0J34tvTVbm8f/vzKVq4cH1Gp1judwdnYGADx58iTHNqmpqUhNTdVf1/3NDR2PbJurqysEwfDovKKAfVfh9/jxYwDS8419F5lKQfovQRTzqh9PRGR6arUa7u7uiIuLg5ubm7XDISIqVJ4+fYoyZcpYOwwisqAnT56gdOnS1g6jQNh3ERVPBem/OFKKiIiIqJCxt5dqUoSHhzNxbwZ378ajUaPDstoqocGwEhfwree+/79BCQgCFrZsiWn/+x8aN26Mv/76K8d9p02bhoULF6Jjx47YujX7aKuXRxs8fvwYTZo0wX///Yfy5csbf8eoSFKr1fD19dX/3xdl7LsKv4ULF2LatGnsu8gkTNF/MSlFREREVMjohsC7ubnxg50Z3LunBuAsq60GwLX0ynBTKAB3d+Dtt4F33oHPgQPA//6H+Ph4g3+jpKQkAECZMmWM+ju6urry714MFfWpewD7rqLAx8cHANh3kUkVpP9iUoqIiIiIihVji1eIDRsBx0OATN8EV6tWDQAQFhaGjIyMHAsG37t3L0tbIiJrY99FhQ1LxBMRERFRsVK7tvxv8lUqAfXqlcySkAKAhg0bws7ODikpKbh48WK2/dLT03HhwgUAQNOmTQsULxGRqWTuuy5dupRtO/susjQmpYiIiIgKGQcHB0yfPh0ODg7WDsUm1a7thubNvaCQ8U44I0PEBx9UznLb+fMxGDXqBjSaugCAFi2mok+fMzh27Al0awht27YNarUaXl5eaNOmjay4dH9v/t2LF1v6f7el+2Kr3NzcEBgYCABYtWpVtu3su8gYpvif5+p7RGQVXH2PiIis6eDBSHTqdDLXqXxKpYD27b2xf38r/W2zZv2HadOuQ6USkJFxA8B0AIBCMRZabUuMGVMVw4YJ6NSpE6KiovDNN99g4sSJsmLiayMRWcLp06fRqlUrCIKA9evXo3///gCA4OBgdOzYkX0XWRSTUkRkFXzxIiIia1u1KhQjRvwDhUJARsaLt8QKBaDVAi1bemHfvlZwc7MDACxffg8ffPDyVL0dADb//+/eABwhCOEQRS2CgoKwe/duKJVKWfHwtZGILOXrr7/G1KlTAQCVK1dGiRIlcO3aNWi17LvIsjh9j4iIiIiKpeHDK+HixfYYNKgiHBxevC2uV68kVq16FX/91UafkEpP12Lq1Gs5HOVNAJ8DqAsgHkAkAD/Mm/edUR/qiIgsacqUKdi7dy/atm2L6Oho3LlzB6+88gp+/PFH9l1kURwpRURWwW9UiIioMElL0yI2Ng1OTkp9IiqzHTsi0Lv3WVnHEgTg558b4cMPqxgVA18biagoYt9FBZF9/UciIiIiomLGSarIFAAAKxtJREFU3l4Bb29Hg9uvXo2DnZ2A9PS8v89VqQRcvRpnyvCIiIhsEqfvERERERVy8fHxWL9+Pd555x1Ur14dTk5OcHZ2Rp06dTBhwgQ8fvzY2iHaPGPnFhgzGWHfvn0IDAxExYoVAQCtWrXCokWLoNVqjTtpMRcbm4bt2yOwenUodu9+iKSkDGuHlCNRFHHq1ClMmDABzZo1Q8mSJWFvb49y5cqhd+/eOHr0qLVDNBn2XbZv37596N69OwCgbNmyaNSoEfsvG2WuvovT94jIKjjMl4hIvkGDBmH9+vUAAFdXV1SpUgWJiYm4d+8eNBoNvLy8sH//fgQEBFg5Utu1dWs43n77b1ltBQH46aeGGD26ap5t582bh0mTJgEA/P39cf/+fQiCAFEU0b17d+zcuRMKBb9Hzs3Tp6n44our2LAhDKmpLz4Iu7qqMGJEZXz1VR04OxeeCSJHjhxBYGAgAEChUKBq1apwcXHB7du3kZCQAACYOnUqZs2aZc0wTYJ9l23L3H8BQJ06dXDjxg1otVr2XzbIXH0XnyFERERERUDPnj1x+PBhxMTE4PLly7h16xZCQkLQpEkTREdHo3fv3khOTrZ2mGZ361Y8Zsy4jg8+uIhx465g//7H0GrN/x1rjx7l4OlpL6utQiEllRISch+pc/bsWUyePBkKhQIbN25EcHAwAODkyZPw9vbGnj178P333xc4dlv2+HEymjQ5jF9/fZAlIQUA8fEZ+OGHW3jjjeNITCw8o6ZEUUTVqlXx888/49mzZwgJCcGlS5cQHR2t/4A/e/Zs/P7771aO1DTYd9mmzP3XypUrAQBnzpzBpUuX2H/ZKHP1XRwpRURWwZFSRETyxcTEwNPTM8dt4eHhqFq1KtLS0rBjxw706tXLwtFZxpMnKRg8+DwOHIiCUilA9+V7eroIPz9nrFr1KgIDvc0aw8KFt/HJJ1fybCcI0nQ/hQLw83PGwIF+GDGiCvz8nLO0CwoKwr59+zBixAj88ssvWV4bf//9dwwcOBBeXl54/Pgx7OyyF18noG3bYzh58hkyMgx/pFEqBQwfXgm//NLYgpEZplar4ezsDJUq59FbXbp0wf79+9G9e3fs3r3bwtGZFvsu25W5/1qwYEGW9/UbN25k/2WDzNV3caQUERERUSFn6EMdAPj6+qJmzZoAgFu3blkqJIuKjk5FixZ/4ciRJwAAjUZEerqoLzoeEZGETp1OYv9+89anGTu2Kj7/vEae7XRf+Wq1wP37SZgz5yb8/f/A559f1Y/qUqvVOHz4MABg+PDh2Y7Rt29fuLm5ITo62qZqDJnStWtxOHr0aa4JKUB6vqxdex8xMWkWiix3bm5uBj/UAUD79u0B2Mb/c3Hvu2wV+6/iyVx9F5NSREREREVcSkoKAMDJycnKkZjHpEn/4v79JIPJB60W0GpFvPXW3wgOjjVbHIIgYNy46ihd2gGCIH8/UZQu8+eHYMKEqwCAy5cvIy0tDY6OjmjUqFG2fezs7PR1ds6dO2eS+G3Nhg1hUKnk/SHS07X47bcIM0dkGrb+/5xZcbqvtoT9F+Ukv//PTEoRERERFWHBwcH6byVbtmxp5WhMLy4uHf/73wNoNLmPhhFFICEhAw0aHEajRoewcWOYUSvgyfXFF1cRG5tm9Gp8Ot9/fwv//afG7du3AQB+fn4Gv3muXLkyAOjbUlaPH8uvQ6RUCoiMTDFjNKYhiiK2bdsGwDb/nzOz9b7LlrH/opcVpO9iUoqIiIioiNJoNBgzZgwAoG3btmjcuHDUzDGlQ4eikJJi3NLiwcHPMXDgOYwbdyXPxJQoikhMzEBSUkaebaOjU7FxY3ie08Vyo1IJWLr0LmJjpRFdHh4eBtvqtunaUlbGrKin1YpwdlaaMRrTWLFiBS5fvgx7e3t88skn1g7HbIpD32XL2H/RywrSdzEpRURERFRETZ48GSdPnoSrqyuWL19u7XDMIi4u3eh9tP+fw1q48A5WrQrNsU10dCq++eYmKlb8AyVK7ISLy074++/DN9/cNFh76MiRJ0hLMy5B9rKMDBEHDkTqpznY2xte0c/BwQEAuDKZAR06eMtOEGq1QPv25i2EX1CXLl3Cxx9/DEBawapKlSpWjsh8ikPfZcvYf1FmBe275H+9QERERERGmzhxIvbs2WP0fmvWrEHz5s0Nbl+2bBnmz58PlUqFTZs22ewHWE9Pwx968iIIwNy5NzF8eCUImYpAXb8eh3btjuPJk9Qs0/DCwpIwefK/+PHH2zhypDVq1866Omx8fEa+Y8ksKSkDjo6OAIC0NMPFt1NTUwGw3o4hXbv6oGxZR0RFpeQ6nVKpBAICPFGvXkmLxWas0NBQdO3aFSkpKRgwYAA+++wza4fEvosMYv9FOqbou5iUIiIiIjKjR48eISQkxOj9EhMTDW7bsmULRo0aBUEQsHbtWgQFBRUkxEKtQwdvuLgokZioMXpfUQTu3UvE6dPReO21UgCkEVJvvHEcT5+m5riPVgtERaWgXbvjuH69Y5akWKlS+U+Q6QgCUKGCs6ypLXKmyBRnKpUCK1e+im7dTkEQkGNiSqkE7O0V+Pnn7MWYC4vIyEi0b98ejx8/RlBQENauXZsliWot7LvIEPZfBJiu7+L0PSIiIiIzWr9+PURRNPoSGBiY4/H27duHQYMGQavVYvHixRg4cKCF75FlPX+eDnv7gr1lffDgxYfkRYvuGExI6YgiEBmZgpUr72W5vX17b7i5Few7XVEEhg+vhGrVqgEAwsLCkJGR8wise/ek8+vaUnZBQT7Ytasl3NzsAACK/3+qKJXSB6MyZRxx9GgbNGxYOD8Yx8TEoH379rh79y5at26Nbdu2wc7OztphAWDfRYax/yJT9l1MShHZGEEQcr3069cvX8fVarX46aef0LBhQ7i4uMDT0xOBgYHYv3+/ie8BEREZcuLECfTp0wfp6emYO3cuRo4cae2QzCo5WYPAwONQq42vK5WZLqkliiJ++OGW7P1++CHrqlHOzip88EEVfeLDWEqlNB2xf38/NGzYEHZ2dkhJScGlS5eytU1PT8eFCxcAAE2bNs3fCYuJ7t3L4fHjblizJgDdu5dDmzal0bt3eezY0QJhYUHw83PG3r2PsHPnQwQHP7d2uHoJCQno0qULrl27hoCAAOzdu9dmpzoVt77L1rH/Kt5M3Xdx+h6RjTK0FGfNmjWNPpZGo0GPHj3wxx9/QKFQoG7duoiPj8eRI0dw5MgRLFiwoFDUPiAismUXL15Et27dkJycjEmTJuGLL76wdkhmt2lTGG7ejC/QMQQBaNbMC4BUNF2tll8XKjIyBcnJGjg5vVi1bebMOjh58ikuXIiFRiN/FT6lUoCjowK///4aSpRQAXDTf7mzatUqNGnSJEv7bdu2Qa1Ww8vLC23atJF9nuLKyUmJoUP9MXSov/6269fj0K/f39i586G++D0ANGhQElOn1kLv3hUsH+j/S01NRY8ePXDu3DnUqVMHf/75J1xdXa0WjzkVx77L1rm5Ze2/FixYkGU7+y/bZY6+SxDzWvuWiIoU3TxeU/5rz5s3D5MmTYK3tzcOHDiA+vXrAwA2btyIQYMGQRRFnDt3DgEBAbKPqVar4e7ujri4OLi5ueW9AxFRMRYSEoLXXnsNz549w8iRI7FkyRJrh2QRjRsfwuXLz3MtYp0bpVJAUJAPdu+Wvqg5fz4aTZv+ZdQxkpLezJKUkm7LwIQJV7F6dShSU7VQKgVotSK0WsDDww4ajZgl+aVQAD16lMesWXVQp467/vbTp0+jVatWEAQB69evR1BQENzd3XHq1Cn07t0bUVFR+OabbzBx4kT9PleuPMfSpXdx8GAkUlK08PV1wvDhlTBwYMX/T3YRAJw+/QwdOpxAaqo2W/JQoZBqh82ZUxeTJtWyeGwajQZ9+vTBrl27UKVKFZw8eRI+Pj4Wj8MSimvfVRxk7r+WL1+O9957D3FxcQgNDUXHjh1z7L+oaDNX38WkFJGNMXVSKi0tDWXLlkVsbCw2btyI/v37Z9k+YsQIrFixAt27d8fu3btlH5dJKSIi+Tp27IiDBw9CEAQ0b97cYCHRYcOGYdiwYRaOzjxEUYSd3XZojK9vDkCaKufgoMS5c+1Qt66UCDp3LhrNmslPSikUgEbT1+D258/TsHPnQ0RGpsDZWYX27b1Ru7YbRFHE+fMxuH8/Cfb2CjRp4ony5XOe2vD1119j6tSpAAB/f3/cv38fgiBAFEUEBQVh9+7dUCqV0GhEjB59CcuW3YNKJSAjQ3qd1z0VPDzssW/fa2ja1Ev2/bNVcXHp8Pf/A2p1epYRUjnZv78VOnUqa5nA/t+mTZswYMAAAFK9nTJlyuTYzsfHB9u2bbNkaCZXHPuu4iRz/wUAderUwY0bN6DVarP0X2QbzNV38esUIsrV0aNHERsbCzc3N/Tp0yfb9uHDh2PFihU4cOAA4uPjbXboORGRNemW1hZFEWfOnDHYzlCB4aIqPwkp3SiYkiXt8fvvr+kTUgBQrpxxNS/KlHHIdXvJkvZ4991K2W4XBAFNm3rJShBNmTIF9evXxw8//IB//vkHgPTB7r333sPo0aP1H+jGj7+CX36RCgfrElLAixXn4uLSEBh4AufPt0OtWsX7y5516+4jLi49zxF2SqWAb78NsXhSSvf/DAC3b9/G7du3c2xXsWJFS4VkNsW17youdP3XggULcOLECdy7dw+vvPIK3n333Sz9F9kGc/VdHClFZGN030CNGTMGN2/ehEKhQOXKldG1a1d07tzZ6GU6Z86ciRkzZiAwMBCHDh3Ktj0jIwOurq5ISUnBiRMn0KpVK1nH5UgpIiLKTVRUCsqW3Su7vSAAFSo4okIFFwwb5o/+/f3g4pL9+9cWLY7g7NkYWcecPbsOpkypLTuGgjL02nj3bgKqVdsvK8nSu3d5bNnS3MyRFm4NGhzE1atxsqd9PnzY1eiEJRG9wPf1VBAcKUVkoxYtWpTl+tKlS/H6669j+/btKF26tOzj6DLglStXznG7SqWCr6+vPltuKCmVmpqaJbuuVqtlx0BERMXP5s3hEATITiy0alUKx4+/kWe7L76ohR49TufZztlZiZEjq8o7uZktW3YXCoWQZ2F1jUbEb79J0wnLlnW0UHSFT3h4slF1yB4+TGZSiojISvK5oC0RFVadOnXC1q1bcffuXaSkpCAiIgKLFi2Cm5sbTpw4gW7duiEjQ/7KQ7GxsQAADw8Pg21023RtczJ37ly4u7vrL76+vrJjICKi4iciIgkqlfzRvZUquchq1717OUyZYri4tSAADg4Cfv/9NXh42Ms+vzmdPPlM9kp/Go2If/6RNxLMVjk6GvcRx9GRU4yIiKyFSSkiG7N//3707dsXlStXhoODA8qXL4/Ro0fj8OHDsLOzw7lz57Bp0ybZx0tJSQEA2NsbfmPu4CDV3EhOTjbYZtKkSYiLi9NfwsPDZcdARETFj6OjUvZoF6VSQFxcOn744RYWLbqdZ1Jm9uy6+PXXAFSvXiLbtk6dyuLMmXZ4442cC7haQ1paHtW6ZbR/8CARx449walTzxAXl26q0AqlDh28ZSc0S5WyR82arIdJRGQtnL5HVEhMnDgRe/bsMXq/NWvWoHnzvGtHBAQEoE+fPti0aRN27NiBQYMGyTq+o6M0/D8tLc1gG920PCcnw0PfHRwc9MkrIiKivLzxRhnMnn1DVluNRsSuXY+wd+8jaLXSlL8GDUriu+/qo23bnJNLgwf7Y9Cgijh3LgZ37ybAzk6BgABP2SOuLKlatRL499+4LAXOc1O58otk2+HDUZg79yb++uuJ/jZHRwXeeaciJk+uVSjvb0GNHFkVa9c+yLOdUilg5MiqsLPj9/RERNbCpBRRIfHo0SOEhIQYvV9iYqLsts2bN8emTZtw584d2fvImZonZ4ofERGRIcHBz7FkyR1s3x6BhIQMuLvb4e23feHr64SICPn1gTKv1nf16nN06HAC27Y1R69e5XNsLwgCmjXzQrNmea+SZ03vvVcZW7dG5NlOoQDq1nVH/frSioM//3wHo0ZdhlKZddRQSooWa9fex/btEfjrr9Zo2NC2Xr8DAjwxfHglrF4davC5o1IJqFjRGZ98Us2ywRERURb8WoCokFi/fj1EUTT6YswSunZ2dgBgVE2patWkN2v37t3LcXtGRgbCwsKytCUiIpJDFEXMnHkdDRocwpo19xEbm470dBHPnqXhl1/uITxcmhZu5MKxAACtFtBqRfTv/zeiolJMHLlltWtXBq+84p7nlDStFpg8uRYEQcCOHREYNeoyAORYjyojQ0R8fAY6djyJhAT57wuKimXLGuHDD6sAQJaknO4xfOUVd5w48UahqRtGRFRcMSlFVIxcv34dAFChQgXZ+zRt2hQAcP78eaSnZ69BcfHiRaSmpsLe3h4NGjQwSZxERFQ8LFp0BzNm/AcA2aam6a6LIuDgoIAgvEhOyU1SiSKQnq7F6tWhJovZGhQKAX/88RoqVHDKNuoJAJT/X6f7yy9rITlZg4YND6F377N5HlejEfHsWSo2bgwzdchWp1Ip8PPPjXDrVid8/HE1NGniiQYNSqJ37wo4fPh1XLwYyBX3iIgKASaliIqJqKgobNiwAQCMGl31xhtvwMPDA2q1Gtu3b8+2fdWqVQCAjh07wtWVhUKJiEie5GQNvvzymqy27u52WLy4Idq0KY1XXnFH06aess+j1QLr1uVdX6iw8/V1xj//BGLChBooWdIuy7YWLUph+/bmuHz5Od599wKuXn1u1LGXL895NLQtqFbNFd99Vx/nzrXD5cvtsXlzM7Rr5w0hP8PviIjI5JiUIrIhkyZNwoYNG5CUlJTl9uDgYLRv3x6xsbEoU6YMPvjgg2z7vvbaa/D398+WeHJwcMBnn30GABg/fjyCg4P12zZu3IhVq1ZBEARMmTLFDPeIiIhs1dat4VCr5U0bi4pKRfnyTvjrrza4erUDFi5saNS5njxJzU+IhY6XlwPmzn0FkZHd8M8/gTh16g2EhnbBiRNv4NChKOzb9xiAlIiTSxSB0FD59SmJiIhMiYXOiWzIjRs3MG/ePKhUKlStWhXu7u54+vSpvh6Ut7c39uzZg5IlS2bbNyIiAg8ePEBCQkK2bRMnTsTJkyfx559/olGjRqhbty4SEhL0x507d65+mh8REZEc//wTCzs7AenpeVcxV6kEXLr0HD16SAXLS5Qw7i2sq6ttveV1cFCiceMXxckjI1OwcmWoUcmozOzt+T01ERFZh229QhMVcx999BG8vb1x/vx5PHr0CHfu3IGzszMCAgIQFBSEUaNGoVSpUkYfV6VS4ffff8fixYuxZs0a3L59G3Z2dmjbti3Gjx+PoKAgM9wbIiKyZVqtzCX1INWQylysu2ZNV/j6OukLoedGpRLQs2fOq+/Zil9/vQ9R7hKFL1GpBLRpU9rEEREREcnDpBSRDenYsSM6duyYr33v37+f63alUomPP/4YH3/8cb6OT0RElFmNGq45rgqXk/R0ETVqvKhbqFAIGDOmGr744mqeo4MyMkR8+GHlgoRa6N26FQ+FQjAq0aeTkSFi1KiqZoiKiIgobxyrS0REREQWN3CgH1QqeW9F3dxU6N0762in0aOrIiDAM8fV6DKbPr02atZ0y3ecRYFCkb+i3QoF0LdvBbRs6WXiiIiIiORhUoqIiIiILM7LywGjRlWBnEXQPvusBpydsw7wd3JS4uDB19G9uw8AaRqaQgEoldJ0P0dHBebPr4fp02ubI/xCpXFjD9mjzgApGQVICal165pwJToiIrIaQczvBHQiogJQq9Vwd3dHXFwc3Nxs+xtsIiLKWUaGFv37n8P27RFQKLKuGqdUAhoNMHx4JSxf3jjX0UC3b8dj9er7uHcvASqVAs2be2HQoIpwd7ezwL0wnfy+NsbHp6Ns2b1IStLk2ValEjBwoB9GjZJGmhERFRTf11NBMClFRFbBFy8iIgKkguebNoXhp5/u4Pz5GP3trVqVwscfV8Obb5YvNiN5CvLauGBBCCZOvJpnu61bm6FvX9/8hkhElA3f11NBsNA5EREREVmNQiFg4MCKGDiwIqKiUhAXlw5PT3uUKuVg7dCKlM8+q47nz9MwZ85NqFQCMjJefO+sVAoQRRE//9yICSkiIipUWFOKiIiIiAoFb29HVK/uyoRUPgiCgK+/fgWXLgViyBB/eHjYwc5OQJkyDhg7tipu3uyEDz6oYu0wiYiIsuD0PSKyCg7zJSIiyoqvjURUFLHvooLgSCkiIiIiIiIiIrI4JqWIiIiIiIiIiMjimJQiIiIiIiIiIiKLY1KKiIiIiIiIiIgsjkkpIiIiIiIiIiKyOCaliIiIiIiIiIjI4piUIiIiIiIiIiIii2NSioiIiIiIiIiILI5JKSIiIiIiIiIisjgmpYiIiIiIiIiIyOJU1g6AiIiIiIiKt7Q0LQ4ejERERDIcHZV4/fVSqFy5hLXDIiIiM2NSioiIiIiokNJqRRw8GIU//4xEYmIGypZ1xIABfqhVy83aoZmERiNi/vyb+P77W3j2LE1/uyAAHTp449tv66NuXXcrRkhERObEpBQRERERkZUlJ2tw86YaAKBWp8PNDfjrrycYNuwCHjxIgkol6NvOnn0D7dqVwfr1TVG2rKO1Qi4wjUZE//5/Y/v2CIhi1m2iCBw+/ATNmh3BX3+1QZMmntYJkoiIzIo1pYiIiIiIrCQ8PAnjxl2Bt/ceNG16BABQufI+dOx4HB06nEB4eBIAICND1F8A4Pjxp2jW7AiePEmxWuwFtXjxnRwTUjoajYiUFA26dTuF1FSNZYMjIiKLYFKKiIiIiMhIx44dw9y5c9GrVy+UL18egiBAEARERETIPsbly7GoX/8gFi26g/j4DP3t6elaHDz4BBqNCK02530zMkRERCTj88+vFvSuWIVWK/5fe/caE/Wd73H8858ZHEG5KK4oCqXFGlfsemdB3O4eKpo9nrV4SaO7tZvgo9qyD3ox0Wyz2mZLNtm2aXpMTR9IG+i63apY2m3dVprYRqvbNdZTu5sxrgcFgXrKbWCAgbmcByjVVmQGZv4zzLxfCQ9m5vf//b+Y+JuZD7+LXnzxwrCB1A1er3TtmluHDl01pzAgxoVi7AJCieV7AAAAQJBKS0vV2dk56uvb2/tVUvKJnE6PvN4RkplheL1+/elPDfrjHxcqPd0+6loi4fTpNl250hNQW4tFqqqq1y9/mR3mqoDYN9axCwg1QikAAAAgSHl5eZo7d67y8/OVn5+vZcuWBXX966/Xq62tf8SZQiPp7/fpb3/7etwFNi0tgS879Pmkq1fH7zJFIJqMdewCQo1QCgAAAAjSiRMnxnT9vn3/DlElUmfnQMj6MsuXX3YE1T45ma8tQCiMdewCQo09pQAAAACTXbrkGvMsqRvS0yeEpiOTvPtuk373u38G3N5ikdaunRnGigAAkUIoBQAAAJjMajVC0k9iolU///mMkPRlhr4+r37967/LCOLXt1gMbdt2d/iKAgBEDKEUAAAAYLJly6bIMsZP4larobKyHCUnJ4SmKBMcPNio9vaBoGaJvfDCQmVkTAxfUQCAiGFxNgAAAHAH7e39qqm5qubmPiUlWbV6dYby8lLH1Gd5+b06caJF0s37QfUGfL3Vamj+/BRVVNw3pjrM9t57zbJYBjcvD8Qjj9yl3/zm3vAWBSAobrdbbrd76LHT6YxgNRjvCKUAAACA2+jp8eipp/5H+/f/r/r7fbJaDfl8fvl8UlFRuvbuXaKFC9NG1feGDbOUmXlUTU1Vw7axWAbDp4EBv6xWQxaL5PX6ZbEY2rIlW6+8snhczZKSJKdzIOBASpJ+/OOp4SsGwKhUVFRoz549kS4DMYJQCgAAAHFjx44dqq2tHbGd3y9dudKjvj7v9We2y+OZO/T6qVNtWrHiYx0//jMtWxZ8cJKQYNHnn/+31q3bpDNn2mWxGPL5eiQ9KsMYvH92dpI++uin6u726OjRFrlcHs2YMVGbNs0et8vZpk+3y2Yz5PEEtn5v+vTx+XsCoRbo2PVdlZWVKiwsDGktO3fu1BNPPDH02Ol0KisrK6T3QPwglAIAAEDcaGpqksPhGMWVfbc88nr9crt9Ki09qfr6/xxVLZmZKTp16r/0178265VXLurMmavq6JCWLJmi8vIf6aGHspSYaJUkLVqUNqp7RJstW7L1xhuXA2o7adL42sQdCKfRjl0ulyvktdjtdtnt9pD3i/hk+P2hOowWAALndDqVmpqqzs5OpaSkRLocAACG9PR4NHPmu3I6PQG1r6lZofXrZ0uSGhoaNHv27FHdNx7eG30+v+bO/UD19T3yeof/GmK1Giovn6OXXlpkXnFAHDKuH4XJ2IVI4fQ9AAAA4CbHjl0LOJCyWg39+c8NYa4odlgshmpqijRpklVWqzFMG2np0in6/e8XmFwdAMBsLN8DAAAAbvLNN+6RG13n9fr19dd9IzeMEg5Hlw4dalR7e7/S0iaotDRzzCcJBuu++1J1+vQDKi8/q2PHrskwBsMqr9eviRMt2rbtbv3hDz9SUhJfVQAg1jHSAwAAADdJTg78I7JhSGlp0X8C3uXLLpWV/UMff3xt6CQ/n0/67W/P6yc/mab9+5drzpzJptUzb16KPvrop7p4sVsfftgil8urjAy7HnxwllJTo//fEwAQGuwpBSAiWHsOAIhWbW39mjnzXfX3++7Qar+kE5KkyZNt6u5ulyRNmTJFFsvgDhlFRUV65513Ar5vuN4bL192KT+/Tq2t/bfdx8lqNZSaatOpUw/o3nuTQ3ZfANGnvLxcBw4cGHrc2toqKTrHLsQH9pQCAAAAbjJ16gT96lfZw+55NKhXUpekrqFASpLa29vV2tqq1tZWdXZ2hrvUgGzb9o9hAylpcAmi0+nRI4/83eTKAJitq6traIy6EUhJ0Tl2IT4wUwpARPAXFQBANLt2rU/Ll9epqalXHs/3Py5fP7BKhw+vUGnprJDcMxzvjQ5Hl+bNOxpw+7NnS7RoUVpI7g0gPvC5HmPBTCkAAADgO6ZPn6jPPivWihXpkiSbzZDVashmG0yjfvADu955pyhkgVS4HD7cOMKMr2/ZbIYOHWoMc0UAAHyLjc4BAACA28jMTNTx4/+hc+c6VF19WS0tfUpKsqmkJEMPPpiphITo//tue/uALBbJ6x25rWEM7qcFAIBZCKUAAACAO1i4ME0LF6ZFuoxRSUtLkO9O+7XfxO8fHycJAgBiR/T/eQcAAADAqJSWzhp2g/Pv8nj82rBhdpgrAgDgW4RSAAAAQIyaPz9F998/bcR9paxWQ0uXTtHSpVNMqgwAAEIpAAAAIKbt379cqakJwwZTVquhSZOsqqrKN7kyAEC8I5QCAAAAYlhu7mSdPv2A8vOnSho8ZS8h4duTBBctStOpUw/ohz/kKHcAgLnY6BwAAAAwid/v14kTrTp6tEXd3R7NmDFRmzdnKSdnUljvO2fOZJ08Waxz5zp06FCj2tr6lZY2QevXz2LJHgAgYgilAAAAABN89lmrtm37XP/6V5dsNkOGIfl80q5dX2rduky99NLcsNcwnk8SBADEHkIpAAAAIMw+/fT/tGrVJ/J4fJIGT7q72XvvNevLL1siURoAABFDKAUAAACEkcfj00MPnZLH45PPd/s2Xq9f9fU95hYGAECEsdE5AAAAEEa1tU1qaekbNpC6wecbnD3V3t5vQlUAAEQeoRQAAAAQRocPX5XVagTc/tixr8NYDQAA0YNQCgAAAAijtrZ+eb3+kRte19HBTCkAQHwglAIAAADCaOrUCUHNlEpLmxDGagAAiB6EUgAAAEAYbdgwK6iZUqtWZYSxGgAAogehFAAAABBG69ZlaubMibKM8MnbYhmcTTVlCjOlAADxgVAKAAAACCObzaK//KVQNptl2GDKajWUkzPJ3MIAAIgwQikAAAAgzFaunKbjx3+mefNSJEk2m6GEBENWq2QY0i9+MVN1dfdHtkgAAExmi3QBAAAAQDwoKEjX+fOrdfJkq44ebZHL5VFGxkRt3pylu+6aJKfTGekSAQAwFaEUAAAAYBLDMFRUNE1FRdMiXQoAABHH8j0AAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikghuzevVuGYYz4c/fddwfVb05Ozh37KygoCNNvBAAAAACIVZy+B8SQ7OxsFRUVDfv6uXPn1N3drcLCwlH1v2zZMtnt9u89n5eXN6r+AAAAAADxi1AKiCFlZWUqKyu77WtdXV3KyMiQJG3dunVU/b/99tvKyckZbXkAAAAAAAxh+R4QJw4ePKje3l5lZGSopKQk0uUAAAAAAOIcoRQQJ6qqqiRJW7Zskc3GJEkAAAAAQGTxzRSIA42NjTp+/Lik0S/dk6TnnntOTU1N8ng8ys7O1urVq7Vp0yZZrdZQlQoAAAAAiBOEUkAcePPNN+Xz+TR//nwtWbJk1P3s37//e48XLFigI0eOKDc3d6xlAgAAAADiCMv3gDhQXV0tafSzpIqKilRZWSmHw6He3l5du3ZNb7zxhjIzM3X+/HmtXr1anZ2dd+zD7XbL6XTe8gMAAAAAiF+G3+/3R7oIAOHzxRdfaPHixbJYLKqvr1dWVlbI+q6vr9fixYvV0dGhZ599Vs8888ywbXfv3q09e/Z87/nOzk6lpKSErCYAAMYrp9Op1NRU3hsBjCuMXRgLQikgSuzYsUO1tbVBX1dZWanCwsJhX3/yySf14osvqri4WHV1dWMp8bZ27dqliooKLVq0SGfPnh22ndvtltvtHnrsdDqVlZXFmxcAANfxxQ7AeMTYhbFgTykgSjQ1NcnhcAR9ncvlGvY1r9erAwcOSBrbBud3ciMQu3jx4h3b2e122e32sNQAAAAAABh/2FMKiBLV1dXy+/1B/6xatWrYPuvq6tTc3KzExERt3LgxLHUnJCRIkjweT1j6BwAAAADEJkIpIIZVVVVJkkpLS5WcnByWe3z11VeSpNmzZ4elfwAAAABAbCKUAmKUy+VSTU2NpPAt3evp6dG+ffsk6Y4ztgAAAAAA+C5CKSBG1dTUyOVyKSMjQyUlJSO2X7lypXJycnTw4MFbnn/hhRf06quvqqOj45bnL126pLVr1+rixYtKSkrSU089FcryAQAAAAAxjo3OgRh1Y+neli1bZLON/F+9sbFRly9fVnd39y3PNzQ06OWXX9bjjz+ue+65R+np6ero6NCFCxfk9/s1efJkHThwQLm5uWH5PQAAAAAAsYlQCohBLS0tqqurkzT2pXubN2+Wz+fT6dOn1dDQoCtXrmjChAlasGCB1qxZo/LycmVnZ4eibAAAAABAHCGUAmLQjBkzgj4Nr76+/rbPFxQUqKCgIARVAQAAAADwLfaUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAIAgdHV1qbq6Wg8//LDmzp2rxMREJSUlKS8vT08//bSam5sjXSIAfA9jF6KR4ff7/ZEuAkD8cTqdSk1NVWdnp1JSUiJdDgAAAdu6dauqq6slScnJycrNzZXL5dKlS5fk9XqVnp6uDz74QMuXLw+qX94bAYQTYxeiETOlAAAAgCCVlpbq2LFjamtr09mzZ3XhwgU5HA7l5+ertbVVGzduVG9vb6TLBIBbMHYh2jBTCkBE8BcVAMB41dbWpqlTp972tYaGBs2ZM0f9/f06fPiw1q9fH3C/vDcCCCfGLkQjZkoBAAAAQRjuS50kZWVlad68eZKkCxcumFUSAIyIsQvRiFAKAAAACKG+vj5JUmJiYoQrAYDAMXYhEgilAAAAgBA5d+7c0CyDoqKiCFcDAIFh7EKk2CJdAAAAABALvF6vysvLJUnFxcVaunTpHdu73W653e6hx06nM6z1AcDtMHYhkpgpBQAAAITArl279Omnnyo5OVmvvfbaiO0rKiqUmpo69JOVlWVClQBwK8YuRBKn7wGICL/fr66uLiUnJ8swjEiXAwCIEzt27FBtbW3Q11VWVqqwsHDY1/ft26dHH31UNptNR44c0dq1a0fs87uzDfx+v/r7+zVt2jTeGwHcgrELsYrlewAiwjAMjowFAJiuqalJDocj6OtcLtewr7311lt67LHHZBiGXn/99YC+1EmS3W6X3W4PuhYA8YexC7GKmVIAAADAKL3//vsqLS3VwMCA9u7dq+3bt0e6JAAYEWMXogV7SgEAAACj8Mknn2jTpk0aGBhQRUUFX+oAjAuMXYgmzJQCAAAAgnTmzBkVFxfL6XRq586dev755yNdEgCMiLEL0YZQCgAAAAiCw+HQypUr9c0332j79u3au3dvpEsCgBExdiEaEUoBAAAAQVizZo0+/PBDGYahwsLCYU+bKisrU1lZmcnVAcDtMXYhGnH6HgAAABCEG0eh+/1+nTx5cth2q1atMqskABgRYxeiETOlAAAAAAAAYDpO3wMAAAAAAIDpCKUAAAAAAABgOkIpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAAAAAABgOkIpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAAAAAABgOkIpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAAAAAABguv8HDIJu7WZrFMYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [source](https://amueller.github.io/COMS4995-s19/slides/aml-05-preprocessing/#8)\n",
    "mglearn_utils.plot_scaling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.686198Z",
     "iopub.status.busy": "2024-05-17T15:07:40.685986Z",
     "iopub.status.idle": "2024-05-17T15:07:40.692321Z",
     "shell.execute_reply": "2024-05-17T15:07:40.690942Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.686180Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.694576Z",
     "iopub.status.busy": "2024-05-17T15:07:40.693845Z",
     "iopub.status.idle": "2024-05-17T15:07:40.715677Z",
     "shell.execute_reply": "2024-05-17T15:07:40.714660Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.694526Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.908140</td>\n",
       "      <td>-0.743917</td>\n",
       "      <td>-0.526078</td>\n",
       "      <td>0.266135</td>\n",
       "      <td>-0.389736</td>\n",
       "      <td>-0.210591</td>\n",
       "      <td>-0.083813</td>\n",
       "      <td>0.126398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.002057</td>\n",
       "      <td>1.083123</td>\n",
       "      <td>-0.923283</td>\n",
       "      <td>-1.253312</td>\n",
       "      <td>-0.198924</td>\n",
       "      <td>4.726412</td>\n",
       "      <td>11.166631</td>\n",
       "      <td>-0.050132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.218207</td>\n",
       "      <td>-1.352930</td>\n",
       "      <td>1.380504</td>\n",
       "      <td>0.542873</td>\n",
       "      <td>-0.635239</td>\n",
       "      <td>-0.273606</td>\n",
       "      <td>-0.025391</td>\n",
       "      <td>-0.099240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.128188</td>\n",
       "      <td>-0.753286</td>\n",
       "      <td>-0.843842</td>\n",
       "      <td>-0.561467</td>\n",
       "      <td>0.714077</td>\n",
       "      <td>0.122307</td>\n",
       "      <td>-0.280310</td>\n",
       "      <td>0.010183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.168196</td>\n",
       "      <td>-1.287344</td>\n",
       "      <td>-0.843842</td>\n",
       "      <td>2.500924</td>\n",
       "      <td>-1.059242</td>\n",
       "      <td>-0.640266</td>\n",
       "      <td>-0.190617</td>\n",
       "      <td>0.126808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18571</th>\n",
       "      <td>0.733102</td>\n",
       "      <td>-0.804818</td>\n",
       "      <td>0.586095</td>\n",
       "      <td>-0.966131</td>\n",
       "      <td>-0.118182</td>\n",
       "      <td>0.063110</td>\n",
       "      <td>-0.099558</td>\n",
       "      <td>0.071541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18572</th>\n",
       "      <td>1.163195</td>\n",
       "      <td>-1.057793</td>\n",
       "      <td>-1.161606</td>\n",
       "      <td>0.728235</td>\n",
       "      <td>0.357500</td>\n",
       "      <td>0.235096</td>\n",
       "      <td>-0.163397</td>\n",
       "      <td>0.007458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18573</th>\n",
       "      <td>-1.097293</td>\n",
       "      <td>0.797355</td>\n",
       "      <td>-1.876574</td>\n",
       "      <td>0.514155</td>\n",
       "      <td>0.934269</td>\n",
       "      <td>0.211892</td>\n",
       "      <td>-0.135305</td>\n",
       "      <td>0.044029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18574</th>\n",
       "      <td>-1.437367</td>\n",
       "      <td>1.008167</td>\n",
       "      <td>1.221622</td>\n",
       "      <td>-0.454427</td>\n",
       "      <td>0.006578</td>\n",
       "      <td>-0.273382</td>\n",
       "      <td>-0.149822</td>\n",
       "      <td>-0.132875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18575</th>\n",
       "      <td>0.242996</td>\n",
       "      <td>0.272667</td>\n",
       "      <td>-0.684960</td>\n",
       "      <td>-0.396991</td>\n",
       "      <td>-0.711754</td>\n",
       "      <td>0.025998</td>\n",
       "      <td>0.042957</td>\n",
       "      <td>0.051269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18576 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "0       0.908140 -0.743917           -0.526078    0.266135      -0.389736   \n",
       "1      -0.002057  1.083123           -0.923283   -1.253312      -0.198924   \n",
       "2       1.218207 -1.352930            1.380504    0.542873      -0.635239   \n",
       "3       1.128188 -0.753286           -0.843842   -0.561467       0.714077   \n",
       "4       1.168196 -1.287344           -0.843842    2.500924      -1.059242   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "18571   0.733102 -0.804818            0.586095   -0.966131      -0.118182   \n",
       "18572   1.163195 -1.057793           -1.161606    0.728235       0.357500   \n",
       "18573  -1.097293  0.797355           -1.876574    0.514155       0.934269   \n",
       "18574  -1.437367  1.008167            1.221622   -0.454427       0.006578   \n",
       "18575   0.242996  0.272667           -0.684960   -0.396991      -0.711754   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "0                -0.210591               -0.083813                  0.126398  \n",
       "1                 4.726412               11.166631                 -0.050132  \n",
       "2                -0.273606               -0.025391                 -0.099240  \n",
       "3                 0.122307               -0.280310                  0.010183  \n",
       "4                -0.640266               -0.190617                  0.126808  \n",
       "...                    ...                     ...                       ...  \n",
       "18571             0.063110               -0.099558                  0.071541  \n",
       "18572             0.235096               -0.163397                  0.007458  \n",
       "18573             0.211892               -0.135305                  0.044029  \n",
       "18574            -0.273382               -0.149822                 -0.132875  \n",
       "18575             0.025998                0.042957                  0.051269  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train_imp)\n",
    "X_test_scaled = scaler.transform(X_test_imp)\n",
    "pd.DataFrame(X_train_scaled, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.717076Z",
     "iopub.status.busy": "2024-05-17T15:07:40.716794Z",
     "iopub.status.idle": "2024-05-17T15:07:40.923698Z",
     "shell.execute_reply": "2024-05-17T15:07:40.922106Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.717048Z"
    },
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5609808539232339"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "knn.fit(X_train_imp, y_train)\n",
    "knn.score(X_train_imp, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:40.925656Z",
     "iopub.status.busy": "2024-05-17T15:07:40.925242Z",
     "iopub.status.idle": "2024-05-17T15:07:41.842287Z",
     "shell.execute_reply": "2024-05-17T15:07:41.841062Z",
     "shell.execute_reply.started": "2024-05-17T15:07:40.925617Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7978563117812038"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "knn.score(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Big difference in the KNN training performance after scaling the data. \n",
    "- But we saw last week that training score doesn't tell us much. We should look at the cross-validation score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "source": [
    "### (iClicker) Exercise 5.2 \n",
    "\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "1. `StandardScaler` ensures a fixed range (i.e., minimum and maximum values) for the features. \n",
    "2. `StandardScaler` calculates mean and standard deviation for each feature separately. \n",
    "3. In general, it's a good idea to apply scaling on numeric features before training $k$-NN or SVM RBF models. \n",
    "4. The transformed feature values might be hard to interpret for humans.\n",
    "5. After applying `SimpleImputer` The transformed data has a different shape than the original data.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](../img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformations and the golden rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### How to carry out cross-validation? \n",
    "\n",
    "- Previously, we saw that cross validation is a better way to get a realistic assessment of the model. \n",
    "- Let's try cross-validation with transformed data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:41.844197Z",
     "iopub.status.busy": "2024-05-17T15:07:41.843644Z",
     "iopub.status.idle": "2024-05-17T15:07:45.553617Z",
     "shell.execute_reply": "2024-05-17T15:07:45.552615Z",
     "shell.execute_reply.started": "2024-05-17T15:07:41.844154Z"
    },
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014541</td>\n",
       "      <td>0.180861</td>\n",
       "      <td>0.696373</td>\n",
       "      <td>0.794236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010841</td>\n",
       "      <td>0.147328</td>\n",
       "      <td>0.684447</td>\n",
       "      <td>0.791467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.011135</td>\n",
       "      <td>0.159440</td>\n",
       "      <td>0.695532</td>\n",
       "      <td>0.789436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.010957</td>\n",
       "      <td>0.167743</td>\n",
       "      <td>0.679478</td>\n",
       "      <td>0.793243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013512</td>\n",
       "      <td>0.101623</td>\n",
       "      <td>0.680657</td>\n",
       "      <td>0.794820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.014541    0.180861    0.696373     0.794236\n",
       "1  0.010841    0.147328    0.684447     0.791467\n",
       "2  0.011135    0.159440    0.695532     0.789436\n",
       "3  0.010957    0.167743    0.679478     0.793243\n",
       "4  0.013512    0.101623    0.680657     0.794820"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train_imp)\n",
    "X_train_scaled = scaler.transform(X_train_imp)\n",
    "X_test_scaled = scaler.transform(X_test_imp)\n",
    "scores = cross_validate(knn, X_train_scaled, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Do you see any problem here? \n",
    "- Are we applying `fit_transform` on train portion and `transform` on validation portion **in each fold**?  \n",
    "    - Here you might be allowing information from the _validation set to **leak** into the training_ step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- You need to apply the **SAME** preprocessing steps to train/validation.\n",
    "- With many different transformations and cross validation the code gets unwieldy very quickly. \n",
    "- Likely to make mistakes and \"leak\" information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- In these examples our test accuracies look fine, but our methodology is flawed.\n",
    "- Implications can be significant in practice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pipelines\n",
    "\n",
    "Can we do this in a more elegant and organized way?\n",
    "\n",
    "- YES!! Using [`scikit-learn Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html).\n",
    "- [`scikit-learn Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) allows you to define a \"pipeline\" of transformers with a final estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's combine the preprocessing and model with pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:45.554849Z",
     "iopub.status.busy": "2024-05-17T15:07:45.554597Z",
     "iopub.status.idle": "2024-05-17T15:07:45.559915Z",
     "shell.execute_reply": "2024-05-17T15:07:45.558816Z",
     "shell.execute_reply.started": "2024-05-17T15:07:45.554829Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "### Simple example of a pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe = Pipeline(\n",
    "    steps=[\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"regressor\", KNeighborsRegressor()),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Syntax: pass in a list of steps.\n",
    "- The last step should be a **model/classifier/regressor**.\n",
    "- All the earlier steps should be **transformers**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Alternative and more compact syntax: `make_pipeline`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Shorthand for `Pipeline` constructor\n",
    "- Does not permit naming steps\n",
    "- Instead the names of steps are set to lowercase of their types automatically; `StandardScaler()` would be named as `standardscaler`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:45.561609Z",
     "iopub.status.busy": "2024-05-17T15:07:45.561188Z",
     "iopub.status.idle": "2024-05-17T15:07:45.569120Z",
     "shell.execute_reply": "2024-05-17T15:07:45.567877Z",
     "shell.execute_reply.started": "2024-05-17T15:07:45.561581Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "pipe = make_pipeline(\n",
    "    SimpleImputer(strategy=\"median\"), \n",
    "    StandardScaler(), \n",
    "    KNeighborsRegressor()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:45.570756Z",
     "iopub.status.busy": "2024-05-17T15:07:45.570353Z",
     "iopub.status.idle": "2024-05-17T15:07:45.621459Z",
     "shell.execute_reply": "2024-05-17T15:07:45.620136Z",
     "shell.execute_reply.started": "2024-05-17T15:07:45.570718Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "pipe.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that we are passing `X_train` and **not** the imputed or scaled data here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "When you call `fit` on the pipeline, it carries out the following steps:\n",
    "\n",
    "- Fit `SimpleImputer` on `X_train`\n",
    "- Transform `X_train` using the fit `SimpleImputer` to create `X_train_imp`\n",
    "- Fit `StandardScaler` on `X_train_imp`\n",
    "- Transform `X_train_imp` using the fit `StandardScaler` to create `X_train_imp_scaled`\n",
    "- Fit the model (`KNeighborsRegressor` in our case) on `X_train_imp_scaled`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:45.624562Z",
     "iopub.status.busy": "2024-05-17T15:07:45.624074Z",
     "iopub.status.idle": "2024-05-17T15:07:46.680277Z",
     "shell.execute_reply": "2024-05-17T15:07:46.679240Z",
     "shell.execute_reply.started": "2024-05-17T15:07:45.624527Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126500., 117380., 187700., ..., 259500., 308120.,  60860.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are passing original data to `predict` as well. This time the pipeline is carrying out following steps:\n",
    "- Transform `X_train` using the fit `SimpleImputer` to create `X_train_imp`\n",
    "- Transform `X_train_imp` using the fit `StandardScaler` to create `X_train_imp_scaled`\n",
    "- Predict using the fit model (`KNeighborsRegressor` in our case) on `X_train_imp_scaled`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<!-- ![](../img/pipeline.png) -->\n",
    "<img src='../img/pipeline.png' width=\"800\">\n",
    "    \n",
    "[Source](https://amueller.github.io/COMS4995-s20/slides/aml-04-preprocessing/#18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's try cross-validation with our pipeline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:46.681790Z",
     "iopub.status.busy": "2024-05-17T15:07:46.681541Z",
     "iopub.status.idle": "2024-05-17T15:07:51.434583Z",
     "shell.execute_reply": "2024-05-17T15:07:51.433489Z",
     "shell.execute_reply.started": "2024-05-17T15:07:46.681769Z"
    }
   },
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.002 (+/- 0.001)</td>\n",
       "      <td>0.001 (+/- 0.000)</td>\n",
       "      <td>-0.055 (+/- 0.012)</td>\n",
       "      <td>-0.055 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp + scaling + knn</th>\n",
       "      <td>0.029 (+/- 0.002)</td>\n",
       "      <td>0.189 (+/- 0.022)</td>\n",
       "      <td>0.693 (+/- 0.014)</td>\n",
       "      <td>0.797 (+/- 0.015)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              fit_time         score_time          test_score  \\\n",
       "dummy                0.002 (+/- 0.001)  0.001 (+/- 0.000)  -0.055 (+/- 0.012)   \n",
       "imp + scaling + knn  0.029 (+/- 0.002)  0.189 (+/- 0.022)   0.693 (+/- 0.014)   \n",
       "\n",
       "                            train_score  \n",
       "dummy                -0.055 (+/- 0.001)  \n",
       "imp + scaling + knn   0.797 (+/- 0.015)  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict[\"imp + scaling + knn\"] = mean_std_cross_val_scores(\n",
    "    pipe, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results_dict).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Using a `Pipeline` takes care of applying the `fit_transform` on the train portion and only `transform` on the validation portion in each fold.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Categorical features [[video](https://youtu.be/2mJ9rAhMMl0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Recall that we had dropped the categorical feature `ocean_proximity` feature from the dataframe. But it could potentially be a useful feature in this task. \n",
    "\n",
    "- Let's create our `X_train` and and `X_test` again by keeping the feature in the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.436286Z",
     "iopub.status.busy": "2024-05-17T15:07:51.435847Z",
     "iopub.status.idle": "2024-05-17T15:07:51.445264Z",
     "shell.execute_reply": "2024-05-17T15:07:51.444099Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.436258Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'households',\n",
       "       'median_income', 'rooms_per_household', 'bedrooms_per_household',\n",
       "       'population_per_household'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.446674Z",
     "iopub.status.busy": "2024-05-17T15:07:51.446394Z",
     "iopub.status.idle": "2024-05-17T15:07:51.455292Z",
     "shell.execute_reply": "2024-05-17T15:07:51.453324Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.446652Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns=[\"median_house_value\"])\n",
    "y_train = train_df[\"median_house_value\"]\n",
    "\n",
    "X_test = test_df.drop(columns=[\"median_house_value\"])\n",
    "y_test = test_df[\"median_house_value\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's try to build a `KNeighborRegressor` on this data using our pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.456643Z",
     "iopub.status.busy": "2024-05-17T15:07:51.456375Z",
     "iopub.status.idle": "2024-05-17T15:07:51.465952Z",
     "shell.execute_reply": "2024-05-17T15:07:51.464715Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.456621Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pipe.fit(X_train, X_train)  # fails"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "tags": []
   },
   "source": [
    "ValueError: Cannot use median strategy with non-numeric data: \n",
    "could not convert string to float: 'INLAND'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- This failed because we have non-numeric data. \n",
    "- Imagine how $k$-NN would calculate distances when you have non-numeric features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Can we use this feature in the model? \n",
    "- In `scikit-learn`, most algorithms require numeric inputs.\n",
    "- Decision trees could theoretically work with categorical features.  \n",
    "    - However, the sklearn implementation does not support this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What are the options? \n",
    "\n",
    "- Drop the column (not recommended)\n",
    "    - If you know that the column is not relevant to the target in any way you may drop it. \n",
    "- We can transform categorical features to numeric ones so that we can use them in the model.     \n",
    "    - [**Ordinal encoding**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html) (occasionally recommended)\n",
    "    - **One-hot encoding** (recommended in most cases) (this lecture)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.468169Z",
     "iopub.status.busy": "2024-05-17T15:07:51.467276Z",
     "iopub.status.idle": "2024-05-17T15:07:51.482884Z",
     "shell.execute_reply": "2024-05-17T15:07:51.481405Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.468114Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vietnamese</td>\n",
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       "      <th>2</th>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mandarin</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>English</td>\n",
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       "      <th>5</th>\n",
       "      <td>English</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mandarin</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Vietnamese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mandarin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mandarin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hindi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      language\n",
       "0      English\n",
       "1   Vietnamese\n",
       "2      English\n",
       "3     Mandarin\n",
       "4      English\n",
       "5      English\n",
       "6     Mandarin\n",
       "7      English\n",
       "8   Vietnamese\n",
       "9     Mandarin\n",
       "10      French\n",
       "11     Spanish\n",
       "12    Mandarin\n",
       "13       Hindi"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_toy = pd.DataFrame(\n",
    "    {\n",
    "        \"language\": [\n",
    "            \"English\",\n",
    "            \"Vietnamese\",\n",
    "            \"English\",\n",
    "            \"Mandarin\",\n",
    "            \"English\",\n",
    "            \"English\",\n",
    "            \"Mandarin\",\n",
    "            \"English\",\n",
    "            \"Vietnamese\",\n",
    "            \"Mandarin\",\n",
    "            \"French\",\n",
    "            \"Spanish\",\n",
    "            \"Mandarin\",\n",
    "            \"Hindi\",\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "X_toy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ordinal encoding (occasionally recommended)\n",
    "\n",
    "- Here we simply assign an integer to each of our unique categorical labels. \n",
    "- We can use sklearn's [`OrdinalEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.484337Z",
     "iopub.status.busy": "2024-05-17T15:07:51.484056Z",
     "iopub.status.idle": "2024-05-17T15:07:51.499023Z",
     "shell.execute_reply": "2024-05-17T15:07:51.497202Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.484314Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>language</th>\n",
       "      <th>language_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <td>Mandarin</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Mandarin</td>\n",
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       "      <th>7</th>\n",
       "      <td>English</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>8</th>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mandarin</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>French</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mandarin</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hindi</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      language  language_enc\n",
       "0      English           0.0\n",
       "1   Vietnamese           5.0\n",
       "2      English           0.0\n",
       "3     Mandarin           3.0\n",
       "4      English           0.0\n",
       "5      English           0.0\n",
       "6     Mandarin           3.0\n",
       "7      English           0.0\n",
       "8   Vietnamese           5.0\n",
       "9     Mandarin           3.0\n",
       "10      French           1.0\n",
       "11     Spanish           4.0\n",
       "12    Mandarin           3.0\n",
       "13       Hindi           2.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "enc = OrdinalEncoder()\n",
    "enc.fit(X_toy)\n",
    "X_toy_ord = enc.transform(X_toy)\n",
    "df = pd.DataFrame(\n",
    "    data=X_toy_ord,\n",
    "    columns=[\"language_enc\"],\n",
    "    index=X_toy.index,\n",
    ")\n",
    "pd.concat([X_toy, df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What's the problem with this approach? \n",
    "- We have imposed ordinality on the categorical data.\n",
    "- For example, imagine when you are calculating distances. Is it fair to say that French and Hindi are closer than French and Spanish? \n",
    "- In general, label encoding is useful if there is ordinality in your data and capturing it is important for your problem, e.g., `[cold, warm, hot]`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### One-hot encoding (OHE)\n",
    "- Create new binary columns to represent our categories.\n",
    "- If we have $c$ categories in our column.\n",
    "    - We create $c$ new binary columns to represent those categories.\n",
    "- Example: Imagine a language column which has the information on whether you \n",
    "\n",
    "- We can use sklearn's [`OneHotEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "One-hot encoding is called one-hot because only one of the newly created features is 1 for each data point. \n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.502084Z",
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     "shell.execute_reply": "2024-05-17T15:07:51.530864Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.502047Z"
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   },
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    language_English  language_French  language_Hindi  language_Mandarin  \\\n",
       "0                1.0              0.0             0.0                0.0   \n",
       "1                0.0              0.0             0.0                0.0   \n",
       "2                1.0              0.0             0.0                0.0   \n",
       "3                0.0              0.0             0.0                1.0   \n",
       "4                1.0              0.0             0.0                0.0   \n",
       "5                1.0              0.0             0.0                0.0   \n",
       "6                0.0              0.0             0.0                1.0   \n",
       "7                1.0              0.0             0.0                0.0   \n",
       "8                0.0              0.0             0.0                0.0   \n",
       "9                0.0              0.0             0.0                1.0   \n",
       "10               0.0              1.0             0.0                0.0   \n",
       "11               0.0              0.0             0.0                0.0   \n",
       "12               0.0              0.0             0.0                1.0   \n",
       "13               0.0              0.0             1.0                0.0   \n",
       "\n",
       "    language_Spanish  language_Vietnamese  \n",
       "0                0.0                  0.0  \n",
       "1                0.0                  1.0  \n",
       "2                0.0                  0.0  \n",
       "3                0.0                  0.0  \n",
       "4                0.0                  0.0  \n",
       "5                0.0                  0.0  \n",
       "6                0.0                  0.0  \n",
       "7                0.0                  0.0  \n",
       "8                0.0                  1.0  \n",
       "9                0.0                  0.0  \n",
       "10               0.0                  0.0  \n",
       "11               1.0                  0.0  \n",
       "12               0.0                  0.0  \n",
       "13               0.0                  0.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "enc = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n",
    "enc.fit(X_toy)\n",
    "X_toy_ohe = enc.transform(X_toy)\n",
    "pd.DataFrame(\n",
    "    data=X_toy_ohe,\n",
    "    columns=enc.get_feature_names_out([\"language\"]),\n",
    "    index=X_toy.index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's do it on our housing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.534684Z",
     "iopub.status.busy": "2024-05-17T15:07:51.534042Z",
     "iopub.status.idle": "2024-05-17T15:07:51.552291Z",
     "shell.execute_reply": "2024-05-17T15:07:51.550893Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.534656Z"
    }
   },
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder(sparse_output=False, dtype=\"int\")\n",
    "ohe.fit(X_train[[\"ocean_proximity\"]])\n",
    "X_imp_ohe_train = ohe.transform(X_train[[\"ocean_proximity\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can look at the new features created using `categories_` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.555066Z",
     "iopub.status.busy": "2024-05-17T15:07:51.554263Z",
     "iopub.status.idle": "2024-05-17T15:07:51.563949Z",
     "shell.execute_reply": "2024-05-17T15:07:51.562297Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.555022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T15:07:51.566394Z",
     "iopub.status.busy": "2024-05-17T15:07:51.565515Z",
     "iopub.status.idle": "2024-05-17T15:07:51.582425Z",
     "shell.execute_reply": "2024-05-17T15:07:51.580709Z",
     "shell.execute_reply.started": "2024-05-17T15:07:51.566344Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>14471</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>15377</th>\n",
       "      <td>1</td>\n",
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       "<p>18576 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  \\\n",
       "6051                           0                       1   \n",
       "20113                          0                       1   \n",
       "14289                          0                       0   \n",
       "13665                          0                       1   \n",
       "14471                          0                       0   \n",
       "...                          ...                     ...   \n",
       "7763                           1                       0   \n",
       "15377                          1                       0   \n",
       "17730                          1                       0   \n",
       "15725                          0                       0   \n",
       "19966                          0                       1   \n",
       "\n",
       "       ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n",
       "6051                        0                         0   \n",
       "20113                       0                         0   \n",
       "14289                       0                         0   \n",
       "13665                       0                         0   \n",
       "14471                       0                         0   \n",
       "...                       ...                       ...   \n",
       "7763                        0                         0   \n",
       "15377                       0                         0   \n",
       "17730                       0                         0   \n",
       "15725                       0                         1   \n",
       "19966                       0                         0   \n",
       "\n",
       "       ocean_proximity_NEAR OCEAN  \n",
       "6051                            0  \n",
       "20113                           0  \n",
       "14289                           1  \n",
       "13665                           0  \n",
       "14471                           1  \n",
       "...                           ...  \n",
       "7763                            0  \n",
       "15377                           0  \n",
       "17730                           0  \n",
       "15725                           0  \n",
       "19966                           0  \n",
       "\n",
       "[18576 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_ohe = pd.DataFrame(\n",
    "    data=X_imp_ohe_train,\n",
    "    columns=ohe.get_feature_names_out([\"ocean_proximity\"]),\n",
    "    index=X_train.index,\n",
    ")\n",
    "transformed_ohe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "___\n",
    "One-hot encoded variables are also referred to as **dummy variables**. \n",
    "You will often see people using [`get_dummies` method of pandas](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html) to convert categorical variables into dummy variables. That said, using `sklearn`'s `OneHotEncoder` has the advantage of making it easy to treat training and test set in a consistent way.  \n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### (iClicker) Exercise 5.3\n",
    "\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "1. You can have scaling of numeric features, one-hot encoding of categorical features, and `scikit-learn` estimator within a single pipeline.  \n",
    "2. Once you have a `scikit-learn` pipeline object with an estimator as the last step, you can call `fit`, `predict`, and `score` on it.\n",
    "3. You can carry out data splitting within `scikit-learn` pipeline. \n",
    "4. We have to be careful of the order we put each transformation and model in a pipeline.\n",
    "5. If you call `cross_validate` with a pipeline object, it will call `fit` and `transform` on the training fold and only `transform` on the validation fold. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What did we learn today? \n",
    "\n",
    "- Motivation for preprocessing\n",
    "- Common preprocessing steps\n",
    "    - Imputation \n",
    "    - Scaling\n",
    "    - One-hot encoding\n",
    "- Golden rule in the context of preprocessing\n",
    "- Building simple supervised machine learning pipelines using `sklearn.pipeline.make_pipeline`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Problem: Different transformations on different columns\n",
    "- How do we put this together with other columns in the data before fitting the regressor? \n",
    "- Before we fit our regressor, we want to apply different transformations on different columns \n",
    "    - Numeric columns\n",
    "        - imputation \n",
    "        - scaling         \n",
    "    - Categorical columns \n",
    "        - imputation \n",
    "        - one-hot encoding        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Coming up: sklearn's [`ColumnTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)!!** "
   ]
  }
 ],
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