lectures/19_survival-analysis.ipynb

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    "![](img/330-banner.png)"
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   "source": [
    "# Lecture 19: Survival analysis \n",
    "\n",
    "UBC 2022 Summer\n",
    "\n",
    "Instructor: Mehrdad Oveisi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
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   "execution_count": 1,
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     "slide_type": "skip"
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression, Ridge\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.model_selection import (\n",
    "    cross_val_predict,\n",
    "    cross_val_score,\n",
    "    cross_validate,\n",
    "    train_test_split,\n",
    ")\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    FunctionTransformer,\n",
    "    OneHotEncoder,\n",
    "    OrdinalEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "\n",
    "# does lifelines try to mess with this?\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import lifelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Announcements\n",
    "\n",
    "- hw8 released, due December 7th 11:59pm\n",
    "- Next lecture on Ethics is likely to be a guest lecture by one of my colleagues who has taught Ethics in the MDS program. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Learning objectives\n",
    "\n",
    "- Explain the problem with treating right-censored data the same as \"regular\" data.\n",
    "- Determine whether survival analysis is an appropriate tool for a given problem.\n",
    "- Apply survival analysis in Python using the `lifelines` package.\n",
    "- Interpret a survival curve, such as the Kaplan-Meier curve.\n",
    "- Interpret the coefficients of a fitted Cox proportional hazards model.\n",
    "- Make predictions for existing individuals and interpret these predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Customer churn: our standard approach (10 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In hw5 you looked at a dataset about [customer churn](https://en.wikipedia.org/wiki/Customer_attrition).\n",
    "- In hw5, the dataset was interesting because it's unbalanced (most customers stay). We used typical binary classification approach on the dataset.  \n",
    "- Today we'll look at a different customer churn [dataset](https://www.kaggle.com/blastchar/telco-customer-churn), because it has a feature we need - time!\n",
    "- We'll explore the time aspect of the dataset today."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "slide_type": "slide"
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   },
   "outputs": [
    {
     "data": {
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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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>4726-DLWQN</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>70.35</td>\n",
       "      <td>3454.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>4537-DKTAL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>45.55</td>\n",
       "      <td>84.4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0468-YRPXN</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>98.80</td>\n",
       "      <td>2807.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>1304-NECVQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.55</td>\n",
       "      <td>149.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>7153-CHRBV</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>57</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>59.30</td>\n",
       "      <td>3274.35</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
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      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "6464  4726-DLWQN    Male              1      No         No      50   \n",
       "5707  4537-DKTAL  Female              0      No         No       2   \n",
       "3442  0468-YRPXN    Male              0      No         No      29   \n",
       "3932  1304-NECVQ  Female              1      No         No       2   \n",
       "6124  7153-CHRBV  Female              0     Yes        Yes      57   \n",
       "\n",
       "     PhoneService MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "6464          Yes           Yes             DSL            Yes  ...   \n",
       "5707          Yes            No             DSL             No  ...   \n",
       "3442          Yes            No     Fiber optic             No  ...   \n",
       "3932          Yes           Yes     Fiber optic             No  ...   \n",
       "6124          Yes            No             DSL            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "6464               No          No         Yes              No  Month-to-month   \n",
       "5707               No          No          No              No  Month-to-month   \n",
       "3442              Yes         Yes         Yes             Yes  Month-to-month   \n",
       "3932              Yes          No          No              No  Month-to-month   \n",
       "6124              Yes         Yes          No              No        One year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "6464              Yes  Bank transfer (automatic)          70.35        3454.6   \n",
       "5707               No           Electronic check          45.55          84.4   \n",
       "3442              Yes    Credit card (automatic)          98.80        2807.1   \n",
       "3932              Yes           Electronic check          78.55        149.55   \n",
       "6124              Yes               Mailed check          59.30       3274.35   \n",
       "\n",
       "     Churn  \n",
       "6464    No  \n",
       "5707    No  \n",
       "3442    No  \n",
       "3932   Yes  \n",
       "6124    No  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "train_df, test_df = train_test_split(df, random_state=123)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can treat this as a binary classification problem where we want to predict `Churn` (yes/no) from these other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5282, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     3912\n",
       "Yes    1370\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Churn\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5282 entries, 6464 to 3582\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        5282 non-null   object \n",
      " 1   gender            5282 non-null   object \n",
      " 2   SeniorCitizen     5282 non-null   int64  \n",
      " 3   Partner           5282 non-null   object \n",
      " 4   Dependents        5282 non-null   object \n",
      " 5   tenure            5282 non-null   int64  \n",
      " 6   PhoneService      5282 non-null   object \n",
      " 7   MultipleLines     5282 non-null   object \n",
      " 8   InternetService   5282 non-null   object \n",
      " 9   OnlineSecurity    5282 non-null   object \n",
      " 10  OnlineBackup      5282 non-null   object \n",
      " 11  DeviceProtection  5282 non-null   object \n",
      " 12  TechSupport       5282 non-null   object \n",
      " 13  StreamingTV       5282 non-null   object \n",
      " 14  StreamingMovies   5282 non-null   object \n",
      " 15  Contract          5282 non-null   object \n",
      " 16  PaperlessBilling  5282 non-null   object \n",
      " 17  PaymentMethod     5282 non-null   object \n",
      " 18  MonthlyCharges    5282 non-null   float64\n",
      " 19  TotalCharges      5282 non-null   object \n",
      " 20  Churn             5282 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 907.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Does this mean there is no missing data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Ok, let's try our usual approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4430\n",
       "1     852\n",
       "Name: SeniorCitizen, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"SeniorCitizen\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "numeric_features = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]\n",
    "drop_features = [\"customerID\"]\n",
    "passthrough_features = [\"SeniorCitizen\"]\n",
    "target_column = [\"Churn\"]\n",
    "# the rest are categorical\n",
    "categorical_features = list(\n",
    "    set(train_df.columns)\n",
    "    - set(numeric_features)\n",
    "    - set(passthrough_features)\n",
    "    - set(drop_features)\n",
    "    - set(target_column)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = make_column_transformer(\n",
    "    (StandardScaler(), numeric_features),\n",
    "    (OneHotEncoder(), categorical_features),\n",
    "    (\"passthrough\", passthrough_features),\n",
    "    (\"drop\", drop_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ky/533nd9l512l5cmmk_kzmzj9m0000gp/T/ipykernel_25627/2961559258.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreprocessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    468\u001b[0m         \u001b[0;31m# we use fit_transform to make sure to set sparse_output_ (for which we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m         \u001b[0;31m# need the transformed data) to have consistent output type in predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 470\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    471\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_remainder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_fit_transform_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36m_fit_transform\u001b[0;34m(self, X, y, func, fitted)\u001b[0m\n\u001b[1;32m    432\u001b[0m             self._iter(fitted=fitted, replace_strings=True))\n\u001b[1;32m    433\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m             return Parallel(n_jobs=self.n_jobs)(\n\u001b[0m\u001b[1;32m    435\u001b[0m                 delayed(func)(\n\u001b[1;32m    436\u001b[0m                     \u001b[0mtransformer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrans\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfitted\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtrans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1041\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    857\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    752\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    755\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    700\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    728\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 730\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    732\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    764\u001b[0m         \"\"\"\n\u001b[1;32m    765\u001b[0m         \u001b[0mfirst_call\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"n_samples_seen_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 766\u001b[0;31m         X = self._validate_data(X, accept_sparse=('csr', 'csc'),\n\u001b[0m\u001b[1;32m    767\u001b[0m                                 \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m                                 force_all_finite='allow-nan', reset=first_call)\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    419\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'no_validation'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    671\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order, like)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1992\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mNpDtype\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1993\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1994\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1995\u001b[0m     def __array_wrap__(\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order, like)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: ''"
     ]
    }
   ],
   "source": [
    "preprocessor.fit(train_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Hmmm, one of the numeric features is causing problems?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, looks like `TotalCharges` is not a numeric type. What if we change the type of this column to float? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ky/533nd9l512l5cmmk_kzmzj9m0000gp/T/ipykernel_25627/2516325513.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"TotalCharges\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"TotalCharges\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    590\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1199\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1200\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: ''"
     ]
    }
   ],
   "source": [
    "train_df[\"TotalCharges\"] = train_df[\"TotalCharges\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Argh!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " \n",
      " \n",
      " \n",
      " \n",
      " \n",
      " \n",
      " \n"
     ]
    }
   ],
   "source": [
    "for val in train_df[\"TotalCharges\"]:\n",
    "    try:\n",
    "        float(val)\n",
    "    except ValueError:\n",
    "        print(val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any ideas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Well, it turns out we can't see those problematic values because they are whitespace!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" \"\n",
      "\" \"\n",
      "\" \"\n",
      "\" \"\n",
      "\" \"\n",
      "\" \"\n",
      "\" \"\n",
      "\" \"\n"
     ]
    }
   ],
   "source": [
    "for val in train_df[\"TotalCharges\"]:\n",
    "    try:\n",
    "        float(val)\n",
    "    except ValueError:\n",
    "        print('\"%s\"' % val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Let's replace the whitespaces with NaNs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.assign(\n",
    "    TotalCharges=train_df[\"TotalCharges\"].replace(\" \", np.nan).astype(float)\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    TotalCharges=test_df[\"TotalCharges\"].replace(\" \", np.nan).astype(float)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5282 entries, 6464 to 3582\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        5282 non-null   object \n",
      " 1   gender            5282 non-null   object \n",
      " 2   SeniorCitizen     5282 non-null   int64  \n",
      " 3   Partner           5282 non-null   object \n",
      " 4   Dependents        5282 non-null   object \n",
      " 5   tenure            5282 non-null   int64  \n",
      " 6   PhoneService      5282 non-null   object \n",
      " 7   MultipleLines     5282 non-null   object \n",
      " 8   InternetService   5282 non-null   object \n",
      " 9   OnlineSecurity    5282 non-null   object \n",
      " 10  OnlineBackup      5282 non-null   object \n",
      " 11  DeviceProtection  5282 non-null   object \n",
      " 12  TechSupport       5282 non-null   object \n",
      " 13  StreamingTV       5282 non-null   object \n",
      " 14  StreamingMovies   5282 non-null   object \n",
      " 15  Contract          5282 non-null   object \n",
      " 16  PaperlessBilling  5282 non-null   object \n",
      " 17  PaymentMethod     5282 non-null   object \n",
      " 18  MonthlyCharges    5282 non-null   float64\n",
      " 19  TotalCharges      5274 non-null   float64\n",
      " 20  Churn             5282 non-null   object \n",
      "dtypes: float64(2), int64(2), object(17)\n",
      "memory usage: 907.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "But now we are going to have missing values and we need to include imputation for numeric features in our preprocessor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "preprocessor = make_column_transformer(\n",
    "    (\n",
    "        make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler()),\n",
    "        numeric_features,\n",
    "    ),\n",
    "    (OneHotEncoder(handle_unknown=\"ignore\"), categorical_features),\n",
    "    (\"passthrough\", passthrough_features),\n",
    "    (\"drop\", drop_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try that again..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor.fit(train_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "It worked! Let's get the column names of the transformed data from the column transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_columns = (\n",
    "    numeric_features\n",
    "    + preprocessor.named_transformers_[\"onehotencoder\"]\n",
    "    .get_feature_names(categorical_features)\n",
    "    .tolist()\n",
    "    + passthrough_features\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_enc = pd.DataFrame(\n",
    "    preprocessor.transform(train_df), index=train_df.index, columns=new_columns\n",
    ")\n",
    "X_test_enc = pd.DataFrame(\n",
    "    preprocessor.transform(train_df), index=train_df.index, columns=new_columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>StreamingMovies_No</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>...</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>OnlineBackup_No</th>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>DeviceProtection_No</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>0.707712</td>\n",
       "      <td>0.185175</td>\n",
       "      <td>0.513678</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>-1.248999</td>\n",
       "      <td>-0.641538</td>\n",
       "      <td>-0.979562</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>-0.148349</td>\n",
       "      <td>1.133562</td>\n",
       "      <td>0.226789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>-1.248999</td>\n",
       "      <td>0.458524</td>\n",
       "      <td>-0.950696</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>0.993065</td>\n",
       "      <td>-0.183179</td>\n",
       "      <td>0.433814</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        tenure  MonthlyCharges  TotalCharges  StreamingMovies_No  \\\n",
       "6464  0.707712        0.185175      0.513678                 1.0   \n",
       "5707 -1.248999       -0.641538     -0.979562                 1.0   \n",
       "3442 -0.148349        1.133562      0.226789                 0.0   \n",
       "3932 -1.248999        0.458524     -0.950696                 1.0   \n",
       "6124  0.993065       -0.183179      0.433814                 1.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  Partner_No  \\\n",
       "6464                                  0.0                  0.0         1.0   \n",
       "5707                                  0.0                  0.0         1.0   \n",
       "3442                                  0.0                  1.0         1.0   \n",
       "3932                                  0.0                  0.0         1.0   \n",
       "6124                                  0.0                  0.0         0.0   \n",
       "\n",
       "      Partner_Yes  OnlineSecurity_No  OnlineSecurity_No internet service  ...  \\\n",
       "6464          0.0                0.0                                 0.0  ...   \n",
       "5707          0.0                1.0                                 0.0  ...   \n",
       "3442          0.0                1.0                                 0.0  ...   \n",
       "3932          0.0                1.0                                 0.0  ...   \n",
       "6124          1.0                0.0                                 0.0  ...   \n",
       "\n",
       "      PhoneService_Yes  OnlineBackup_No  OnlineBackup_No internet service  \\\n",
       "6464               1.0              0.0                               0.0   \n",
       "5707               1.0              1.0                               0.0   \n",
       "3442               1.0              1.0                               0.0   \n",
       "3932               1.0              1.0                               0.0   \n",
       "6124               1.0              1.0                               0.0   \n",
       "\n",
       "      OnlineBackup_Yes  PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
       "6464               1.0                  0.0                   1.0   \n",
       "5707               0.0                  1.0                   0.0   \n",
       "3442               0.0                  0.0                   1.0   \n",
       "3932               0.0                  0.0                   1.0   \n",
       "6124               0.0                  0.0                   1.0   \n",
       "\n",
       "      DeviceProtection_No  DeviceProtection_No internet service  \\\n",
       "6464                  1.0                                   0.0   \n",
       "5707                  1.0                                   0.0   \n",
       "3442                  0.0                                   0.0   \n",
       "3932                  0.0                                   0.0   \n",
       "6124                  0.0                                   0.0   \n",
       "\n",
       "      DeviceProtection_Yes  SeniorCitizen  \n",
       "6464                   0.0            1.0  \n",
       "5707                   0.0            0.0  \n",
       "3442                   1.0            0.0  \n",
       "3932                   1.0            1.0  \n",
       "6124                   1.0            0.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_enc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Before we look into survival analysis, let's just treat it as a binary classification model where we want to predict whether a customer churned or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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[i], std_scores[i])))\n",
    "\n",
    "    return pd.Series(data=out_col, index=mean_scores.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns=[\"Churn\"])\n",
    "X_test = test_df.drop(columns=[\"Churn\"])\n",
    "\n",
    "y_train = train_df[\"Churn\"]\n",
    "y_test = test_df[\"Churn\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### DummyClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = DummyClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fit_time</th>\n",
       "      <td>0.005 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_time</th>\n",
       "      <td>0.003 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_score</th>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_score</th>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         dummy\n",
       "fit_time     0.005 (+/- 0.001)\n",
       "score_time   0.003 (+/- 0.001)\n",
       "test_score   0.741 (+/- 0.000)\n",
       "train_score  0.741 (+/- 0.000)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"dummy\"] = mean_std_cross_val_scores(\n",
    "    dc, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = make_pipeline(preprocessor, LogisticRegression(max_iter=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dummy</th>\n",
       "      <th>logistic regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fit_time</th>\n",
       "      <td>0.005 (+/- 0.001)</td>\n",
       "      <td>0.129 (+/- 0.045)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_time</th>\n",
       "      <td>0.003 (+/- 0.001)</td>\n",
       "      <td>0.016 (+/- 0.002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_score</th>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.804 (+/- 0.013)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_score</th>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.809 (+/- 0.002)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         dummy logistic regression\n",
       "fit_time     0.005 (+/- 0.001)   0.129 (+/- 0.045)\n",
       "score_time   0.003 (+/- 0.001)   0.016 (+/- 0.002)\n",
       "test_score   0.741 (+/- 0.000)   0.804 (+/- 0.013)\n",
       "train_score  0.741 (+/- 0.000)   0.809 (+/- 0.002)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"logistic regression\"] = mean_std_cross_val_scores(\n",
    "    lr, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3516,  396],\n",
       "       [ 637,  733]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, cross_val_predict(lr, X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = make_pipeline(preprocessor, RandomForestClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dummy</th>\n",
       "      <th>logistic regression</th>\n",
       "      <th>random forest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fit_time</th>\n",
       "      <td>0.005 (+/- 0.001)</td>\n",
       "      <td>0.129 (+/- 0.045)</td>\n",
       "      <td>0.608 (+/- 0.102)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_time</th>\n",
       "      <td>0.003 (+/- 0.001)</td>\n",
       "      <td>0.016 (+/- 0.002)</td>\n",
       "      <td>0.049 (+/- 0.009)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_score</th>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.804 (+/- 0.013)</td>\n",
       "      <td>0.790 (+/- 0.013)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_score</th>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.809 (+/- 0.002)</td>\n",
       "      <td>0.998 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         dummy logistic regression      random forest\n",
       "fit_time     0.005 (+/- 0.001)   0.129 (+/- 0.045)  0.608 (+/- 0.102)\n",
       "score_time   0.003 (+/- 0.001)   0.016 (+/- 0.002)  0.049 (+/- 0.009)\n",
       "test_score   0.741 (+/- 0.000)   0.804 (+/- 0.013)  0.790 (+/- 0.013)\n",
       "train_score  0.741 (+/- 0.000)   0.809 (+/- 0.002)  0.998 (+/- 0.000)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"random forest\"] = mean_std_cross_val_scores(\n",
    "    rf, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3529,  383],\n",
       "       [ 719,  651]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, cross_val_predict(rf, X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- This is was we did in hw5. \n",
    "- What's wrong with this approach?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "And now the rest of the class is about what is wrong with what we just did!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Censoring and survival analysis (15 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Time to event and censoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine that you want to analyze _the time until an event occurs_. For example,\n",
    "\n",
    "- the time until a disease kills its host.\n",
    "- the time until a piece of equipment breaks.\n",
    "- the time that someone unemployed will take to land a new job.\n",
    "- the time until a customer leaves a subscription service (this dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our example, instead of predicting the binary label churn or no churn, it will be more useful to when the customer is likely to churn (the time until churn happens) so that we can take some action. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tenure\n",
       "6464      50\n",
       "5707       2\n",
       "3442      29\n",
       "3932       2\n",
       "6124      57"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tenure column is the number of months the customer has stayed with the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although this branch of statistics is usually referred to as **Survival Analysis**, the event in question does not need to be related to actual \"survival\". The important thing is to understand that we are interested in **the time until something happens**, or whether or not something will happen in a certain time frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Question:** But why is this different? Can't you just use the techniques you learned so far (e.g., regression models) to predict the time? Take a minute to think about this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The answer would be yes if you could observe the actual time in all occurrences, but you usually cannot. Frequently, there will be some kind of **censoring** which will not allow you to observe the exact time that the event happened for all units/individuals that are being studied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>57</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tenure Churn\n",
       "6464      50    No\n",
       "5707       2    No\n",
       "3442      29    No\n",
       "3932       2   Yes\n",
       "6124      57    No"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What this means is that we **don't have correct target values** to train or test our model.\n",
    "- This is a problem!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider some approaches to deal with this censoring issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Approach 1: Only consider the examples where \"Churn\"=Yes\n",
    "\n",
    "Let's just consider the cases _for which we have the time_, to obtain the average subscription length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>1304-NECVQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.55</td>\n",
       "      <td>149.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>8098-LLAZX</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>95.45</td>\n",
       "      <td>396.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5540</th>\n",
       "      <td>3803-KMQFW</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.55</td>\n",
       "      <td>20.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4084</th>\n",
       "      <td>2777-PHDEI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.05</td>\n",
       "      <td>78.05</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>6772-KSATR</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>81.70</td>\n",
       "      <td>81.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "3932  1304-NECVQ  Female              1      No         No       2   \n",
       "301   8098-LLAZX  Female              1      No         No       4   \n",
       "5540  3803-KMQFW  Female              0     Yes        Yes       1   \n",
       "4084  2777-PHDEI  Female              0      No         No       1   \n",
       "3272  6772-KSATR    Male              0      No         No       1   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "3932          Yes           Yes     Fiber optic                   No  ...   \n",
       "301           Yes           Yes     Fiber optic                   No  ...   \n",
       "5540          Yes            No              No  No internet service  ...   \n",
       "4084          Yes            No     Fiber optic                   No  ...   \n",
       "3272          Yes           Yes     Fiber optic                  Yes  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "3932                  Yes                   No                   No   \n",
       "301                    No                   No                  Yes   \n",
       "5540  No internet service  No internet service  No internet service   \n",
       "4084                   No                   No                  Yes   \n",
       "3272                   No                   No                   No   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling     PaymentMethod  \\\n",
       "3932                   No  Month-to-month              Yes  Electronic check   \n",
       "301                   Yes  Month-to-month              Yes  Electronic check   \n",
       "5540  No internet service  Month-to-month               No      Mailed check   \n",
       "4084                   No  Month-to-month               No  Electronic check   \n",
       "3272                   No  Month-to-month              Yes  Electronic check   \n",
       "\n",
       "     MonthlyCharges  TotalCharges  Churn  \n",
       "3932          78.55        149.55    Yes  \n",
       "301           95.45        396.10    Yes  \n",
       "5540          20.55         20.55    Yes  \n",
       "4084          78.05         78.05    Yes  \n",
       "3272          81.70         81.70    Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_churn = train_df.query(\n",
    "    \"Churn == 'Yes'\"\n",
    ")  # Consider only examples where the customers churned.\n",
    "test_df_churn = test_df.query(\n",
    "    \"Churn == 'Yes'\"\n",
    ")  # Consider only examples where the customers churned.\n",
    "train_df_churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5282, 21)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1370, 21)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_churn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure', 'MonthlyCharges', 'TotalCharges']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessing_notenure = make_column_transformer(\n",
    "    (\n",
    "        make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler()),\n",
    "        numeric_features[1:],  # Getting rid of the tenure column\n",
    "    ),\n",
    "    (OneHotEncoder(handle_unknown=\"ignore\"), categorical_features),\n",
    "    (\"passthrough\", passthrough_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "tenure_lm = make_pipeline(preprocessing_notenure, Ridge())\n",
    "\n",
    "tenure_lm.fit(train_df_churn.drop(columns=[\"tenure\"]), train_df_churn[\"tenure\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.062449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.198645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.859455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.865562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.154842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.757932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.932070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.720893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.818041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.263541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure_predictions\n",
       "0            5.062449\n",
       "1           13.198645\n",
       "2           11.859455\n",
       "3            5.865562\n",
       "4           58.154842\n",
       "5            3.757932\n",
       "6           18.932070\n",
       "7            7.720893\n",
       "8           36.818041\n",
       "9            7.263541"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    tenure_lm.predict(test_df_churn.drop(columns=[\"tenure\"]))[:10],\n",
    "    columns=[\"tenure_predictions\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What will be wrong with our estimated survival times? Will they be too low or too high? \n",
    "<br><br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "On average they will be **underestimates** (too small), because we are ignoring the currently subscribed (un-churned) customers. Our dataset is a biased sample of those who churned within the time window of the data collection. Long-time subscribers were more likely to be removed from the dataset! This is a common mistake - see the [Calling Bullshit video](https://www.youtube.com/watch?v=ITWQ5psx9Sw) I posted on the README!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Approach 2: Assume everyone churns right now\n",
    "\n",
    "Assume everyone churns right now - in other words, use the original dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
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      ],
      "text/plain": [
       "      tenure Churn\n",
       "6464      50    No\n",
       "5707       2    No\n",
       "3442      29    No\n",
       "3932       2   Yes\n",
       "6124      57    No"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenure_lm.fit(train_df.drop(columns=[\"tenure\"]), train_df[\"tenure\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>tenure_predictions</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.400047</td>\n",
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       "      <th>2</th>\n",
       "      <td>22.332746</td>\n",
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       "      <th>3</th>\n",
       "      <td>12.825470</td>\n",
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       "      <th>4</th>\n",
       "      <td>59.885968</td>\n",
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       "      <th>5</th>\n",
       "      <td>7.075453</td>\n",
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       "      <th>6</th>\n",
       "      <td>17.731498</td>\n",
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       "      <th>7</th>\n",
       "      <td>10.407862</td>\n",
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       "      <td>38.425365</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.854500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure_predictions\n",
       "0            6.400047\n",
       "1           20.220392\n",
       "2           22.332746\n",
       "3           12.825470\n",
       "4           59.885968\n",
       "5            7.075453\n",
       "6           17.731498\n",
       "7           10.407862\n",
       "8           38.425365\n",
       "9           10.854500"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    tenure_lm.predict(test_df_churn.drop(columns=[\"tenure\"]))[:10],\n",
    "    columns=[\"tenure_predictions\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What will be wrong with our estimated survival time?\n",
    "<br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
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       "      <th>3442</th>\n",
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       "      <td>No</td>\n",
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      "text/plain": [
       "      tenure Churn\n",
       "6464      50    No\n",
       "5707       2    No\n",
       "3442      29    No\n",
       "3932       2   Yes\n",
       "6124      57    No"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will be an **underestimate** again. For those still subscribed, while we did not remove them, we recorded a total tenure shorter than in reality, because they will keep going for some amount of time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Approach 3: Survival analysis\n",
    "\n",
    "Deal with this properly using [survival analysis](https://en.wikipedia.org/wiki/Survival_analysis).\n",
    "\n",
    "- You may learn about this in a statistics course.\n",
    "- We will use the `lifelines` package in Python and will not go into the math/stats of how it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
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       "      <th>3932</th>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>6124</th>\n",
       "      <td>57</td>\n",
       "      <td>No</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      tenure Churn\n",
       "6464      50    No\n",
       "5707       2    No\n",
       "3442      29    No\n",
       "3932       2   Yes\n",
       "6124      57    No"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Types of questions we might want to answer:\n",
    "\n",
    "1. How long do customers stay with the service? \n",
    "2. For a particular customer, can we predict how long they might stay with the service?\n",
    "3. What factors influence a customer's churn time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Kaplan-Meier survival curve (10 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we do anything further, I want to modify our dataset slightly:\n",
    "\n",
    "1. I'm going to drop the `TotalCharges` (yes, after all that work fixing it) because it's a bit of a strange feature.\n",
    "  - Its value actually changes over time, but we only have the value at the end.\n",
    "  - We still have `MonthlyCharges`.\n",
    "2. I'm going to not scale the `tenure` column, since it will be convenient to keep it in its original units of months. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Just for our sanity, I'm redefining the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = [\"MonthlyCharges\"]\n",
    "drop_features = [\"customerID\", \"TotalCharges\"]\n",
    "passthrough_features = [\"tenure\", \"SeniorCitizen\"]  # don't want to scale tenure\n",
    "target_column = [\"Churn\"]\n",
    "# the rest are categorical\n",
    "categorical_features = list(\n",
    "    set(train_df.columns)\n",
    "    - set(numeric_features)\n",
    "    - set(passthrough_features)\n",
    "    - set(drop_features)\n",
    "    - set(target_column)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "preprocessing_final = make_column_transformer(\n",
    "    (\n",
    "        FunctionTransformer(lambda x: x == \"Yes\"),\n",
    "        target_column,\n",
    "    ),  # because we need it in this format for lifelines package\n",
    "    (\"passthrough\", passthrough_features),\n",
    "    (StandardScaler(), numeric_features),\n",
    "    (OneHotEncoder(handle_unknown=\"ignore\", sparse=False), categorical_features),\n",
    "    (\"drop\", drop_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessing_final.fit(train_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's get the column names of the columns created by our column transformer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "new_columns = (\n",
    "    target_column\n",
    "    + passthrough_features\n",
    "    + numeric_features\n",
    "    + preprocessing_final.named_transformers_[\"onehotencoder\"]\n",
    "    .get_feature_names(categorical_features)\n",
    "    .tolist()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_surv = pd.DataFrame(\n",
    "    preprocessing_final.transform(train_df), index=train_df.index, columns=new_columns\n",
    ")\n",
    "test_df_surv = pd.DataFrame(\n",
    "    preprocessing_final.transform(test_df), index=test_df.index, columns=new_columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>3442</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.458524</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.183179</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Churn  tenure  SeniorCitizen  MonthlyCharges  StreamingMovies_No  \\\n",
       "6464    0.0    50.0            1.0        0.185175                 1.0   \n",
       "5707    0.0     2.0            0.0       -0.641538                 1.0   \n",
       "3442    0.0    29.0            0.0        1.133562                 0.0   \n",
       "3932    1.0     2.0            1.0        0.458524                 1.0   \n",
       "6124    0.0    57.0            0.0       -0.183179                 1.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  Partner_No  \\\n",
       "6464                                  0.0                  0.0         1.0   \n",
       "5707                                  0.0                  0.0         1.0   \n",
       "3442                                  0.0                  1.0         1.0   \n",
       "3932                                  0.0                  0.0         1.0   \n",
       "6124                                  0.0                  0.0         0.0   \n",
       "\n",
       "      Partner_Yes  OnlineSecurity_No  ...  PhoneService_No  PhoneService_Yes  \\\n",
       "6464          0.0                0.0  ...              0.0               1.0   \n",
       "5707          0.0                1.0  ...              0.0               1.0   \n",
       "3442          0.0                1.0  ...              0.0               1.0   \n",
       "3932          0.0                1.0  ...              0.0               1.0   \n",
       "6124          1.0                0.0  ...              0.0               1.0   \n",
       "\n",
       "      OnlineBackup_No  OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
       "6464              0.0                               0.0               1.0   \n",
       "5707              1.0                               0.0               0.0   \n",
       "3442              1.0                               0.0               0.0   \n",
       "3932              1.0                               0.0               0.0   \n",
       "6124              1.0                               0.0               0.0   \n",
       "\n",
       "      PaperlessBilling_No  PaperlessBilling_Yes  DeviceProtection_No  \\\n",
       "6464                  0.0                   1.0                  1.0   \n",
       "5707                  1.0                   0.0                  1.0   \n",
       "3442                  0.0                   1.0                  0.0   \n",
       "3932                  0.0                   1.0                  0.0   \n",
       "6124                  0.0                   1.0                  0.0   \n",
       "\n",
       "      DeviceProtection_No internet service  DeviceProtection_Yes  \n",
       "6464                                   0.0                   0.0  \n",
       "5707                                   0.0                   0.0  \n",
       "3442                                   0.0                   1.0  \n",
       "3932                                   0.0                   1.0  \n",
       "6124                                   0.0                   1.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_surv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- We'll start with a model called `KaplanMeierFitter` from `lifelines` package to get a Kaplan Meier curve.  \n",
    "- For this model we only use two columns: tenure and churn. \n",
    "- We do not use any other features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmf = lifelines.KaplanMeierFitter()\n",
    "kmf.fit(train_df_surv[\"tenure\"], train_df_surv[\"Churn\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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s2bKFgwcPxjoUU0KULl2aatWqUbFixZwXDlNRaofSClihqv7jE8zDjS/Q2Ps7qg4dPsJ/Jy+nda2KdG8aVtseYyKyf/9+Nm3aRJ06dShTpgwikvNKxuSBqrJv3z7Wrl1LYmIiSUlJUdluUbrLqwrHRuPztc1n/gm8URunici0zZs3R7zT+DjhzYnL+XrexojXNSYcmzdvJiUlhbJly1oyMQVCRChbtizVqlUjN+fFYIpSQhHcIC+BpgelqkNUNV1V01NSIi9hiAhNq5dn6aY9Ea9rTDj2799P+fLlYx2GKYGSk5PZv39/1LZXlBLKNgKXQir7zM8XjVOTWZyxGxuMzOSHrKws4uOL0tVnU1zEx8eTlZUVte0VpYQyD2ggImX9prfk2PCX+aJJanl2ZB5i616rMDX5wy51mViI9veuKCWUsbjxyS/PniAi8cCVwLf5eYdXk+rucsTiTbvzaxfGGFPkxaScLSKXeX+e7D33EZHNwGZVnSgi9YFlwJOq+iSAqs4SkdHAyyKSAKwAbgcaANfmZ7xNUpMBWJqxhy6NquXnrowxpsiKVQnlQ+9xm/f6de/1E95rAUpxYnw3AO8ATwPjgLpAb1WdkZ/BVq+QSHJSPEusYt6YsAwdOhQRYenS469E//7771SpUoX27duzZcsW0tLSEBGuueaagNvp2bMnIkLXrl0LIuwTDBo0iPHjx58wfcCAAcRibKWhQ4fy9ttvF/h+wxWThKKqEuTR05u/0ns9yG+9fap6r6rWUNUkVT1FVX/M73hFhCap5VmSYZe8jMmtX375hbPOOosmTZowfvx4qlVzpf3k5GQ+++wzdu8+/v9r1apVTJo0ieTk5FiEC8ATTzwRMKE8+uijfPrppwUejyWUYqJJajJLM6yEYkxuTJw4kV69etGmTRu+++47KleufHTe2WefTUJCAh9//PFx64wYMYK0tDTat29f0OHmqFGjRoUyrlizhBKmJtXLs2XPQbbZnV7GROS7776jT58+dOzYkW+++YYKFSocN79MmTJceumljBgx4rjpI0aMoH///rm6EykrK4tnn32W5s2bk5iYSK1atfjrX/96XJuLrKwsHn30URo1akRSUhLVqlWja9eu/PTTT8CxO6AGDx6MiCAiDBo0CDjxktfKlSsREd544w0efPBBatSoQXJyMv369SMzM5OlS5fSq1cvypcvT+PGjRk2bNhx8S5dupT+/fvToEEDypQpQ8OGDbn99tvZvv1YW+6ePXsyceJEfv7556Px9OzZ8+j8FStWcO2115KSkkJiYiLt2rUr8FKU3fwepsap7k6vJZt2c0rDqjGOxhR3T3w+j/nrd8U0hpa1KvD4+a3ytI1x48YxcOBAevbsyaeffkqZMmUCLnfddddx5plnsnbtWurUqcPUqVNZvHgx/fv3Z+LEiRHvt1+/fnz++ecMHDiQLl26sGDBAh599FFWrlx5tCT0/PPP89JLLzF48GDatWvHrl27mDZtGtu2uSZtU6ZM4dRTT2XAgAHceuutANSpUyfkfp999ll69uzJsGHDmD9/Pvfffz9xcXHMnDmTm2++mfvuu4///Oc/3HDDDaSnp9OqlTu+69evp06dOrz88stUrlyZ5cuX88wzz9C3b1+mTJkCwOuvv06/fv04fPgwb775JsDR5LxmzRpOOeUUUlNTeemll0hJSWH06NFceumlfPbZZ1xwQcH0nWsJJUxNq7vruEsy9lhCMSZM99xzD40aNWLMmDEkJiYGXa5Hjx7UrVuXkSNH8sADDzB8+HC6dOlC48aNI97n5MmTGT16NMOGDeO6664D4KyzzqJKlSr069ePWbNm0a5dO6ZMmcI555zD3XfffXTd888//+jfnTt3BqB27dpH/85Jo0aNjpY+evXqxeTJkxkxYgQjRoygX79+AKSnpzN27Fg++uijowmle/fudO9+bGin7PferVs3Zs6cSfv27WnZsiUVKlQgKyvrhHgGDRqEqjJx4kSqVq16dP9r1qzhscces4RS2NSsmES50qWsHsUUiLyWDAqLc889l3HjxvHss88evVwUiIjQr18/RowYwb333svo0aN55plncrXPr7/+mtKlS3PppZce1wr8nHPcSBmTJk2iXbt2dOzYkWeffZaHH36YPn360KlTJ0qXLp2rfWbr06fPca+bN28OuJN7tsqVK5OamsqaNWuOTjt48CAvvPACw4cPZ9WqVcddmlu0aFGO9TVff/01ffv2pWLFise95169evG3v/2NXbt2nXCpMT9YQgmTiNC4erLd6WVMBF566SVq1KjBE088QVJSEg888EDQZa+77joGDx7ME088wd69e7nyyitztc+MjAwOHjwYtH+0rVu3AvDQQw+RlJTEyJEjeeaZZyhfvjyXXXYZ//jHP47egRYp35sNgKMJKtB036Tx4IMP8sorr/DYY4/RpUsXkpOTWbt2LZdccklYfW1lZGQwfPhwhg8fHnD+1q1bLaEUNk1SyzNxcfR65jSmuBMRhgwZwoEDB3jwwQdJSkrinnvuCbhs06ZNOeWUU3juuee45JJLqFSpUq72WbVqVZKSkpg8OfCAsLVq1QIgISGBgQMHMnDgQDZu3MgXX3zBvffeS2ZmJqNHjw64bn4ZNWoU1113HY888sjRaXv2hH81pGrVqnTr1o2BAwcGnJ/9nvObJZQINEktz0fT17Ij8yCVyuataGxMSREXF8fQoUM5ePAgf/nLX0hKSuK2224LuOz999/P8OHDufPOO3O9v969e/P888+zc+dOzjzzzLDWqVGjBjfddBNffvklc+fOPTq9dOnS7Nu3L9exhCszM5OEhITjpr3zzjsnLJeYmHhCex1w73nKlCm0atUq6I0PBcESSgSyK+aXZuwhPS3g8CvGmABKlSrFu+++y4EDB/jzn/9MYmIiN9xwwwnLXXLJJVxyySV52lfPnj25+uqrueyyy7j33nvp1KkTcXFxrFy5ki+//JLnn3+epk2bcuGFF3LSSSfRoUMHKleuzMyZM/n666+P3tEF0LJlS8aNG0fv3r2pXLkytWrVypdf+71792bYsGG0adOGxo0b88knn/DLL7+csFzLli15/fXXGT16NI0aNSI5OZlmzZrx5JNP0qlTJ7p3786dd95JWloa27dvZ+7cuSxfvrzAGkNaQonA0VuHLaEYE7H4+Hg++OADLrroIm666aaQd33l1ciRI3nllVd4++23GTx4MImJiaSlpdGrVy+qV68OuDurPvzwQ1577TUyMzOpV68e999/Pw8//PDR7bz66qvcddddnH/++Rw4cIDHH3885M0FufXKK6+gqkf33bdvX95//306dep03HIDBw5k0aJF3HTTTezZs4cePXrw448/Uq9ePaZNm8agQYN46KGH2Lx5M1WrVqV169Zcf/31UY83GClJY3ykp6frtGnTcr3+kSNKq8e/4apOdYvNXTgm9hYsWECLFi1iHYYpoXL6/onIdFVND2db1lI+AnFxQuPU8nbrsDHGBGCXvCLUJLU8vyzbGuswjCmRDh8+HHLk1Li4OOLi7HdyrNiRj1CT6sls3LWfXfsPxToUY0qcM888k4SEhKCPG2+8MdYhlmhWQolQE69ifmnGHjrUq5zD0saYaHrzzTcD3jabLbcNEk10hJVQRORm4D1V3ZvP8RR62cMBL9m02xKKMQWsWbNmsQ7BhBDuJa83gPUi8pqItM3PgAq7OpXLkhgfZ6M3mqgqSXdbmsIj2t+7cBNKI9wwvZcAM0VkiohcLyJJUY2mCCgVJzRKKc+CjbHtWtwUHwkJCQXSGtsYf/v27TuhhX5ehJVQvCF5H8SN4X4VkAm8jSu1vCQiJeom+u5NU5iybCurt2bGOhRTDKSmprJu3ToyMzOtpGIKhKqSmZnJunXrSE1Njdp2c92wUUQaA28B3bxJPwF/V9VxUYot6vLasDHbxp376fb38VzTqR5PXNg6CpGZkm7Xrl1kZGRw6JDdPWgKRkJCAqmpqTn2QhxJw8aI7/ISkWSgP3Ar0AaYAXwEnA+MFZHBqvpYpNstSmpUTOKCk2rzwbS13HNWUyqXs44iTd5UqFChQLoXNyY/hd0ORUTSReS/wHrgBWAWcKqqpqvqc6p6GjAIuCM/Ai1sbunekH2HDjNy6qpYh2KMMYVCWAlFRKYDvwKnA08CdVT1elX91W/R74AScS9tsxrJ9GiawrApK9l/6HCswzHGmJgLt4SyHjgPaKKq/1DVbUGWmwE0iEpkRcCt3RuyZc9BPpu5LtahGGNMzIWbUP4BTNYANfgiUl5EugOo6kFVLTHXgE5tVJXWtSswZPJyjhyxu3OMMSVbuAllAtAyyLxm3vwSR0S4uVtDlm/ey/iFGbEOxxhjYirchCIh5iUCJbYS4dw2NaldqQxDJi2PdSjGGBNTQW8bFpE0oKHPpHQRKe+3WBngRmB19EMrGuJLxXFztwYM+nw+kxZvpnvTlFiHZIwxMRGqHcr1wOOAeo9XOL6kot7rLErIrcLBXH1KPd76aQXPfbWQro2rERcXqkBnjDHFU6iEMhT4EZc0xuOSxny/ZQ4Ai0Pc9VUiJMaX4m+9mnH3qFmMmb2Oi9vXiXVIxhhT4IImFO9urVUAInI6MENVgw9EUMKd37YWb01ewQvfLKZP65okJZSKdUjGGFOgwu0ccqIlk9Di4oQH+zRn3Y59DJ+yMtbhGGNMgQtVKb8cuFhVZ4vIClydSTCqqo2iHl0R06VxNXo0TeHV8Uu5Ir0ulcpaH1/GmJIjVB3KRGCXz9/Wci8MD/RpTt9/T+b1H5fxUN8S1au/MaaEC1WHcoPP3wMKJJpioEXNClzSvg5Df15Jv1PqU69q2ViHZIwxBSLs3oZN+O7r1ZSEUsLDn/1hAyYZY0qMUHUo10WyIVUdnvdwioeaFcswsE9zHhszj49nrOOyk+02YmNM8ZdTO5RwKRBWQhGRusBLwNm4Ni7fA/eoao6t7UWkAa6jyrOABOA34G+qmvdhGKOs3yn1GTNrPU99MZ8eTVNISU6MdUjGGJOvQl3yahDBo2GQbRxHRMriGkk2x7XE7w80ASaISLkc1q2KG2a4NW60yKu8WRMK45j2cXHC85e2Yd/Bwwz6fF6swzHGmHyXU8PGaLsZl3yaqepSABGZAyzBJYkXQ6x7O1Ad6OGz7nhgOfAEcEU+xJsnjVOTufOMxrz43WIuareJs1tWj3VIxhiTbwq6Uv4CYGp2QgBQ1RXAz8CFOazbGVjit+5eYDJwnoiEunwXM7f1aESz6sk8+tlcdu0/FOtwjDEm3wRNKCKyXERO8v5e4b0O9lgW5v5aAXMDTJ9H8PFWsh0GDgaYfgDX63GhbFhZOj6O5y9rS8bu/Qz8aI4NxGWMKbYKumFjFWB7gOnbyHks+kXA2SJSVVW3AohIHNDJZ9snEJFbgFsA6tWrl5uY86xd3Uo82KcFg79cwIvfLea+Xs1iEocxxuSnWDRsDJSYwunv/Q3gLmC4iNwFZAIPc2wM+yMBd6Y6BBgCkJ6eHrPiwU3dGrA0Yw+vTlhKo9Ry1iOxMabYKeg6lO0ELklUJnDJ5ShVXQ5cC5wMLAXWA6fibkEG2BC9MKNPRHjqotac0qAKAz/6g+mrSnSP/8aYYijshCIiTURkmIgsFpG93vNQEWkcwf7m4epR/LXkxLFWTqCqHwO1veUbq+rJQHlgTTjtWGKtdHwcb/Q7mVqVkrhl+HTWbMuMdUjGGBM1YSUUEekJzAbOA6YCr3vP5wN/iEiPMPc3FugsIkfbrXhDDZ/mzcuRqh5W1QWqukxEagFXAv8Jc/8xV7lcad66viMHDx/hotd+ZsysddY9izGmWJBwTmYiMh3YD/RS1T0+05OBb4EEVU0PYzvlcIlpH/AIrj7lKSAZaJu9bRGpDywDnlTVJ71pCcDfOXazQCvgQW+5M1U10B1gx0lPT9dp0wpHo/pFG3dz/8dzmL1mBz2bpfD0Ra2pU9k6kjTGFC4iMj2c8zuEf8mrJfC8bzIB8Abdep7Al7FO4LUbOQNYDIwA3gVWAGf4bVuAUn7xKa5V/ZvAV8A9wNu4JJdjMilsmtVI5pPbu/D4+S35bcU2znlpEsOnrLTSijGmyAq3MeBaINhoUaWBdeHu0KvruDSHZVbid+eXqmbhLrkVG6XihBtOa8DZLavz8KdzeWzMPBZt3M0TF7QivpR1BG2MKVrCPWs9DzwhIrV9J3qvHweeiXZgJUmdymV5Z0BHbuvRiHd/Xc0tI6az90BWrMMyxpiIhOq+3r/34GRgmYhMBTbh+tXq7P3dA3f5yeRSXJzwQJ/m1KlchsfGzOXKIVN4+/qOpFZIinVoxhgTlqCV8iKykvBbx6uqhtXjcCwVpkr5UCYszOCO92ZQuWxpht3YkcapybEOyRhTQkVSKR/WXV7FRVFJKABz1+1kwDu/k3XkCO8M6Ej7ejn1TGOMMdGXH3d5mQLWunZFPr79VCqWSeCa//7Kj4syYh2SMcaEFHFCEZFUEann/8iP4Eq6+lXL8dFtXWiYUo6bhk3j05lrYx2SMcYEFW5L+TgReUZEtuL6zFoR4GHyQUpyIqNu6UynBlX4y+jZvDp+ibVVMcYUSuGWUO4B7gD+iWsf8gzwNC6RLMONxGjySXJSAu/c0JGL2tXihW8X83/vz2TfwcOxDssYY44TbkK5AXgS1x4F4FNVfRxogWvUaJe88llifCleurIdD/Rpzrg/NnD5m7+wfse+WIdljDFHhZtQGgLTVPUwkIUbIRFVPQS8DNyYL9GZ44gIt/VoxFvXpbNySyYXvPoz01ZaN/jGmMIh3ISyE8huYbce8B1yMJ4goyWa/HFmi+p8+uculEssxVVDpjLsF+sDzBgTe+EmlJkcG/P9G1w3LFeLyOXAs8CM/AjOBNekejJj7+xKz2YpPD52Hn8ZPYvMg9ZdizEmdsJNKC/jhtwF13fXRlxPwaOBBODOqEdmclSxTAJD+qdz3zlNGTN7PRe/9gsrtuyNdVjGmBIqVy3lRUSARkBZYIFXl1LoFaWW8pGatHgzd4+ayb5Dh7n37KbceFoD67HYGJNn+d5SXp2lqjqnqCST4q570xS+vLsbXRun8MyXC7ng1Z+Zs3ZHrMMyxpQgkYwpX0lEnhCRb0Vknvc8SEQq5WN8JgI1K5bhv9edzBv9OrBlzwEueu1nnvx8vtWtGGMKRLgt5U8CluCG3E0C5nvPDwGLRaRNvkVoIiIi9G5dk+//2oNrTqnH2z+voM+/JvPbCru92BiTv8Itofwb2Ao0UdXuqnq5qnYHmgLbgFfyK0CTOxWSEnj6ojaMuqUzqnDlkCkMGjvPSivGmHwTbkLpCDyqqqt8J3pD9T4OdIpyXCZKOjesytf3dOO6zvUZ+stKK60YY/JNuAllK3AgyLz93nxTSJUtHc8TF7Zm1C2dOaLKlUOmMHjcfPYfsv7AjDHRE25C+Q/wNxE5bjxaESkD3Ae8Fu3ATPR1bliVr+/uzjWd6vHfySs499+TmbVmR6zDMsYUE6HGlH/S9yVQH1gtIl9ybEz5vsA+XHsUUwSUS4xn8MVt6NWqBgM/nsOl//mFG7qk8X9nNqFimYRYh2eMKcJCjSl/JILtqKqWik5I+ac4N2zMjZ37DvHcVwsY9fsaqpQtzX29mnFFel1KxUmsQzPGFBJRadioqnERPAp9MjEnqlgmgWcvacvnd3alYUo5HvzkD85/5Semr9oe69CMMUWQ9c1haF27Ih/ceiqvXtOeHZkHueLNKbw1ebn1YGyMiUjQOpRAROQ8oAeuu/qtwERVHZcfgZmCJSKc17YW3Zum8LcPZ/P0uAXMXL2D5y9rS/nEiL4mxpgSKqwzhYgkA18A3XADbG0FqgJ/FZHJwHmquiffojQFpkJSAm/0O5k3Jy3n718vZNGm3bzRrwONU5NjHZoxppAL95LXM0AHoD9QRlVr4kZtvM6b/kz+hGdiIXtkyJF/OoXtew9y7r9/4l/fL7F2K8aYkMJNKJcCj6jqu94wwKjqYVV9F3jUm2+KmS6NqzHurm6c1bI6L32/mLNfmsi38zZa3YoxJqBwE0pVXIeQgcz35ptiqEbFJF67pgPv3XQKSfGluGXEdAa88zuLNu6OdWjGmEIm3ISyAjgvyLy+3nxTjHVpXI0v7+7Go+e1ZMbq7fT51yT+9uFsNuzcF+vQjDGFRLi377wJ/FNEyuOG/t0A1ACuAm4C7s2f8ExhklAqjj91bcAl7Wvz2oSlDJ+yirGz1zPgtDRu6daQquUTYx2iMSaGwh4CWESeAf4ClM6ehOsw8kVVfTh/wosuaykfXWu2ZfLSd4v5dNY6EkrFceFJtbjhtAa0rFUh1qEZY6IkkpbyYSUUEamISx5lgM64dijbgKmqWmSaVVtCyR9LM3bzzs8r+WTGOvYdOswpDapwY9cGnNWiunXjYkwRF9WEIiLxuC7qL1bVz6MQX8xYQslfOzMPMer31Qyfsop1O/ZRr0pZBnRJ4/L0OiQnWceTxhRF+VFCWQfcrKpf5jW4WLKEUjCyDh/h2/mbePunFUxbtZ3kxHguPbkOV3WqS/MadjnMmKIkPxLK87jhfy/Ja3CxZAml4M1as4O3f1rBV3M3cOiwclLdSlyZXpfzT6pppRZjioD8SCi3Aw8BG4ExuLu8jltRVd8OM7i6wEvA2biK/e+Be1R1dRjr1gOeAk4HqgFrgQ+AZ1V1b07rW0KJnW17D/LpzHV88PsaFm3aTVJCHD2bptK3bU3ObJ5KOesvzJhCKT8SSk5jo4Q1HoqIlAVm4yr4H8ElpadxA3S1DZUURKQcMBNIAAYBq3Fj3T8BjFXVK3PavyWU2FNVZq/dyacz1vLV3I1k7D5AYnwcPZulMKBLAzo3rIKIVeQbU1hEklDC/VnYIA/x+LoZaAg0U9WlACIyB1gC3Aq8GGLd04AmQC9V/dabNkFEqgD3iUhZVc2MUpwmn4gI7epWol3dSjx+fiumrdrOl39s4Is56/lm3iY6plXmzjOa0L1JNUssxhQxYSUUVV0Vpf1dgLvVeKnPtleIyM/AhYROKNntX3b5Td+Ba/FvZ58iJi5O6NSgCp0aVOGBPs0Z/fsa3pi4jOvf/o2T6lTksvS6dG5Qhcap5S25GFMEhN2wEUBEGgOdgNrAOuA33+QQxvobgTGqeqvf9NeBy1U1JcS6ScAcXP3N7bhLXp1wLfc/VdU/57R/u+RV+B3MOsLHM9by5sRlrNzqCpxVy5XmlIZVOL9tLXq3rmHJxZgCFPVLXt7J/HVc9/W+dSWHRWQYcIeqHghjU1WAQA0htwGVQ62oqvtFpCvwMTDPZ9ZbwJ0hYr8FuAWgXr16YYRoYql0fBxXd6rHVR3rsnpbJlOXb+XX5duYsnwrX/6xkbNbVmfwRa1JrZAU61CNMX7CrUN5AbgWeBwYBWwCqgNXA48BmcBdYW4rUJEox5+cXlIbDaTiElt2CeUx3KBftwfcmeoQYAi4EkqYMZoYExHqVy1H/arluLJjPQ4fUf7303Je+HYxZ780icfPb8nF7WtbacWYQiTchHIV8ISq+g6ktRwY7P1D/4XwEsp2XCnFX2UCl1x8/QnoCTRW1WXetEkishMYIiJvqOrsMGIwRVCpOOGW7o04s0V17v9oDvd+MJsxs9bzQJ/mtKhpjSWNKQzC7b4+EfgtyLxfOVZhnpN5QKsA01sSfLyVbG2A7T7JJFt2XC3CjMEUYY1SyvPBrae6bvRXbafPvyZz53szWJphI1AbE2vhJpTvgXOCzDsHGB/mdsYCnUWkYfYEEUnD3RI8Nod1NwKVvRsDfJ3iPa8LMwZTxJWKE/7UtQGTB57OHac3YvzCDM55aSL3jp7Fgg3+NwEaYwpKuA0buwEjgHHAhxyrQ7kCN8BWP2B99vKqujzIdsrhGjbu41jDxqeAZFzDxj3ecvWBZcCTqvqkNy0Nd5fXRmAwrg4lHTcE8WKgk6qGbIBpd3kVT1v3HODNScsZPmUl+w8doVNaFa7vksY5raqTUCrc30zGmEDyu6W87woSYBqhWs173af4dr3yA67rlZU+y6ThRoF8QlUH+UxviWslfyqu65U1uJLN4HC60beEUrztyDzIB9PWMGLqKtZs20eNCkmc0SKV9PqV6ZhWhTqVy1glvjERyo+Ecn0kAajqsEiWLyiWUEqGw0eUHxdl8P5vq/l1+TZ2H8gCIDU5kdMaV+PMFqn0aJpinVMaE4aoJ5TiwhJKyXP4iLJ4026mrdrO7yu2MXnJZrZnHiKhlNC5YVXOblmdPq1rkpJswxcbE4gllCAsoZjDR5QZq7fz/fxNfLdgE8s37yVO4NRGVTmvbS16t6pB5XLh3rRoTPFnCSUISyjG36KNu/lizno+n72elVszSSgl9G1Tk+tOrU+HepWtzsWUeJZQgrCEYoJRVeat38VH09fy8fS17D6QRcuaFbju1Pr0bl2DSmWt1GJKJksoQVhCMeHYeyCLz2atY8SUVSzcuJs4gXZ1K9GjaSo9m6XQpnZF4uKs5GJKBksoQVhCMZFQVWat2cGERZuZuCiDOet2ogrNayQzsE9zejZNsUtiptizhBKEJRSTF1v3HOCHBRm8OmEpq7dlcoo3jkv7eiE7yjamSItKQhGRxyLYp6rqUxEsHxOWUEw0HMw6wqjfV/PvH5awZc9BTm+WwuXpdTmjeSpJCTmOhG1MkRKthJLTOPK+whpTPtYsoZho2nMgi/9NXsF7v61i064DJCfFc17bmlzYrjYd6lWmdLx1+2KKPrvkFYQlFJMfDh9Rflm2hU9nrOPreRvJPHiYsqVL0TGtCl0aVeW0xtVoVauC1beYIskSShCWUEx+23sgi8lLtvDLsi38smzr0W71m9dIpl/n+lzUvjblE8MdhsiY2LOEEoQlFFPQNu3azw8LMhg5dRXzN+yifGI8l3SozUXta9OuTiW7/dgUevmSULyx2W8HmuEG3DqO1aEYE5yqMnPNDkZOWcUXczZw8PARUpITOatFKme3rE6XRtWsQt8USpEklLDK3iJyHfAKMAw4CXgbSAAuADYD7+YuVGNKBhGhQ73KdKhXmcfPb8WERRl8N38TY2et5/3f1lClXGmu7lSXfp3rU7NimViHa0yuhNt9/QzcuCNPAYeAdFWdISKVgR+B/6rqq/kZaDRYCcUUNgeyDvPL0q28/9tqvl+wCRGhd6saXN8ljY5p1peYib2ol1CAJsAk4Ij3KA2gqttFZDBuBMVCn1CMKWwS40txevNUTm+eypptmYyYuopRv61m3B8baJxanqs61uXSDnWsB2RTJIR7o/w+IE5dcWYj0NBn3h6gVrQDM6akqVulLA/1bcHUh87k75e1JTkpnqfHLeCUZ37grvdnMnX5VkrSTTSm6Am3hPIH0Bj4HpgMPCQiK4As3JC8C/MlOmNKoLKl47kivS5XpNdl4cZdjPptDZ/MWMvY2etplFKOqzvV47KT61gPyKbQCbcO5Uqgoao+KyLZiaWuN3s3cJGq/phvUUaJ1aGYomrfwcN8MWc97/22mpmrd1A6Po7z2takX+f6tK9byepaTL7J93YoIlIOOBUoC/yiqlsi3kgMWEIxxcH89bt477dVfDpjHXsPHqZlzQpeo8lalC1tjSZNdEU9oYhIKVU9nOfIYswSiilO9hzI4rOZ6xg51Y3bUqlsAtd1rs91XdKoVv6EpmLG5Ep+JJRNwPvACFWdnsf4YsYSiimOVJXpq7YzZNJyvluwidKl4rg8vQ43ntaAhinlYx2eKeLyI6H8B7gcqAwsAoYD76nq6rwEWtAsoZjibmnGHt6avJxPZqzj4OEjtK1TkfPb1uLctjWpVckaTJrI5VfXKwnAuUB/oC+upfwkXHL5WFV35y7cgmMJxZQUGbv2M2bWej6fs545a3cC0KlBFe46owmnNa5qlfgmbAVRKV8RuBLoB3QB9qtqoS9bW0IxJdHKLXsZ98cG3vt1Net27OPUhlW5r1czTq5vI02anBVIb8MiUheXUO4AalrnkMYUbgeyDvP+r6t5dcLSoyNNdmlUjXpVy1KvSlnqVilrXeubE+RH1yvZG07G1aX0B7oB+4HPgRGRBmmMKViJ8aUYcFoDLk+vy9BfVvL2TyuYsGjzcctULVeaelXLUr9KWepVLUeHepXo3iTFutk3YQm3Uj677uR8IAlXdzIC+LAo1J1ksxKKMceoKjv3HWL1tkxWb8tk1dZM1vj8vWHnPo4o1K1Shn6n1OeK9LrWp1gJlB93eR3B3d01AhhZ1O7uymYJxZjwHcg6zHfzNzF8yip+W7GNxPg4LmpXm3vObmJd7Jcg+ZFQOqrq73mOLMYsoRiTOws37mLElFV8OH0tcQK3dm/ErT0aWsv8EsCGAA7CEooxebNmWybPfb2QcXM2UKNCEn/r1YwL2tUioVS4HZeboiYqCUVE3gaeUtUV3t+hqKr+KcI4C5wlFGOi4/eV23jqi/nMWbuTauUTuezkOlyRXsda5hdD0UooK3C9CM8WkZVAqKKMqmrDEPMLBUsoxkTPkSPK+IUZjPp9DRMWZXD4iNIprQp/6taAc1pWt8aTxYRd8grCEoox+SNj134+mrGWUb+tYfW2TFrUrMDdZzbmnJY17JbjIs56Gw7CEoox+Svr8BHGzl7PK+OXsmLLXprXSOaG09I4s0V16wG5iMrP3oZHqmqRPSNbQjGmYGQdPsLnc9bz6vilLNu8FxHoUK8yZ7WoTq9W1a2upQgp1L0Ne122vAScDQhu9Md7ctqWiAwCHg8y+4CqJuW0b0soxhQsVWX+hl18Pz+D7xZsZO66XQCcVLcSl7Svzfkn1aKKNZYs1Aptb8MiUhaYDRwAHsFV9D+NG/mxraruDbFuHaCO3+RywNfAp6p6RU77t4RiTGyt37GPL//YwMcz1rFgwy7i44SezVLp26YGZ7aoTsUyCbEO0fgptL0Ni8jdwItAM1Vd6k1rACwB7lfVFyOMoz8uoZ2nquNyWt4SijGFx4INu/h05jrGzlrPxl37iY8TujSuRp/WNbi4fW2SEgp9f7MlQqHtbVhEfgCSVPU0v+kTAVS1R4QxfA+0BuqoalZOy1tCMabwOXJEmb12B1/P28jXczeyamsmzWsk88rV7WlSPTnW4ZV4kSSUiJq3ikiyiNwoIhOAFcDDwGRcp5HhaAXMDTB9HtAywljqAKcD74aTTIwxhVNcnNC+XmUe7NOCH+/ryTsDOrJlzwHOe+UnRk5dRUlq2lDUhZVQRORcERkFbAT+602+BVcyuVpVvwxzf1WA7QGmb8NV+EeiPy7+YaEWEpFbRGSaiEzbvHlzqEWNMTEmIpzePJWv7u7OKQ2r8shnc7lt5HS27T0Y69BMGMItoXwOnAQMBhqo6umq+nYuu64P9HMjNy2frgNmquqckDtTHaKq6aqanpKSkovdGGMKWkpyIkMHdOSRc1swfmEGPf4+gZe/X8zu/YdiHZoJIdyuQjsDc1R1fx73tx1XSvFXmcAll4BEpBPQHLgnj/EYYwqpuDjhpm4N6dE0hRe/W8zL3y9h6C8rubV7I67vUt96Oi6EciyhiEg88Auu3UhezcPVo/hrCcyPYDvXA1nAe1GIyRhTiDWpnsx/+p3M53d2pX3dSjz/9UK6Pj+BV8cvYec+K7EUJjkmFK/CexMQja5XxgKdReRoR5Iikgac5s3LkYiUBq4CvlRVqxQxpoRoU6ci79zQiY9vP5WT6lTkhW8Xc9pz43n2qwVk7M7rxRMTDeHWoYwEborC/v4LrATGiMiFInIBMAZYA7yZvZCI1BeRLBF5LMA2zsNdNgtZGW+MKZ5Orl+Fd27oxJd3deP05qn8d9Jyuv99Aq9NWMrBrCOxDq9EC/ci5ErgGhH5HZcANuBXua6qOY2ZgqruFZEzcF2vjMBVxv+A63plj8+iApQicMK7HndX2Bdhxm6MKYZa1qrAK1e3569nN+W5rxbyj28W8cmMtTx1YWu6NK4W6/BKpEjGlA9Fw2nYGGvWsNGY4mvCwgweHzuP1dsyubBdLW7r0YgWNSvEOqwiL5KGjeGWUBrkIR5jjMl3pzdP5dRGVXn9x2W8MXEZY2atp03tilyRXocLTqpNxbLWT1h+swG2jDHFzra9Bxkzax0fTFvLgg27KB0fR5/WNbiqYz06N6xio0lGwEZsDMISijElz9x1O/lg2ho+nbmO3fuzaFCtHFd2rMslHWqTmpzjqBclXn6Mh7KC0GPKY2PKG2MKs/2HDvPlHxsY9dsaflu5jTiBzg2rcl7bWvRuXcPGZQkiPxLKUE5MKFVxXdfvAcar6o0RxlngLKEYYwCWZuxh7Oz1fDF7Pcu37KVUnNC1cTUubFeLc1rVoHyitcLPVmCXvESkEm6Aq3+raqFvtW4JxRjjS1WZt34XX8zZwOez17Nuxz6SEuI4u2UNLm5fi55NU4mLK9n1LQVahyIilwKDVbV5njZUACyhGGOCOXJEmbF6O5/NWse4ORvYnnmI5jWS+es5zTirRWqJrcgv6IRyLjA6nBEbY80SijEmHIcOH2HcnA28/P1iVm7NpF3dStx3TjNOa1y1xCWWghqxMR43WuJ/gSOqekquNlSALKEYYyJx6PARPp6+ln//sIT1O/dTrXxp0utXIT2tMh3TqtCiZgVKx0c0TmGRE/WGjV5L+WCZZxdwbpixGWNMkZFQKo6rOtXj4g61GTtrPVOWb2Xayu18PW8jAKXihPpVy9IktTxNUpM5qW4lujetRmJ8oe84JF+EeyvDk5yYUPYDq4CvVHVnVKMyxphCJDG+FJen1+Xy9LoAbNq1n2krt7Ngwy6WZOxmScYevl+QweEjSnJSPL1b1eCCdrU4tWFV4ksV7xKML2vYaIwxUXAg6zBTl29j7Kz1fDNvI3sOZFGjQhKDLmhJ79Y1Yx1eruV7HYqIVASaABtVdW3EG4gRSyjGmIKw/9BhJizM4NUJS5m3fhd9WtfgiQtbFcmW+ZEklKBlMRHpJSLPBZj+MJAB/AqsEpH3vAp6Y4wxQFJCKfq0qclnd5zG33o144eFGZz94iQ+mr6W4nxVKNTFvduApr4TRORs4ClgIW489zeBK4G78yk+Y4wpshJKxXHH6Y358q5uNEktz30fzuapLxYU26QSqmTRHpc8fN2Aq4zvpaobgex7sq8B/pkfARpjTFHXOLU8H9x6Kk+Nm8/bP6/giCqPn9+y2LVpCZVQUoFlftPOBn7KTiaecUD/aAdmjDHFSVyc8Nh5LSklwls/rUBVGXRBq2KVVEIllN1AuewXItIE1yHkVL/lduGG6zXGGBOCiPDwuS2IixOGTFrOYVWevKB1sekvLFRCWQhciCuB4P2twLd+yzUANkU/NGOMKX5EhAf7NCdOhDcmLmP6qh30bV2D3q1r0Di1fJEusYRKKC8Bn4hIFVzCGAD8Afzst9zFwOx8ic4YY4ohEWFg72bUr1qWD6et4Z/fLeaf3y2mYUo5rkivyw2npRXJ1vYh26GIyF3AX4EqwG/Abaq6xGd+HWAucL+qDsnnWPPM2qEYYwqjTbv28+38TYybs56py7eRVrUsj5/fitObp8Y6NBsCOBhLKMaYwm7yks0MGjuPZZv3cmbzVB47vyX1q5bLecV8EpWGjcYYYwpetyYpfHV3dx7q25ypy7fS6+VJjJ29PtZhhcUSijHGFDKl4+O4pXsjxt/Xkza1K3LX+zN55ssFZB0+EuvQQrKEYowxhVT1Ckm8e1Nn+neuz5BJyxnwzu9s33sw1mEFZQnFGGMKsdLxcTx1UWv+fmlbfluxjfNf/Yn1O/bFOqyALKEYY0wRcEXHuoy+tTPb9x7k7lEzC+XlL0soxhhTRLSvV5nBF7fh95Xb+fcPS3JeoYBZQjHGmCLkova1uezkOrwyYSm/LNsS63COYwnFGGOKmCcvbEWDauW4Z9Qstu45EOtwjrKEYowxRUzZ0vG8enUHduw7xF8/nM2RI4WjgbolFGOMKYJa1qrAo+e24MdFm/lXIalPsaF7jTGmiOrXuT6z1+7kXz8soVpyIv07149pPJZQjDGmiBIRnrukDdv3HuSxMXOpXDaB89rWilk8dsnLGGOKsPhScbx2bQfS61fmL6NnMXnJ5pjFYgnFGGOKuKSEUrx1fUcapZTn1hHTmbVmR0zisIRijDHFQMUyCQy/sRPVyicy4J3fWLJpd4HHUOAJRUTqishHIrJTRHaJyCciUi+C9VuIyIciskVE9onIIhG5Oz9jNsaYoiC1QhIj/tSJhFJx9P/fb6zdnlmg+y/QhCIiZYHxQHPgeqA/0ASYICI5jiAjIunAr0AicBPQF/gnUPTGyjTGmHxQv2o5ht/YicyDWfT/329s3l1wDR8LuoRyM9AQuEhVP1PVMcAFQH3g1lArikgcMAz4QVUv8NafoKpDVPXFfI/cGGOKiBY1K/DODR3ZsHMf17/9G7v2HyqQ/RZ0QrkAmKqqS7MnqOoK4GfgwhzW7Qm0BCx5GGNMDk6uX4U3+6ezJGM3Nw2dxv5Dh/N9nwWdUFoBcwNMn4dLFqF09Z6TRGSqiBwSkQwR+beIlIlqlMYYUwz0aJrCS1e2o2FKOeLjJN/3V9ANG6sA2wNM3wZUzmHd7NY6o4FXgQeAdOBJoC5wcaCVROQW4BaAevXCrvs3xphi4by2tQqssWMsWsoH6sUsnNSZXZoaqaqPeX//KCKlgOdEpKWqzj9hZ6pDgCEA6enphaMHNWOMKYYK+pLXdlwpxV9lApdcfG31nr/zm/6t99wu92EZY4zJq4JOKPNw9Sj+WgInlC4CrAsnlnCySzeFbzxMY4wpQQo6oYwFOotIw+wJIpIGnObNC+Ur4ADQ2296L+95WpRiNMYYkwsFnVD+C6wExojIhSJyATAGWAO8mb2QiNQXkSwRya4rQVW3As8Ct4nIMyJylog8ADwGDPO9FdkYY0zBK9BKeVXdKyJnAC8BI3CXq34A7lHVPT6LCq71u3/CexLYDfwZuA/YAPwDeCqfQzfGGJODAr/LS1VXA5fmsMxKAtz5paqKa9hojRuNMaaQsd6GjTHGRIW4H/0lg4hsBlblcvVqwJYohlPS2fGMPjum0WXH06mvqinhLFiiEkpeiMg0VU2PdRzFhR3P6LNjGl12PCNnl7yMMcZEhSUUY4wxUWEJJXxDYh1AMWPHM/rsmEaXHc8IWR2KMcaYqLASijHGmKiwhGKMMSYqLKGEICJ1ReQjEdkpIrtE5BMRsVG6ciAil4nIxyKySkT2icgiEXlWRJL9lqssIm+JyBYR2Ssi34tIm1jFXdSIyNcioiLytN90O65hEpG+IjJJRPZ4/+PTvO6hsufbsYyAJZQgRKQsMB5oDlwP9AeaABNEpFwsYysC7gMOAw/heof+D3A78J2IxAGIiOB6mO4N/B+uO54E3PGtE4ugixIRuRo4KcB0O65hEpFbcZ3TTseN+Ho58CFQ1ptvxzJSqmqPAA/gbtxJsbHPtAZAFnBvrOMrzA8gJcC063Bj2Zzhvb7Qe326zzIVccNB/zvW76EwP4BKwEbgau8YPu0zz45reMcwDdiH65g22DJ2LCN8WAkluAuAqerTLb6qrgB+xn3RTBCqujnA5N+959re8wXAelWd4LPeTuBz7Pjm5O/APFV9P8A8O67huRE3KN8bIZaxYxkhSyjBtQLmBpg+DzfCpIlMD+95gfcc6vjWE5HyBRJVESMiXXGlvT8HWcSOa3i6AguBq0RkmTf+0lIRucNnGTuWEbKEElwVAo9zvw2oXMCxFGkiUhs3ls33qpo9smao4wt2jE8gIgm4geheUNVFQRaz4xqeWrg60X8AzwHnAN8Br4rI3d4ydiwjVODjoRQxgVp9njBOiwnO+xU3Blf3dIPvLOz4RmogUAYYHGIZO67hiQOSgQGq+ok3bbw3JPmDIvJv7FhGzEoowW3H/ULxV5nAv1qMHxFJwt0l0xDopaprfWZvI/jxBTvGx/FuV38YeBRIFJFKIlLJm539uhR2XMO11Xv+zm/6t0B1oCZ2LCNmCSW4ebhrqP5aAvMLOJYix7s88zHQCeirqn/4LRLq+K7W44eENi4pJwEjcSey7Ae427S3A22w4xqueUGmZ5c+jmDHMmKWUIIbC3QWkYbZE7zi8GnePBOE19bkXeBM4EJVnRpgsbFAbRHp4bNeBeB87PgGMgs4PcADXJI5HViKHddwfeo99/Kb3gtYq6obsWMZMescMgiv8eJs3L3qj+CupT6Fu+7a1n6dBCci/wFuw13r/8Jv9lpVXeslnZ+AusDfcL+wHwTaAiep6poCDLnIEhEFBqvqI95rO65h8Bot/oBrHPowsBy4DLgZuEFVh9qxzIVYN4QpzA+gHu6yzS5gN/AZkBbruAr7A1iJS8CBHoN8lqsCvI27Vp2J9w8e6/iL0gO/ho12XCM6dhWA14BNwEFgDnCNHcvcP6yEYowxJiqsDsUYY0xUWEIxxhgTFZZQjDHGRIUlFGOMMVFhCcUYY0xUWEIxxhgTFZZQihlvSNicHitFJM37e0CsY84t730M9XndU0QGZY8K6TM9+73eVOBBRpGIDPDeR1oB7/dkEcn0eo0udESknfe5n9DvVqAhknO5j7+IyBz/75Y5nh2c4udUv8dG4Bu/aRcDG7y/x8UmzKi4GNd7QbaewOMU3+/1ONxntqGA9/sP4G1VXVfA+w1XO9znHqgjx2h5A0jFDQdugrDu64sZ9es3S0QOAFv8p3sCTSsyVHVmrGPIKxFJVNUD4SyrbiTMQKNh5hsR6YDrJ+z/CnK/hY2q7hOR4biOON+JdTyFVXH9JWdyEOiSl4gMFZG1IpIuIr+IyD4RWSQi53rz7/UuM+0SkTEikuK3zXgReVBEForIARFZLyL/9LqxDxXLFyLyvc9rEZHN3jbK+kx/V0R+83l99JKXiAzC/UoFOJR9ec9vV6VE5EkR2SAiO0TkcxGpE8ax6uUdj50issc7Jo/5LXOSiIwVke3ecftZRLr5LZN9fE/NPr7A30XkSxGZHmC/NcWNJHiP9zrgJS8RuVlEZnj73S4iE0Wki8/8siLyvIisEJGD3vPDYV6+uRmYo6rH9c7rHfuRItLfOx77RGSyiDQRkXIi8qaIbBWRTd53IN5v/WYi8qn3OewTkaki0ttvmUHe+20iIuO8Y79KRB7Ljt37/maf4JfIscu6/sfoLu997/aOTyu/+Tl+xsAooKXvsTXHs4Ri/FUAhgNv4S4pZQAfi8g/cb9U7wDu8f5+zW/dkbiONN8DzgWeBf6E63k4lPFAFxFJ9F63Bari+qnq6rPc6cAEAnsL+J/3d1eOXd7z9SDQGDee+N3e/JCxietteiywArgSN874i0A5n2U6AL/gLrncDFyKG2/jexE52W+TFXEnpveBPrhjNRzoICL+Q0tf4z0HGjs+e98vAEOAGcAVQD9gEq4fOrwT+TfATcC/vH2+hRtX5R+h3runNzA5yLzuuKGIB+IuBTXC9X33Lq7vu6u82O4FbvGJuRau08WTgDu9uHcA40SkT4D9fIr7jlyE60/vCY5dehoHZNeRXM6xz933smA/3Pfxbtwgb/WAMdlJLpzP2DML169fb0xgse5MzB75+8B11DgywPQ03Al7gM+0od607j7T2nrTFgGlfKa/CBzKngZ085a7zm8/13rT24WIsb23TA/v9T24jvq+A571pjX3lunt996G+rwe5C0TH+S9TvSbfp83vVaI2C7zlqkQYpkfgAVAaZ9ppbxpnwU4vhf6rV8G2Jn9Xn2mzwK+9Hk9wFs/zXvdGDgMvBgitv7+n6k3/WFch4ipIdat7q17c5Dv1Tagos+0u7zl3/JbdgYwwef1C7gRPBv7Ha9FwIwAn+cNftv7A/g2wHFpHCBOBZYACQE+0y7hfsY+60723bc9jn9YCcX426uqk3xeL/Sev1fVw37T43Ej24H71XYQV5qJz37gRsAD92s2mNm4k9MZ3uszcL9Ix/tNO0TwX8vh8L8BIXvQr3oh1pnl7XeUiFwmIqm+M0WkDNAD+BA44vO+BfieE993Fn5d+qvqPtwv+2tFRLzttsH9gh8eIrazcFcZhoRYpjewCvglwOeSAHQOsW4t7zlYvc0UVd3p8zr7u/KN33ILcV3AZ+sOTFXVpdkTvO/W+0A7cWOO+PL/3OYS+jPz952qHvJ57f+5zyLEZ+xnM8eOi/FjCcX42+H7QlUPen/6D3eaPT27fiQVKA3swf1zZj8yvPlVg+1QVY8AE4HTxQ1j2x13aWsCcLJ3gjkd+F1V90b+lo7a5vc6uzI8aB2Pd9LrhftfGQFsFJFf5digS1Vwv64f5fj3fQh3OaeyX11Fhl9izjYcd9Lt6b3uj7tsNCbE+8k+pmtDLJMK1A8QW3ZdVNDPhWPHJdhNA8G+E4Gm+x7jKgS+U20jLhFX9pse6HMLWS8XxvpkbyOMz9jXPlyJ0gRgd3mZaNkK7Mdd+gpkfQ7rT8BdCumKG8RsIu6EuhdXAugJvBmNQCOlqhOACV4dz2nAk7jr/Wm4BHwEV58UsDThJcyjL4PsZiKwGugnIhOBq4GPvNJLMFu859q4y0WBbMXVDVwRZP7KENvPHnfd/wSfV9uAGgGm18AdH/8EkO9CfcaqusVn0SocO+7GjyUUEy1f4ypnK6rqD7lYfwKuhPMo7jr6DgARmYyrTK2GuwQWSvYvzzK4ZBRV6m7vHS8i5XElhwaq+rsX40le3EdCbiT4tlVE3sXd9PApUIfQl7vAXVI7gqvw/muQZb7G3SSwR1UXBlkmmJW4HwkNc1guUhOBe7yT9UoAr2R6JTBTVSP97Hw/9zwJ9BlzfAJpwLHSnfFjCcVEhar+KCLvAx+JyIu4f7ojuArxvsBAVV0cYv25IpKBG4fe9+6j7JLLAWBKDmHM957/KiJfAYdVdVpu3k82EbkNdwnuS2ANLrE9iCtxzfUWuxd3Z9U3IvI/3OWcakAH3E0LD4S5u+Hett/w9jUx1MKqukxEXgLuFZFk3J1Kh4FOwEJVHY274+oG4AfvTr3ZuMTdCHc300Wqmhlk+wdF5Fdve9H0Eq4i/TsReRx359Sfgaa4u7Eilf253yEiw3CX9Ob4XK4NKczPGBGp5MX4Qi5iLBEsoZho6odrAHcj7i6iA7hfud/ghlnNyY+4SzO+JZHsv6fmcPkHXGX367iT02O46/ESXuhBzcbdavssrj5iG+6W12uz41HVGSLSEdcO5t+4W4M34+5ueiPcHanqQhGZBqTj7vjKcThVVb1PRJbi3vP1uEuEc/BuhlDVQyLSC3gAV5Jp4C2zDFfZndNJdzTwDxEpl8f6K9+Y14tIV+B54D9AIq5i/FxV/ToX25strh3SLbjbtuNw73NlmJvI8TP2nIs7Xp9GGmNJYUMAG2OC8m6IWAv8WVVHxjqeWPJKvVtUtX+sYymsLKEYY0ISkYdx9RsnhVNqKo5EpB2uq6LWvrc7m+PZJS9jTE5exN0aXZOc79YrrmrgGlhaMgnBSijGGGOiwho2GmOMiQpLKMYYY6LCEooxxpiosIRijDEmKiyhGGOMiYr/B0hDomTv7C80AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.survival_function_.plot()\n",
    "plt.title(\"Survival function of customer churn\")\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What is this plot telling us?\n",
    "- It shows the probability of survival over time.\n",
    "- For example, after 20 months the probability of survival is ~0.8. \n",
    "- Over time it's going down. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7406285497917455"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_train == \"No\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "What's the average tenure?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.6391518364256"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(train_df_surv[\"tenure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "What's the average tenure of the people who churned?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.854744525547446"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(train_df_surv.query(\"Churn == 1.0\")[\"tenure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "What's the average tenure of the people who did not churn? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.816717791411044"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(train_df_surv.query(\"Churn == 0.0\")[\"tenure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's look at the histogram of number of people who have not churned. \n",
    "- The key point here is that people _joined at different times_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[y_train == \"No\"][\"tenure\"].hist(grid=False)\n",
    "plt.xlabel(\"months\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Since the data was collected at a fixed time and these are the people who hadn't yet churned, those with larger `tenure` values here must have joined earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Lifelines can also give us some \"error bars\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.plot()\n",
    "plt.title(\"Survival function of customer churn\")\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We already have some actionable information here.\n",
    "- The curve drops down fast at the beginning suggesting that people tend to leave early on. \n",
    "- If there would have been a big drop in the curve, it means a bunch of people left at that time (e.g., after a 1-month free trial). \n",
    "- BTW, the [original paper by Kaplan and Meier](https://web.stanford.edu/~lutian/coursepdf/KMpaper.pdf) has been cited over 57000 times!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can also create the K-M curve for different subgroups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = train_df_surv[\"tenure\"]\n",
    "E = train_df_surv[\"Churn\"]\n",
    "senior = train_df_surv[\"SeniorCitizen\"] == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "\n",
    "kmf.fit(T[senior], event_observed=E[senior], label=\"Senior Citizens\")\n",
    "kmf.plot(ax=ax)\n",
    "\n",
    "kmf.fit(T[~senior], event_observed=E[~senior], label=\"Non-Senior Citizens\")\n",
    "kmf.plot(ax=ax)\n",
    "\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It looks like senior citizens churn more quickly than others.\n",
    "- This is quite useful!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cox proportional hazards model (10 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We haven't been incorporating other features in the model so far. \n",
    "- The Cox proportional hazards model is a commonly used model that allows us to interpret how features influence a censored tenure/duration. \n",
    "- You can think of it like linear regression for survival analysis: we will get a coefficient for each feature that tells us how it influences survival.\n",
    "- It makes some strong assumptions (the proportional hazards assumption) that may not be true, but we won't go into this here.\n",
    "- The proportional hazard model works multiplicatively, like linear regression with log-transformed targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ConvergenceError",
     "evalue": "Convergence halted due to matrix inversion problems. Suspicion is high collinearity. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-modelMatrix is singular.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36m_newton_rhapson_for_efron_model\u001b[0;34m(self, X, T, E, weights, entries, initial_point, step_size, precision, show_progress, max_steps)\u001b[0m\n\u001b[1;32m   1517\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m                 \u001b[0minv_h_dot_g_T\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspsolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0massume_a\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pos\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)\u001b[0m\n\u001b[1;32m    252\u001b[0m                            overwrite_b=overwrite_b)\n\u001b[0;32m--> 253\u001b[0;31m         \u001b[0m_solve_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m         \u001b[0mrcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpocon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36m_solve_check\u001b[0;34m(n, info, lamch, rcond)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Matrix is singular.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mLinAlgError\u001b[0m: Matrix is singular.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConvergenceError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ky/533nd9l512l5cmmk_kzmzj9m0000gp/T/ipykernel_25627/2468730739.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlifelines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCoxPHFitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df_surv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tenure\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Churn\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/utils/__init__.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(model, *args, **kwargs)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_censoring_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRIGHT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, df, duration_col, event_col, show_progress, initial_point, strata, step_size, weights_col, cluster_col, robust, batch_mode, timeline, formula, entry_col)\u001b[0m\n\u001b[1;32m    287\u001b[0m         \"\"\"\n\u001b[1;32m    288\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoalesce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m         self._model = self._fit_model(\n\u001b[0m\u001b[1;32m    290\u001b[0m             \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m             \u001b[0mduration_col\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36m_fit_model\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_fit_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbaseline_estimation_method\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"breslow\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 610\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_model_breslow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbaseline_estimation_method\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"spline\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_model_spline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36m_fit_model_breslow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    621\u001b[0m         )\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCensoringType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_right_censoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m             \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    624\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/utils/__init__.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(model, *args, **kwargs)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_censoring_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRIGHT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, df, duration_col, event_col, show_progress, initial_point, strata, step_size, weights_col, cluster_col, robust, batch_mode, timeline, formula, entry_col)\u001b[0m\n\u001b[1;32m   1249\u001b[0m         )\n\u001b[1;32m   1250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1251\u001b[0;31m         params_, ll_, variance_matrix_, baseline_hazard_, baseline_cumulative_hazard_, model = self._fit_model(\n\u001b[0m\u001b[1;32m   1252\u001b[0m             \u001b[0mX_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m             \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36m_fit_model\u001b[0;34m(self, X, T, E, weights, entries, initial_point, step_size, show_progress)\u001b[0m\n\u001b[1;32m   1377\u001b[0m         \u001b[0mshow_progress\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1378\u001b[0m     ):\n\u001b[0;32m-> 1379\u001b[0;31m         beta_, ll_, hessian_ = self._newton_rhapson_for_efron_model(\n\u001b[0m\u001b[1;32m   1380\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_point\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_progress\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_progress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1381\u001b[0m         )\n",
      "\u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/lifelines/fitters/coxph_fitter.py\u001b[0m in \u001b[0;36m_newton_rhapson_for_efron_model\u001b[0;34m(self, X, T, E, weights, entries, initial_point, step_size, precision, show_progress, max_steps)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                     )\n\u001b[1;32m   1526\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1527\u001b[0;31m                     raise exceptions.ConvergenceError(\n\u001b[0m\u001b[1;32m   1528\u001b[0m                         \"\"\"Convergence halted due to matrix inversion problems. Suspicion is high collinearity. {0}\"\"\".format(\n\u001b[1;32m   1529\u001b[0m                             \u001b[0mCONVERGENCE_DOCS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mConvergenceError\u001b[0m: Convergence halted due to matrix inversion problems. Suspicion is high collinearity. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-modelMatrix is singular."
     ]
    }
   ],
   "source": [
    "cph = lifelines.CoxPHFitter()\n",
    "cph.fit(train_df_surv, duration_col=\"tenure\", event_col=\"Churn\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Ok, going that [that URL](https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model), it seems the easiest solution is to add a penalizer.\n",
    "  - FYI this is related to switching from `LinearRegression` to `Ridge`.\n",
    "  - Adding `drop='first'` on our OHE might have helped with this.\n",
    "  - (For 340 folks: we're adding regularization; `lifelines` adds both L1 and L2 regularization, aka elastic net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "cph = lifelines.CoxPHFitter(penalizer=0.1)\n",
    "cph.fit(train_df_surv, duration_col=\"tenure\", event_col=\"Churn\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can look at the coefficients learned by the model and start interpreting them! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <td>0.812874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <td>0.311151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_No</th>\n",
       "      <td>0.298561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <td>0.280801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner_No</th>\n",
       "      <td>0.244814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <td>-0.282600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <td>-0.302801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <td>-0.330346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_One year</th>\n",
       "      <td>-0.351822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Two year</th>\n",
       "      <td>-0.776425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           coef\n",
       "covariate                                      \n",
       "Contract_Month-to-month                0.812874\n",
       "OnlineSecurity_No                      0.311151\n",
       "OnlineBackup_No                        0.298561\n",
       "PaymentMethod_Electronic check         0.280801\n",
       "Partner_No                             0.244814\n",
       "...                                         ...\n",
       "OnlineBackup_Yes                      -0.282600\n",
       "PaymentMethod_Credit card (automatic) -0.302801\n",
       "OnlineSecurity_Yes                    -0.330346\n",
       "Contract_One year                     -0.351822\n",
       "Contract_Two year                     -0.776425\n",
       "\n",
       "[43 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph_params = pd.DataFrame(cph.params_).sort_values(by=\"coef\", ascending=False)\n",
    "cph_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks like month-to-month leads to more churn, two-year contract leads to less churn; this makes sense!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# cph.baseline_hazard_ # baseline hazard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# cph.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Could we have gotten this type of information out of sklearn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6464     No\n",
       "5707     No\n",
       "3442     No\n",
       "3932    Yes\n",
       "6124     No\n",
       "Name: Churn, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>4726-DLWQN</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>70.35</td>\n",
       "      <td>3454.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>4537-DKTAL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>45.55</td>\n",
       "      <td>84.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0468-YRPXN</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>98.80</td>\n",
       "      <td>2807.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>1304-NECVQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.55</td>\n",
       "      <td>149.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>7153-CHRBV</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>59.30</td>\n",
       "      <td>3274.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents PhoneService  \\\n",
       "6464  4726-DLWQN    Male              1      No         No          Yes   \n",
       "5707  4537-DKTAL  Female              0      No         No          Yes   \n",
       "3442  0468-YRPXN    Male              0      No         No          Yes   \n",
       "3932  1304-NECVQ  Female              1      No         No          Yes   \n",
       "6124  7153-CHRBV  Female              0     Yes        Yes          Yes   \n",
       "\n",
       "     MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "6464           Yes             DSL            Yes          Yes   \n",
       "5707            No             DSL             No           No   \n",
       "3442            No     Fiber optic             No           No   \n",
       "3932           Yes     Fiber optic             No           No   \n",
       "6124            No             DSL            Yes           No   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "6464               No          No         Yes              No  Month-to-month   \n",
       "5707               No          No          No              No  Month-to-month   \n",
       "3442              Yes         Yes         Yes             Yes  Month-to-month   \n",
       "3932              Yes          No          No              No  Month-to-month   \n",
       "6124              Yes         Yes          No              No        One year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "6464              Yes  Bank transfer (automatic)           70.35       3454.60  \n",
       "5707               No           Electronic check           45.55         84.40  \n",
       "3442              Yes    Credit card (automatic)           98.80       2807.10  \n",
       "3932              Yes           Electronic check           78.55        149.55  \n",
       "6124              Yes               Mailed check           59.30       3274.35  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.drop(columns=[\"tenure\"]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "I'm redefining feature types and our preprocessor for our sanity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "numeric_features = [\"MonthlyCharges\", \"TotalCharges\"]\n",
    "drop_features = [\"customerID\", \"tenure\"]\n",
    "passthrough_features = [\"SeniorCitizen\"]\n",
    "target_column = [\"Churn\"]\n",
    "# the rest are categorical\n",
    "categorical_features = list(\n",
    "    set(train_df.columns)\n",
    "    - set(numeric_features)\n",
    "    - set(passthrough_features)\n",
    "    - set(drop_features)\n",
    "    - set(target_column)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = make_column_transformer(\n",
    "    (\n",
    "        make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler()),\n",
    "        numeric_features,\n",
    "    ),\n",
    "    (OneHotEncoder(handle_unknown=\"ignore\"), categorical_features),\n",
    "    (\"passthrough\", passthrough_features),\n",
    "    (\"drop\", drop_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor.fit(X_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "new_columns = (\n",
    "    numeric_features\n",
    "    + preprocessor.named_transformers_[\"onehotencoder\"]\n",
    "    .get_feature_names(categorical_features)\n",
    "    .tolist()\n",
    "    + passthrough_features\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = make_pipeline(preprocessor, LogisticRegression(max_iter=1000))\n",
    "lr.fit(X_train, y_train)\n",
    "lr_coefs = pd.DataFrame(\n",
    "    data=np.squeeze(lr[1].coef_), index=new_columns, columns=[\"Coefficient\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <td>0.787653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <td>0.600509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <td>0.291008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <td>0.258659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <td>0.251646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_No</th>\n",
       "      <td>-0.169654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <td>-0.204406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <td>-0.461593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>-0.743315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Two year</th>\n",
       "      <td>-0.765519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Coefficient\n",
       "Contract_Month-to-month                   0.787653\n",
       "InternetService_Fiber optic               0.600509\n",
       "OnlineSecurity_No                         0.291008\n",
       "StreamingTV_Yes                           0.258659\n",
       "PaymentMethod_Electronic check            0.251646\n",
       "...                                            ...\n",
       "MultipleLines_No                         -0.169654\n",
       "PaymentMethod_Credit card (automatic)    -0.204406\n",
       "InternetService_DSL                      -0.461593\n",
       "TotalCharges                             -0.743315\n",
       "Contract_Two year                        -0.765519\n",
       "\n",
       "[44 rows x 1 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_coefs.sort_values(by=\"Coefficient\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is some agreement, which is good.\n",
    "- But our survival model is much more useful.\n",
    "  - Not to mention more correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- One thing we get with `lifelines` is confidence intervals on the coefficients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Vnodb2bj5ZooVwE/BP7TzjksBQzJBDglPYZvuH+D56+AG06vAhtlpZUl3YEXcjgS2Cq12xE3HM5Jbd7ACyycZ2YP58Zssz/5os4AngB+kvP4FAsitIfFgZ3N7IZyDcxsqqTsf8WHCobiObjhtx1wSZr394B1gB06OKeTcMNoBLB7mTZH4VXTfpoJ0koai//ejAC2KV6gEOgMgqAEvUEvpDfMIQjKEYZNEHScSsUJ3gB+gHtk7sfLN58EPJL7+YXUNq9k317D5qXMqAEws3ckvZP6ynJnNsCNnC+TFyljHF5eObv2QUmrAD8BNgbWBX4HDJW0m5ld3I7+VsGNtxM7MTfnc7xIRIcws8nJANubZNikn9/FK6J1pM9GSacCIySdBLxcotkQ4ObMqEnXTZd0I15koFS/IdAZBEErulpsEULwMZi7CcMmCDrOe3g4VP/iieTJmJjL23jMzCYm78wLZpbPBs8S4NujZJ9RSsx0Zq6vvkAf3JNyZKkO8oUBUjL+7emFpJWBe4DTcPHQqvrDvVkAnRlT8E4q1TwnnA3cJGl13KO2M3BuQSS0vZyOV0A7mpyhmKMv7kUr8hYQCoBB0AtZa621enoKJelOscUQfAzmRsKwCYIOYmafS7oX2ETSwqXybIBfpfe7u3FqeZrxksdn4YZJKyp5VMzsRUn/Bg6StHS1/UnKdGiWa2N+M3GvVp4lSzXEc1PmlFvxELC9gSfx8tznV7qgLczsQ0kn4PpBpYRpG4FlShxfhtKGaRAEPcyhhx7a01NoQXeKIOYFH4t8FQUfg7mLMGyCYM44BQ/BOh7XYJmFvNzzYXioWX2uCta+khbAw7Ra19NsA6VS0LnPG+JelZ+YWYtzZjZDUgOwBu41KmnEpPn0NbO3S5xeFQ8Dy5/7A7AlvvajzOz1wjUv4gbEHpLOt/J15V8BVi8c2zw3L8O1jtpD5gFbBNdPmkUyus7Dk/cHA+PMrFT4WCXWT++PSfpWeoZn488/m+uGufYTgM0lLWFmHwBIWgIPQxvfzrGDIOgGTjnllF5l3GQiiN1BJvjYUzo23bnWYN4jDJsgmAPM7C5JRwFHp4IBFwNNwPfxzbOA7+K5F3vguRyPkRPv7IRpPIbnwzxb5vyfgHuBOyRdgIdFLZXm2MfMDscrjr0q6UrcqzEV95xsD/wczxP6Mb6pH4gn4s8AtgWGSDoCWCvrz8xM0rC03rslnYvnsvw/YGkzy0oyXwH8RdJwXKB1MB1P5M/I7sPBkm4DviiE/l0A1OPG3rZzMM4SJIHOVHXtaEp7f44BtgDuSnk4lq5bFA9fC4Kgl/H444/39BR6lBB8DOZWQscmCOYQMzsG3/wvxmzhxv1wL0otMMrMtjaz69IlD+GVuPpSJpyrneNPN7MHzaykuICZPYYXMpgG/C3N7wzc4Lo3NZsO/AX4Bu6FuivN7Vt42FZWzewhM7sIWBv4L/5vyEC87HG+P1L1sk3SxwuAG/FKX1Ny0zshXXsAcD1u+OzS7pvQkptxD8p+wAN4wYZZmNm7uBflzTSnjtIA/EFSFmZ2IfBSsZGZ/Rf34EwHLsILF3wIbBClnoMg6C00NjYyZcqUVq/GxoiYDeYewrAJgk7AzG43s5+ZWZ2ZLWRmK+FekBbinWYmM/tLXrwT+IaZCbhQSbwTNyjWVhLvNLPx6drxub42NLP1VRDvNLP+QH/lxDuBR/GQr73S/PqZ2a/M7FZJa+CV2v6Me0xewUsT15jZuqlCV3G9z5nZ9rgXCmDTXH8/kHR1Krt8C/BN3KD6upmtYWYX5rr6OTAI934YMABYJq11TG68oWbWT9Kikm6S9KZcJHTDdK/ybb/AjZmvAyume5sJeV4q6UDc4FoaeLgDXrP70vvw/HsqLf2ddGx88Za1c4wgCAKam5upr6/vcrHKxsZGBg4cyIABA1q9Bg4c2OXGTXetM5j3CcMmCLqAnHjn2DJFBWC2tyAvcb0znmNyILAbXrb5hkJZ5WrJxDtPw7VS3sTFOwfm5vl9vMR0X1xsc1vcszNO0tol+uwjaX5Ji6TzfwaewTVsMlbA9Wv2ATZLc9gd92jMQtIf8FC1d/Cy0tsB11Giylxq3xf3HK0CrNcBb8d8uEFzDPBpWu9M4LZU5rq9ZAKde0kqKzqaE+isw0MQdwVqcIHONTowbhAEXUxvEedsbm5m5MiRXb7hnz59Ok1NTTQ0NDB58uRZr4aGBpqamkrm23Qm3bXOYN4ncmyCoGsoK96ZIzu3fO7YZ8AWZvYZQC8S78woGmnPp/nOKkpgZtdkPyehzvvxMKyLJe1vZtMk1aQ5XGdmeYHKO0otRNIK6dyHwI9TOFl7WRR/Lq8Dw8zsaklX4x6qv0jaFS9lXZaC4Cd0kUBnEAQ9y913381GG23UdsNuolh6uav6L1fKubvGD4I5JQybIOgaKol3VmJsZtQkeoV4Z451gC9w78e38CT4OyWtl1VUS0bLcODXuNG2QO76lXCP0HrA4lRXavk7+NqfBbYxsw+ruKYUHwLPmtmQ7ICZfSDpFrz4wgZ4XlRZUqW7WXSVQKekvfB8JFZYYYUqlhYEQWcyevToXmXY9LRgZU+PHwTVEoZNEHQNZcU7c2TnXssdKwYy9xrxzsSjOa/Fw0nH50288tph6fiFwMa4p+IJvHraD3Htm2zs9gh4DkntD54DoyajVDnrt3G9nUfxIguVeKPEsU4X6Ex5TecDDBo0KPJzguArTkNDA/369euy/qdOnVrReOnp8YOgWsKwCYIu4Ksg3pnOv53EOL8HIGlhXN+m3szOyNpJ+m7h0ryA59NU5jy8EMOlkj7Ph7oxOzSuWpHPb5Q59nrSmJlY4nwLUnjgLEKgMwiCrqZciFhnUwwJaytELQh6G1E8oAuQNDRVqcpeH0h6UtIBHUwCn6uQtJWkP5U43j93T/YqcX6xdK9M0rGdOG5WNWzj9vbZzvHrk6Bkxin4Bvv4Em0z8c73gQdz1w3P3aNRuUsOLwpzzilmNgMvWbwpsIaZTSy+2upD0rJ4Lk+W87IQ7gX6rNB0aOHzf/DQsL1SPyapvvxU7QDcALtC0na5c6+k96LI5y/K9LWOpNckXZrGXQIv1vBAmfbVcjaeu1Pq93YCsLOkk7MDOYHOCXM4bhAEXcAhhxzS01MAXKxyxIgR1NbWduk4NTU11NXVMXjw4BYV0QYPHkxdXR01NTVdOn53rTOY95nnN9k9zHZ4qE1N+vnveInZo3pyUt3AVngo0mllzn+Aa5UU8yu2Zc7K4rY1brdShXjn+3gY1KJ4AvoDeNni81IXb9LxXJ1q+RNeGvmvkmZQWrwzz48k5XNsDsVzbs4FMLP3JT2Ii2O+iXtmdsc9M7NIuS1HAH+XdA1eEvsVSYcCn5jZ34sTNbNhaezLU4jcv83sTUkTgCOS5+gdvLLct8us923cQ/MtSVvhxuVieKW0DlOFQOevgZ0kPUQIdAZBr2fAgAFtN+oGamtrqa+v7/Jx+vbty6RJk0pWP6upqelysc7uWmcw7xOGTdfyhJlNSj/fmcrsDmPeN2za4lpgV0kDkp5Lxq7ANbT+dn+uxcyOkfQIcBCee7IoXoXsYlyc8lrgCzN7MIU4vW5mD2bXJ4OoK+f3mKRHcf2Yv+EhX+8Cj5GMlQKZhovhOSKPAvuY2cO5NjsA5+Aelo+BK/Hy1TcXxj5T0lu4cfQL3MvzHBWMDDM7WNLnwGXJuPkXbsick+b/CfBP3HPyjxJdTMBLUK8J/BsvSPBzM3ux3Jjt4MK0lpUKc/5veraf4gKdAh4kBDqDoNey//7795qSz11FY2NjjxkyQdBVRCha9/IIsISkpZUTMZT0saQXJB2fKlUBIOlMSW9LyleVQtLiKWTrhPQ5C7XaStJ5kholNUk6XVKfNNZ9kmZIekbSz4oTk7SBpLtSvzMk3SFp9UKb8cqJPkr6SNLT6ZvvrM0YXJNkuVxI1ZTCcPcB/8M3pNl1/YCfUCbXQ9IASZdJelfSTElPSNq6neMumu7pe6mfSyXVFsapSW3eSOO8IOkgqWVihaS1JDVI+kTS65KOpIx3pZR4p5kdamaNhXYys78Ujk1JApNv5Y5taGYbSlpK0jnAZfim+VylEL9MvDN37y5JRsSywBBJZ6Rz4/GE+aVwQcsFgUlm9itg6XQfhwCr4R6mJ9N8anGjbG2gIX+f0px/jodafR24ExfinAZclb/vZna1mf0ILypwmpn9yMxuTnNbA7ge2Df3N3KEmR1mZvMnowYzm2pmvzSzWjNbBi8L/Vu8xPQz8jDQ3+du64fADfjvy8LATZImSlq/+OxK/V0AE9OzmpRrtzVuNH0T90j+QtKvCt1dZmaLm9li6d4cqSQ0Whw3CIKgK0Ure0KQM0Q4g+4gDJvuZQAetvMh1YkYno2Hrm1NS3bCw2eK30iPwitQ/RYXDhyWjl2Mf4u9DZ6sfK2kpbKLJG0O3JXmtTOwI64E3yApr7ECbYs+HgPcin/rv256FecPcCkejpaxMx62N77YMM3hIWAN3PPxK9yjcE1u81jNuGfgnoYd8RCgbdOxbJz5gFtwYcxT8c3n7Wmtx+XaLYUn/C+Fb473x59hOR2TNpGLXhZfZcPQ5CWV78fzQ+rT+03AOXLhy6zdAOBhvLLYCNzAGJnmDrAf8DjwX2bft/0Kw10GTMbDqQ6v9j7lqHjfy6zvh3ho3rfxZ7556r9iWR5JW+K/ywvi4X1b4r/7RQHNwcDBeDW43+J5QTfnDd1q/y7U/UKjQRB8BehK0cqeEOQMEc6gO4hQtK6lj7xYwBK4KOI2wE1m9hEecgWUFzE0s2fl+QN74+E8GXsDd5rZ/wrj3W1mWfL82LQxOwAYbGb3pbHeBJ7EN4oXpbZnABPMbMvcnO7BvSoH4wZSRkXRRzN7WdK7wKf5kKoSXIxrf6yT2u0CXGpmVmI/X497QzYws2np2B1pc3k0cGOV495rZtmm/0652vwekoaameHhUOsDu5nZmFy7xfCckdPM7D18o70Y8DMzezXdh7HMTmRvLz+mdbI9+Ab56jLXHIhv1r+b06oZlzbmIySdk8oyj8SFQtcws3yp4osA0u/YdGD+CvftajP7v+yDpC2o7j5ltHXfS/FX3MOzTvp7gTaqx6W/ozPwLwx+kqvoNi6d3zXXvAZY08ya0rm3cI/qFpKuSG3OAO7FDbGMFn8X6hmh0SAIupjepGHTFeKVPSHIGSKcQXcQhk3X8nzu5y/xb76HQdUihuBemyskrWRmL0n6AbAWpdXKbysx/sqZUVOY0/JpHivh34ofr5YV2z7CvzEfQksqij5Wi5n9T9L9wC7ynInvlFkTuDfkVuD9whzvAE6RVGNm1Xy9dEvh81N4Fa9v4KFeQ/Dn9K9Cu0uB3+PejJvS+4OZUZPWM0PSTXQsP+hJYI8Sx0uJPWZshnuxJpe4J3vg9/O/eMWzmwtGTXu5rvC52vuU0dZ9b4GkRXFj75ScUVMNq+DG3omlylSbWf/U/xTggcyoyc0J4JL0yvg2LY3OgbT8u+gSoVGFQGcQ9Ch77FHqn+SeoSf0XUJTJphbCcOma9kaD6/6AHjFWmqZVCNiCL6pfAv30hyCh669QcuNY0ZT4fOnuFbJLMzs0+QRycZYOr1fkF5FXi18bkv0sT1cjH/b3Qd42MxeKNNuabywwK5lzi+Je7vaoi3xy75Ao5nNLLR7K3cePE+llPZKKfHHaviwmtLKBZbGN9mlPD0wW8dlSaoTwaxEUVyy2vuU0V7R0To8TLa9826P6Gcxv2lm+rv4B26krAGMLnFdlleT/V10idCohUBnEPQow4cP57jjSkXWdj9dIY7ZE4KcIcIZdAdh2HQtT+cTjDNUvYghZvaZpNHAfnIdjO2BU222+vucknmGjiCF7BT4tJPGKcWVeLjPnsAfK7SbhuutnFTm/Jx4I/I0An0lLWhm+XVn4orZvXqT8kKP3cU0PKfjwDLnMyPxPQqlljtAcWNd7X3qKE24R6i9886LfnaUN8xsoqTM8Gjr76KzhEaDIOhFTJ48ue1G3URXimOGIGcwrxGGTc9QrYhhxnn4BuuqdG2pMrYd5QVgCrCamZ3YSX3OxPM6KmJmzfLKbmsBV1Roejse3vSMmX08p+NWYAJernc7PGwwYyd8I5vloDwAHCppeTN7DVxcFE+i7y5uB/4AvGpm71RodyewjaRlzazoecmYieeBVUu196lDmNlHku7DRS2PbuOZ53kR/13eQ9L5FfJ3qqHav4u80GjJvJocZmYHpNDLKyTtaGZXzcEcgyCYh+lK0cq8IGeRrhLkDBHOoDsIw6YHsCpFDHPtX0/5G1vjxQde68S5mKT9gRskLYh7Ud7DvQ/r4Rvn9gpePot/o78vMBEXXHyqVEMzq0ag8Ci8ste9ks7EN5x1uNr8imaWVSOretwy3IaXoj5X0teBZ/CCAnsAJ+QS4k/HK4fdKakeNwwOxTVbOsISktYpcbypQnje6Xg1rwZJp+Mb8cWAVfFiEVkhiBF4oYj/SDoeD6VaDtjMzLJy28/iHsHf4nk9H5QaV9JQWlbtuzR5Ea/Fq/0V71N7WTjdzxvxsMsJwAOSTsVDvVbEE/7/UOri9Ls8LM3nbknn4lXy/h+wtJmNqHYibfxd1Kc+G8xsiFoKjT6F3/NzgWetSqHRaucVBEH30Bs2310pWtkTgpwhwhl0B2HY9BxViRjmuAo3bM4rc77DmNmtcq2S4XhewSJ4vsSDuIhhexkNrIPnz9Ti1cL6z8H8XpU0CN9QHo9ro0zDQ38uyjWdo3HN7MtUSe54XBl+SdyI+hNeNjtr956kn+JhdBeluZyL/z11RHz1e7gXqMgtwBZl5vq+pPXSeIfhxkozbuBck2s3RdKPcMHKE3DPzOu4jkvGSXji/Wg8EX4CsGGF+W6Hh6MdCGyCV95rpHCfOsDCaS1TzWy0pB/jVe/+jnsqX6GlYdUKM7tB0iZ4GecsZ+zljsyrwt9F5gkaLGkzayk0mj2vDWhdNCHfdymh0SAIeglnn312T09hjmlLgDN7BcG8hOYsWiPoLiRdhleKWrFUxacg6GpyHpuVrKU45T3A2mbW4dgFSQvhRRkmA3uaWanE/S5F0kIlCiKUajcGrzb3Hh5694Ms7E3SxsBYvNz0+M6Y16BBg2zixPbWlgiCYE645ppr2Hbbbdtu2AGam5sZNWoUw4YN6zLPUCbA2dRUrCnkoWaTJk3qUqOmO9YYzLtIetTMBnXk2hDo7OVIWkfSPnjY0Wlh1AS9kEfwcLofSbpa0lRJH0t6QdLxklrkPUkaL+k+Sb+U9LikmXhoX5at+w9Jll5DC9dsLOkxSR9JelrSVsXJSFpD0o2SmtI87pc0uNBmTJrnupL+I+lj4OR2rPlL3Cu0NuXLlOfH21nSk5I+kfSepEskLduO8YIg6Eauuabrant0h1BlTwhw5gkxzqCniFC03s8DeHLyRbimTTAXIGk+Kn9xYGb2RXfNp4sZgOfYDMRLl4/BS5yvhofKrYhX88uzMvA34Bhc8LIJNxCuxUPmbkzt8lo+38bD/07AvSUHA1dLWjXzIEn6Pl5B73G82t5HeIn0cZLWM7NHc/19DS9a8Vfgz7QzRyqFvT0EHC3punJfOiRNmvPwsM4jgG/i4Y4/kvT9tko/B0Ewb9KVgpU9IcDZnf0HQTnCsOnlmJl6eg5Bh/gn8LsK59vKY+nN9EmioEsAv8ENkpvMbFaFNLkozP24vtDFkvY3s3wZ6KWATc3sidw176cf/2dmpSqrLQUMyQRiJT2Gl97+DW4oAJyCa8xslJWilnQHno91JLBVrr/FgZ3NLJ9v1F6G4+Wgd8Z1mVogqQ9uvI03s+1zx5/HDbDdcQOveF0IdAbBPE5ParqEnkwwrxKGTRB0DfXAmRXOf9BN8+gKns/9/CVe8nmYpBp8o/9rYHlggVy7lWipbzMlb9RUyUuZUQNgZu9IegdYASCFvG2AGzlfJuMrYxxejjrP55Qv1lEVZnaXpLuBekmlCgCsgoupDi9cd5+kV9J8Wxk2IdAZBD1Ld4hzdoUIZkZPCHC2Z/wg6CrCsAmCLsDMpuAV1eZFtsbLL38AvGJmnwCkcscb4+FnTwAzgB/ilf8WLvRRTlOnEo0ljs3M9d0X14c6Mr1akSqQZSFj73RSOOCf8QqCewAvFc5l2bml1vtW7nwQBF8xukMEMwQ4g68aYdgEQdBens5XRQOQtDCwJVBvZmfkjn+3TB9d4YVoxj1IZ1EiLAy8pHdnz8HMHpJ0I/AXPJ8nT2aMLVPi0mVwvaUgCHoZw4cP5/LLL++SvrtDqLInBDjzhBhn0FOEYRMEPYikajbXr5hZ/04Yazwwv5mtX0XbrXBdmlXxXJp3aBlKVmQh3FvyWeH40HZMMSu1vEjFVmUwsxmSGoA1gMfaqCC4OFAjaUUz+187h1ofWE7SvWY2JB37C+6lynSM1gTG47pCb+PFEzJdHZIG0beAU9s5dhAEczndIVTZEwKceUKMM+gpwrAJgp5l3cLn64An8RydjDa1VToTSX/Eq4/9E0/Gn4FXJNu33DVJMPRB4GBJb+JVy3bHhUOr5W3ceNpe0n/TuJMLRQfa4k/AvcAdki7AQ8CWAr4P9DGzw1O7xXGDbUW8KltHyAQ6bzezpyRdAeyYb2BmX0g6CjhP0qXApfg9OQ4PW6soOBoEQdBRQoAz+CoShk0Q9CDF6l9J0+W9MlXBuotDgOvN7Pe5Y3dL+ozKG/EdgHPwULCPgSuBA6kyQd/MvpS0B578Pw7/92k3vHx0VZjZY5J+AIzAk/K/BrwLPAacm6q1LVChi2r5EngGOFbSHUmg8yhcb6pPYU7nS/oIOBS4AS/ffivwf1HqOQh6J10lztnZNDY29phXJgh6IyHQGQS9HEkDJF0m6V1JMyU9IWnrEu3WkHSdpGk5gcwjSrRrS+SyL57Y3gIzG2NmymnG1OdD6cxsipn9HLgG+MjMDjCzW3Cdm3sk7SfpNOA7wPcl3Sypf2GYUbgRsh+uYXNuKus8shhCJ2ln4H3cwzNL9NLMnjOz7c1sadxjMx64HjgN+BTYHPhZ6mZsTgx0w+Kay3Bf6reFQKeZvQxslto8UeZao2vyi4Ig6ETmxLBpbm6mvr6+y8UpGxsbGThwIAMGDGj1GjhwII2NpeqtdC7dtdYgqJYwbIKgFyNpeeAhPG/kIOBX+Mb/Gkm/yrX7IS7m+u3UbnN8I1+s55mJXJ6Gb8jfxEUuB+baPAz8TtKhklbuxOUcgZd93g3YHzcK7pRU9KBsgIeUDcdzU2YCt0laJWuQdF4uAZ5L6zgcN1YmSFq80N9PUn8jccPjf2l8gD/i4YDr4ve1apL+TSbQWfbf0nbONQiCXsB+++3X4Wubm5sZOXJkl2/2p0+fTlNTEw0NDUyePHnWq6GhgaamppKenM6mu9YaBNUSoWhB0LupBwRskMs1uSMZPEcDN6Zjf8XzU9Yxs4/SsbtL9FeNyOU+wNXAycDJkqYBY4ELzezOOVjLB8CWWVK/pBdx78eu5BLrgW8APzazV1O7u4BX8AT9XTogelkHrG1mb+Xa1qUfn8vC/iTNV9C+KWIlykN3iUBnEAQ9S2ds1Iulljubtko3d/X43TVGELSHMGyCoHezGZ6P8X5h030HcEoSxfwc+DFwSs6oKUdFkct07EVJa6U+NwXWwbVrtpd0pJkd28G1XJ2vVGZm90uaintL8obNg5lRk9p9IOkWZhdaaK/o5YN5o6YCR+G5OeV4BehfGLNLBDqTl2cvgBVWWKF4OgiCuYCeFqjs6fGDoCcIwyYIejdL4x6NXcucXxLPG5kPF81si7ZELgGv5oVXF7sXQNI3gduBEZLOMrOmqmbfkrfLHCtWTmurXXtFL6sVAz2fyoUOylWn63SBTjM7P82HQYMGRU5OEHQzAwYMmOM+Ghoa6NevGA3ceUydOrWi8dLV41czhyDobsKwCYLezTQ8ZOmkMuffwKtwfUn7Siu3CzN7Q9JoPD9nJTwP5xMASQua2ae55kuW6eYbZY49UWW719PP7RW9rMowMLM38PvZLkKgMwjmPY477rg57qNciFhnUwwHaytELQjmZaJ4QBD0bm4Hvgc8Y2YTS7xmpvCz+4CdJXVI3DJPyt8pxarpPQvreiW9r567thZYr8z1v84n2Uv6MV7c4IFCu3Xyc5C0BF4MIWuXF73MzzsTvZxQZvw8cyQGWoK/4IbK/oXjnTHXIAi6mdGjR3f42traWkaMGEFtbW3nTagENTU11NXVMXjw4BYV0QYPHkxdXR01NTVdOj5031qDoFrk8gtB0DaShuI6JitlJX/bce1WwIpmdloXTK1TkTQMeNXMri1xbiu8wtaquMDjO8DjwLlmdnsnjD0FuM/Mdk6fV8C9I68BZwJT8GT41XED4ud41a8Z+Cb5RVzNfiouPrmmmf0h9TUemL9E2eQpeHL70PT5feAeXCx0MlAD/AL3RlxlZr9N7V4FShlBM5lt/IzHCyBMxr0XfYE98bygE/CCAquZ2We5ufTBtV7qU1+H4QKb3zWzF1O7vYDzgMtoKXo5Hfh+pg9TvJ+5NS+Z5ngzXnhhJvCCmX1QYj0tkDQG2BgvGLAhcHTS4LmMlgKdu5nZmGrnWo5BgwbZxInh2AmC7mTHHXfk8ssv7+lptEno2ATzIpIeNbNBHbk2QtGC7mIrfDPY6w0bYBjuAWlh2Ej6Ix6K9U/gFNyY+DbuTdgI9650Kmb2qqRB+Cb/eODreHja08C/8cpoz5rZ9OQBORr4O7AQ7lHpiLL9YbghczQeAvYFbjAdjuvMZHyJlzuuwY2od/AiAGsCa+EFB/L/494C7IKH1S2CG08HZEZNjgm4QXQ87tF5Fvh5ZtTAnItemtk0SQektU7AjamfpHGrZUO82MCx+L04Cq8uNz/uubmlM+YaBEEAYcQEQTWEYRPMtUhayMzKJXR3BYcA15vZ73PH7gb+UUnHpJ2sUlyTmU3FE9MrYmaPA7+scH7DMsf7Fz6fC5xbxVwBJhW9IUVyIpyfp/cfteXxM7PRQMVYEDO7FPeA5MdaqNCmP2Uws/NwT0q7yHm26gvHXwaKmjxl5xoEwdxNc3Mzo0aNYtiwYV0eipWJcTY1ta7bUldXx6RJk7rcuOnO9QZBR4kcm2COkDRe0n2V1OxT6M7vgOVyKu9TcueXknSOpNclzZT0fArfyY8zNF03RNJVkppxbwGSpki6VNL2kp6TNEPSREktQq5S2w0k3SXpg9TuDkn5HJEpeO7DTrm5jkmn+zI7xKoF+TLGqZ8Bki6T9G5a0xOSti60qU/9r57m8SFwpaSzJb1d1FSRtJCkJkmj0ucN0/UbFtptLel+SR9Kmi7pYbUU85xf0hHpPs+U9IakUyW1qIw2p6TnMqbEqW9Kuj7Nb5qks4q5QZIWlXSSpMmSPk3vwws5Otn6t5H0D0nvUrqiWr7fH0oal8aekX4XflhoM0bSVEnrSXpE0idpLX/Italndmnoz7LflXSuf/o8tNDvBpLGSno/jf2kpLyRHARBL+Gss85qs013ilOGGGcQVEd4bILOIFOzPwF4DzgYV7NfNX0zfwweQvUDINtgzwSQ67Dcj4cm1eO5GD8Dzkkemb8XxroM+Bfwa1r+/g7GNUOOxKt1HQPcLKm/mTWnsTbHQ4FuwfMjwEORGiR9z8xew8OnbgWeTPMBeDe9Pwz8TtL/gBvyoVF55InvD+GhWQel638LXCNpKzO7sXDJDXgI10l4SNMnwL64hsytuXZbALW4in1J0ub7b8D1uDH5IZ6f0j/X7FLcs3MS8B/g/+H3qz+wbbm+yw/ZStTyC6ucvHcpcCVwNvBDPIRrMWBo1ieu0/OdNK+ncC2dI3Hj8uBCf38HbsPD3MoaZ5K+h4edPZvGMjy8boKkddI486Xxa/BQv1OASfjz+5ukD8xsDO5N6gf8HlgfD9cri6QtgWvw3/W98b+T1XAjOgiCXsbkyZOpq6truyHdK4QZYpxB0AZmFq94VfVi9mZwYO7YeOAzvKBAdmxpfKP359yxMcDUEn1mhshKheP/wDd/8xfGPr1EH1OAJqAud2xQar9j7tgk4K7CtTVpnFGF/i4tMc7KwH9Tv5au+xewaaHdBbgxs2Th+Fjgidzn+tTPgSXGehH4V+HY9Xg+TfZ5w3T9hrm1fABcW+EZDk7X7Fo4vlM6vmY7fh+m5O5F/rVx7vyYEr8/5xb6GZ5+X1ZOn3dJ7YaUaPcpsHRh/ddVOd+rgWagtvD8G/F8qux5lHt9jOctZUVXsvbzF8bpn44PTZ+V7sVEYL4q57pXaj9xhRVWsCAIupcddtihzTaTJ0+u9O9Fl7wmT57c6+YQBJ0NMNE6uFcNj03QGbSpZl+BzXDvxuTCN/934Hkl38GNiYzryvTzgLUUjXwqva8AIGkl3LN0fGGcj/AywkPamqiZvShpLeDHuDdlHdzDs72kI83s2NyabgXeL7GmUyTVmFk+bqDUmi4FDpe0hJl9IKkvXgFtRIUprgcsThJ2LMNmuHFwTWFud6b3IbTWlanEbbjHJc8LbVxzZeHzFXgC/g9xg24z3ID4T4k5Hovf97zXq9zvRJEhwM2WPHgA5kUXbsQ9WAfgVdJG4Pf6x7T0xPwCGIlXNWvPV5er4J6ZE60QslgOC4HOIJhr6A1CmL1hDkHQGwjDJugMqlKzL8PSwEDc61OKothjORX5FnMws5mSyM1h6fR+QXoVebXNmXq/XwD3pheSvolXQxsh6axkXC0N7JpepViSltXCSq3pEtwj8Gu8stn2eGL6ZRWml92rSpvupYEF8RC1Sn1US6OZtbcWcTEPJvucCYwujRsCc/o7UaRvmbZv4d6+N4A3JE0DmszsoXwjSZnIZnsNm2qeSxAEcyndKYQZYpxBUJkwbIKeZhqei3JgmfPFb/87+u31tPR+BDCuxPlPO9Kpmb0haTSeY7QSnoczDWjAc1hKUVS3b7UmM5ss6X48F+jC9D7ePA+oHO+l9+XwctClmIaH/pX72q04t67gG8Azhc8Ar6f3aXiu1W/KXD+l8Lna34lGXESzyDK0Ns7rJC1gLUtRF+dZLfnnEgTBXMAee7RZiLJbxSnzYpxFQowzCGYThk3QXcyktMr77cAfcEHMd7pw/BfwDfFq+Df0F+bOfQS8K+lxPGem5FwlLV/GsFg1vWcV024H1gWeMbOPC32MTz9uWMWcL8GLKGyY+tutjfYbZOMnb9X7eGjXKDPLlOZuxwsmfM3M7qpiDi2Ql24eClzc3mtz/AYvk52xPV404eH0+VlgO6BPCa+J4eFg9R0YdwKweRbel/pbAg9DG19o2wcvpHBFYZ5vA69J+gI3ZsF/V7L+pgIPFvp6Ef/d20PS+Sl+OAiCXsxGG23UZpva2lrq6+u7fjJA3759mTRpUo/q2HTneoOgo4RhE3QXzwJ9Je2LJ0V/YmZPAafjFacaJJ2OGyCL4cbCYDPbsjMGNzOTtD9egWzldHgEnkOxJu6x6YMbNtOAwZK2wI2V98xsCvC0pHvwnI7JeOL5L4B9gCvNLAtnOwrfpN8r6Ux8U1sHrI57Bv5S5bSvxCucXYonrl/TRvu812k8XoFrQ+AySZub2U5mNl7Sv/CqdaeleX6JJ7z/AjjMylR7S/TH79t9Va6hFL+QdAqeM/PD1N/FuXFfx6uT3SDpZLxC3YJ4jtTDdFwP5hi8stxdkk7CPT2HAYviYqR5PgBOlrQU8BKwAy4weyJeSa1PmjvAwZJuo0xltPS7NwwvUHC3pHPx4hL/Dy+EUClvKgiCHmDHHXfk8ssvb7thN9K3b98Q4gyCNgjDJuguRuNJ38fjJYtfAfqb2fuS1sONgcPwcJ1m3MBpayPfLszsVklDgHPSoeF4zsWDwJlm9oCkq4CrcIPmSvzb+ItwL8Vh+Ob/aDws6Qv82/jDgVG5cV6VNAj3KhyPl7qehoeHXWRmz1Y532ZJN+F5Nv/KvAxV8Fu8JPLBeJ7KTLywQMbOuJds93QPZuLG1x20oQPTSeyc5rYvboz9Axc/zcgMhCvx6mADgBnAy3ip7ikdGdTM/pu8X8fhz1T4s9/AzJ4sNJ+Oe2jOAL6L35cD8UIWh+NG2SZ4Sej98N9fUSZMzcxukLQJXgUwy/F6mdzvTRAEQSkaGxt71FMTBHMVHS2nFq94za0vSpStLpy/Ds9DWTR9XhTPl5mMb8Qn4wbBfOn8ssDnwB9K9HUYblx8PX0ej+fK5Nt8Hdd0eQ03Ml7Dw9AWyrVZA68E1oR7b+7HPVr5fuopXX74SuCj9POGqc02uEHxLtCczi2AVx2bktY5JX1eoHBt8bVhbqw9cQ/LJ3huyQVA38J85k/35dnU7l08RG7V3LMpvvqnaw2oL/S3GV7Z7mM8/O56YJVCm/G4l2lj4DE8/PBpYKsSz2wMJUqTF+7BL3Ej5qbC+ankSlynY6vgv1PNaY4PAptV+/u69tprWxAE3Us15Z6bmppsxIgR1tTU1GXzmDZtmtXV1ZUsu1xXV2fTpk3rsrHNumeNQVCEOSj3PEvFOwiCWdwKLAQMSuWGs9LTZ+BlgEfj37yfAmBmb+IFCXYp0dfOwO1m9m6Jc0iqw0UyfwuchnuE/g83MhZMbb6f2vTFDYdtcQ/QOElrV7GeAfimOs/fcQ/DLswWxrwI90ZcjIdsXYgbIBel848B+6ef/4jn/aybjiPpRNxAG4cLsR6KGx23SeqTG/sK3GtyK7BVWtOzuIF4C25MgefZZGOUrHwmabN0zYf4PdwXD/m7T1IxWT8Tkj0NN+zexEPyBpbquw0+TvPcIol7liRVzbsPN0wPwPOLmoFbJP28A+MGQdANrLXWWm22aW5uZuTIkTQ3N3fZPKZPn05TUxMNDQ1Mnjx51quhoYGmpqaSnpzOpDvWGASdSYSiBUFrslyZZfHcivXxcKV70/G7UnL+CEknmRc9uAS4VNIqZvYCgKQ18U32MRXGOghYERhkZo/njv8r9/MpaU4bmdmnqe87cI/DkbhxkKdPml9ffKM/iNmJ7hkPm9mssj+SVk9rHZnmKzzB34CRKSfmKWZXqXvOzB7MXd8fN2RGmtnRueMv4hv7XwLXS9oIN8wONLO/5eZzfe6al9OPT5jZJCpzLPA/4Odm9nm6/gE8RPBg4E+5tkvhop8vpXaP4cbNb/CQwfYyGg+hOx4ol2n8Jzy/at1sLZJuxQ2543AdoCAIehmHHnpo1W2LJZg7k7bKOXfl2N3RfxB0NmHYBEFrlN6N6sUir8O9BrswuzjALnhoVF5MssimwCMFo2b2RKRF8GpnxwNfFuYwDtipxGWf5H7+FM/jOKLQpihqmQmUXgrcxewKaxmPlZl/xiZ4wv9lhTk+hOerDMGNl03x+/qPNvprE0mLAd8Hjs+MGmhRKru4hqqEZM1saDXjm9lnkuqBiyVtbGalyogPAR7MG2hm9kUq4HBUCbHWbG174flFrLBCNTq3QRB0JqecckrVxk1PilaGYGYQtCQMmyBozfLp/U2qFIs0s48kXQPsJOlIfJO/A3CVmX1S5trs+mLiep6+eAWuI9OrFZLms5aK9uvgCfhNeBntUnMvhnb1zR3fG1gifV4Qz+c5HzdG1gbOLdFfJoBazsOyZO690QplsDtIHW6ElhPd/Fbh2JwIyZbjMjxc7zhK6yP1BUoZrW/hc6+jpVgrAGZ2Pn7PGTRoUJSHDoJu5vHHS37XVJKGhgb69evXJfOYOnVqReOlK8euZvwg6G2EYRMErdkc93o8SvvEIi8BfoeHri2Ch7Jd0sZY71FZuLEZL8d8FmW0YwpGDcCjeQ9GGYqb5WzTv0wWSgezQswAnjKziZIWL9NfJoC6KW5QlTv/Hl72e5FOMG6a8HWUE92cVuJ4p2JmXyZD9lpJpUqTVxIFNUobW0EQzEWUCxPrTIohYW2FqAXBV5UoHjAXI2moJMu9PpD0pKQDCuFA8ySStpL0pxLH++fuyV4lLl0wvb8k6djCtdvgie/nmtlHeLWu5YEPzWyimU0E+uG5GhPN7L3c5ZmBcTgehjYFaGhjGXcCP5S0RqmTZjYj9bEG8Fgafws8fC2bT5tIGp/ELc8s02TT9F4MOctC3bL8opnpfZaAaTJ+zsE36iukOR0AXJ3N0cwmp+Z34p6KSrLepcaoT/OfRbo3jwLb5YsTSPoWXt56QrHj3O9G27LiVWJm1wGP4LlJxX9TJwDrpjE3TnPogxc6eNyqL+EdBEEvo7a2lhEjRlBbW9tlY9TU1FBXV8fgwYMZMGDArNfgwYOpq6ujpqamy8aG7lljEHQm8/zm9yvCdniZ2Zr089/x0KCjenJS3cBWePne08qc/wA3MM4vHB+U+3m5pG2zAm4wbAeMZXZOymXAbnjBgFPxsLH98Yppm+Hlgj9KbbON9xB88356KltYidOBHfEKZ8fiCfpLAVsC+6SN759ww+IOSReQQqwkHQf0MbPD2xgj4wNgteJBSYviye+fAzWSRuBV2NbFw9/+ZWb/Tc1fTO12l9SIGyGTU9vtgTMlrYIbfwtLGorn34w2s3vM7J4UsneapOXxAgULpHt2i5mNxxPrAfaXdBEeBpivqpbnSLwq2s2SzgYWxwsgvA+cWuV96QyG40ZbkdPxqm9fAzaStDCue7My7hkMgqAXUo04Z21tLfX19V06j759+zJp0qQe07HpjjUGQWcSHpt5gyfM7EEzu9PM9sQ1O4b17JR6BdcCP5Y0oHD8x7mfh+LlnI/DSzxvj2uMfAKeIA78DM8v2QsvUTwEWAzf/H9aYtzF0/lL25qgmTWn+VyHe3puxzfkn2d9m9ljwA/w0Kq/Mbus9HeZ7Umphv9SWkByG9wQuwE3fnbH1/l7XL/nd7n5TsO9MWvg3ohHgO+m379h+D0agifuL43nnzQBs5L28XtcjxumNwL/xA2uN9MYT6bzv8Qrqj3C7JyfFpjZ7biBUIvr9ZwLPAesb2ZvVHFPOgUzG4v/3RWPv4GLoYKXyL4az7vZPM09CIJeyN13393TUwBCnDMI2k1HBXDi1fMvyghNAien40vjG+KrcY/Ox3i53uOBRXLtz8SV1Rco9LM4vtE9IX3eMPW7FXAenh/QhH8r3SeNdR+uEv8M8LMSc94Ar7r1QWp3B7B6oc142hBTxEUUi4JlU9K5/unzHngy+5G56/rhifU/SW2OLYw9APfSvIt7I54Atq5y3Oz+/Crd0/dSP5cCtYVxalKbN9I4L+Cln1VotxYeivYJbpQciXskrJ2/K9k9PQqYVDh3J65VM4aCMCVuxDyQnnUzLi65eaFNdr+HFu7TlEK7ikKnnbVm3EPyGP773oQbYOsV5ro3cDRuTDUDNwH9yvTVYcHRwu/FxrlrVsSNvfuBukrrCYHOIOh+ygl0dqdgZU+Lc5qFQGfQMxACnUGBAfjm/UM8xOoJYB+8dPEZ+DfyF+ban40bQVsX+tkJ9zwUS/OOwo2S3+Kb82Hp2MX4t+/b4BvhayUtlV0kaXPcqPkQF67cEf8mviGFJeVpS0zxGNyr8C6zRRyL8wc3KvLCmTvjRt74YsM0h4dwb8RBuIHyGHCNpF+1Y9wz8P98dsQ3z9uS05GRNB8ePrUb7p35Jb4RPg33HGXtlsJDtZbCvSb7489w9xLrrJZLgBUlrZfG+CbwU8oUJsANgdF4iN5vgYl42Fe7xCWrETpN7eZozZL+iocePoYXfNgZ92oVayYfAQxM/R6IP8fLCn11huBoqTmuhXv7nsONnVLFFoIg6IV0p2BlT4tzQgh0BnMfkWMzb9AnbRyXwDdz2wA3med+XJM1kqs23o+Xl71Y0v5mNs3MnpU0Af8W+8pcv3sDd5rZ/wrj3W1mWdL+2GSwHAAMNrP70lhv4t90b85s5fozgAlmNqt6lKR7cIHFg2kZPldRTNHMXpb0LvCp5YQiS3AxLqS5Tmq3C3CpmVkSscxTj4dkbWAecgWe17I8bqDcWOW495pZFn50Z8o52UPS0PRNxC/wymm7mdmYXLvFgIMlnWZelOAg3LD8mZm9mu7DWFxXp0OYa7zcB+yKb653xr1G99DSAMzaH5L9nAyyu/D8kH1on7hktUKnHV5zMnoPwnOb8kUlbinR/BUz2zF37deBU9KzfhPPYzoUN2SzAhNf4DlG7RIcLczxp3jY4VXAXmb2RVvrCoKg99EdwpU9Lc7ZXWMEQWcShs28wfO5n7/Ev3keBiCpBg/3+TVe3WuBXNuVmF0S92zgCkkrmdlLkn6AhwRtU2K84ob2eWDlzKgpzGn5NI+VcC/M8YWKbR/hoU5DaElVYoptYWb/S2KNu0j6HPhOmTWBfxt/K/B+YY534JvekmKKJShupJ/C83e+geuXDMGf078K7S7F81rWxUOj1sXFHV/NrWeGpJvwMMSOcjFwsqQDmW3ofVnC0EPS2ngY2A+ArzNbvPSFVo0rU63Q6ZyseWM8b7BYLKIUxWf0Tnp/tXB8RHpl/U+g44Kj2+FrOM3MioKpLQiBziDoWQ455JCK53uDtktvmEMQ9DbCsJk32BoPr/oA/yY6Lwh5Ib4hOwoPSZsB/BDXRckLE16Hb7r3Bg7Bv5F/A99gFymGznyK5ynMwsw+TRvlbIxMwPGC9CpS3FB2ppjixXheUR/gYcvptBRYGvdk7Frm/JKUEFMsQXHuWfnibO59cZHKmYV2b+XOg4cyPV2i/7ermEMlrsSLEBwFrE4ZjZ7kvbgLD6v6A/6MPse9GP+vnWNWJXTKnK0566OarxiLzygrNLAPXkJ6d2DfQpu8AGdHBEe3xfN+LmyroYVAZxD0KAMGFGvOtKSrhTGh58U5q5lDEPQ2wrCZN3jazFopvqfSslsC9WaWz/H4brGtmX0maTSwn6ST8cpVp1rbQo/VknmGjqC0Qnup6mKdxZV4GNyeeGWqckzDk9ZPKnO+s6psNeIilQuaWX7dmZBjdq/exL08RUodqxozmy7pRrwK20Qze65M083wMsW/MbNZxkIqD91eqhU6nZM1Z5pCy9F+j1L2e/6CuRBpVhK8MwVH98K/NBgvaSMze76N9kEQ9BD7779/xZLP3SmMGeKcQVA9YdjM2yyEeymK35IPLdP+PNzwuCpdW014TbW8gG9eVzOzEzupz5nkRBzLYWbNkk7AQ+uuqND0djwU6pk2NqlVjVuBCXj+xna0TFjfCTfwstydB4BDJS1vZq8BpDycX87B2Bln4h6kyyq0yQyYWb8/klbGy1O3N/D6dtxj8WEbG/o5WfM4PMRvLzxna04Ym/pawbyUcznuxA3EPXD9qEpMx0uH3wbck4ybckZlEAS9kO4UrMyLcxbpDnFOCIHOYO4jDJt5GDN7X9KDeEL6m/i3y7vj32iXav96ymXYGi8+8FonzsUk7Q/cIGlB3IvyHv5N/HrAq2ZWTmizHM/i35bvi1fr+sTMnioz/tFV9HcU8DBwr6QzcUOsDg/XWtHMsspcVY9bJAlWZqFI56Wk9WfwggJ7kEpNpzC+bfHQwTsl1eMG1aF4OFOlvgeY2ZR0rJ7SWjd7AGua2VYVpjsO92RcnMRJlyWVXcaLO7SHy3AD4DlJx+OVzxbE865+xWyh0xVxY7yqNedJhR1OB/4kaQk8Z+cLPPTyeTP7d7WTTX2dxGzB0Ql4KeflccHU7+JFL1bFC3RkgqPP4ZUBbwNONhcczff7QRJ2vQU3bn5qZs9UO68gCHqW7hSs7GlxTgiBzmDuIwybeZ8dgHPwnJqPcYPiQODmMu2vwg2b8zp7ImZ2q6QheDGD0bjX4y3cQ1H1pjPHaDzp/HhcoPEVvDxxR+f3agpBqk99fh0POXqa2ZXdOmvcD3Av1mF4nsYUZm+YP8Ar3E3HSzGfkcafhgtQzo8bYdUwglwJ6RzHkCtBXQoze0bSTqSKcMDLuHEyusqx8319loyj8/Ewx0Nwo+1lfJOfheT9JY21Hx1Ys5kdImlSuv53aYz/4p6V9s75z5Kew0tO748bdK/hzwfcCPt9Ws9habxh6dxyJMHREv1+KOkXeP7a3cm4KZVXFARBD7HRRhv16PghzBkEHUNefTYIHEmX4aFGK5rZlz09n3mNnFflIlysdMVUAhpJmaF3LR4uuImZlcpHaqvvvMfGgOPM7C+duIYpwHgzG9rO67L5rVQqJ2xuIXmSRuDG0mq4QO4n6dxAXHgzX8p7jhg0aJBNnDixM7oKgmAOaW5uZtSoUQwbNqzLwrMaGxsZOHAgTU2t0/vq6uqYNGlSlxk33bG+IGgLSY+a2aC2W7YmBDoDACStI2kfXITxtDBqupxL8Cph6+eObY2HYV2TbyhpvKTxxQ4kTZE0ptwAyagBGC7J0qs+nRuTDJSsbf90fj9Jp0l6R9JHkm6W1L+txUgaIOkySe9KminpCUmlBFPb6qfcvPaWdLSkNyU1S7pJUqtyQJL2lPSkpE8kvSfpAkl9C20OlPScpI8lNUma2JG54p7Hb+LenLbWtZmkB9KY70u6PoW4BUHQCxk+fHjJ490hWNmTwpwhyBnM7UQoWpDxAPAh7kk4u4fn8lXgFTz3ZRe8Eht4menr8OdQNTldmOyLikywdX1cTHIMs0ML20r6PwIvC74bXqL5eDzfZTUzK1mqOeWWPIRrwRwEvIsbyNdI2srMbmzPeirM6z94jtjSwKl43s4GuXmciBcN+Buel7McrpGzuqT1zOyLFFp3Kh5e14CHQ36P2SW228MTeOjm4ZLON7MPSjXK5dTcjd+XxdP490la08xe78DYQRB0IZMnT654viuFK3tSmDMEOYO5nTBsAgDMrLU6Y9DVXAycKumPeJGCjYGft7OP9Wld9a4Y5vW6mT1IdXwAbJl57CS9iBtHu1Jafwg8J0nABmaWlUG+Ixk8WX7OnPKKme2YfUhFF06R9E0zeyN5lQ4FRuYLReTm/0tcUHNd4L+FYhK3zsG8jsSLPByEr7UUx+KFBn6elU+X9ADwIm6I/al4gUKgMwh6NT2p7RK6MkFQnjBsgqDnuAovu/xLPCztLVwQc0g7+ngCT14H2ALP/fgVsxPXH2nnnK7OhyGa2f2SpuIGQTnDZjPcOHg/5z0CuAM3PmrMbE5jJ24pfM6q0K2A6wttgnusLivM4SG8CMMQ3LB5BNdq+jtwA/CfVI2tQ5jZi5IuwisPnlk8Ly9V/X3g+LwmlJlNlnQ/OY9Tod8Q6AyCHqSt/JKuFMfsSWHOEOQM5nbCsAmCHiKV/r0eD0frD1xmZl+mUs/V8qGZTQSQtHo69lSueEB7p/V2mWMlS4QnlsY9OruWOb8kblzMCY2FzzPT+8K5OUBrb1V+DuBesoXxamb7AZ9JuhX4U3bPOsBIXIPoMFprP9Xh3qxSFdLewg3aIAh6GWefXTkiuzvEMUOYMwjaTxg2QdCzXIx7I+bDS3OX4hOglBJbV5TF+UaZY09UuGYanq9yUpnzb8zhnKohC4HbFGhdSiidTxXozsM1hOpS+1PxcuM/6sjAqUz4ecABtC6j3oSXiV6mxKXL5OYdBEEv4pprrmHbbbdtdbw7BCt7UpgzBDmDuZ2oihZ8ZZC0laR7U8WvjyW9kqpTbZZrs6Gkeknd9bcxFtcWOreCUOMrwMpyYVMAkh7QEmXaT85VMvsUT5Cvll/n1y7px0A/vLhExsLA71K1sk2B2/EE/GeS9+g9XOj0EVwEdCadz5qF97HAl8AKZjaxxKtVJrCZNSXRzitxEdb2jv1eMo7AtYIM1+EB2CqNMQN4FNhOUp+sA0nfwoVpJ7Rj3CAIuolrrrmm5PFMsLIrN/6ZMGe+Ilr26spSz9A96wuCriQ8NsFXgpSgfwauCn8KLtz4bWBzYCN8cw6wIZ6nciy+Ue5SzOwLyntqMq7AE8n/mco7D8ATzt8v034bZoc+PQtsLul23HvwhplV8qAsAVyfPBBfB07AdVkuLtH2AzyMbjhuyNyb8kyG4F6mRXA9nqKg52aS3ioce9/MxlaYV5EX8+9m9rKkk4AzUxnlCWkOy+P5N6PN7B5J56d5P4BXcVs5raHdAp7A1/Dws8PN7B1JZwB/LtHuSNwrd7Oks/GqaCPx53dqB8YNgmAep2/fviHEGQQdIAyb4KvCIcD1Zvb73LG7gX901DsjaQHg80xgs6tIG/J98DVsCzwO7ExB7ybH4zkvyQF4+eObgIXwDXV9heFOAAbiJaIXA+4BDihT6vla4NfAPsCg1O/xuLbLx6nN8yWu+3uJY8/QPq/JR4V3zOzPkp7DdWX2xz0or+EFGV5Kze7HS1nvghsmbwCX4sZsexkL/EHSKDN7CzeY98XzamZhZrdL2jyNcSXuRRsP/F8bRmYQBPMQjY2NJTVoampqwogJgk4iQtGCrwp98WTtVuRKG9cze4P7WSZqmc7lBSxPlvQGnsBem85vI+nBJGrZLOkqSS3q9EraHk+wfw94QtLjkn5XYkr34KFNa6RwuRl4pbMf48nmU3GjrA/wXG4dY/BN+6xQNDO7H0+cvxr3DP029bc6bhwVGYx7fJYAnk7zuEcthUCzKmzX4sbDNmY21cz2ALZLx7ZIbWaVmU7z+3k69gleUOAGYFUzy4yaj4BFsspmZjYllSK/JIlpjiox5zwz0ruAz9MaTjazqam/i/BqY6/jZbL74mW22/Ka5bk+vR+T3oenvpuZXcTgicI1jWluX+BfKC2OG1ZBEPQgzc3N1NfXtxKkPO644zp1nMbGRgYOHMiAAQNavQYOHEhjY7E+SudRbo1BMC8Shk3wVeFhPC/kUEkrl2kzmtkljdfHSxyvW2gzHA9f2gvYGvgkeVOuwcO+fg3sjRsOEyTl82BWxA2MnfAcjJuA0en6IrvgIXL7AX/ADY6LcQHP/+Kem1uBEyX9oor1D8Y1U47ERSL74KFRtYV22wPjgC1xr83lJOOtBB+lde+SO7Yr7hX5X7FxTqjywzSHffH7dJ+krOraxbhxsGnh8i3SPC4pt8BqnoOk9XEPzQT8GWyHVzIrt8ZKvImX694r5cyUm9f30nh1eGjerngxiAmS1ujAuEEQdBLNzc2MHDmyyzf906dPp6mpiYaGhhY5Mw0NDTQ1NZX05HQW3bXGIOgNRCha8FVhH9yoOBk4WdI0PJToQjO7E8DMpibNFoCH8rojOd4Gts7CzyQtjlcDu9DMds8aSXoIz//4PTAq9X987vx8eDjSsvgG/9zCODNxocxM0HF1XATySDM7Nh0bjxtX29G2yGQNnsjflK59C0/u/wVuvGR1oZ9Knhdy7cqFvIEbImOTYfIe8Bs876QUbQpVmtmDkl7CjaX8mnYBnjOzR0t13I7nsA7QbGbDcpffmdoKN/jKUuJ34iTcgBoB7N76CgCOwp/nT5NXB0ljgSnpum0qjRkEQddTLK08bNgwzjjjjE7vv1yp5uL4nUlX9h0EvY0wbIKvBElIcS08nGtTfIO7NbC9pFnGQhVcX8ipWRc3GorCkFPx/JIhJMNG0kq4Ov0QvNRv5jEtVTVsbGETneWq3JFb0+eSJuEJ8m3xQGbUJPICl+AhUgCnF667AQ/pKsc9+Fp3BCbjBQOupJBn0k6hykuBwyUtkbR++uIhbJXyYKp9Do8AdZIuxYsy3JcZG8DvgAsrjAGzDcBs/o2STgVGpOIFL5e4Zghwc24czGy6pBtxcdbWg0h74V5BVlhhhVJNgiDoREqVVr7xxht7dPwgCNpPGDbBV4ZUgeze9ELSN/FqaCMknVXY+JejKLSY5VSMK9M+85AsjnuIPgIOxzfAn+LemlLf9Bfn8mmF4wvTNi0CuM1sZhLvzK5dNr2/U2j3haT3ynVqZibpMtyj8gpwo5m9nyuDnNEeocpL8EIEv8YNje2BBYDLys2DKp+DmU2QtB0e3ncdgKQJeJW5m4AfVBijHKen/o7GwwyL9KX8uov3iTTP8/FcIAYNGtSlxSmCIICGhgb69es36/OBBx7Y6R6bSsZLcfzOpK2xg2BeIgyb4CuLmb0haTReBnolPA+nzcsKnzOBxaF4Za8iH6T3dfHN+2Azuy87WfAu9CTZxnvp/MGkvbJUG9deDBwBrIYXOShF1UKVOS/OzrhhszMw3sxeqzCHap8DZnY1cHUyNjfEw8luB/qZWbsFM83sQ0kn4KWbTynRpJHy6+66jOEgCKqmGCK26667lgwZm1OKYWFthagFQdA+esumKgi6FEnLl9kYr5res4ppWVjYIuQ2wxX4T2o3MFXcKsei6X1W2eTk1diyijG6g6nptR0tw7G2oo1/J8zseUln4bo3d5RpM0NSJlRZn7xneaHKYgnoS4BzJG2IG4W7tTH/ap9Dfk4f4gUUVsSN2yWBd6u5tgRn416fUiGNE3AtoSXM7AOAVMzgl3ieVRAEPURtbS0jRoxoJUi57bbbduo4NTU11NXVlfSc1NXVUVNT06nj5Sm3xiCYFwnDJviq8LSke/Dwo8l4PsYv8KICV5rZq6ld9s369BSq9REwiTIJ9ClX4lDgLElfB27DhReXw/NGxpvZ5fjGe3pqNwLXiPkLnnA/p2V/B0kaY2ZDO9qBmX0paSSu6zMauAqv4nY4vp6SYqWSptAyjGxzSf+j9P0ahHttMqHK3+HV3d4jCVWm8toj8XyYvwFZkHulAgZVPwdJRwPfwHOD3gD6AX8EnjCzVkaNpL/iYWZrm9nThdP9JT0FnGtmB6W+zy8xvWPwqm53pTwcwwssLIqHrwVB0EPU1tZSX1/f6vh+++3H2Wef3Wnj9O3bl0mTJvWIjk25NQbBvEgYNsFXhcNwQybb2H6BV8s6nJTcn3givU/HtVwWxUPURpbr2MzOk/QacCieRL8ArpNyb9afmb0raWt8A381vqk+A8+/6Ig4ZKdjZqNTeNZBePjX03jOyE24kVCOO5gt+lmDb+LrS7RbFxf/3B8vMJAl4q9fFKo0s2ZJN+F5Nndmno425t/mcwAewg2Z0/F7/w5eFe3IMt0eiYfXjZa0XqZ5lDgef47D0+cL09grFeb13+R5Og64KK37QWADM3uyrXUFQdD9dKQ0clsCnNkrCIKuQ10smh4EcxWShuIb1JXMbFLu+D34t/ZdFy/QTiQtlIoATME9EkO7YIwf4IbdrmbWSkMmjX2fme1cOH4fsICZ/ahC3/XAiCTAmR0zYKSZ1XfKAjoBSYPxcLI/mdmodGxf4CxgIzMb35XjDxo0yCZOnNiVQwTBPEtzczOjRo1i2LBh7QrF2nHHHbn88surbp8JcDY1ta5BU1dXx6RJk7rUqOnoOoOgNyLpUTMb1JFrQ6AzCKrjEWAJST+SdLWkqZI+lvSCpOMlLZJvLGm8pPskbSnpaUkzJT0v6TfFjiWtIelGSU2pz/vTZjrfZkwac11J/5H0Ma7JUxJJAyRdJundNPYTyWOUb7OypOskvSPpE0mvp/VsLeknkg7CQ7a+wD0Wb0saJ2nVkoO2ZDruMcmPZ8mYqRpJ9cnYKfZzrKQ/Spos6QNJEyStVmjXJ7V7U9JHku6WtGp752FmDXgOzbHpvvYDTgTOy4waSdtIejCN0yzpKkkt6jRL2lHS45I+lPS+pKck7d2e+xEEQfvoqDjlgAED2tW+JwU4IUQ4gyAjQtGCoDoG4Bv8gXhY0xg8WX01XIBxRbwscZ6BeJ5IPR7ytC9whaR3zeweAEnfBxqAx4E98ZyefYBxKfQpL0j5NVx75a/An4GPS01U0vJ4yNU7eFjZu8BvgWskbWVmWd7KzUBzmtd7wHfw0Kvz8DLEmd7MEam/JXEdoNrWQ86q7pYlxW+C5xB1FTsDLwAHAgvi1chukLRqTidnJH6fTsHLQH+f2Tk7rZAqCnQOBzbH782n+H37v3TdPsA5uKfvaPwe1AMTJH0vafGsj+vz/A0PV5sPL1xR2+6VB0HQbtorUrnnnnsyZcqUdvffEwKc3dF/EMwthGETBKXpkzbrSwC/wdXhbzKzWVoqaSN8P+6duFjS/oVywd8A1jWzB1P72/FSxEcDmUfmFOBVPKTp09TuDjy/5Ui8KlnG4sDOZnZDG3Ovx/M4NsjN545k8BwN3ChpKTwXZMucoTMB36Bn63saz2/JlzC+rsR4O6ZXnn9QwaPUCXwGbGFmnwH4o+Aq4IfAf+QV54bhif2HpWvGSvqMVKigBBvgHqpK9E/vmyWDZXG8XPSFZjZLj0jSQ3gO1+/xHK51gGYzG5br685ygygEOoOgU+lpHZeeHj8IviqEYRMEpXk+9/OXuDjkMEk1+Lf3vwaWp2W41Urk9FiA1zKjBmaJXV4F/J+k+YCF8M308cCXaqlpM47WYo+f416WttgMuBV4v9DnHcApaQ3TgP8BJ0r6Bp6j81Khn0eAoXKBzjuBx7MyzQVuw71W4MUWfpg+f4YXCugKxmZGTeKp9L4CXoHuu3jluasK111NecPmUdoW6Pw70MfMsrLW6+IFEy4r3Oup+O/QENyweQSok3Qp7nW7z8yayw0SAp1B0Lm0VwCzvQKdPSnAWc34QfBVIQybICjN1vjm9APgFTP7BEDSNcDG+Mb9CWAGvpE/C1i40MfbJfp9Gw+d+jr+99cH98yUrMolab5cJa53yhgWRZYGdk2vUiyZyiNvgnt3TgCWlDQZOMXMMq/NH3B9n93xil6Nki4GhpvZR7n+Gs0sn91+b/JmnSzpLDN7too5t5eisGWmP5Q9g2XT+zuFdqWeCQCp8lrFLH1JM2n572YmaDquzCVNqe8JkrbD7+l1qa+sIMF/K40ZBMGc014BzMUWW6xDgpkhwBkEPUsYNkFQmqfzVdEAJC2MC2rWm9kZuePfLdPHN8oc+xTPe1kE9wadBVxcqoNCeeFqv7mfhuftnFTm/Bup7/8BuyYjZA3gAOBsSVPM7LYkYHkEcIRcSPPXeNL8p3j57Eo8k96/C3SFYdMWb6b3pXNzgdLPZE7IPHRDC+NkzCpTbWZXA1en8LUN8edzu6R+heccBEEn0V3ilD0pwAkhwhkEGWHYBEH1LIR7WD4rHB9apv3yktbJ5dj0AbYDHk4b2RmSGnCj4rEkkjkUT0InXfMRbgQJWKTgwSnH7XiI1DNmVrLAQB7zmu9PSPoTnhOyOh5elm/zCnCqpJ3S+bb4XnovJXrZHxdJbStXaE5YHjfAtqNl3sx2ndD3d1OltktwY/ADYKCZXSRpDzy/aICZTSl1cTIYb5a0Iq5ltCQl7lMQBHNOR8UpzzrrrHa170kBTggRziDICMMmCKrEzN6X9CBwsKQ38Upiu+Pq9qV4G/i3pBH4xnVfYOX0nvEnXEDyDkkXAKuk49fiYW4X4HkjR+OCkndK+mUbBstRuPbMvZLOBKbgVc5WB1Y0s90lfQ/fVP8bmIQbbEPxPJ67ASQ9gFcRewr4EM8HWgMXmcyzlKR10s+LAD/C85CeTGvrCTbBw9P2kfQBs6ui/T6d7wwPyU64B+tQ4CxJX2d2JbktU/np8WZ2uaRMGPYe3GPWDxcKfcLMwqgJgl7G5MmTqaura9c1IcAZBD1PGDZB0D52wCuHnYWXW74SLzlcKql/El4Z7Hi8sMAUYIes1DOAmT0mF8EcgZcCzv4n/RpwgZlNAJD0UzxJfaPU5x/KTdDMXpU0CM+fOR7P55mGV1rLjJK38Gpsf8I32Z/gBswWuRLT9+IV4Q7H/634H3CQmf2tMOTP0gvcmHgl3aMTc6WXe4LpeLL/73Ej4iHceLsfeH8O+34C+CZwjJltK+k13MDJDLyDcAPxifT5oTSH03ED9R28IEPJ3KogCHqWv/71r1UJdDY2NvaYlyYIghKYWbziFa9OfgHj8cpX7b1uKJ5LM7DM+etwI2TR9HlRPFdjMh56NRn3lsyXu2bD1Oe2uP5OE77pvwwvJJDvf348r+Z53Eh5A68itnCuTf/U3964J+lNXNflJqBfob9FcXHLabjX50Zg/XT90ELbDYC78NCuGXgVt9VL3Ve8gMNjuO7P08BWuTZjUv/515R0bpf0+e20vrdxb86q7X22uEFrwKDcuT3Ssf65Y8INnRfSM3oTOBOoqWa8tdde24IgaJumpiYbMWKENTU1zXFfO+ywQ5ttpk2bZnV1dcV/awywuro6mzZt2hzPo1o6c+1B0NMAE62D+6/5CIJgbuJWPNdnUCovfAe+mT4D+DkwGvcCnFLi2lH4f7o74MbPr/Dyx3kuxYU1L8cFKU/APR6X0ZojcBHS3fFN/rol2p2X5ncargX0Quq7BZI2x42aD3HxzR1xDaGGpL+T59tpvVmfb+JJ+QPT+WPw+9QE/BM4GDhB0v/h+S+f4cKdm+BiqE/QMaHMc3Gv17FttDsuzXUsLl56Mm7A3pLKfgdB0Ak0NzczcuRImpubu2W86dOn09TURENDA5MnT571amhooKmpqaQnp6vo7rUHQW8lQtGCYO7i1fS+LG6grI8LcWa5LHclscoRkk4ys3y542fMbLf08+2SGoFLJf3UzO6SNBj4LfA7M8uqtI3LtVvTzJ7I9feKmc0S5kw5JqdI+qaZvSFpFdxAGW5mJ6Zmd6aqYPsU1nUGMMHMtsz1dw8e/nYwLraZsRQwxJLujqTHcOPmN8DxZvaypHdx78i3cZHTGjz86yPgX2Z2Qbp2ftzTlP1c5Iv07VErzGxmyp0ZLWlI7hnMQlJfPNzvIjM7IB2+I83vEmAL3IsVBEEnUSy53BG22GILpkyZUtU45Uo5d8Y8qqU7xwqC3kwYNkHQBZjZhl3UtbIhcCHOV4D/FDbld+JehHVouWm+stDXVXiZ6XVxb8lmuDFwTYn+wMUmn8gdv6XQX14k8w28iMB8Jca9gpxhI2kl3AA5vjDuR8ADadw8L1lOTNTM3pH0Tho3z+fF5yDpQmAHSa+n+bZlVPwEDz0rxxjg/3CvTCl1vHVwD9ulheNX4NXvNig1B0l7AXsBrLBCcVlBEFSitwhV9pZ5BMFXiTBsgmDuIgvLehPXaPkWrctPZyxZ+NxCnNLMPpXUxOyqbkvj4qEfVtlftSKZRVHM4udM5PKC9CryauFzcdxs7KJAainyoqPfxosI3ILnAc0s0f6FSp2Z2ReSjgKukPTzEk2y7OE38wfN7HNJ03Lni/2eD5wPMGjQoGr1i4IgABoaGujXr98c9XHggQdyxhlnVGwzderUisZLZ8yjWtqaSxB8VQjDJgjmLjbHiwc8iifkT8ZDsEoxpfC5hTilpAXxKmyvp0PTUt/l/nd8o51zzTbz38BDykrOg9kil0fgifxFPm3nuGWx8qKjU82sLdHRclyJV447Fs+7yZMZYcuQE/BMnqklmb32IAg6iXKhYe1hscUWq7qPYhhYWyFqQRB0HWHYBMFcgqRt8IT/M8zsI0m345XOPjSz56vo4jd4Mn3Gdnio2APp8+3AYcDXzOyuTpjyQ7hezG9w4yFj+0K7F3AjbLVcLs6cMhPX1CmLtV90tFw/JukveMnvbQunH0xz2R4P98v4Lf7v74SOjhsEQUtqa2sZMWIEtbW13TJeTU0NdXV1JT0ldXV11NTUdMs8oPvXHgS9lTBsgqAHkLQVnlS+Kl796x3gceDl1GRNSZsCP8D1UDbHDZGxuMcBvALZbnjBgFNxQcwF8RCrX+ElkD/KDbtayjG5AhcKPQ5P2L8LwMzGS/oXXmHsNFzk80u8vPMvgMPM7MV2LHNd3HA6OlX/egSvRPaLfKNkGOwP3JC8SFfi4qffANYDXjWz00rcwyl4KF7G7yT9rtDGcD2eNfGQt/GUFh1dTdIYMxtaxbpqgTUkfQ58x8xeNLNbJN3PbD2fvwK/NrPGdC+PkDQDr9b2/3Dvzn20zlMKgqCD1NbWUl9f3yl9rbXWWm226du3L5MmTeoVOjadufYgmJsJwyYIuhlJf8SrgP0TL8s8AzdGNsc3veCJ/Rkb4Zot2wNXZ1W6zOwzST/Dw6D2Agakvl7GN8zFEK4DcYPn30AfvBrYHwttdsbzUHbHS0LPxL0pd9A6N6YtbsE1dHYEDsGNrrvT5/vyDc3sVklD0pijcW/LW7jH499l+t8aT8wH1/dZEJiK6+ssjJd0XofZxQe2oLTo6BVpTpl4abX0wXV8Mg/UcEoXGhgOvIsXTNgPDz+7GDjCzL5s55hBEHQDhx56aNlzIcoZBL0XlalkGgRBFyHpVeBRM9u6xLn5ss2upHpgBLCAmX3eRp8L4FXAWv1BS9oQuAfYxMxK5bDME0gaD8xvZusXji+I5wfdny8nnTt/D7AiLqrZ5j+Iuft5J+6BWsvMnsydnwqMq9L70yaDBg2yiRMndkZXQTDP0tzczKhRoxg2bFinhGOdcsopJY2bxsZGBg4cSFNTU6tzdXV1TJo0qUuNm85eZxD0RiQ9amaDOnJtiMMFQffTF/dGtKKEUQPwmSRLYVVI6p8+7yfpZElv4J6V2nR+G0kPSvpIUjNQXxxH0vaS7pb0rqQPJT1eDONK7UzSsZIOlvSKpBmSbpG0dHpdKel9Sa9JOqxw7dB0ff/csSmSLk3jP5f6myhp/RJjH5jafyLpYUnrpc9j2rrBhXv6Ke6V+bmkpQpjrICHo11SjVFT4Ey8QEJbAp1IWkXSdZKaJX2cns9m7RwvCIIydLZA5eOPP17yeE+LcoYQZxBUJkLRgqD7eRjPB/kfcEOZvJXRQD/g97gI5xcl2gzH81b2wsOiPpG0D3AOrpFyNJ6/c3Jqn0+mXxG4Gk/q/xIP1xotaREzK1b22gV4Gg+j+gYwCg+lWgK4DS9LvB1woqSnzOzWNtY/GFgFOBKvwnYMcLOk/mbWDCBpjzTOBXhY3reBy0nGWwe4CNgfT9o/K3d8Z1wb6OKUB1Tpyx4zs/xz+Bg3as6WtI6ZPVjqIknfxEPvPgAOwEtM7w/cImkLM7utg2sKgqBAZwlVzpgxo6RAZ0+LcoYQZxBUJgybIOh+9sGNipOBk5OeyVjgQjO7E8DMpqaQJoCHyoSivQ1snXkaJC0OnJT62T1rJOkh4EXcOCD1f3zu/Hx4bsiywL60Llk8E9gym4Ok1YGDgCPN7Nh0bDye87IdniBfiRpgTTNrSte+hRtovwAuT/MZAdxmZnvk5vkWcE0bfZfEzB6R9CywKy0Nm12AB8zsxeQJauW1yjEBzxnKMxrPHzoez4UqxZ/wstrrmtkkAEm3As/iBRxaGTYh0BkEHaMztVxuvLEt/d6uHT8IgvYThk0QdDNpE70W8GNgUzzBfWtge0mzjIUquL4QPrUubjRclnRSMqYCz+NemVEAklbCPTpDcI2VzFNRSqRybMGwykpL35Fb0+eSJjFbQLQSD2RGTeKp9J7t4Pul11GF624AKuYatcHFuFdp5fQMfohXpds3na/Hw8vK8UHxQCrgUI97fDYuk8M0BHgwM2rSdV+kCnRHSaoxsxbxKyHQGQQdo6tFMXtalDOEOIOgMmHYBEEPkEKa7k2vLFzpdmCEpLMKG/9yvFn4vHR6L1cgIPOQLI57iD7Cq4O9jFdQ2xevhlbyuhyfVji+cJuzni1aCYCZzZRE7tpl0/s7hXZfSHqviv7LcSnuWdkV+Et6n0mqumZmU2gtaloNl+H6P8dR+t73xUt5F3kLD4OrA7o2MD8IviJ0lijm3XffzUYblXPChihnEPRWwrAJgl6Amb0haTReBnolPA+nzcsKnzMV+6HkVO5zZB6HdXH9l8FmNqvscsHL05NkBtvS+YOS+gBLtW5eHWb2uqRxwM6SjsbzbW6s0ois1O+Xko4ErpXUquoabsgtU+L4MvgzbCxxLgiCdtDZApWjR48uadj0tChnCHEGQWV6y0YmCL4ySFrezF4rcWrV9J5VTMvCwhahRBhUCf6T2g00s0qaLIum989yc6oDSm3KybUZihclKC/w0DlMTa/tgAuzanA5MiHOy8xs53b2fRHuYTkBN5IuntPJApjZdZIewUPZlgMWz52eAAxLxRGmwCwj7bfA42ZWzbMNgqAC3SVQ2dOinCHEGQSVCcMmCLqfp5N2ynXAZDwv5hd4UYErzezV1O7Z9H6wpNuAL8ysrKCJmU2XdChwlqSv40np7+Mb7Q2A8WZ2OW4ATU/tRgCL4aFZ7wFf69yltp/kARkJ/CN5sQAewAVIF8cLHRyHi162l+vwtR+Eh7rdPscTns1wXNumyOm4F21sut/T8QpzK+OirEEQ9EIqCXFGuFkQ9E5CxyYIup/D8L+9o/GN8L/x8LDD8SpdGTcDZ+Ob4AfwymEVMbPzgF/h5ZQvwY2bkfiXGE+kNu/ixQr64NXZTsCre106pwurFkl9KoW+mdlo3PjYJB3qj3twPgcmmdmDZvZye8c1s4/x8tECLm9L+LSdfY9ldmGF/PE38JLdz+CluK/G8242N7PONKyC4CtBc3Mz9fX1XarlsueeezJw4EAGDBjQ6jVw4EAaG7sugrQ71hcE8yph2ARBN2Nm55rZr8zsW2a2sJktZmZrmdnJSUwya/eFme0PHIiXa54p6Snge3h406wwLElLSTpH0uu4V2JZ4BAzW8TMBqbyzz9Mgpnr4Po438YLANwCnG9m9Wam1N+Kkm7BtVr2lnQGsFAa7mozU6508Z6SnsSru60q6QJJfc1sTGo3JY17HF5K+sd4oYHv5tYqM6sv3KdRZvat9PGfzBYhfax4TyVtgxcf+H4SwbwqiW/m20yRdGm6dy8C+0hqkLSSpMUknSdpmqS3JZ2aN7wkLSzpdElP4wbn28CBklalJSel90MKx4fgHqeFgA9xI6eaPKogCAp0h0hl3759e0yIM0Q4g6DjRChaEPRiJG2C54TcCByM54WMwjfxL6Y2NcD9eC5OPR7e9jPgHEkLmdnfC91eAvwL2Ab3FNXjBs6I1N+CeNW0RXAhyXeAvVP74vxOTPP6G557sxwuWrm6pPUKgpZDgf/hm/4ZwBsV1j0gjd2QDq2drp8MXC9p/pyuTilR0npggqTvFXJYhuAG3WHAgvi9vCbNaxKwfWrzF7xa3NnpuoVSv8fixQ364p60ByWtamZvUYZ23qMgCKqkK8UqDznEv5voCSHOEOEMgo4Thk0Q9G5G4rk2eSHOp4BHSYYN7tH5FvBdM3spHRsnqRYvH31OIeTqcjMbkWv3I2AHkmGDi1SuiAtKPpjGvI3ZejOkY/3xjfpIMzs6d/xF4D7gl8D1+UuATVM4WFt8DKyOl2QG2Cx3bnoaZyW80EIlUdLfk7R7EosDm5nZ+6ndMngluofN7JDkpbkHz335jaTz03Uz8ByoL8zMUvL/HbjnZgc8j6YVHbhH2fkQ6AyCNuhJPZfQkgmC3kkYNkHQS0mb50HACXkhTjN7TNLkXNPNgIeAyYW8lTuAPYDvAP/NHb+lMNRTwMa5z+sCr2VGTRrzS0lX4p6QjE3wcNaiIOhDuPExhJab9turNGpIHpDNAFJVtH/iXpk8r6UxqhIlTTyQGTWJWWKjyQjJ31fIVY5LPJc0h/JFFlapsJT23iMgBDqDoBq6Ugxzzz33ZNy4cpJgXTt2iHAGQccJwyYIei9LAQtQEKpMvJ37eWlgIK034RlLFj4Xs15nMjt/Bjw/521aUzyW6cxMqnLcoqBoe3izVEU4SVWJklb4nBcbfQP4Qfo8Avghs6uWDQZOw/N7DsEryH0J3EplUdL23qMgCKqkK8Uwf/rTnzJu3LgQ4gyCuYwwbIKg9/IebqwsXeLcN4CsLPQ03Pg5sEw/L7Rz3DeB1cqMmScTBN2U1gZD/nxGV3geqhUlbZNUuGEigKRpwGeZMSXpILwaW75gwwJ4rk0186v2HgVB0AbdIVK51157cfLJJ/eIEGeIcAZBxwnDJgi6kJyoZcaHeKL6P4BzzexzSVOA+4pik2b2haSJwLaS6nM5NmvjFbYyw+Z24A/Aq2ZWyrvTXh4AdpO0jpk9KGkhPJH/iHT+qVR97Rnca7FCKnXcLUiqB0akCm7VipLOKYsCnxfC1U7BS2bn+XPh81h64B4FwbxMd4hUnnrqqT0mxBkinEHQccKwCYLuYTs876Mm/fx33BNzVBvXjcC1bq5LiexL4Xkub+EbZvDE9d8CDZJOxz00iwGrAoPNbMt2zvUiXFPnWkl/BvYEfoQn0C8EHICHUG0HXAucKWkVvIzyJ8DyeG7JaDO7p51jV8NokrBmO0RJ55Tbga2AI3PHDgSaK11kZi9LOonuv0dBEMwBkydPpm/fvl1qwARB0PmEYRME3cMTme4LcKekgcAw2jBszGyspJ1wA+c6PFfj4HTd+6nN+5LWS8cOwzf1zbiBc017J2pmn6Yy02fiCfsLA/cCl+M6NBPMbApwmqT5gJ1wj87+eLjZa8BdwEutey9NKks9s8r5TcWNxOzzeZJew6uP7YjnJb2e5vxEtXNog3/gxsie6XMTUEcbhk2a358lPccc3qMgCLqPmTNnMmXKlFbHu9pbEwTBnBECnUHQMzwCLJFLfkfS9pKekzRD0kRJ6wOY2eVmtoqZLQScABwHrAlsKukSScuaWZOZHWRmA/Ak+DuBs3BxzRkppG1SXlgzjbkBnhj/YWp3h6TVzex/ZvaLdA7gbDM7LxPczK43sy/N7BIzW8fMFsOT7d/AS0a/kOtPZvaXNOZ4SfdJ+qWkxyXNBPaT9IykUobYOsBwSVul6+tTpbQ8d+Jelal4WemvAd9ktlcLvMLcB5JelzRT0vPAymluLYoPmNlQM+uX+/xlmv+P0qHD8EICbwG75S59h9mGX3aPl8W9MwPxL5P+BxxnZgckIy0I5nqam5upr6+fJ0QlGxsbue222xgwYECr18CBA2lsLNZfmbuYl55VEBQJwyYIeoYBwBd4zg24AXEwHur0Wzx342ZJy0g6R9K2kk7BxTWXS9cdhQtxTpC0eKH/cv3VZg0kbY57DT4Edsa9HUvgIW3Lp2bP42WJT5S0s6RiAYFZVNlfxsq4YOXf0xruSmvbQlJdoe3OeCW3W8uNDVyBG3y34iFje+L6P8umuWUippvjoXybAzfhIqZ/qNBvOQwX8fwefn9LImkxPPzs53j+zVZ4ee1LklZNEMwTNDc3M3LkyHliszx9+nQ+/vhjGhoamDx58qxXQ0MDTU1NJfNu5ibmpWcVBEUiFC0Iuoc+ScdkCeA3wDbATWb2kSTw3Js1zawJQNJbuFdnE2AZPCxsGeBzfKP8ZzN7OglRNgC744ZCRrn+foGHlIELU07I5+BIugf3KBwMDDOzDyXtjOvIXJLa/A/PZTnTzDIdmKr6y7VdCtepyevrTMeNk+0l/QP3tvQBtgf+naqWtULSRsC2wIFmlr8H1+d+bq+IaZuY2W2S7gOOlnR1met3A1YCfmJm49Ox25KBeKykC8zsi/aMGwS9mWJ55LmRtko6z+1rnNvnHwSVCMMmCLqHvAHwJXAZLTf6D2RGSOKp9L6cmW0t6Tt4FbJ9zWx01sjM7pP0Cp4on9/Ul+tvBQBJKwHfBo4vCEd+hFdFG5Ib46ZUDWxT4CfAj4H9gN9L+qWZjWtPf4kpeOGBb9Gas9NrJPAobgRdXKJdxqa4B+UfFdq0V8S0Wv6M5/IMxYsaFBkCvJ4zajIuxavlfYfZz2YWyZuzF8AKK6zQgWkFQc/wVRCW/CqsMQjmVsKwCYLuYWs8/+MD4BUz+6RwvkXQtpnNTJ6cTPwxy1YtJXL5Fq31VNrqL8vtuSC9irya/2BmM/DiBdcBSFoHF8U8Ec9daVd/aR370FIYFGALvFDClrimzCg8N+jBEn1mLAk0mtnHFdq0V8S0KsysQdLtwFGSLinRpC/ln1l2vlS/5wPnAwwaNKgr9H+CoEtoaGigX79+bTfsxUydOrWi8TK3r7Gt9QXB3EwYNkHQPTydT9rvAJmhskyJc8uQhCXbQSYMeQRuoBQpGfaVkfRt7sQ9IR3pz8yslKfiBby62RrAeOCXeMGESrwH9JW0SAXjprNFTPMMx+//PiXONQKrlDiePccQ6AzmKcqFb82NFEO22gpRC4Kg5wnDJgjmDl4A3sbzTWZ5RFKZ528Bp3agvynAamZ2YrlGkpYAZGbTC8f74LkjmTeiqv7awsw+kHQDsAteXW1hUm5PBe7EdXf2wIsRlKKzRUxnYWaPpWpuf8bn/H7u9ARgO0k/NrP7c8d3xA2t5zpzLkHQU9TW1jJixAhqa2t7eipzTE1NDbW1tSW9GnV1ddTU1PTArDqPeelZBUGRMGx6IQW1+lXM7MXC+Q2BTNRvk2Kp2nb0PyArS5vU3O81s7sLbccAG5pZ//aMka6dggskDu3g/FYq5+WYk3l1F+mejkgf5y+cG4hrmOxGFZjZF5KOAs6TdCmeo7Ecnmz/ErN/X9pizVQqeSVcU+UGSQsCV+Kej28A6+EGwGm4t+GeFGZ1F74ZXxY3IlbHc20wM5NUTX/VcDGwA55jc5+ZTa7U2MzuSYbFaan62t24ls0Q4JaU39JZIqZXlDl+JPA0HvI2IXd8DO4lulbScDwccSe8KMTeUTggmFeora2lvr6+p6fRKfTt25e7776burpigcZ5Q8dmXnpWQVAkyj33bj7Av7kusms615mMADYqcfwYPD+kt9Fb51WO7ea0g5R3sQvwXeAG4GRgLLCBmX1Y6doy/d2Kb/4XwxPf70h9LoMn/IMLgp6CGzFn4kbDebjhsJ2ZndPO/qphLJ6Dshxte2sytsfLOG8F3IhXcVuN5FEys/dxA+tWXIPmjtRmS2Z/SVANfyl1MFWHazXXlJu0Ae5VOhF/bmsAu6TnGQRBL+TYY4/t6SkEQdABZBZ5qb2NnMfiInxTtKKlByVpEXzTdy1eiamzPDaGiwaW3Lh1hK702MwN5Dw2d+Kb7IFZ0YC8x8bMxnTjnIYyD9zbziKF1Km9pZ67k0GDBtnEie1NoQqC7qW5uZlRo0YxbNiwuT7EqbGxkX79+vHxx61T9urq6pg0adJc7bWZl55VMG8i6VEzG9SRa8Nj07u5BM+fWD93bGtc26OVQntSdB9f4viUFLpVkpyK+3BJll716dyYZKBkbfun8/tJOk3SO5I+knRzKglcEUkDJF0m6d2k/v6EpHZ7XirMa29JR0t6U1KzpJsktSpfI2lPSU9K+kTSe5IukNS30OZASc9J+lhSk6SJHZkrnlz+TTz0q611bSbpgTTm+5Kul1Qq+bx43RhJUyWtJ+mRtK4pKi8+uVR6DtMlvSHpb5IWLvS5rKSL0/2ZKem/ck2bfJuh6b6vU0V/i0o6SdJkSZ+m9+GSKv47JGl+ScdIejn3vO6TtH6hXTXP1CQdJ+lwSZPxogY/kPR5qXsl6TBJn0n6evrc6m9M0tclnS3ptXSfXpN0iaSFcm3WkHRj+j36WNL9kqIsUTDPMC+JPoZAZxDMvUSOTe/mFVwjYxdchBE8DO06ZivWdwbr4qFCY/AwI/BcgEocATyB54gsDRwP3ClpNTMrWVJXnv/wEJ6ncRDwLp73cI2krczsxjlbxqx5/QcXrFwaT6q/DPd8ZfM4EReM/BtegWs54FhgdUnrpXyWndK1R+P3fhFcZb4jX9M9AVwFHC7pfDMrGUYoaTPgFjzc67fA4mn8+yStaWavtzFODfBv4CQ8hGx74G+SPijhFboE+BcuFLouHsbVRMoJkrQYnitShyfFvwbsDFwiadESYVRt9Tc/Hv71HTyM8ClgHTw3pS/+PMpxGP77Mhy/lzV4ielZz6KaZ5rrbyguGnoIMCP9PA7YRdI5tGTnNO8myetl55FUh/++9U3j/Rf/vdsSWBCYKen7+O/Q48CeuLbPPrhA6Hpm9miFtQfBXMW8IP4YAp1BMBdjZvHqZS9842W47sbu+AZxYTxp+3M88XjD1Gbj3HXj8dCvYn9TgDEl+u+fO2bAsSWuHQNMyX3un9o+C8yXO/7jdPz3Fca9ADdmliyMMRZ4otT6K9yjcvOaUGh3SDr+zVy7L4CjCu2y+W+VPp8JPDaHz7E+9Tk/sHJ6dkelcwPTuaG59hPx8LT5c8cG4Norp7Ux1pjU3/Yl7u0rzA47ze7tyEK7m4EXc58PSO02LLQbhxumfdrZ3y6p3ZBCu+G412TpCmu7Gbi2wvmqnmnu9/wNYJFC253SuUqvoRT+xnDD8wtgrQrzuwuvfrZg7lifdOz6MtfslX4fJq6wwgoWBL2dyZMnt/X3M9e9Jk+ePE+vsbi+IOgtABOtg3uv8Nj0fq7CN9m/xMPS3sI3SkUl9+7majP7MvtgZvdLmop/W19KoBFc8+RW4H21Vn8/RVKNFcoKd4BbCp8zrZQV8A3tJngI5mWFOTwETMfv6/XAI8B+kv6OJ3z/x8w+6uikzOxFSRcBB0s6s3g+eUi+DxxvuXwPM5ss6X5yHqcKfEHrEMUr8ET+5WjphSt1nzbOfR4CvG5eUSzPpXiOzneYfW+r6W8z3MD6T+G+34l7OtbBk/5L8QhwhKTjgNuAh80sr4tT7TPNuN1a691ch3tSrgAyr80w3PPyM9z4mowbN3k2BR4xs8dLTVyeE7cB7tH8sjC/cbhB1QoLgc5gLmVuF6+EEOgMgrmZMGx6Oea6Htfj33j3By4zsy9LRMV0N2+XObZchWuWxkPpdi1zfkl8IzonNBY+z0zvWb7H0um9XOJ8pkB/cbrm93hJ488k3Qr8yVLBhQ4wEt/IHgb8o3CuDhDlVeq/VUX/TdY6DDB7TkXDptR9Wij3uW+FuWTn87TV39L4GkqGKTL7vpfieOATPCzsz8CHkq4GDjWz96j+mWa0WpeZfSTpKrwy4B64obQRcIWZ/SdrV+LvbkngyQpz74t7Z45Mr1ZImi//JUEQzM3MS+KVIdAZBHMfYdjMHVyMfyM+H67vUY5P8PyDIl1RvuUbZY49UeGaaXiuwUllzr8xh3OqhkzpfVM8xK/k+eQKPQ/XjalL7U/Fc1h+1JGBzexVSefhYV43F0434eEBy7S60I9Vo1BfJ2mBgnGTPae28nOKNOIaNqXmQpXzyTMN93j8psz5KeUuTOs5CThJ0jLAFsBpwKJ4LlJVzzTfZZmhLgF+hxfrWAQP/Wyr3PR7VDbmm4EvgbPwv+NWhFETzAvMS6KPNTU1LLLIIiHQGQRzIWHYzB2MxQUPm83smQrtXgG2lbRgFqojaQiwRBVjfIpv5qrl15Lqs02ZpB8D/aisV3I7Hqr2TIlQoO5iLL7RXMHMxlZzgZk1Af+W9CNg7zkc/zjcCzQ8O6CWgqw7pvv6RTr3LTxXZH559bpK5b37ANvSUkRye+BV2jZs1ix8ngBsJ+nYNNesPPSOeI7Nc4X2y0mqVL769jS3D801X1ogaSlJJ+AhlwNw79XLuEF/hpllXpY38Xs4DtfWgQ480zLcg3u1dsH/FqYwu2hHOe4E/iJpDTNr5bkxsxmSGnDtmsfCiAnmVeYl0ce+ffuyzz778Mc//rHVuRDoDILeTRg2cwFpk1vJU5NxBZ50/E95eecBwJ+A96u49llgc0m34996v2FmlTwoSwDXJw/E14ET8MT3kt9KJ44CHgbuTXkmU/AQrNVxrZ7dC+03k/RW4dj7c7J5NbOXJZ0EnCkvozwB93Qtj+dqjDZXsj8fF0F9AN/Ir4xveO/s6Nhp/HcknYGHVBX5CH9mN0s6G6+KNhIvOvBJ+lyJD4CTJS2FP4sd8DyXockDVYk1C5/HAAcCWQnkwZJG4Pdob2tZZawaLsMr6N0l6VQ8fGtB4Ntpnv1Tu7/hSfMAa+GG5O8k/QN4LB3/Ie5VOQ+qf6ZtTTCFeF6WxlwAOL2K+3Y6buyNS0bgU8BSeG7OPuYV8P6EVze8Q9IFuHG2FJ5T1cfMDm9rbkEQdC+nnXZaT08hCIIOEIbNPETakO+DVwLbFi8vuzMlNG9KcAC+qbwJz40YiVf1KscJeGWvMbjS/D3AASVyPPLze1XSoNTv8bhBNA14GhcjLfL3EseeYfY39R3CzP4s6TlcV2Z/PDTpNbwow0up2f34RnwX4Gt4mNylpPLFc8gpwL64UZfnKjzJvhb30H2Kb4iXBa6mdeJ6kem4h+YM4Lt4fs2BZlbq3lYkeRo2SOOuB5wLvADsYmaXdqC/zyT9DDgcN74H4KWWX8aNmveB9czsndxld0kahRswmzJbB2ht4GTcc5P1X80zrYZL8Bwo8Ofd1rqak7fy2LS2JfH7fjf+/DCzxyT9AP/d+Rv++/Qubqid2465BUHQRTQ2NrbQp7niiivYfvvt5wkPTRB8pehoObV4fTVfzC6rvEdPz2VeeTG7ZPJP8ZCqwblzO+KaRVukNvny3jvjno9P0msGsGyh7yn4Bn17PHxsBu4RWT/XZjytS4GOL8xtHdzrMh038v4GLFzi92Jo+nwIXkDg64X5CNeN+Vf6/Jt03a+rvFeGGxF/xHN2PsA9NKsV2m2KV+B7E/eEPY3r3PQpc392B57Hixtsnc5tjH858AlemGAPCmXGU7tF8RygycyunjacluXQF8cN9VfTfXkbD6dbta01r7322hYEQds0NTXZiBEjrKmpqV3XTZs2zerq6kqWRK6rq7Np06Z1yzyCIHCYg3LPFRW/gyDoVvKCrBklBVkl7YV7F57DRTEfxau4TZBUDFkbjG/qj8ST7fvg4W616fx++Ab+v3gO1LrpWJ5LcO/KNng55P1xMdRy/BM30nYrHN8U99ZkQrAb42Wqb63QV5Gdgc3xULnd8FLeNxRKKa+Ie2t2T20vwj2Fx9Gan+DhYiNxj9l/JX0Hz+/5EDcK/5zG2yh/YU54dA/cU/ZzvLz2kbhnLuN03IgbiYfH7YMX2qhtx7qDIKhAc3MzI0eOpLm5uV3XTZ8+naamJhoaGpg8efKsV0NDA01NTS08OV05jyAI5pwIRQuCdpIU6PtUamM5LZp2cjFwqqQ/4qFqG+Ob5fz4fYBjcK/K9unYb4CV0mt33KOSUQOsaV4EgZS39AjwC+ByM3tW0nRcGPTBMvO63MyyMLxxqZDCDpQJzTOzRkn/BvaSdEr6BgY8f+UFm62PszzwrrVPI+gzYAtLYY+pBPNVeO7Nf9L4s0K80vNqwHN6DpH0Z2uZxF8HrG1mb+WuuRz3Tv0sm1sqAjCZ2SWvSfdgfWADM7s3HbsrzWmEpJPMw+vWxUu15zWermvHmoMgqJJimeZq25cr49zR/oIg6H7CsAnahbmGS4+L6PQwv2N2FbNydPQeVSPIugqu3TKrspqZDQWQNAUXhMwbNg9kRk0iL1paLW0JcJbibPxe/RQ3hpbF1/V/7Ri3FGOtZS5Xfj3/AUhj1eMemG/S8t+6pWlpnDyYN2oS6wC35g0uM3tT0n9wb1BGtcKjjwBDJb2Xzj1uFQowJI/cXgArrNCexxQEQWeLT4aYZRDMPYRhEwTt5ybgB13RsVUnyJplspYT0KwonmlmM1N/C1M9bQlwtsLMHpY0EQ+7GoeHa31Oy0IRrwGbSFq0HV6biiKskubDjYlv4sbN88DHwFa4MVhcd6n7uCxeDa/I27Q0bKoVHv0D/mx2x8PhGiVdDAwvtW4zOx84H2DQoEFtVWYLgiBHQ0MD/fr1q7r91KlTKxovnd1fEARdRxg2QdBOzGwa7ReobA9tCbJmG/tyYp4TSxzvKc7BRU6Xww2bq8wsb5iMA/bEw+2qqd5XDd8GBlGo4Cbpl2XalzIc3sSNliJFYdqqhEfN7EM8J+mIpE30a+BEvNjAYWWuDYKgA5QLKWuLYghZWyFqQRD0PsKwCYLeR1uCrC/gnoPtgVk5G5LWw70Hp3ZgzJlUJ+TaXv4F/BW4HA8VK5Y3vhZfz0mS7jWzd/MnU3jXz8ysGApXiUXT+ywviqQFgJ3a0ceDwC/ynqQU3vZjWnp4KgqPlsLMXsHzqHZiDkuXB0Ewm9raWkaMGEFtbW27rqupqaGurq6kl6Wuro6amppumUcQBHNOGDZB0A1I2gqvvLUqbkC8g1ciy2/0fyRpZ+BoMysryGpmX0g6CveEXIqXK14OD3F6ibbzf0rxLLCfpN/i1c8+MLMXOtDPhZLGp1wszOzjJBZ7EPCUmf2nsJbPJW2DG3NPJPHSzOO0Bp5n8jxwS8ofAhguaTituVDShXio4JfAcZK+wA2cg3LtFpM0A89nKsexuFflRUnfwDVqhuIG5ZcAklbGPVJvU1p49FfAVmb2kaQH8PC4p/BKaxuk9bVbYygIgtLU1tZSX1/f7uv69u3LpEmTWlQ/++Mf/8jf/va3DunYdHQeQRDMOVHuOQi6mFTh7Drc6Pg9Xn742HQ6Xz74R3iVsTb/LlMOxi64EOcNuGDlWLw614eVri3DSXiRgtF4ovt5lZuX5Vha56xkBkTJPs3sWXyTfwluPNyMh+Lthnt09k5Nt07vY/AqY1mJ6m1yY6+bzs+HGzQXA2fhZbRPTO1+jnt1yhoVaU6b46FmfXCj8Sy8rPb7qdLaaFzT57vAP3Aj7FZc7+d3eCGDT1OX9+Lhapeltf0aOMjMzig3hyAIuoeiOCcQ4pxBMJei2VVYgyDoCiS9CjxqZluXODdfVnpYUj1u2CzQVrnoFFr1uc0Ff8CSjsM1YL5pZu0ThKjc73i8RPX6heML4gbH/Wa2ZYnr7sELAPSv5v5JGoyLgB6Oe91uwb1KZwEb5UpXdwmDBg2yiRN7U9pUEHQ9zc3NjBo1imHDhnVpSFdjYyMDBw6kqamp1bm6ujomTZrUpcZNd60zCOYmJD1qZoM6cm14bIKg6+lLy/LCsyhh1AB8JskkWTrXP33eT9LJkt7Ac2Jq0/ltJD0o6SNJzZKuktSiRrCk7SXdLeldSR9KelzS74rzSeMcK+lgSa9ImiHpFklLp9eVkt6X9JqkwwrXDk3X90+f15L0Ll7eeQLwUOpvoqT1S4x9oKQpkj6R9LCk9dLnMdXe6HRPPwWuAH4uaanCGCvgYWCXtGXUSPq7pB3wfyfHAifgmjf/wr0/52VGTZXPYMd03z9M9/ApSXsTBEErukvksrPFOdtLiHkGQecSOTZB0PU8DPxO0v+AG8zsxRJtRgP98FC19YFSGifD8TCxvfDwqE8k7YPneVwIHI3n79QDEyR9z8w+SNeuCFyNb8i/xHVxRktaJC9mmdgFeBrYD68CNgoP6VoCuA0vQ7wdcKKkp8zs1jLrvg4vd/wJXq3tyPTzMcDNkvqbWTOApD3SOBfgoWvfxgsO1Jbpuy0uAvYHfot7VjJ2xjWGLq6ij4XxEL1v4CFln+I5MsOAZpIeTzXPIBlyl+L6QofixtKqc7C+IPhK0NVil50tztnR8YMg6BzCsAmCrmcf3Kg4GThZ0jTcA3Chmd0JYGZTJWX/wz1UJhTtbWDrzNMgaXF8432hme2eNZL0EPAibiSNSv0fnzs/HzAe12rZl9aVymYCW2ZzkLQ6nnx/pJkdm46Nx3NetsPzSlphZv1Twv/XgI0zkVBJb+EG2i+Ay9N8RgC3mdkeuXm+RQdLQJvZI5KeBXalpWGzCy5YWsq4LPaxZ/6zpI3x5wawWTJYqn0G6+BV7obluryz3NgKgc4gAHpeHLOnxw+CoH2EYRMEXYyZvShpLbxU8Kb4JndrYHtJs4yFKri+ED61LlADXKaWqvdT8UpiQ0iGjaSVcG/CENx7koWhzqQ1YwuGVVbG+I7cmj6XNAlYvop5P5AZNYmn0nu2Y++XXkcVrrsBF/TsKBfjXqWV0zP4Ie4l2bcjnZnZuFTdbH4zy+5Ftc/gEaBOXsXuCuC+zFtVZqwQ6AwC2i+O2V46W5yzs8cPgqB9hGETBN2AmX2BV8a6F0DSN3ENlBGSzips/MtRrDaWCUiOK9M+85AsjnsaPsIT4F/Gw6r2BXYvd12OTyscX7jNWc8WFAXAzGZ6UbFZ1y6b3t8ptPtC0ntV9F+OS4Hjca/NX9L7TODfc9Dnp6Ryz4mqnoGZTZC0HfAHPEQPSROAP5nZf+dgPkEwT9Nd4pghzhkE8wZh2ARBD2Bmb0gaDZwBrITn4bR5WeHztPQ+FCgl5Jnl16yLC3cONrP7spMFD0NPkhlsS+cPSuoDLNW6eXWY2euSxgE7Szoaz7e5sUojslqqfQaY2dXA1cnQ3BAPYbtdUr+siEQQBE53iVx2tjhnewkxzyDoXHrLxiYI5gkkDaWlQOaHwGvA2cC5KYRrCnAfkJXbySqmZWFhi5DbEFfgP6ndQDOrJPS4aHr/LDfPOqBVKeREH0kH4Zv1FZn978TJkg4zs5eqmFt7mJpe29Hy3m2Vxl5TkpmZOtD3Rbh2zAm4kVRN0QAAJB0A/B34WZYLlTu3FC5q+hDVPYNZJJ2hmyWtiBu2SwLvVjuvIPgq0F0il6XEOcEFOseMGdPlOjYh5hkEnUsYNv+fvTOP02s8///7Y60tMmNtRZpolG9paaUtSoQfSu2UqlpS+y60apeJfRd7ERUEraXEGhIkxl4iSq1hIiLINhOSSGzX74/rPpkzZ86zzD6T3O/X63k9ec65z33uc58z3Ne5lk8k0jbsiS/Wu+GCk1cBW0u6DDdcfol7au4ys0nhmDfD918kPQp8a2YFBUzM7HNJJwLXSFoFr1g2C1gDL2k8xszuwA2gz0O7QcByeGjWdDyxP8tueM7LRcALwG9x/ZbewE9wodFWw8y+kzQYuDF4se7GDaqTw/W8jRdgaA734dd+PB7qNrIJx16Di2peL2l9M5uT2jcE/+/nIbiBWPQeBI/RasBTuMZOD+BYYLyZRaMmEulAKisrGxkwyy+/fBTnjES6INGwiUTahvFmNgFA0rHAOcDOwHbAUsCS+MJ9SOqYh3DPzpF4Ir3CpyBmdr2kj/ASwvuEfj/Gc3nGhzbTJO0GXIpXZ5uCewoqqdfOSbMOMNDMrgjj/0HYvifwQZnX3yTMbGgI0ToeL8n8BvAn4EHgEzN7oZn9finpbrw62R2lhE8zx5qkg4DXgHPxMs/gWja/AQaY2ae44VP0HuCenWOBy/F5n4pXRTujOdcViURazsyZM3N1ato6/CwSibQdUaAzEmljgk7MP3AjpScwCS+RPAl4LRGtBDYxs6PMbFUzW8zX1doXrw42H6/wdZuk72dOcS2+kD4UmIgn4/8MX0AnY3jSzH6OG1YT8YX6icDjoZxzwi/D96epY4eZmcxsQjoXxMz6A6dJekLSF7iH43Fg+VSbXkAPSc9I2ikIVM7HPUB7Sro31XaImf0Q93RshFeP6w78UEGsNCHkBz0KVAZBz2mSRkpaN9VmZUnXAdvjSf/bhzLKZRPC7s4EjpG0MbBjuL6RSeiZpC2AvwB9cX2hp4FdzexAM0u8cN/g+jbL4iGBc4EPzWxKU8YTiXQ0dXV1VFVVdXlByZkzZ9KnTx969+7d6NOnTx+uvvrqjh5ii1lY7lUk0hSiYROJtA+9cdHN2eH35vhi+Aw8qX1xPO+ie3JAWITfBrwF7I57eH6LCz8uMB6a0N8OwBNhDPvi3oUVgGpJSdnmt/HQrQsk7StptUIXVGZ/CT/GxSmvCtfwRLi2HSVVSOot6RJJuwAnAXNwD0lNuP4s/8SNs0fwXJxD8FC+74exdQOeBXbAxTJ3wL0/10k6ptA1FeBy4GVcPPRS3Ng6tNw5CLk0D4Rr+QPuubsMDwmMRLoUdXV1DB48uMsvlj///HNqa2uprq6mpqZmwae6upra2lpGjmxK1GrnZGG5V5FIU4ihaJFI27B48CqsgOdp7A48aGZzQ6njbsCGRUQrFwfOxnM09k46lfQ2UI2Xab4ydb6i/YU2VwBjzWyXVH9P4eFlf8HDz2YHL9E/cMMDSR/g3pGrzSzRtCmrv1TblYFtzWx8qm0tbpzshXul1sdLMq8CfIl7Pk4mU5Ja0lbAHsBxZpaeg/tT/z4OrwT301Sxg9HB0Bsk6bpQyKHUfwO/DWWn/wy8iucYHW5mHzVhDn6Bhx8eYWZJ3MuTJc4biXRqsuWRuxqlyjnffPPNbLrppu08qtalq9+jSKQ5RMMmEmkb0gbAd3hlroGpbaVEK9fByx+flu7UzJ6R9CEerpVe1BftLwh0/gg4L7OYnws8jwtJJud4UFIvXEx0Szyf5EjgIEk7BaHKsvsLTEwbNeE8HwUtl/3M7HpgO0k74d6NrZK8mmAIptkWL319Y3ZHiu3wvJaazPgeAw4GfiLpc9yLUowtcePyzRA29/8IwplNmIPxePjZPyX9A3jazBpo9mQJ3rpDAXr27FmsaSTSISzsopKjR4+md+/eHT2MSCTSRKJhE4m0DbvhVdG+wHMp5mX2lxKtTPJjsqKc4Pkv2XI9pfpLNGJuCp8sk9I/QgWw+6gXk9wYF6G8AM8laVJ/Ba4DvPzyzZJ6m1kNsB8woUSxgJWAmWb2ZZE2qwJ9SJW4zunjbepzigrxTurfXwFfm1mS71PWHJjZBEm/xUPsbgOWlvQf4G9mNjbvpGZ2A8GA6tu3b1a/KBLpcKqrq+nRo0dHD6PZTJ48uahxtvXWW3PjjcXenXR+Sl1jJLIwEg2bSKRteCOpitZMEkNl9Zx9q+M5H00hEZI8BTdQsnxV7GAze0HS47gnpDn9FVqc34sXHdhX0hXATrjmTDGm40UDlili3MzAK48dV2D/O2b2FU2fx+w5oIw5MLOngKckLY17wM4CHpbUy8ymt2AMkUiHUCiEq6uRDddKfh933HELxfVFIosa0bCJRDon7wCfAXuT8gZI2hTPHbm0Gf1NBNYzswsKNZK0AqBULkiyfXFcdyfxvJTVXzFSYqYP4J6aKbiH6bYShz6O594cjBcjyGMkcAwwKRv2lVRYk3S+mZ2a2SfgfbzYw+1mtm+RcTSag9Q19TazidkDzGw+8GQo/jAinCcaNpEuQ/fu3Rk0aBDdu3fv6KG0iG7dulFRUZHr0aioqOAnP/lJB4yqdVlY7lUk0hSiYROJdEJCwvqZuEbKcGA4Lvp4Li6QeXMT+zNJRwEjJC0F3IUvqFcDNsUNgMvw3J6nJN2GV/uailcaOxhP7j+yif2Vw7+BYcBg4JkQklbsWp4K+S6XhcpjT+LaMf2Ah81sDF7J7A94dbLLcSNkOSApB/0F8CdJp6VCy8Cry/XCq7IVpcAcrBN2nynpDTO7TNLhYWyPAB/hhRROwQ25N0qdJxLpTHTv3p2qqqqOHkaLqaysZMKECQV1bI4++mjuuOOOnCO7DgvLvYpEmkI0bCKRToqZ3SBpLq43MwIvKfwInpsxu+jB+f09IqkfXpBgKLAMnq/zAvCv0GwCcDGwNZ4ntHI473hgTzO7p4n9lUN1OG4NPESrHPbGc1YOwIsyzMLDyoaGsc0K3q0zQ7s1gDrqc2buw8szbwGMSfW7PzAW96SUJGcOkjLcq+AFBMAFPrfHQ+xWxcMMnwH+VCJPKBKJRCKRSBOIOjaRSCuSFrMs0qZXXohTOK4qs224mW1gZkub2Upmtp+ZfZJp05T+nscrtH0WNn2Bl4M+X9IYM6szs7Pw8tT34x6bZXGvTYOCBSHs6jngHNzg+govazwVL42cnLM/sL+khyXNDWKaVwBLhybfmdn3w3hvkHSIpNeC8OZ0YE082T/N12Fc/8DFSSuBU81sQbK/mdWa2fFm1tvMlgrCp0ncyUe4AbNf6nq+B/weL2jQgLCvFqiTNFvSp5IelLSumT1vZjuaWQWupwNwTJjrZM4fwg0aw71LnwPTsueJRCLtIyxZSqBz/vz5bXbuKJwZibQd0bCJRBYhJG2DGzZv41owlwBDcAHNpE1TxS1vw/NSdgeuA47CQ62S/pYCRgE/D/sG4B6R03PGdwFwLZ6MvzPurdoOeDTk+aQZEMb21/A9paxJqOdW4PeSlgm/d8WNjnty2i6NaxKdE851BJ4P9IKkvAIPzb2mSGSRpz2EJUsJdP7yl6UKJjafKJwZibQdMRQtElm0GAy8CeyW5JZIeh14BXg3tClL3DLV5x1mNijV7tfAH4Fk2wHAWsAmKW2aR6nX2iFs64Uv+gcHr1Gy/V08dGsnGopwChf9bG441314RbbdJN0VxjkCFwdtgJnNwvOMkjEtjmvifBau9fK8EzTjmiKRSKAtBSZLCXRuttlmTJw4sU3PHYlEWp9o2EQiiwhhMd4XOD+dMG9m4ySlE/ZLilsC/01tfzhzqtfxHJ2ETYCP0to0ZvZdMCaqUu22wb3It2fO+yIeutWPhkbAyBbmqNSF79vDJ+GP4buBB0jSXsBf8AIBK6Z2rUNhmnpNybmiQGdkkacjNVii/ksk0jWJhk0ksuiwMh5qlad6/1nq3+WIW6aZmfk9n/r8GfD8nM9oTHZbInhZKD8pe95Cop/l8kvg18AVuGHzOzzM7Du8BPWC16qSdsILItyCe72mh3aPUC+CmkdTrwmIAp2RCLStCGhHCnRG4cxIpO2Ihk0ksugwHTdWVs3ZtxowKfy7pLhlE8/7CbBegXOmSQQvt8UT9bPMyPxu0YLfzF6WNA44Fa+QdpmZvQQg6SsaiozuDUwwswHJBklLkimoUGTM5V5TJBIJtIcIaCGBzmWWWSYKdEYiXZBo2EQiiwhBG+dlYA9JVakcm43wZP7EsCkobtlMngf+LGnjVI7NYsBemXajcC9ITzMb1QrnLUkIiTsbD7/7R5GmywLfZLbtB5RK/m/3a4pEujrtISxZSqBz5ZVXbrNzR+HMSKTtiIZNJLIIIGlX4ATg/4DuwBxJr+EJ7HvgOjLfhebFxC03N7Ndyjxnf+ApPM/kA+Dfkk7FvUGHA93S7c3sfUkXAldLWgcvxzwPL/e8DTDUzJ5KHfJbSX8I+78B3sLzVa4OOja9gBrgz2Y2LIxpQHacZvZ3SfOANyX1NrOJOZczEtg1zMdDwEbAsdTn6eTSjGuKRBZ52kNYspRAZ2VlKWds84nCmZFI2xHLPUciCzmSjsUrgL2HC1Ceiyeu/wr4M54Q/ykucplUANsUzx85CS8a8A9gF9xQKZdxeOGAl/BF/Hi87PEtuMFxTvYAMzsVT5rvB9yFVyk7CQ/jei9cT7/QfC3gSlz8cne8JPVR1Bck+CScP13cYECBsT4c2hbK27kRn7c/hPPsgFc0m1WgfZOuKRKJtC8zZ84satTce++9HTCqSCTSUpQqjhSJRBZCJE0CXjGz3XL2LQb8AE9uP9fMzm7v8ZVDqOgmXEvmHVw3Z2szm5NptxywaaGwL0ljgCXMbLO2HXHr0bdvX3v55Zc7ehiRSIdQV1fHkCFDGDhwYKuFbiXinLW1jdPeKioqmDBhAkcffTR33HFHq5yvLa4hElmYkfSKmfVtzrHRYxOJLPxU4h4ZJC0j6TpJe0jaAtduGQXMBYZK6i3pdknTJM2XNF5SA4NIUpUkk7S2pIclzZb0oaQzg6GUtOsf2vVPbZOk4yW9I+krSZ9IujqIgqbPYZLOlXRyKEX9FfBT4BBgFeCYrFEDYGZzEqNGUq/Qz4DwewywBfCbsN3CNiQNCL97hd/DUm2yn/T1bCDpAUm1kr6U9KykBkH7oa/Jkn4uqVrSXEnvSTq8vNsXiSy6tIWYZSlxzjxPTkuIgpyRSPsRc2wikYWfl4ADJH2Ah5etDlyNlxqeA1QDe+L/PXgRz4E5HpiGh17dK2lXM3sg0+99wM14Ts5OeBnkj8K2QpwLnIILYz6Ia+KcDWwgaQsz+y7VdgCem/PXMM4puD7Op2bWyIWR8uokJIn9iwUNmaOA28L2w8K+QiuYs4G/Z7ZdAvycUGRB0i/wuXsVN7jm4rlDoyVtamavpI7tBtwBDAHOwkMAr5P0TsyxiURK05qilqXEOSdPnsycOXNaTaAzCnJGIu1HNGwikYWfw4F7gIvCZwbupbnZzB5PGkm6CTcMtjCzpAzxY5LWxBfjWcPmUjNLjJjRkrbCxS1zDRtJlXgBg1vM7OhU/9Nwg2PHzDkEbJsW4QxjmVjgOt8Hfpiz/abwGYwbMkukxULzMLP3Q3/Jef+K5+DsYWYfhM0X40bOVmb2VWj3GPAGcAawa6rLFYAjEyNG0tN4Ceg/kpO3FAU6I5GGtKfuS3KuBx7I/icvEol0dqJhE4ks5JjZu5J+DvwGX0xvDOwG7C3pDDNLkvi3wz06s4KHI+Ex4GJJ3cws7eFIJ+WDL+h/XmQoG+PCncMz2/+JG0Nb0NCwGZk2aspgJxoLgz6AGzQP4R6f/k3oD1ggznkhcJKZ3R+2LRPGex7wXWa+RgN/ynQzN+2ZMbP5kt4Dcq2WKNAZiTSkNcU6SwlkVldXY2asueaa7XK+SCTSekTDJhJZBDCzb4GnwwdJP8BLGA+SdI2Z1eLCnfuHTx4r0TB0a2Zm/3zge0WGkdRPbVB5zMy+kTSDxmKXeRXKPsJzbRphZq+nfyf5MsDEJHRNUvawokjaAA8hu8nMLkntqsRD2s4In7xjF0uF1uWJc5aar0gkEmgLsc5C4pw9evTg1FNPbbXiAZFIpP2Ihk0ksghiZlMkDQWuANbG83Bm4DkjFxY4bEoLT5sYQqsD/0s2Bm/HSuH8DYaZ08doYBtJG2VyWFodSavhHp8XgCMzu+tw3Z9rgFvzjs/kC0UikWbQFmKWpcQ5u3XrlnNU84mCnJFI+xGrojUDSdtKelTSDEnzJL0r6UJJFS3oc0xSoSn8blRRqrWRtJikP0t6SdJMSXMkvS/pn5J+1VbnbSmSJkoalvrdP1TqapPnOVUxqy57jyUtEfZVtdK5qiQ1K/RI0kBJu+dsLxRPsW74/jR8jwR+BvzPzF7O+cxvzrhSvIB7KfYP1/mLsP0P+EuWsWX0MRSYjgteLpfdKWlZSVsXOX4+sEypk0j6Hq43MwfYM3iVFvxNhops1cAGwLi8+SrjWiKRSAkSMcvWNAoScc50RbTkM2HChFYX52yLa4hEIvlEj00TkSunn4srnB+Mv4XeCBfc20PSlmb2USucKhE3fLMV+irEJbh6+hBc1PBr4Md4/sWv8bf4nZHdaBgS1R8YhAs+tuVb8hXx+3xyG55jKG5gNIeBwDPAvzPb35D0FF7FrAav0PU7vKjAXWY2KbQ7E7/nT0u6Gk/SrwDWB9YyswObOS4AzGympMvwqmgA3UJp5HPCuLM5O4X62AP3pIyTdBWe27MkLjiaFEoYXaCLN4EjJf0BLw7whZm9k9NuCPALvDLbuiGEbTH8bz4R1TwBD+17LBRe+ARYORy3uJm15XMSiURaQGVlZVEDZo899mjH0UQikdYiGjZNQNKW+CJsiJkdn9o1VtJ9wCt4WMqWLT1XSNIuWrmpJYTk56OAq8zsr6ldo4Br2sr70RIkLW1m883s1Q4awuPAMZKGmNmnJVs3AzObDLR2bdCTcEPmLGA14FvgXdxAG5I69yRJfXEj9zxcL2YGbjjc0kpjOQ34Bs9LORYvKX0rcEq5oVtm9nTIfTkRL0vdAzfK38JDw64tcviFwDq4Abk8bphskdNuXdxYuj1n35bAx2Y2TtIvcaP6StzwnYa/lMiWio5EIu3IzJkzc/VounXrVpZHJho2kUjXpNMtXjs5f8M9NKdkd5hZDXAB0F/Sr5PtIXTlHEnHSqqR9IWksZLWK3aivFC0EK72jKStJY0LQn9vSNo15/hSwoHLAUtRH4aUvZ4Gi8wy+kvabSFplKRZIbTtNUkHZeajKnNMAyHFsC0RNdxE0nOSvsRLFTcIRQt9DQqHfa16EcWl5SKTl+eMMQktWze7rwRJ9bDTSjWU9CtJo+XilXMkPVFOeF9eKFo5z5CkiXip4z+l5mAYgJn9HTckXgO+xP/u5wLPJ2WKQx/DcGP6JuBD3AC6C9douU3SYZLOwjVgZgH3SVpQpsjMBphZL0mHSHoN9zzNAPaTl3omjDFJtl8cz7c5EmgQQmdmMrPTw7h+GZ6pGeGZ/wCvUHa0mf3IzJbGCwq8C/wFmCZpPPDz0M+wZG5xr8rfgOfwMLM6SdfiBs+SZjYxnL8/nthfB1xhZiLnhYWZvQX8C/fifI17w1an4Uujg/GXBW/LRU+nSLoU2C6cJxLpctTV1VFVVdUpRSdnzpxJnz596N27d6NPnz59mDkzW/ekMUcemU2ra3s685xGIl2FaNiUiTzBeQtglJnNK9AsKVW7VWb7vsAOwHG4MF9PYIQalogtlx/hCd+X4YvBT4B7JPVJjfUX+MKtEl+U7oEvMEdL2gjAzKbjYUl/lXS4pIJiGeX0F9rtAjyBG0yHAbsA/yBfW6QcVsRLAd8JbI9Xp8oyFF+IA2yGh+9tEvJBbsaFKbOVpw4DxprZ200czye4sOWhkgpek6Sf4fkiFXgo0/74gnds8DQ0h1LP0G64kfoYYQ5wkcmy71+g2JyfAvQBDgzj2ISMR0PSBbjHZDSwM+5V2Q54VC6g+Qn1Rsz5qbHmhqFJWj5c07f4XCaepyVSbdbEhUU3wD04O+Nek3sl7ZzT7Qj8/uyMi4veileE2zbTbkegO66xk4ukY/DQv6nAAbjQ6X1Ar1Sz4cDp+FzuEK77IPK9QZFIl6Curo7Bgwd3ykX4559/Tm1tLdXV1Q3yZ6qrq6mtrc315GTpiOvqzHMaiXQVYiha+ayEJx1PLNIm2ZdN1v4a2NHMvoYFJWfvxnMCnmviOFYG+pnZe6GvcfhicS88fAjKFw7cB1/EXhfaTMHfsl9vZun8mpL9yS/qCmA8sGXK41Mo16Eclgf2NbMRhRqY2WRJSejWi2b2TWr3dfgb/D0Ji9NgdGyMCyM2hwtxw2gQvsDP40w8Sf3/mVldOO8o/PkYRMY7USZFnyEze1XSfGB6jvhkU4QkG8256ssmf2hm+6S2r4Lr2/wgVFnrhRsyg83srFS7d/Ecmp3M7H5JSSjhB6WEMvGQsArgb2b239T2Yal/V9E0YdErzeyK9Aa5psx+uI5Pwn7AW4Wqr0nqhv/N3Wdm6Xv6WKrN5nhxhAPMLKmeNlrSTGC4pA3NbHxe/5FIVyBbMrkzkC7bnFciupwxz5kzh4kTJ7byyIrTGecyEulqRMOmfJomgNGQUcmCNJDobfSk6YbNe4lRA2BmUyVNDX01STjQzF6QtA4eYrM1/ub8AGCApD+b2a1N6G8d3DNzQSuWuf0GF1ZsFmZWExbwh1H/1v0wPA8im2Bfbp8zQxjRIEkXklKnT9EPeCgxasJxn0t6ABeRbA7Neoaa8jwEis151quSHsMUYBvcC3x75jwv4sUe+uFFN5rCe3g42PWSrsE9bdniHE0VFr0v5zzDgZMlrWBmX4TQue2pD3PMY1PcELyhSJvtgK9w71F6bI+H7374y4AGSDoUOBSgZ8+CztRIpMPpisKT5Y75gQey70QikUhnJxo25TMdz0/oVaRNsi+78MoTMoTmifPlBQenhf6aJBwYQrZGhg+Sfgw8hYe63Vpuf7hHC1o38X1qEJZsCdcCD0paHw+92xf4ezq3pBlcDhyDewOyhgH4nOWJS36Kex+aQ3OfoaYKSRab81JjWDV8Tyhw/EoFthfEzGbJi3acgd/LFST9DxhkZvemztsUYdG8e3Mb7vn5PR7CuDeFiwek+4Xiz/yqeGjm7BJ9NMDMbiAYTH379m1W+e9IpD2orq6mR48epRu2I5MnTy5qvHTGMUPpcUcikdJEw6ZMgo7F07g44PcK5Nkk8fxPtuPQstTRAuFAM3tX0r+A4yWtWm5/kqaHn2uUGN98fKGXptCCtzUWdI/gIWCH4cnzK1D8DXtJzGy2pPOBS/Ewrywz8QTyLKuTb5i2JXU07XloyZwnYWDbArVF9jeJEKq1R/B49MVzfe6StIGZvUHThUUbXWPw7j2LG743h+8xJUq3p5/5Nwq0mQHMAwqtVloqehqJdCiFwr06A9nQrlIhammGDh3KwQcf3FZDi0QibUQ0bJrGxXj4znm4hsUCJPXGy+o+bWYvdsDYADCzOZLSwoG5RoykJYFKM/ssZ/e6uHdqlpnNL6c/vCLVROBgSTeYWaEF8oe4LkqaHYpeVGkSz8EywBfpHcHouh4vbbw5MNrM8sLHmsq1+DNwTs6+scAOSVgTgKQV8DC0Ma1w7kI0Ep8s93loJUbhRlRPMxtVpF36fpVNyJ96QdIZ+EuE/8MNipF4GOX/zOzLJo+6ntuA6+SVCDfBizQU4zncE3MoqbyaDCPx/y6saGZPtGBskUinonv37gwaNKhTik5269aNioqKXO9HRUUF3bp1K9nHk08+2e6GTWee00ikqxANmyZgZk9IOhM4KyRK34q/mf4FvnCehSccN0JekvkE3GhI/qt6rKRPzSwRZOweStKe1biHJlGOcOCKwCRJd+Fejcm452RvPLfgIqtXmi/Zn5lZyCE5FnhO0hA8l+X/gFXNLMlV+CdwuqTT8NLCm1NmIr/qSxqDVzs7INPk85BU/zRe/vf74RpuwsOMNsDf/K+Ah4XdbWYDyjh1Uhb6bUk/MbN3g8F3FvXen13DOcCrke0IPBHycAxf3C5L6Xu7YbjWXknp4SYwG/co7ohf3/TQR7sISZrZ++F6rw65W2Nxb8WaeP7NUDN7CvgMN5wPk/RfvOxyTSrxfwHhWg7Fc3Nq8DLlx+IG7POhWWsJi96F69EMD+O7N7M/8cJth3tzvpB0CnCVpHvxsLUv8Hs4z8yuMrMxku7EKxdeFsb5HR62+ju8bPW7ZY4vEuk0dO/enaqqqo4eRi6VlZVMmDChRTo2HUFnntNIpKsQyz03ETM7G1/4L4eHrDyO63DcCvS1ehX3NL/EE5bfw8u8pt8Ep0tDd8eTlVt0X8xsXDjnDHyh9jheseyn+AIXPOfgdFyw8WK8TPOtuOFwGG6oNaU/cO/F0fgb+ZvwalSH0rCS3Pl4yeSj8cXq/1HAGMxhN/xN+qe4MfYq8F/gN/gitBY3IvrhC9sdw/in4YvsT8KYfo8bGU0VnVychobJzdSr0C8gVO/qj8/xLbgnYDZeteu1EudIFrl5eSCl+Ba//ruA/xAMrSbcvxZjZqfi97xfGMcI3KirJcxV8Botgz97o8NYCxVVeA83Ms4AHsXn/BtgG3MxU8LfXF881PA83HN0HV40oeyw0FDs4UE8tOz+xNuWIjG8nk8dczVeda8Hbtjciz9fNanj9qU+f2cEcA/+/L+HG3mRSCQSiURaARWOGIq0FpImAa+Y2W45+xYkbqtebHLJTNnivD6XBL4pEvK10CNpDLCEmW2W2b4UnrvwrJntIqkCL3c8xMzOkPQUsBbQq5z5C6FJT+EGwTa48ONrqf2T8RC3Aa1xXc2l0Hy0sM+lU567VkMuQnquBRHOjqKtrq816du3r7388ssdPYxIpAF1dXUMGTKEgQMHdrrQqUSgs7a2capfRUUFEyZMKOm1qa2tpaKiubVemkdnntNIpD2R9IqZ9W3OsdFj0z5U4l6GRuQYNQBfK6jHh329wu8jJV0U9Gbm4x4eJO0u6QW5KnudpLuVEdyUtLekJyVNkzRb0qs5oVxplfu/SPpQ0hxJD0taNXzukjRL0keSTsocOyAc3yu1baKk4eH8b4X+XpbUaPEt6bjQfp6klyRtGn4PK3Oekzn9Cg95217SDni42GLAtWFetgBua4ZReDXuScnLq8leyzqS7gv348twf7Yr47hmzWEwarYAfpM8O2Fbsr+3pNvD/Z8vabykBoa2pKpw3PqSHpM0G/e6pJ+LYyXVSPpC0lhJ6+VcQ9HnMXmugdNSY60qMic/DnM5NTwbk0KfaZHOlSVdJ+njcH1vy0sm581tv3B8HfCipL9J+kpSoyIWkt6UdH/4d/J3OCDTZgtJo8LfxRxJr0k6KNPmkLB9nqTpkm6Sl5SORLoknVlMsjUEOmtqakq2aW0685xGIl2FmGPTPryE54R8AIwoEFM/FA9nOQjYDA8rynIaHrZzKB4WNU/S4XjYzc14mNQKeNjLWEk/S4XTrIWHwFyAx/j3A4ZKWsbM/p45z354UvaReLjQEDxMbQU8HOgGPPzmAkmvm9kjFGdzXOfmDDzn4mzgIXkeSR2ApIPDeW7ChSd/hCu1dy/RdyFuAY7CNVkm4QKJn0g6FdckulWS8HksSMZz9iVu1FwraWMrIC4p6Qe4IOUXeMjRrDCWhyXtaGaPNuN6Ss3hkXhuyOJ4KCGEEsdyocoXganA8Xju0x9wbZVdzSwr1jACvw8X4s9Kwr7AO8BxeGW7i4ERktZN5qnM53ETPJxrGHB96LtYyeSH8OpuR+CVyNbA81MWC+fsBjyLh7dV4WFgv8ULASxtZldl+rsduBMPDVsCD2c8P8zJtUkjSRvhoZK5ZbJDm13w8LNn8XmfDqxHfS4Yki7AhWKvxAVM18Cfo/UlbdoKJc0jkQ6jM4pKtoZA5+DBg7niiitKtmtNOuNcRiJdDjOLnzb+AD/GF08WPtPxhdW2mXZVYf8Sme29wvZxhPDBsH15fNH8j5z2XwEDC4xnMXxBdyPwWmaf4XkeS6S2XRa2n57atgS+UL45tW1AaNcrtW0inl9RkdrWN7TbJzWej4BHMmPZPbQbVuA6xgDPFJn3/wEvZra9BTwX/t0/dU8KfXql2m2Na5u8DzyZ6nNyeozAJXgeSJ/UtsVxo2BciWelWXNYbD5wI2UasFJm+yhgfM7zd1xOH4bnhCyZ2vb7sH3Tpj6P4bhzyvjbWTm03blIm8TYWzuz/Ub8b22JzNxentPHKOD5zLYheHnupTN/hwPCb4V78zKwWIGx9cJfUpyZ2f6b0NeuBY47NPT7cs+ePS0S6WzU1NSU+m9nh39qamq63Jjzxh2JLGoAL1uJ9UGhT/TYtAPm2jA/xxcz2wIb44nwe0s6w8xKhjYF7g83PGETvMJaVul9MvA27pUZAiBpbfwNej+8ulMShpiXXzDKGnoq3g7fC0ramuv6TMArXpXieTOrTf1OK9aDe6p64NWt0ozADYTmcivuVfpxuAe/wiucHRH2v4In1RdjCilRVjP7OoRN3SppazMbnXNMP+AFM5uQOu5beXWsMyV1M7PSsRANKTWHxdgOL7YwK/OcPAZcnDOe+wr0M8rMvi4whudowvPYBGYAH+D3cTW8Glm2YMN2uEeqJuf6DgZ+gr9YSMi7vtuAWyStbWbvhX72Bu6ywjk46+CemQuscBntbfC/teycvIh71PrhRTQaYFGgM9JF6Ixil60h0Hncccd1iMcmCnRGIi0jGjbthHm4ydPhk4QrjQQGSboms2gtRLZSVqL0nre4hiCSKGl5/I30XLza2fv4G/QjgLxSuNmxfFVk+/coTQNRSvNSyaSO/X74nppp963qhT+bw3C8Stb+eAW4/XFD7l+h/y/wt+JFCWNNczte6etc8ue+Eq/YluVT/C1/BSFMrAmUmsNirIpf+/4F9q+UGU+himxZcdFkwZ+MoaznsSmYmUnaBvcmnQ+sJKkGuNjMrkudtw/wdX4vjQRg867vXjwMbV88121bPAzztiLDS/otFj+SzMmEAvsLidNGIl2ChVWg87jjjuu01xWJRAoTDZsOwsymSBqKl91dG8/DKXlY5ndSfnYAHnaVJcmv2QR/s7y5mT2T7My8Qe5IkoXmqumNkhbHQ5GahZl9LGk0sK9cc+YPwANlGpHF+v1OLhL575BjkWUm9ZonaVbH72HWQGhrZgDVeM5MHlMyv5vrISj3eWwSZvYBsH/IidoAz1u6VtJE83ylGbhRfFyBLt7JdplzjjmS7gP+hBs2+wIfmNmzRYaWGN1rFGmTzMm25Bt2jbR7IpGuQGcWk2wNgc6tttqqZJvWpjPPaSTSVegsC9uFGklrmtlHObsS4cekYlpakb2cReBzoV0fMyumybJs+F7wRlteAjlvUd4RTA6fPfGk84RdafkzegvuYTkfN5JubUFfo0Ko0gQzu0/Sf/Ak/mx1wbHAQHllsw3xwg1X4IbVq9ZYH6W1mI8n62cZiRu3/zOzL4scv3GJ/vcLlcZWwA2JrMFQ7vMI7u1bpkSbBoQwzPGS/o0X2dgZL2YxEjgGmGRmU4t0UYrbcCP4t/jfxsUF2l0i6Wb83k8EDpZ0Q/AunQOcZmaJm28UXoChp5mNasHYIpFORWcWk2wNgc599tmHO+64oy2GV5DOPKeRSFchGjbtwxty7ZT78IpN3fCqTofjMfyJqOeb4fsvkh4FvjWzgqFSZva5pBOBayStgi/yZuFvkLfA8xHuwBecn4d2g3Bx0dPxN84rtu6lNp3gARkM3Bi8WHfjxsDJ+PUUyl8oh/vwaz8eX4yPbOFw05yGa9tkuRz3WozCvSHr4YUHfgzs0Irnz/ImcKSkP+Dhhl+Y2Tt47tJLwNOSrsYX4xXA+sBaZpaEI+YaNpKODf+sxb0lc/CqdXum2zXheUzGuoOkkaHfKWaW9Rwh6We4UfgvPJxrcVzg9lu8AAf4fP8BqJZ0OW5wLYe/ONjczMo14Efj9+sm/GXA8BLtj8er0Q0DnpT0d0I1NEmDzWyQmb0v6ULgaknr4EbvPDw3bRtgqJk9Veb4IpFImVRWVpZlwEQikYWLaNi0DyfhhsxZeNz+t3jlsZNpmEz9EB7nfyS+GFX4FMTMrpf0EV5Gdh+8atfHeC7P+NBmmlyz5FK85PMUfLFYSb12TodiZkNDLtDxeBjQG3hY0IP44ri5/X4p6W78Df8dVkL4tIl9j5JrxfTPbJ8i15i5EL/vS+JzvYOZtdiwUmFRyQvxhPaheIWysUB/M5skqS+ep3IesAoeAvUG7tEqxV/D90Nm9mD495OSRuGG+gLKeR4DR+Pljx8ElsbLH+eVVf4UL9d9Al5gYh5etGAHM3s6nHOWpE3xv5mTcEOqDjdw7i3j+pKxfyfpjnC9z6eLP+QwFjcEN8QNlDNwg2ipsH9iqt9TJb2Fl/w+Cg+F+wh4Aq80F4lEWpmZM2e2yGMTiUS6KM0tpxY/8dPWH7ximQH7dYKxDAhj6RN+j8G1arbGy3DPxQ2FXVPHDKNxKc+Jqf0r45ovH+NhZG8DhxY4bz/ck1VHKNGML56H49W73sI9KS8Dm+WMfwt8If1FaPcYsH5q/8ScsQ4L+2YD15U5T73x0L9p4ZrGA7tl2lSF/tcP45iNV8C7FviMxuXOl8a9OkPC7/7h+P6ZdrvhejKzcS/dS6TKROMvck4J8zwfN/AvBb7XxGchmffLcW2jNVL7ziFEzaW2LRm2T8RD8CaG30uWc76NNtrIIpHORm1trQ0aNMhqa2s7eiiNmDFjhlVUVOSWUq6oqLAZM2aU7OOiiy5qh5E2pDPPaSTSntCCcs/Z3IBIpEOQ1FvSJZJ2kbSlpCOpD90r+617O/Mj3PN1Ga658wlwj6Q+Yf/ZeJnlaXiOyyb44jstKrkDvtDfAfdeXCfpmJxz3Y7Pxe9xT1/C5rj44xl4ONbiuHBn96SBpB1wo2Y27g3bB8+TqQ7inYRxfYobGslYzw77EoHZEyX9uNBkpIRAN8A9bzvjRt+9knbOOWQE7vnYGTcSbsULSGybabcjLtRasEJZmLN/4+GGB+BhcveRKtWNGyOn48KvO+B5Vwfhc9sczsPLkRcU8Azcgt+zW/FruRn3LJXjLYtEOiV1dXUMHjyYurq6jh5KIz7//HNqa2uprq6mpqZmwae6upra2tpcT06WE088sR1G2pDOPKeRSFchhqJFOgtf4m/w98fzP2rxnIeTzWxuW520jMpw34a3B3msDPSzoKsiaRxu3OwFnGeeXzEN+MrMXsgcexyej/FTq9dlGR0MkkGSrrOGYXP3mNnfcsbQDdjQQqU3SZ8C/8FD4JJ8liuAsZbKNQk5Xx/gRtFAM3tV0nxges5YD8dDGC8CLpI0A88futnM0jlGVXjo5BZmllT7eiwYPGcBD2T6vdLMGghFSHoP2A83CBP2A94ys1dyrj8xEs8D7jOz3VO7Hku12Rw3/A4ws6SAxGhJM4HhkjY0s/F5/RfCPMRzCHCSpIvN7P2csa0P/BEYbGZVYfPjkr4FzpZ0gZn9N3tcJNJVyJZU7gyUKutczpivv/56DjvssNYeWlE641xGIl2NaNhEOgVm9ikutNhuhKplNSWabYmHneXxXsoowcymSppK+aKZLRWVhBLCnUGY9UfAeZnzzAWex0PcimLlC8y2hhDocOBkSSuY2ReSKoHtKZ4LtimeU3RDkTbb4WFg92bGlhhm/WiYA1Qul+A5cYNxb1iWZH6zhQiG4x6xLWh4nwEI1ecOBejZs5zHKRLpGLqioGS5Y77gggvaeCSRSKS1iYZNZFFmCp7HU4xsSeM0eXo08ylfNLOlopKNxmCNhTsTbaCbwifLpJxtjbDyBGZbQwj0Ntzz83s8ZGtvPEelWLhYuUKZS+HheMX6aBLmhQsuwg3H83OaJFnK2Wv9NLM/2+8NBEOtb9++zdUVikTanOrqanr06NHRw2jA5MmTixov5Yz5uOOO44orrijaprUpNe5IJFKaaNhEFlnM7Cs82b4jaLGoZBPOA540Pzpn/1fN6dTyBWZbLARqZjWSnsW9HzeH7zGWrwOVkBbKfKNAmxl4RbVCq4ZGpaabwFXAQLwgQFaYNDE8V8dLcJP6nYwrEumyFAr36gxkQ7tKhailWW655TrtdUUikcIsEoaNpAE0FH6ciyd0v4prYdxjZi3RSil1/jEAZta/DfquomGYziy8lPQQq9cMaek5euHVuW41V4FvdSRtiAtyXmlmMzP7jIY5Cu2KpP7UPz+9cD2V9P7JwGgzG5Bz+HzyhShzRSVTz2pvyhNpTY9jAI3FQt/Bq3CtZ2al4ipyx9oEgdlyhUAT+pNvbN2GF1HoH/r7c4l+nsM9MYeSyqvJMBJP2F/RzJ4oY2yNSIUuNjBGzGxuEOa8mnox3ISx4Xtv4NzU9j+F76ebM5ZIpKPp3r07gwYNonv37h09lEZ069aNioqKXO9HRUUF3bp1K9lHe4tzQuee00ikq7BIGDYp9sTDVZbGcxB2wA2bQyXtVOZirDkc2Ub9ptkM18epBA4Bbpf0PTP7Ryv03Qs3np7BE87bgg3DOYbTOMRrE4qHGbUnA8lfjBfiTaBS0hG4d2iemb1OYVHJH+Jej0KhZ8UYQOZv2sxM0lHACElLAXfhHo7V8NyUSWZ2WWqsm0vaETdWppvZRMoXmC1XCDRhS/Ln8i5c42Y4XlSiaFW8kItzCnCVpHvxsLUv8GdqnpldZWZjJN2JV627LIzzO/zZ/h1wkpm9W+w8+D3ZJIwvy424/k2Dim5m9r9w3qqQ2/Nc6OMM4M5YOCDSVenevTtVVVUdPYxcKisrmTBhQot0bJ588km22mqrthheQTrznEYiXYVFzbAZbw1F924L4o134xWf8srsthgze7Mt+s3wYlJFS9LjuK7JQCDXsCki8tjpyKnS1ZH8TtIGTWg/FE+2Pw8vWfwh0MtKiEq25r0xs0ck9QNOC+NZBjdcXgD+lWp6Cr5Avyu0uUXSYZQpMGstFwJN+qmT9CCeZ3OnmZX0XJnZ1aEi3Im4YfM1/jdwdqrZvvjf+IFhLubj92Mkrp9T6hzzgRckNfLumtlXwXs6LOfQA/AXAgfi5aan4OF6g0udMxKJdAxDhw5td8MmEom0As0VwOlKHzLiijn778Pj75dNbVsWX3zU4HkINfhiaLGw//u4hsUxOf2dhC+sVgm/x+B5Auk2q+CChB/hC6yP8BCcpVNtNsBL5Nbib66fBTbP9FMVri0rangXMNcaChruji9cpwF1VoZ4YOrY7Kd/6lyHAK+FOZyOJ6lXZsazRJiXN0O7afiCct3U/cl+eoVjDajK9LcdXtXrSzz87n5gnUybMZQQ0Szz+UnmYCdcTPPBzP7JBDHL1LZ1wnNVF8b4ArBdE57VXqltEykhxBmuNTt/Y1L7my2cmboH5wDH4n8LX+BhVuvlXMPu4Xrnhuu/G+iZ2p93r6uKzMmPw1xODc/OpNDnEqk2zRY7Bf6GP/sr5Zz7TeD+8O9e4fgBmTZb4OWvZ4V78xpwUKZNyb+RQp8o0BnpjHRmMcnWEOj84x//2A4jbUhnntNIpD0hCnS2mEfw8LS+sEDbJCm7ewVebnYoHj5yMYCZfYKH0eyX09++wEgzm5Z3MkkVeEjKH3Bxx9/hi6sl8cpNSPpFaJOElu2Bv/0eLWmjMq6pN75wS3MVrjOyH77Ig9LigeOAo8K/j6VevHFcGOcFuIE2GhdaPBE3Oh6VtHjq3P/EcwwewXNpDsEXjd8HHsYXzeDhgsk5csOxJG0XjpmNz+ER+GL8GUlrZJqXEtFsCl+Gce4oaeNCjULFsGdww/RoXNemDnhY0vbNOC+UFuI8Es8Z+y/183dkGE9LhTMT9sXDN4/D81564iFuCzy/kg7HQ8fexD0uh+H3ZqykFUKzTcL3sNRYhxa59odwj9YRwG/x53U+IZ+oFcROh+Pz+Yd0o/B39n8UFwbdBRc/XSpc6y64l/SHqTbl/o1EIl2Gziwm2RoCnR1BZ57TSKSrsKiFohUiyRH4fvj+I56zsoWZJcm9T4QyuoMkXWie8H0bLu63jpm9AwuS4NenYQhMluOBtYC+ZvZqavudqX9fHMa1lXn1LiQ9hnsczsCNgzSLh/FV4gvAvviCPs1LZnZw8qNc8UBJSSjdW5YKCwvJ1CeG489KbX8XX9jvBNwvaSvcMDvOzK5Mjef+1DFJxajx1jBcMI9z8NCe7a0+/O55PDzqL8AJqbZFRTRLnCePoXguxXlAoTiFE/Dckk2Sa5H0CL7YPxd4tBnnLSrEaWZvSvoc92JkQ/eqaKFwZuBrYEcz+zqMAdzz8SvgOUnL417Omy2VTyPpRfzeHIQXtXghHPtxzlgbIGllvOraLmaWHmc6s7fFYqeSngT2k5TWwtkf95Y+Gow3ZY4R/jc2HtjS6guQjE616UUZfyPF5iAS6cx0RlHJ1hDo3GeffZg4cWIrj6w4nXEuI5GuRjRsnGTBkpSf3Q6PvX8uR8zvHDxn4gE8PGY27gE5PbTZDw9JyS4W02wL/Cdj1NQPRloGD285D/guM4bR1FdUSjMv9e+v8NyHUzJtsoKIzRIPTLEN/tb89swYX8T1Svrhi7Zt8bm9sUhfZSFpOeAXwHnpxarVlwneInNIS0Q0G2FmX4dcilslbW1mecnv/YAX0gaamX0bksjPzBGqLIeiQpwlaA3hTIBRiVGTM4YkKb4bjZ+HyXhoWD9SOTllMgM3Yi+QtBoeXvdepk1riJ3ehnsp83SF5oTvbBGDdXCD6gIrXFWx3L+RBigKdEa6CF1Rd6UrjjkSiZRHNGycNcN3Evq0Kr5gKSqeaF7m9V7gT5LOwBcwfwTuNrN5BY5Njn+tyP5KPDTmjPBphKTFMoupjfGk7lq80lXe2LOhXc0SD0yRiD8W8rCslPqeaa1Tda4CN0TzwtQ+JRUCFGiJiGYhbsfD9c4lv6pXJR4Wljc+4dfQVMOmlBBnMVpDOLPRGPB5TI8heR4KVY2rLbC9IGZmkrbBvU7nAytJqgEuNrPrUudtqdjpvXiOzu24MOamuDfmYOr/Vr+Hex6z/ZYSBoXSfyMNsCjQGekiRIHO1iMKdEYiLScaNs4OuMfjlfB7Bh6Dv1eB9hNT/74Nr3q0GV5J6vsUickPTMdzBgpRh5eivQbPfWlEzhviVzLhNrmHZX63VDww2b8t+YvWZP90vOTxMq1g3NTi17F6zr7VaQfBQzP7Lhiy/w45FllmUnh8Rr6x1Za0WDizCecBz9/KClVCE3V5FgzGtZP2D6FfSd7StZImmtmjtILYqZnNkfRvPPTzUEknAB+Y2U1JmxBWliYtDFqIcv9GIpEuSRTojEQinYlF3rCRtDue0HuFmc0Nm0fib2Znm9nbJbp4Cn9jux9u2EzEF5HFeBw4XdIGZtbIcxMWWdX4Im5ckTCXllKueGDyZj4r3jgKN8B6mtmoIud5HE/SPhgvYJBHoXM0IMzNK8CekqrM7FsAST/E37IX6r9VMbP7JP0HD9nLFuEYCwyU1MtcB4aQJP4H4FUro3xxM5kPrJCzvanCmc3lOdx46WNmpco7f0WJe50lVEoZH4yOg/BctkcpIHbaDG4D9pX0W7wIwMUl2r+L/70fLOmGML4s5f6NRCJdis4sJtkaAp0dQWee00ikq7CoGTYbhmTkpfC8gB3xKlyjaJiPcjte9ekJSZfioShL4RW2dsbLBc+FBW/vb8crIi0JXF5ggZPmzPA9PoQT5fEJnq/wmKSbwu+V8fySxc3s5HIuWNIYCoe7ZMUDwa/7h3hY24OSXsXn4xvgQEkz8QX0O2b2vqQLgaslrYMv6OfhoX3bAEPN7CkzeyqE7F0WEtafxOeqH/CwmY3BE+sBjpKU5Dr8NymckOEMvCraQ5KuBZbHNUFmAZcWmYte5IhYNoFRkqrNLMlNOg032gAOkDQsXMvl4TyjJA3Cw7yOxMsW71Dmuf6Jhxc2hfeB/yfpZDwc7ItQ1KKpwpnNwsw+l3QicI2kVXCjYxbu0dgCz49Jkv5r8Dl7Fi+SMcXMsp4jJP0MDwn7Fx7OtTg+t9/gzxEUFjtdFy+PnudVy2M07r26CS/3ns09S7gZL+9tkgYC/waelPR3vJz2/wGrmtmgcv9GyhxfJNJp6Mxikq0h0NkRGjadeU4jka7Colbu+W5c++Qx3EOxNO6t2C6dExPyU36LJ7sfiide346HnD2Hv21OcxsuvrgchRdDaTbBq1mNoD4UZV44T7+wfwfgl2H/lfgC+grgp9R7UlqDA/AQpePwPIaVcQ2c7akvwfxLPPxnA3xh9h9gIwAzOxWfo37huBF4/kktkE7y3jv0vyteWOEfwHqEfIfguarCq0Q9E87xg7wBm9lIfH66h3P+Hdd32SxvcZyiFzCIlhn0m8vLTRPewI/JGd8UPDTxf3jexj143s0OYezlcHrpJo24GV/4V+Hzd30YzyS8St5reEGKUWFcW1BvHLQKZnY9bvyvg/9dPIobnUvg1cMS7sEN7jvDWA8t0OWnuOFzAv7c3Ik/Fzua2SvhnLNwb90j+LP3GP587YJ7VMsd+3d4tbU18GINhfJizkkdMwI3UMANogfCtUxMtSn3byQSiXQSDj744NKNIpFIp0OlnQuRtkbSROAZM9u3Dfoeg5cA3qxEu0l4ns5uOfuyhQq6FCE3Y0l88fsUsE2BambF+hiG50lMxw3bXyaeOUlb48bClsFj0yEEj1QNcIiZFdOFaUqfwsVa8zxnLel3AG6IrV3EgGgXJC1tZvNLt+wY+vbtay+//HJHDyMSaUBdXR1Dhgxh4MCBnS50aubMmfTp04fa2sZpbRUVFUyYMKGk1+a0007j3HPPLdqmtenMcxqJtCeSXjGzvs05dlHz2HQZJPWWdLukaZLmSxovKc/o2EDSfZJmSPpS0juSsmWekbS1pHGS5kp6Q9KumSaV1FdDa0DaqJFUJamRNSxpWDDQkt+9JJmkIyVdJmlqOPdD2SRsSRMlDZd0iKQJkuaFsW6Zc559Jb0W2kyXdJuk7xfo70BJb+OGyA7Uv70fFcZmkvrnXXMBvsPD4DbCxT6LUs5YCxw3Jhikye/+Yaw7S7o69DUtXGP30KYXbtQA3Ji6vgGpfnaX9EK4D3WS7pbUoJZwobmTNCD0t3F4Lj+XNEXSlZK+l+ljWUkXSqqR9FX4Pk1SIqg5ADdqAN5LjbVXkTnZR9KrkmZLmiXpdUmHZdpsIekJSV9ImiPpMblWU3Zun5G0U+hvPnCkpP/JwyWz5/11GNuu4Xej51/SEpJOkvRmuNfTJI2UtG6qzcqSrpP0sfzv+W15SedIpEvSmcUkW0Ogs6ampmSb1qYzz2kk0lVY1HJsugSqV4qfiot5TsNzCO6VtGsiVCjpV3go1ITQbjIuZvizTJc/wsPYzsc9Dn8B7pG0bupt+Ut4zsMHwAgze7eVLucUPATpz3jp2/NwEdD1MiWpt8ANhtPwHJ6TcGHEDaxe/PRQPLzqX6HfH4T+fi3pF2Y2O9XflsCGeBjU1HDdR+GV5o7Fw58A3lFDfZEs36ZzpsxshFxw8ixJ9xXyZDVxrOVyBfAQsA8e6nURngt1AB7Stzue73E+9TpK74fxHI6Hn92Mi3KugIesjZX0s0xBg+zcTQSSLNzb8HCw3fGQySo8pGpQOM8SeCjYT/DCCq/juUJn4MbzX/D8qHPwcLs9qS+XnFtmWtJmeIjnlbjY5WJ4/kz3VJsd8BCvh4HE83kSnnfzMzP7KNXlj0NfZ+MaOTPxsNTBkioyekH7hv2P5I0t8E88xHIInqfzPTzs7PvA25K6Ac/ixRKqcAP0t8B1cm9RuxS8iETags4oKtkaAp1z5syJAp2RSFfEzOKngz/4wnF46vdNuDGzUqbdKGB86vfTwEfAskX6HoMn4q+d2rYqviA+NbXtx7iIoYXPdHwBu22mvypCgarM9mHAxNTvXqGfN4HFUtt/E7YflLn+r/DKUcm2FfAF5W3h9+LAZ8BTmfNuFvo7NtPfXGD1TNv+oe3WmTEW+/RPXd/k8O//F/btH35vnWlb9liL3LMxOeO+JdPuajw3S5nrOTjTbnk8if8fme29wrwPLGPuBoS+B2e2PwS8m/q9X2jXL9PutHCuVTP99Snj7+OvuA5SsTYTgCcy27rhz/GQzNx+B2yYabsm/jdxWGrbkvjf4bWFnn9gq1L3FDfq5pH6GwzbbwzjWyLnmEOBl4GXe/bsaZFIZ6OmpqbUfzs7/FNTU9Plxpw37khkUQN42UqsDQp9osemc1JSKR6vCvUbXKhwbk4fad6zlFq7mU2VNJWUar2ZvSvp56HPbfG37LsBe0s6w8zOyXZaJvdYyqthZs9Kmoy/7b8p1e4F8yT3pN0Xkh4O7cA9FKviC2RS7Z6R9CHu8bky019uaF2KKXhhhGJkNVAwsyckPYlXk7sz55imjrVcHs78fh33NKxGgTDCwCb4Iv/2zPM0GXgb9y4MSW0vNnd5Y9g69Xs74EPgucy5Hse9NBtT700ql/8AFZKG496RZ8ysLtkpaW3cK3le5pxz8WIh/WjIRDMbn95gZh9JGosbZtenrmVlCmhJBbbFFyM3FmmzHe6Brcn5ez4Y9279NzOeKNAZ6RIsrAKdHUEU6IxEWk40bDon5SjFf4WH5JTju84ThJxPRrXeXBPm6fBB0g9wjZBBkq6xhiE65fJZgW1ZUcNS7ZJMz7xwpU9T+ynSrgHmCfHNzco+FXgBX5hmK1s1dazlkr2PScL797INM6wavgsVTMje12JzlzeGpTPn+iHuJcwjt/R4McxsrKQ9ca2a+wCCEXKCmf2X+uu7iYbGcsKkzO9C13crcLOk3mZWgxs5E8zshSLDWwn3JhXTB1oV6EMrzkkk0llYWAU67733XvbYY4+2GlokEmkjomHTOSlHKX5xPKSmmOp5izCzKZKG4rkda+N5OPMAJC1lDStlFVqcrVZg2/gy230c/p0sqFfPabc6jQ2UNn3LbWYvSnoAzxM5PLO7qWNta5KS4gPwEtRZsoKhLZm7GXgOyV4F9k9sTqdmdg+eF7Y8Hpp3ITBSUg/qr+8U8o23bEW3Qtd3L56Dta+kK/DS4+eXGNp0oFLSMkWMmxl4rtJxBfY38gpGIp2dziwm2RoCnR1h2HTmOY1EugrRsGllJG2LJ/L/Cte1mYS/Zb6gCR6PBkrxChWyzKx/OEd/vMLXa/gi7KwSb4zLGfealkqwllRFSAgPPCfpYzzZGlzccVxo2x0vpZxdIAP8XlJVEo4m6TdADzxEKM3GoWjCJFxj6EK8klkS+vQO7sHZm9RbeUmb4h6CgsKcKRIPR5MU70MVrwMAyySXn44baNnQqmaPNcz7FrheUFNJPHzZ63sOvzd9zOyWZvTbFEYCewCzzeztvAbhedkx/GzSvTAvuvCQpLVwg3slfL4nAuuZ2QXNG/aC8McRuKdmCu4Juy017v40/JsAD7E7GffcFSoCMBL3Nk0ys6nNHV8k0pnozGKSrSHQ2RF05jmNRLoK0bBpRSSdii/K78cXOjPxSl8nAXtI2tIaVmcqRFYpfkVgSUmnA2sBA3HD53t4Ls7zki7Fw9LWwhOjj2ni8N+Q9BRuhNXgxQTA324/iVfI+gleUes7vKTwIDwM6W9AoSpfKwD3S7oeWAV/A/4ejfMWPsMXiYRzP44bhmeDh8lJOhO4PuRaDMe9VeeG/m4u4xrfxXOTDpQ0Ezd03rGGFcGKIfxenhzG9LqkfwF/TDdq4ViH4gKXzeFA/Jr2lvRfYA5QY2YzJJ0IXCNpFVw0c1YY0xZ4oYI7mnnOLLfjFfCeCM/ka8BSeA7Mznj1sO648QNwlKRb8DCt/1qOXo6ks3Dv3VO4wdEDr2w33symhTZHASMkLYWLYE4Px2yKGxSXlTn+W/H7ORjP5alJ7RuHG6oHJRvM7KlQJvqyYJg/iRcd6Ac8bK5rdDle1bBa0uW4IbYcXtltczPbpcyxRSKRMqmsrOy0BkwkEmk7omHTSsg1V87BKzAdn9o1VtJ9wCv4oqmRNksWM5skqS9egek8vGzs1/gi9BYz+xzP70g8IGfhb4uXxhO3y1nkZzkJ+F3oazXqNY5OBS4LC85qSd/iidLJAnJyOGZrPEQoy/l4fsEwfDH3FHC0NSz1DO6hGBP63h1PSt/eUmWnzewGSXPxkr8jcGPqEeBvVkb55LDAPzpc61g8nG/LcN5ymA8cI2lIKrm+ioxh05KxmtlkSaVFFgrzAp7TMRr/+/4zMMzMrpf0URjPPvji+2M8n2p8oc7URPFKM/ta0m9x4+9QoDduYL2Pe9/ShssDeLjXIfjz1pv8ULUXcUPmcjw/aSpu+J6ROu8jkvrhBRuG4p6gT8N8/KsJ1zcqHLcG/lynr+3zUPgiy974M3UA/tJhFl7wYGg4blbw1p0Z2q0B1OEGTiPtnEgk0nJmzpzZIo9Ne4tzRiKRVqK55dTip1F51kfxt8TfK7D/b7j349epbYYbQ8fiXpIv8AX3epljx5Bf/rd/ps0zuIExDq8I9Qawa85YNsAXlbXAl7jGxuaZNlXhHEtktu8Vtv8ste2XwD24kfMlvmC7JrTLlh7eLZxvNvA57pn6jFDuOpmTVPtlgQfxhO8N8q49tBsQtvdKbZuIe0sOwcsBzwtzs2UT723S9+b4Iv2q1L4lwr6qzDG/wo2L2eGYJ4BflXGuKv+zbLCt5HMSrjVbNnRYE+/5sHAPN8HD177Ew716hf4Owxf7n+AL8weBHjnXcAjuqZmH/03cBFSGfUlf2c+AInPyS9zgmIE/1x+QKsEc2vTGvUXTcAN0PLBbgWd6fbwi2Wzc6LwWfwazz/rSYb6GFPq7K/JM75x5Rk7BK9DNx71Ol1LgvxXZz0YbbWSRSGejtrbWBg0aZLW1tR09lEbMmDHDKioqckspV1RU2IwZM0r28cEHH7TDSBvSmec0EmlPaEG55+StfKQFhBKuWwCjzGxegWZJHsZWme374rkkx+Fv13viITXN8aYlQpyX4V6PT/CE6z6psf4CX7RW4gvQPfAF42hJG+X0ubhcWX2ZsP9UPAH9jVSbnvhC8nC8tO0V5CSPSzoGF5Ccir/d3hMPfcu9VkmVuHGwDrCpmb1W1iw0ZAvgBPxN/t74wvJRSes0o69PcO2YQyX9sFAjST/DDY8K3CjaHy+3PFbSBs04L5R+TnbDPQ2P4YbJJoQwvibe8xXxksp3AtsD6RC1U3Dv24FhHJvgxkT62i/ADYXReOjZifgz8aikxakXEgX35iVjzZaRTvpbPlzTt/hcJl7FJVJtEkHbDfD8tp1xA/ZeSXlhfSPw+7Mz7gW6FfdybZtptyMeNncbBSjyTPdKNRuO52Pdgd/D8/FwtgZzF4l0Jerq6hg8eDB1dXUdPZRGfP7559TW1lJdXU1NTc2CT3V1NbW1tbmenCynnXZayTatTWee00ikqxBD0VqHlfDQl4lF2iT71sxs/xrY0UJoliSAu/E3/s81cRwr48KI74W+xuELyb3wkDaAi/EE/a0s5DNIegw3VM7AcyDSZA21t8N409o0C8Jp5BfwLB6qdjkefkbQ3jkPuM/Mdk/195ikw7IXIqkn9W/Vf2Mhl6IZrBaOnxT6fQIP1zsd2E/SYlDUwM9W0LoQ91wMwhf4eZyJG1D/z4LeiqRR+DMwiPqFfVMo+pyY2auS5gPTrXF54qbc8+WBfc1sRLJBUq/wzw/NbJ/U9lVwXaUfmFfQ64UbMoPN7KxUu3dxb+JOZna/pFfDrg9yxpplXdxA/Jt5aeeEYal/V+H5T1uYWVIh7bFg8JxF4+IOV5rZFekNkt7DiwY8ktq8H/CWmb2SN7Biz3SqzeZ4fs0BZpbklY0OOV7DJW1oGU2dSKQrkS2p3BkoVda5nDHPmTOHiRMntvLIitMZ5zIS6WpEw6Z1UAuOHWUN801eD989abphU1SIU9IyuAfjPOC7jFdoNPCnnD43xt+WL4ZX9DoJeFzSpmb2Wei3G+4R+T1uuC2ZOv7F8L0pvmi+IXsCM+uV2fQT/NrfBHa3MvJnilBK+PNMGle6SvMhvnBOjp8ZkuIHSboQzx3J0g94yFIikub5GUlOSXNo1nPSjHv+DfBQge7yxDmTMUwBtsGfk6wQ6It4iFY/vLBGU3gPD3u7XtI1wFhrXICjpKCteV5awn055xkOnCxphfCMVOIeq2LPRsFnOjO2r3DvUVawFHxOxmcPknQonqNEz549s7sjkU5DVxSULHfMDzzQVC3hSCTS0UTDpnWYjucj9CrSJtmXXZQ1V3Qxj1JCnJV4wvwZpBKv00haLO2NAV4xs2/Cv1+S9DTuBToBN3LAixVsjRsJ4/Gckl/heTbJuROdm3JeSfUL7f/SQqMGSgt/3kDhhTz4/GXDtS7Hy/eeRb4xWElhcc6KYoMtQnOfk6be86nmQq3NGUMilDmhwPHNEeecFQpznIGHuK0g6X/AoJSnsBxB27Rhk3dvbsMN2N/jz/PeuIFeLFysnGd6Vdx7Weg5zp0TM7uBYDD17ds36zWMRDoN1dXV9OjRo6OH0YDJkycXNV7KGfOjjz7K9ttv39pDK0qpcUcikdJEw6YVMLNvwoJ/G0nfK5Bnk8T6P9mOQ8tSh5dqvobG5ZYByBg1efs/kzQd+BmApO8Bu+DJ8wvCeyT9NHPo9PC9Bg3zc/K4Hs/1GC7pm3SoG/WhcUtljmmqQOjH4CKkuLehINk8FDObLel8PAH84pxDZlJYnDPP+GxL6mjaPW/JIjoJA9sWT7ovtL9JhFCtPYLHoy+e63OXpA3M7A3KE7Rt0GXOOWokPYvnMt0cvsfkeIfSlPNMz8Cf2UKrlaLPXiTS2SkU7tUZyIZ2lQpRS3PEEUe01bAikUgbEg2b1uNiPLTnPNybsQBJvXHvxtNm9mLOse2Cmc2RVI0nWY8rZcTkIen7eC5PkvOyNO4RyJZvHpD5/Rz+1vpQUjkIhYdqR0v6BvinpH3M7O6w78PwvT714TzgSeV5bKyU+KikFWgo/NlcrsXv8zk5+8YCOyRhTanz7kT5paWbw3wygpetcc+bwCjciOppZqOKtGuWUGrwHL4g6Qz8RcH/4QZFA0HbJo+6ntuA6+RCnJvgRRqKUc4zPRL/21/RzJ5owdgikU5F9+7dGTRoEN27d+/ooTSiW7duVFRU5Ho/Kioq6NatW8k+jjzySK699tq2GF5BOvOcRiJdhYXasJG0K774XBcXipwKvAr83cxGhjb98TKuZ7Vk0WdmT8hFGc8KSdS34m+tf4FreszCk5FbFUkD8DfML+L5EaU4AdcueUzSTXhYzsphnIub2cmZ9r8OlbyuCb8n4Dk3f4cFoUKvAheEilhX4m+w10h3EvIWTgGukgsa3o6XLd4QmGdmjVTbzWxg0M25Q9L6eKjblrjhcErwHE3F367/qMD1fobnBFXhC+qTSAl/lslm4XucpB+aWa2ZzZcLRyb5Ff1T7c/GK2o9EfJwLJx3WTLaKDn0L7G/GPOBrSXtiIe9TTeziTT9njcLM3s/XO/VoercWNxbsSaefzPUzJ4K274EDlJGSDTbZ7iWQ/HcnBr83h2LPzvPh2ZZQduJeMjf+sBaZlaoyEOWu/Dnd3gYX1ZjZsPU95hynmkzGyPpTrw64WVhnN/hoam/A06ylFZTJNJV6N69O1VVVR09jFwqKyuZMGFCi3RsOqIyWWee00ikq7DQlnuWdCyeJPweXlp1B+rfrqdLLvfHE4RbPBdmdjaecLwcbmw8DhyJGzl900nsrUiSCF+WiKKZjcN1QWbgi7jH8fLMP8UXv1meod6oMfwt+2/MLJ2wPgY3dsDfcn+KlwPOnvtqvBxuD3wReC+e01BTZLx/wctXn4YvYMfhhswLYfzD8IpfeZ4T8MX1pbgn7V94Psj2zVxMrkB9XhH4PX4v2yhU7+qP53XcgnsCZuNVu0qVrB7XjHElrIQ/B3fhApFVYTxNvefNxsxOxQ2RfmEcI/A5q6V+rlbEn6NESPQ/FC6q8B5uZJyBa0XdjBvw25jZ5HDOSXiI2mv4fR4FXIcXTSg79DMUe3gQN8rvT7xtKd7NfJf7TO9Lff7OCFzz6ehwbXk5YJFIJBKJRJqBzBbOvFRJk/DE991y9i1Ilg5v8gcBS6aS5Av1uSTwjS2sk1aA4NV6Cl+k/x5YzczmpPa/hxtAA4BDzGxo+4+yMZImAs+Y2b4t7GcA9YbqZsCPzOzTsG8JPAxvsJlVteQ8LaW1rjfT59JmVpbR3IQ+e+EL/w5/Vtri+lqbvn372ssvv9zRw4hEGlBXV8eQIUMYOHBgpwudmjlzJn369KG2tnGqX0VFBRMmTCjptTnttNM499xz22qIuXTmOY1E2hNJr5hZ3+Ycu9B6bPBqUJ/m7cgxagC+lmSSLOzrFX4fKekiSVPwt+Hdw/7dJb0gaa6kOkl3B+2VBUjaW9KTkqZJmi3pVUkHZMcTznOOpL9I+lDSHEkPS1o1fO6SNEvSR5JOyhw7IBzfK7VtoqTh4fxvhf5elrRZzrmPC+3nSXpJ0qbh97Ccqfs37rXZPXX8pngYWK6IoaTtJD0v6ctwDfcrJY4p6VpJnykjSCppaUm1koaE3/3DdfbPtCvnPuwT5n52GMPrytHOKYPEK1RSuU3SrySNDuecI+kJSb8q47iq5BlMbUuej2Ml1Uj6QtJYSeul2kzEy3H/KXmO0/dQ0gaSHghz+qWkZ+UaK+nzDJM0WdImkp6T9CVwUepv4TBJZ0n6JMz1g5IalRaSdIik18IzNV3STfLyyWmjBuDG1FgHFJmTX0oaJWlGuM8fSLo206a3pNvD39p8SeMl7ZZpUxXOtb6kxyTNxosQtPQZ3C3M52xJn4e/o51T+5eQdIqkt8PYpki6VF54IxLpknRmMcnWEOhsb6MGOvecRiJdhYU5x+Yl4ABJHwAjCoQeDcVDSA7C38Tnlbk9DQ+VORRPkp8n6XA81OVmPGdiBTzUZKykn6VCWNbCw04uwOPq+wFDJS1jZn/PnGc/PBH6SLxq1xA8hG0FPATnBjzk5QJJr5vZIxRnc2AdPIRnHp738ZCkXlYvGnlwOM9NuNjjj3B19O4F+pyLh9rsR70hsz8uyPlBtrGk7fBQuSdxkcLl8fl6Ri5M+HG4xiPwSlrpaypH9b3ofQjNVsFzJq7ExSMXw3OuuksSfk8LkvHifQJcDQyUdImZfZh3TDj3WFyHZwBuDJ4cxrVxGeFoeewLvIOH+C2FF6sYIWndMMbd8Pl7jXrdnWlhPL/Aq4a9ChyC38fDcaHITTMClCsC/wQuAU7Fw8ASTsET5g/Ew8guxcOvtkhd+wXAX6if7zVwg3B9uRH8CW4Y/xs4n3rxzDw9ICQtjyfmv4TP5Rd4fsqmqTZr4jlmU4Hjw3X/AdeO2dXMsmIUI/Bn/kL873IezX8GjwnXej9wAB5y+Asaln4fjofaXYjP3//hf4+9gD0K9R2JdAU6o6hkawh0/vOf/2Tvvfdu7aEVpTPOZSTS5TCzhfID/Bj4L76oNLw0653Atpl2VWH/EpntvcL2cYSQvbB9ebwQwD9y2n8FDCwwnsVwQ/JG4LXMPsPj9pdIbbssbD89tW0JfPF2c2rbgNCuV2rbRDynoSK1rW9ot09qPB8Bj2TGsntoNyy1rX/YtjWen/QtvmBdGi9ffEhqvg5OHfcynkeQvq7eeOjWZalt7wJ3ZsZxP/Bmzhj6N+U+AH8FZha4J0mfxT69UnPcB/cE1iXnDffE8HLXSb/3hDbdU9u6hbn6d4nntgqvCpd9Pt7DwyWTbb8P2zfN3PfhOX0+AbwFLJXatnjYdn9q27DQ5y4F/hbGZrb/NWz/Qardt8CZmXa/Ce12zfR3cHasOWNPntufFWlzE27MrJTZPgoYn/O3flxOH815BrvhhlbBe4q/YDBg/8z2P4XtGxY47lD87+flnj17WiTS2aipqSn1384O/9TU1HS5MeeNOxJZ1ABethLrg0KfhdZjY2bvSvo5vqjaFtgYf6u9t6QzzKxQsnmW+8MkJ2yCL2iy6uqTgbdxr8wQAElr456EfriGSRL6lxfTP8oaegfeDt8Lysia6+VMwKtMleJ5M6tN/U6rxIN7qnrgCflpRlC8utpT+LXug4cULYMniTcQnpS0HP7m+rz0dVm9XsgWqebNUX0v9z78B6iQNBz3RDxjwWMFvIIn1Rejgc6Imc2UdCkwSF4BLM/T0A94KHUezOxzSQ9QOEm+FKPMLF1SO30/n8tpD4CkZfC5Pg/4LjNXo2ksMPoNhQVLsyWy02OYglc+W4zG9+RFvJBCP9xYaArv4Ubi9ZKuwY2rrLbMdrinZVbmvI8BF0vqZmbp2JP7cs7TnGdwU9zAvqFIm+1wQ/vezNiSUuX9cFHbBlgU6Ix0ERZWgc7jjjuOK664omib1iYKdEYiLWehNWwAzBXUnw4fJP0A15UYJOmazMK/EFmV8kRdfXSB9rXhXMvjb4zn4mFI7+MLnCPwUJ7c41J8VWR7ObH5DYQgzcsTkzr2++F7aqbdt/IyyrmYmUm6HQ9H+xB4wLzkc0WmaQUg8lXeP8XzQRKao/pe1n0ws7GS9gSOISxoJY0FTjCvXlYyKzvMW5rLQ39n0dgwAPfqFLru7DyVS1bYMzGOSz0Llbh35ozwaYRSxTSAqeHvpjljSO7JhALHFxJRLUh4trbEx34tsIKk/wGDrF64dVU8JHL/IudNGzZ596Y5z2ByPcXiR1bFQwdnl+gjEumSLKwCncstt1ynva5IJFKYhdqwyWJmUyQNxUvdro3H7Zc8LPM70doYAPwvp32SX7MJvnjf3MyeSXZmE5Q7kGRxt2p6o6TFcY2TYtyK51ushwsl5lGLz93qOftWJ6VCb81TfS/3PmBm9+A6Isvj4UQXAiMl9bBmaBeZ2WxJ5+M5JhfnNJlJ4evOGgdtTR2eR3INft8akZmDlngHknuyLY0N8vT+JmFm44E9wt9OX/zZu0vSBmb2Rui3Gr+veUzJ/G50jc18BpMXAGvg+XF5zMBzeAq9hs2OLRLpEnRmMcnWEOi85pprSrZpbTrznEYiXYXOsshudZRSm8+wbvhOKqalVdCzuhV5PBfa9TGzW4q0WzZ8LwgfCl6NXco4R3swOXz2xBdyCbtS4rkws7dDWNAqFFBcN1e8fwXYU1JV4gWQ9EM8hCcryNkc1fdy7kN6TLPxAgpr4cbtSoQE+2ZwLS58mRfSOBbYIQlrApC0Ah6GNibdUPWlpBO+Dtvvw3PC7mnCmObjz/ECwn2oBjYAxjXHkGsCo3AjqqeZjUo2qr4S2p/Dd/pvrmxCSOMLks7ADer/ww2KkXgY3FZmdlELxp99Bo+XVIfnoe2T0z4pq30cBf4OwthOAlY0sydaMLZIpFPRmcUkW0Ogs6amhoqK5jrYm0dnntNIpKuw0Bo2wBuSnsLDj2rwfIzf4dWg7rJ6scw3w/dfJD0KfGtmBcOTQq7EicA1klbBK5bNwt/aboG/5b0DX3h/HtoNwkU7T8ff8q7YupfadMzsO0mD8ZK7Q/GqaGvhYXOz8AVqseOPLuM0Z+B5GQ/Jy/MuDwwO/V+aaVtK9T17/rLug6Sz8CpzT+Fvx3vgyvXjzay5Rk0S2ncW+fkVZ+MVtZ4IeTiGL26XxcPX8tgTNzQPw71Q83HD5tAmDOtNYHNJO+KG+3Qzm4gbYE8Dj0m6CffWrYznQC1uZic34RwFMbP3w/VeLS/pPRb3Vvw8NEleKnyGezL2lvRfYA5QY2aNPDrhWg7Fc3Nq8L+jY3Gj9vnQ7Ey8Cto5kj7DiyhUAOsDa5lZXuhnHtln8CY8hOzGEH6Z1m7aGjds/hmu4148bO0LYENgnpldZWZjJN2Jewwvw73E3+EFFH4HnGTNE4uNRCJFqKysLMuAKcQll1zCHXfc0YojikQi7cHCbNichC8ckoXtt3jlo5MJyf2Bh/C370fiCySFT0HM7HpJH+HlbPfBY/E/xheP40ObaXIdjUvxt+5TcC9BJcUTktsNMxsawrOOxxdpb+A5Iw/iRkJL+x8paQf8eu/C84PGAH8zs2xSfp2kB/Echzutsep7Xv8l7wOeuH4snhdTiecUPU6BfJMmcnM499qZcf03vPU/Fxc1FfACsIUVLvU83swmhBLZmNneku7GDc5yOQWvuncX7g25BRhgZuMk/RK/D1fihvU0vOJftux4izCzUyW9BRwVPka9d/Sz0Oa7UGr8PDxHagncmzMsp8v3cCPjDDwv7Au8IMQ2ZjY59DcpPDs7hD5XwQ2nN/A5KHfsec/gUEl/wOcpMTKXxsuMP4U/d/fiz8HtuMftLdy4TdgXz8k6EC8fPx83vh5L5iQSibQeM2fObJG3JhKJdGGaW04tfhbOD14lzID9Onosi8KHVCnpAvvvw70ey4bfy+K5JDW4oViDL5YXSx3TP/S5B24s1OLew9tpXBJ5CdwgehtfcE/BjfHvpdr0Cv0dhr8o+ATP3XkQ6JHpb1n8RcEM3NvxAK4RZbiRlW67BV6K+gvcG/IYsH6mzRjgGbzU+Di8GMcbhNLRoc0wGpdMnRj2LY+HPU4K1/cZbkyt24R71CuM8drw+/Iw3rXC75VxQ+fjcI63gUMzfayOG1mJ0O8n+EuVVUudf6ONNrJIpCtSW1trgwYNstra2nY754wZM6yioiK3jHJFRYXNmDGjrH7++Mc/tngsHXH9kcjCAC0o95yUH44sgsjV2i+RtIukLSUdSX3oXtFQsEi78QjuIegbkucfAw7GvX/b4yKzZ5BfxGAI/j/0P+LGz840ztkZjodI3oF7PM7HBWvzqoGdgmv5HIjnlWyS0+76ML7LcE2kd0LfDQievCdw42df3POxAlAdBDfT/Chcb9LnJ3hoV5+w/2x8nqaFMW2Cl3YHN0L2wkMgt8FDUcdTWIS2EebhfKcAh0v6C+4BPNXMPpDUDReo3QGvqrYDbvBdF8Q7E24L4zoxjONYPPRwWSKRhZS6ujoGDx5MXV1du53z888/p7a2lurqampqahZ8qqurqa2tzfXk5HHwwQe3eCwdcf2RyKLOwhyKFinNl3gewv54TkIt/jb7ZDOb25EDaw/kdZwXL9bGGmoLdQRJLtj3cQNlMzyk7emw/YlQjnqQpAvNLF2++39mlhRhGClpJjBc0v8zsyckbY7nphxgZknFtNGpdhuaVyRL+NBSSfQht+liST8wrzi4Dm6gnGZmF4Rmj4dwx8Mz13UFrkmzS6q/p4APgL8AA1NtVwb6mdl7od043LjZC9dJel/SNOArM3shc55NgNvN7KbUtjwdm1JcE853CZ4/lxS/OA6vfvjTZHz4HHbH78l14RnaBDeG0oZgU8IMI5EuS7bscnucq1BJ53LHstZaazFx4sRWGUskEmk/omGzCGNmn+ICgosqW+B5EgWR1Du8se8oknwvw+/Vh8BzOWKP5+AitA+ktt+V6etuvOTzJri3pKnikaUEOn+NC3Rmz/tPUoaNXLj2R8B5mfPOxQsC9Msc/17KaMDMpkqaSr3YbDH+AwwI2kyPA69aYZ2egpiZSToH95ida/XV5bbD87hq1Fgc9GDgJ8B/wzhODMb0k8Abwd2ei6RDCTk9PXuWc5mRSOelM4lOdqaxRCKR1icaNpFFmVfwnKJidLTOSBKW9QmuOfRDUiXEM2TFHhskppvZV5Jq8cpx0HTxyFICnYnoazYhPvs70U66KXyyTMr8ztP+mU95QrXH4MULDsSLOcyUdCvuVWqqV/KrzDf4tfSh9D35A1684W94iOAnkv4OnGM5JbjN7AZCxb2+ffu2RFsoEulwqqur6dGjR7uca/LkyUWNl3LHctxxx3HFFVe06VgikUjrEw2byCKLedWrgqW9Owk74MUDXsET8mvwkKg8JmZ+r5b+IWkpPOTw47CptcUjE9HX1fCQstxxUC/UeQoe+pjlq5xtzcJcu+gU4JSgofR74IJwjpNa4RQz8Ep7xxXY/04Yx1RCpbgQsncAnvczDS88EIkstBQKC2tLsmFgpULUsiy33HLtPuZIJNJyomETiXRSJO2OJ/xfYWZzJY3EK53NNrO3y+hiL+Afqd974qFiif5La4tHvohrtOyFGw8Je2favYMbYeulcnFaSiNx0ixm9iFwqaQ/4bllrcFI3Cs0KZPfVGwc7wCnSjq8FccRiXQ6unfvzqBBg+jevXu7nbNbt25UVFTkekoqKiro1q1bWf38/Oc/L92oBB1x/ZHIok40bCILLZJ2xcUp18Urbk0FXgX+bmYjQ5v+eHnks/JCgtqRDSWtjIeG9cQFPvcERuEeB/AKZH/GCwZcCryGazWdAFQD22XCq9aTdDOe4/JjPBRrbGLEWPnikRuH/m6UNMnMHqchq0v6jnq9nrMkLUbQmwl9LSDkqxwFjAhepLtw4drVgE1xI+Gy9DHhPj0FbGlmY3Lm702gUtIRuBdunpm9Lul5PO/odTzkbgtgA8rUt5F0D7AV8H85+/rj2ldT8Wpul+NG23L4M7e5me0iaUXcM3U7Xgr6a2AX3HuWnctIZKGhe/fuVFVVtes5KysrmTBhQot1bE488cQWj6Ujrj8SWdSJhk1koUTSsXjlrX/gpZDn4AnrO+AL1ZGhaX889+EcfFHfUSQVsubhC+VxuKfjniTJ3My+lvRbXGT2UKA3nnD/FjCWxiFcx+Een3/h1d8exMsMp2mKeOQ8YD8aL8a3xY2GFfCyxuOBv+JG2pN4pbRn0geY2SOS+oVzDsW9LZ/ihtG/cuZnHF704M2cfYQ+NsYFOrvjRRZ64WKte+FztgQeIne8mV1ZoJ8sRwH/A67GK6MBIGkZXAz1eWAnvOT2SXj+Uh1u4CQl0+eF8R+C50h9F/b/ycxGlDmOSCRSgtYU5rz44otbxbiJRCLti4oU5olEuiySJgGvmNluOfsWS7wzkqpww2bJUqWdJS0JfFOsmlVnIOXd2MbM8nJYmtvfLXiOympmNie1/z3ccBkAHGJmQ1t6zs6EpH1xg203M7s/bLsQNxI3DKFlbUbfvn3t5Zc7eypYJJJPXV0dQ4YMYeDAgW0akjVz5kz69OlDbW1to30VFRVMmDChScbNPvvswx13NJLgKkh7XWcksigg6RUz69ucY6NAZ2RhpRL3ADQix6gB+FqSSbKwr1f4faSkiyQlivHdw/7dJb0gaa6kOkl3S2pQl1fS3pKelDRN0mxJr0o6IDuecJ5zJP1F0oeS5kh6WNKq4XOXpFmSPpJ0UubYAeH4XqnN/wzf/SW9Ffp7WdJmOec+TtJESfMkvSRp0/B7WM7U/RsvO7176vhNcU/YbXlzLWk7Sc9L+jJcw/0heT7Zf62kzzKlkpG0tKRaSUPC7/7hOvtn2pVzH/YJcz87jOF1SYfljTcPMxsOPARcK6m7pF/g4X9ViVEj6RBJr4V5nC7pJkkNVlFhrt8Kc1Eb7kkjwzsSWZhoL5HK1hLmbC5RjDMS6RzEULTIwspLwAGSPgBGhFyRLEOBHsBBuPBlnr7JaXiuyKF4ONc8edL3dcDNwFl4CFYVMFbSz0K1NYC1gHvwRPrvcH2WoZKWMbO/Z86zH/AGcCSeazIE15xZAXgUL/27J3CBpNfN7JEy5mDPMP55wNnAQ5J6mVkdgKSDw3luwkPhfgTcQTDecpiLh1ftR70hsz/wLA2roBH63w7XvnkSL3e8PD5fz8jFPz8O13gEHs6WvqYdwzhyDabQf8n7EIy54cCVwIn4y5x1ge7yPKBiL3cspXlzOB6SdjmwIR5ud0kYxwW4qGhyjjXw0Mb1JW1qZt/KCxZcGsZZjYfe/Qw3wCORhZ62FqtsLWHOhDlz5jRJoDOKcUYinQQzi5/4Weg+eLL8f3EPg+HJ6XcC22baVYX9S2S29wrbxxFCNsP25YFZwD9y2n8FDCwwnsXwFwk3Aq9l9hnwbnoMwGVh++mpbUvg+Tc3p7YNCO16pbZNBGqBitS2vqHdPqnxfAQ8khnL7qHdsNS2/mHb1nh+0rf44n1pXGPmkNR8HZw67mXgvcx19caT5y9LbXsXuDMzjvuBN3PG0L8p9wHP9ZlZ4J4MSz0feZ8xmfYHh+1fAT9Nne9b4MxM29+EtruG31cD45rw/B4a5u/lnj17WiTSVampqSn2N9bqn5qamk51/kgk0nSAl62Z67/osYkslJjZu5J+ji8wt8UTy3cD9pZ0hpmdU2ZX94c/soRNgG7A7Znwqcl4xat+uBcESWvjb+j7AatT7x2YT2NGWcMcn6Sc82Opa/pG0gTqRTuL8byZpYPNXw/fSZhWj/A5M3PcCKBYrtFT+LXug2vqLINXNatIN5K0HPAL4Lz0dZlZjaRn8epkCcOBkyWtYO5lqQS2pz5MMI9y78N/gApJw/EQvWcseKxwo/bqIuf4Iv3DzIZKOiv0kcznNvh9zY7jReDzMI77wziOlHQVPsfPWRGBUIsCnZGFjLYW6WwtYc6E5557jk033bTVzh+JRNqHaNhEFlrMw4ieDh8k/QCvhjZI0jWZhX8hPsn8XjV8F0rKrw3nWh4v1TwXr8j1Pv6m/wi8AlnucSm+KrL9eyVH7Z6UBZjZfEmkjv1++J6aafetpOmFOjUzk3Q7Ho72IfCAmc2SVJFpWoGXf87OH3ju0w9Tv2/DjYzf42FlewNL4uWRC1HWfTCzsZL2xCu/3QcgaSxwgpn9l8aipqX4iobV55JxTCjQfqXwfSs+9wfh4YZfS3okjKOpY4hEuhztJdLZUmHOhFNPPZV99tmnNYcWiUTagWjYRBYZzGyKpKF4Gei18Tyckodlfs8I3wPwnIssyVv+TfDF++ZmtqDUcTZJvgNJDI5V0xslLQ6sXOLYW3FtnfXwctJ51OJzt3rOvtWpn8e0F2df3LDZFw8D+6jIGMq9D5jZPbhWz/J4SNuFwEhJPazl2kXJOLalsRG6YH/w+l0PXB+MwG3xnJt/Ab9u4RgikU5Le4lUtpYwZ3OJYpyRSOegsyyyIpFWRdKaBRbG64bvpGJaEha2DJnQo8CNkm4M/56Nv+GfB6xtZsVEHpcN31+nxlSBCzO2GZImUtowAQ/ZmowXGLg5tX1XSvx3wczelnQNsAqpULlAer6+A06UNA24Knh7foiLcC4pqcrMqkLbOmDHUPVsE+DP8gp1g1NtwEPhBDyH368+Je5Detyz8QIKa+HG7UrAtHKOTfE94E+S9gZ+gnvlvgN6mtkoSZOB0WY2oMg4aoF/Sfo1UHZ1tkikK9JeIpWtJczZXKIYZyTSOYiGTWRh5Q1JT+HhRzV4Psbv8OpWd5nZpNAuEXz8i6RHgW/NLCsasiduBHQL/14fOFXSynjFsll4Mv0WuKfhDnzh/TlwjaRBuBr96XgRgxXb4HqbhJl9J2kwbogMxauirYWHzc2ihFipmR1dYNf/8CR78EID5+BGxAaSRgODQ/9/xiuLpY/bEc+3+RKvvpY2uBKOCuf/XNKJ+PyuQoH7EHJiVsMNoil4XtGxwHgza6pRk2Zx4Cwz21uuaXN1KGP9PeD7kgbg+TdDzewpSTfghtjzePjfj8kXO41EIs2gNcU5Af7617+2xrAikUg7Ew2byMLKSbghkyxsv8Wrb51MSO4PPARci+c9nIl7A5Tpa7yZJTkUj0vqg4cPrYMn0S8JfIzn8owHMLNpQaPkUrzk8xR8gV9J8aT45rJUUw8IyfDLA8fj4V9vAH8CHsSNhObwhZm9EP79gqRxeHGBP+M5NGOAv1ljUct54XsNvELaFyEnKEtiiGJm10v6CC+xnHsf8CT+Y/EyzZW4UfE4cEYzry/hcWAvSeeb2amS3sKNrkrcoOsJPIFXhQMvif1n3JhZEX8ehtM2z0Ik0inoquKcAL17925S+yjQGYl0EppbTi1+4mdh/1BfSrlPZvtFYfuvcaNlMu5leAc4D1gm034M8AwehvYGHv72NrBXzjk3AB7A8zW+xBfEm2faDAvn3AT3DH0JXBH2TSRVqjls640n4k8L5x4P7JZp82PcuzWT+vLYdxNKNePlla8CJoU+PsMT99dN9TERGJ5zTY+QKXUczlGV+l1FSEVpRptzcOOlBveKjAXWy7RbPLT7BC/o8CQeltjgHGU8E/3DMTvhRtSDmf2Tc+Z/nTC3deFevQBsV+45N9poI4tEuiJJqeW2LoGcnKe6utpqamoWfKqrq5t9/j/+8Y/NGkMs9xyJtBxiuedIpF3pjXuA+uBGwjB8Qb0e7vVZC6/slaYPLuBYhXsNjgD+KWmamT0FEBTtq4FXcW2YuXjo3Ogg9PhKqr8V8fLFlwCn4gvmRkhaE/daTMU9M9Nwscx7JR2KL+6r8bLH3+KL9k9Cn1tTX6L6crxQwKm4F2IlvJR298anXFAgYQXcANgGD8NrK/bFjcrjcM/VxcAISetafanpwWHsF+MG2S9wA7K5fIkbStdK2tjqvVQNCJX4nsGfj6NxT9hRwMOSdjSzR1swhkikS9DVxDkhCnRGIl2VaNhEIqVZPCzWVwD2wkUsHzSzBeWI5XFTz+J5NbdKOsrMZqT6WA3YJFkASxqJ55WcBSRlfC7Go04SOgAAc+tJREFUPSJbmdlXod1juJfnDDyxP2F5YF8zG1Fi7FV4aN0WqfE8FgyegbiH4QC84MAs3LtysnkO0rBUP5sAt5vZTalt9+Wcb5/wSXMj7uVqK74GdjSzrwFCCNvdwK+A50LRhoHA383spHDMKElf46GCixWqVmcNtYWyDMUFQM/Dw8/yOAEvfb2JhXDGUOb5TeBcPDeoEcHoPBSgZ8+eeU0ikS5DR+u7NPf8DzzQkncfkUikI4iGTSRSmrdT//4OD+saKKkbcBqeO7ImnuORsDapksbAR+m3+uZ6MXcDf5O0GLA0nvR+HvBdZqE9Gs99SfMNnh9Uiu1wY2VWps/HcENqU9ybMAEPMXsqjCXLf4ABQePmceBVc52gLI9SL/q5LG5cnIkbH0eVMd7mMCoxagJpMdLngJ/ixRvuzhyXvGI9gwI5N5J6WwGdGTP7WlIVbshubWZ5mjr9gBesPkcrufd3AmdK6mZmjTKeLQp0RhYiupo4J8A///lP9t4763hv/hgikUj7EA2bSKQ0u+GL4C+AD81sHoCke/FwrTPxkLQ5+EL+GhqLaH6W0+9neOjUKvjf4uIUX2QvZvW6K1MLGBZZVgX2D588VjKvMLYN7t05H1hJUg1wsZldF9odg5fIPhD3NMyUdCtwmpnNTfU30xpWlXs6eLMuCqKob9L6zMz8Tkp4FxUjxZP7wT1KNxToe0qJc9+OF6o4l3yx0Eo8tDDLp7gnrQL38kUiCy1dTZwT4OSTT26tYUUikXYkGjaRSGneSL9xB5D0PbwYQJWZXZHa/tMCfaxWYNtXeN7LMrg36BpcALMR1lBMsty3+DPwHJoLC+yfEvr+ANg/GCEb4Pkg10qaaGaPmmvAnAKcErRofg9cEMZ/Un7XC0gENH9KqqpZO5IWI02LeS4XvqdY4xLfZWFeNvsM4N+S8jSKZlJYpNRobJRFIgsNXVmc87TTTuPcc88tu30U6IxEOgfRsIksEgRdkbQuylzcoHgVuBO4x5qmQr807mH5OrN9QE7bDYEV00nmkhbHNXFeCuedI6kaNyrGlTuWEAqVLhlswA6S9jHX0xmJ58f8z8xyCwykCdVIxks6ATgIWD+UMh4A3GpmH5jZh8Clkv6Ea/qU4mfhu5huzOrheirNrMFiPxHqLOM8hXgd96btiYfaJexZ6kBJl+Deqo3M7I3Mvh8DrwF/x0P1zqa+2ELCWDxssVcS0hbu/R/wcL48UdhIZKGgK4tz1tTUNKl9FOiMRDoH0bCJLGokYptL4zkYO+CGzaGSdipn8Q9gZrMkvYALe36Cl0c+ENdhyfIuXkntX0GscxpeFe3H4TvhBFyD5TFJN+GehpXxCl6Lm1mx2IjN8KpmI3BNmNuDV+lM4CU8JOxqvCRzBW6QrGVmB0r6Ga6x8y8812Zx3JD5Bi+L3As3nnpIuh+YjecDbQDckhnHypI2Dv9eBi+JfRpuADxdZPyJV6OSxl6MTfB7djDNwMxqJQ3BRVW/oL4q2kGhSTEj8gy8GtxQSZtm9g3FPV6n4RXi8sQ2L8fnclS495/jmkk/xp+9SCTSClRWVjbLgIlEIgsX0bCJLGqMz4SV3RaS+O/GK3cd04S+/ghch4ePfYkLUR5H46T+uXg54ovw4gBr4wbGH5NSzwBmNk7SL3Ej4kq8pPM0YBzuFSjGi2b2jaQvcS/BpsBAM/uHpL54/sx5eD7PDDwka1g49lO8GtsJQA/cMHodrzT2iqT+oV1/3DBcAvgAON7MrsyM47fhA57r8mGYowtKVBgrSMrL1ZzDEwbhOS0H4Zo3L+IGx7MUESM1sy8lHYTP6bHUC3/uhBuTW4Uco1GSxuBzlD5+iqTN8FDA63CDejywg5mNbMkFRSILCzNnzmxVb0trEEPKIpEuSnMFcOInfrrShwJim6n99+EL+mXD72XxxWgNnkdSg7+ZXyzs/z7u0Tgmp6+T8BC1VcLvOmBWps0qwLXAR7gB8BFwG7B0qk05Yp1V4bqWyGy/C5gb/t0/tNkdT5SfBtSFfUvieiwTw3VODL+XzByb/fRPnesQ3CMzD/dc3QRUZsazRJiXN0O7aXiY3Lqpe5P99ArHNhLRxKu9PR/mZRZwP7BOps0YXENma9w4nIuXzt417N8z9L159h7m3NOrcU9Vb9z4mwVcl9q/Oy68OTfc77uBnpk+9sFDH2eH418HDit17ijQGekIamtrbdCgQVZbW9um55kxY4ZVVFTk/f1bRUWFzZgxo03Pb9Z+1xqJRMqDFgh0ZuPBI5FFlUfwt+l9Q1nkx/DQpyuA7fGwozPwEsmY2Sd4SNN+OX3tC4w0s9yckqCr8hyeZ3EZ8Dvgb7iRsVRo84vQphI3HPbAPS2jJW1UxvX0xhfYaa7CvRb7UZ8LdAtwMl6wYEc8D+kk6kPMxlFfpvlYPCxsk7AdSRfgBtpoPGTrRNzoeDTkkiT8E68c9giux3MIbuR8H3gYN6bAjY3kHJ+Qg6TtwjGz8Tk8Ag+te0ZSNhTwR/g9HIEbr1/j4qQXAtfjxsgzeefJcDJujF2Pe8/q8HuGpMOBe8P1/B44LIxnrKQVQpvNgOG452fXcJ030ljgNBLpFNTV1TF48GDq6ura9Dyff/45tbW1VFdXU1NTs+BTXV1NbW1trientcm71nvvvbfNzxuJRFqfGIoWiTiTwvf38RCzzXBRyyQv5IkQCjVI0oVmNhX3sAyXtI6ZvQMgaUN8UXt2kXMdD6wF9DWzdCngO1P/bopYJ7iIKLghdATQF1/Qp3nJzBbkqUhaP1zrYDOrCpsfl/QtcLakC8zsv5KSSmZvWUqLR1Iv3JAZbGZnpba/ixsLOwH3S9oKN8yOs4aha/enjnk//HO8ZSrQ5XAOHgq3vYXwNknP47lMf8FD6hJWxrVklsJDBn+IJ/gfjs/3KeHtEIVEOgPzcGNsVPi9nZl9IWl53LN3s5kdmLqeF8N4DgKGABvjXrKBqT7zcnIikU5FtoRyW/VfqCRzW5+/0Dnuvfde9thjjzY/dyQSaV2iYROJOEkCh+Eehw9x1fr038jj+KJ6YzxE7D7ca7AfcHposx8eZpSWrB6fOde2wH8yRk39QKRlaJpYJ/jCO+ErfDF9SqbNfZnf/cL38Mz24bhhtgXw37wxBrbBjYTbM2N8EU+S74cbL9vi83pjkb7KQtJyeOL/eZbK2TGzGknPhjGnec/M3gv/7h/6+AQYYWaHp/rthYcbFmNLPPxtCTN7LGzbBOhG4zmYjAu79sPvxX+ACknDce/VM2ZWV+Q6DwUOBejZs2eJYUUibUdHi0529PkjkUjXIho2kYizZvj+BNc7+SGNSzknrARgZnODSOefgpbJYrgH5G4LIp5Fjn+tyP5KmibWCW5sfYvn40wys7yxZ0O7Kgts/zSzvxCrhu9CHpaVUt8zrcyKcyWowI3QvDC1T/H7liZPJ2Y+jQVUpwC/LHHud3CjMT3vyRzkiXOC3w/MbKykPfHiFPcBSBoLnGBmjYxHM7uBIBrat2/fcjWLIpFWp7q6mh49erRZ/5MnTy5qvLT1+csZQyQS6TpEwyYScXbAvR6v4LksNcBeBdpOTP37NuAAPHRtGTyU7bYS55pOflnohDqaJtYJ8IqVrjqWXSAni/7VgfdT25PSyzNK9Jfs35awgC+wfzpQKWmZVjBuavHrKCR6WWrMuYRwv5IinTmV2ZLzDaCh+GfCAp0aM7sHuCeEr/XHQ9hGSuqRcz8jkU5BoRCx1iYbDlYqRK2taYo4ZyQS6TxEwyayyCNpdzzx/YrghRmJ54TMNrO3Sxz+FB52tB9u2EwEqksc8zhwuqQNzKyR58bMmiXW2QzGhu+98cT+hCTULckvmh++l8kcPwo3wHqa2SgK8ziefH8wXsAgj0LnaECYm1eAPSVVmdm3AJJ+iJe4LtR/W/Ecbrz0MbOspk8uZjYbeEjSWnge1EoUFy+NRNqd7t27M2jQoDYve9ytWzcqKipyPSYVFRV069atTc8P7XetkUik7YmGTWRRY0NJK+PJ5D3xSmB74eFNSQ7I7cCf8YIBl+JhY0vhFbZ2xssFz5W0K14E4Ha8EtaSwOVJMnoRLsdL/46WdA5e9ndlYBfgcHM1+paIdRZiM1IhU2b2P0l3AoMlHYLr5iyP/3dhMvADPMfmXby09YGSZuJGyDtm9n6oLna1pHVwQ2keHta3DTDUzJ4ys6dCyN5lktbERT+XxPNPHjazMXhFMYBzJe2EJ/ffnBROyHAGXhXtIUnXhjEPxnObLm3GvCxA0kTqw9m+DX1OwA3YPC2h2cC/cUP1xLBtBl744VM8LPEOSSPwZ2cgXvK5B0EXp1D1vEikI+nevTtVVVVtfp7KykomTJjQoTo2edd62mmncccdd7T5uSORSOsSyz1HFjXuxhPAH8O9FEvjwong+ROE/JTf4obOoXiJ4tvxkLPnknZ4ZbIT8NCz7sByNE7Eb0RIGv8NnmtxMq7nciluPCRjGIfnfMzAxTofx9/u/5R6T0pT2Sxn20t4Ps/KwArhfA/j5Zy3CmOZARyNe5DG4onwG4V9p+Jz1A/XzhmBl4uuBd5LnWdvXHNnV7ywwj+A9Qi5MsFzVYVXc/sONyJ+kHcR5sKWO+Bzfldo+xawmZlNKXcyivAYXhRgc7x09wNh/G/g85TmktDm33gOzre4RtEGeGjc+NAuCfU7Dfd0XYjP5Q6tMN5IpMvSGcU5I5FI10WlXy5HIgs3kgbg+i1rl1FqOH3cMGBrM2u1zFZJS5vZ/NItm9zvRLwS176Z7ZPw/Jzdco7JFiho7rnb5JragiLztDxu8Pwc+LGZTQ7V6+qAa83s+Jy+Fsxfc58x8OIBL79cMv0nEmkxdXV1DBkyhIEDB7ZLWNbMmTPp06cPtbWNU/QqKiqYMGFCmxs3ha55n332iR6bSKSDkPSKmfVtzrHRYxOJZJA0RtIzkraWNE7SXElvhNCzpM0w3IOzhiQLn4mp/StLuk7Sx5LmS3o7lPBNn2dAOK6fpLsl1eGlkpE0UdJwSXtLekvSHEkvB6HH7Hi3kPSEpC9Cu8eCRk2yfyIeXvWn1FiHhd2V1FdBa0DWqJHUW9LtkqaFaxovabdMm6rQ//phHLOBuyRdK+mzrFaMpKUl1UoaEn73D8f3z7TbTdKzkmZL+lzSS5J2Tu1fQtIpYZ7nS5oi6VJJ2epnzSLkxRyJ5wAdFjYvh4coljV/kUhnp71EORM6qzgnEDVsIpEuSsyxiUTySRTrz8erev0Fr2i1bnjjfjYecvRLPHcCQgK8pG7As/giuAqvsPZb4LrgvcgmuN+Oi0X+noZ/k5sD6+A5JfPCOR+S1CvRQJG0Ax7+9TAeEgUeClYt6Wdm9hGwGx5Ol4R7QX2y+kvAAZI+wLVd3s2bjJAb8yIwFRcYnQb8AbhX0q5m9kDmkBHATXjI1Xdh/EfgFdQeSbXbEQ8pK1hJTtIxeDje/bgxORvPNeqVajYcFwS9EA8X/D98vnrhhSAK9Z3330Cltn+b5EyZ2WuSpuBhhJjZdEk1wF8lzQIeMbNJOf1FIl2K9hDFTJ+ns4lzQjRsIpEui5nFT/ws0h+8VK/hla0AxuAaNmun2qyK50+cmto2DJic019iiKyd2X4jbiQtkTnv5Tl9TMTzVCpS2/qG9vuktk0Ansgc2y2cZ0imv+E55/kxXiDAwmc6bmRtm2l3E27MrJTZPgpPgE9+V4V+jss517vAnZlt9wNvpn73D8f3T13LF8C/i9y/zcMx+2e2/yls37DAcb1S113o0z9zzPPAW6nfG4e5Tdp/HObqV8WesTKeyUPx8tMv9+zZ0yKR9qCmpqbU30ObfGpqajp8HNkxHHHEEe038ZFIpAHAy9bMNV302EQi+aQV6zGzqZKm4pXUSrEd7t2oyXgEHsNLHv8ENyYS7ivQz/Nmlg4+fz189wSQtDbuWTovc565+AK8X6mBmtm7kn6OeyG2xRfquwF7SzrDzM5JXdMjwKyca7pYUjczS8eN5F3TcOBkSSuY2ReSKoHtgUFFhrgpXvXshiJttsOLLtybGdvj4bsf9Un8afJEOR8Ibc8Mv9/J7BcpPSAzeyFUhNsS2BovOnAAMEDSn80sV4eoFBYFOiMdSHuIYkLnFudsr3C8SCTSukTDJhLJp1zF+jxWBfrgXp88Vsr8/qScMZjZfLlAZDKGRPX+pvDJUlZYlLkWzNPhg6Qf4JXaBkm6JhhXqwL7h08eKwFpwybvmm7DPTq/xxPp98bLPt9eZHjJXBWLSVkVz3WZXaKPBliOKKekr4CZZlYoW39NoIG2kXlhhJHhg6Qf4+WhL6OAwGok0plpb1HMzibOGYlEui7RsIlEWp8ZeC7KcQX2Z70AzX0jn6jen0JKnyZFngZMScxsiqSheI7R2ngezgxcePTCAodlyyw3uiYzq5H0LJ4LdHP4HmOeB1SI6eF7Dbzcch4z8NC/Qq9+W6MENJI2xEtQDy3WLnjB/gUcL2lVM5vaGuePRNqa9haq7MzinL17927zc0cikdYnGjaRSPOZjxcIyDISOAb3mPwOX8QDrGP5yfnvBU/MNmaWZ6AUYuPw/SszuwC8KhnwtJk9mWlbgYeYNUDSmgUMi3XD96ehqtps4GfA/8zsy3IGV6DE8W14EYX+eNjWs5ImmlmvAt08F859KB72lsdIvGDCimb2RDljKwdJi4fzr4TP9TV4mN/1Yf+BuKdsVzMbkTl8XeBLXOAzEukStJcoZ0JnFecEOPfcc9v83JFIpPWJhk0k0nzeBColHYGHNM0zs9eBy/GKYdXAC6Htl8A1kuaa2S6ZfmbjeSTNZafgIbgLz1f5dyiFPMnMLgttXgT6StoRL0883cwmAm9IegrPianBk/V/BxwO3GVmk4LR9SruEXla0tV4wnwFsD6wlpkdWOZY78IrnA3H5+RIXCA0l5CLcwpwlaR78bC1L4AN8fm+yszGSLoTr1p3Ge5h+g4vDvA74KQCBmUhVpa0MZ5PcxNwLV6kYQngj8GjtSouzvkd8HtJy+LhcivhIXbbAxdZY/2e7SRly0PPMrNRTRhfJLLQUFlZ2SmFOIcOHcrBBx/c0cOIRCJNJBo2kUjzGYq/yT8PL1n8IdDLzGZJ2hRPQE9KMAs3DE5OHb9k+H6MIiWJy2Av4CDqQ6T+H+7F+FeqzbF4Vba7cC/TLXilrpPwxf9ZwGp45bd3wziHpI6fg1dlq8KvdxU8BOyN0FdZmFmdpAfxPJs7zey/ZRxzdTAGTsQNm6+Bt/Byzgn74l6yA4HTcG/aRHxuPyt3fIHfhs93eN7Qp3gI2s5m9lBocxXwDT5vvwEuxnN95gH/w7VubszpO1vqm9B+/ZztkchCy8yZMzvUU1OKJ598Mho2kUgXJAp0RhZ5zGyYmSkJlzKz/mbWSAjTzHqZ2YDU7zlm9kczqwjH90rtqzVXoz8xbNoRT3B/JdXlnPAZlj6PpDHARDPbN7N9InCLmVVlhvaqmSV6MAAr4h6j50JoGrihsqaZLWtmAqokGf7fgAm4sfMdnvS+m5ldFJLr09c/2cwONrM1cH2dJ3HPyU2JWKeZVYW5+CY7f6l+9gxt9pE0TClhU9wYAVhH0lmSPpELlx4A7GFmy5hZNzP7tZk9JOkQSa/hIWJn4J6zH5jZima2gZn9Da9Q9pakL4MY6MtZYdHU2HqFscnMFjezCtzz8xpwjqQlgzdsL+AYMxuMG4bXAB8AS+NiqOuEfycMB84JbebjRuGzwOZmFo2aSKegrq6OqqqqNq8INnPmTPr06UPv3r0bffr06cPMmXm1W1qH9rrGSCTSMUTDJhJpHz7Eq47tl9q2Px4CVqiaV1PZJHwPC//ehBKJ7njhgbWBPwNHARsBj0tastABKbHODXCxzp2BcXi55Z0LHddETsEryx2IF2HYhEz1NEkX4GFio8MYTsRLPz8a8mOQ9CfgUlyb53e4ts09QNmvhIOR9mdgPdw4uRa438wSj9hw4HTgDmAHXNT1oMx4T8Ln6krcG/Rn4ImmjCMSaWvq6uoYPHhwmy/6P//8c2pra6murqampmbBp7q6mtra2lxPTmvRXtcYiUQ6hhiKFom0H7cCl0o6Fs9P2RrPxWgVgqYKwMdm9kKp9oEvgF3M7DsASe8Cz+BGV14JafBwNAFbmFlSme2xYPCchWvBtJQPzWyf5IekVXC9nB+EHJdeuCEz2MzOSrVLxr8TLv65Ca4ZdG4YMwR9m4zmzXfJHORhZuMlXYiHudUCR4Q+Nse9YwekNGtGS5oJDJe0oZmND+N43MyuSHX7YBPmIxJpN7Lll9uq/0LlnNvy/OX2fc0117TZGCKRSNsRDZtIpP24G7gaX3T/EM/deIIyhDTbkHvSC3oze1bSZHwhXsiwaapYZ3N4OPM7LU46BdgG9zjfnhnDi3heTD/csPkPXqDgQ7xkdCGSnKNinIUbNkPNLCkAUK446H+AUySdCzwKvJQN9Usj6VC8Ehw9e5ajCRuJtB7FRDMXhfMD1NTUUFFR0dHDiEQiTSQaNpFIOxEqfN2Ph6P1Am43s++Cl6WjyEus/4ziRkBTxTqbQzbIPqkulhUnnUA+iSjnreGYo4Dv4wn/z+KV69IiotMpgZl9Fe5V2iApVxz0PLywwL7AqcBsSfcAJ5pZo3Ob2Q3ADQB9+/Ztrs5RJNIsqqur6dGjR5v1P3ny5KLGS1uev9S5Ey655BLuuOOONhlDJBJpO6JhE4m0L7fi3ojFgD8WaDMPL7ucpS3yMVYrsG18kWOaKtbZFiQhcNvioWG5+83McN2Z6yVVhPaXAqeb2a9baRwlxUHN7Gt8vi6UtDpeTOIyYFk8lC0S6TQUChFrbbJhYaVC1CKRSKQU0bCJRNqXUXjJ5Toz+1+BNh8Ce0haKglXktQPWKGM/r8iXzS0EL+XVJXKsfkN0AN4vsgxI/FQtbLFOtuAUXgVt57lasCYWS3wL0m/xssxtwZNFgcNYWxDJf2OWOY50ono3r07gwYNonv37m16nm7dulFRUZHrOamoqKBbt7z3Oq1De11jJBLpGKJhE4m0LwvKIEs6PLNvVAh1+gwPYfqHpGFAb+AEylOxfxPYQdJI4ALgWzP7VZH2KwD3h5LJe+GVyAw4U9JOwN9zjjkTF8EsV6yz1UUpzez9kMx/taR1gLG452RNPP9mqJk9JekGvEDCBOBXwDt4KODj+T3XI2l5PLfnY7wss2X2D8JLTI+khDiopBF4yehxuIfp53h+zvXNnYNIpLXp3r07VVVVbX6eyspKJkyY0CE6NuVeY9SwiUS6JtGwiUTal00yv+8DPgJ+iYto/gfPJ/kV8FdcuPNVPDfj3jL6PxovKfwgrqPyUYn25+Nimafj4pzj8PyT5fDSxVtlDzCzSZKaItbZJqKUZnaqpLfw/JmjcIPsI7wgw3uh2bN4aeUDcZ2fT/HyzIPK6H+2pENw79CRuFYNAJJ+gufKXIwXFCglDvo0sGcY57LAJOAivFpbJLJQUY74ZmVlZacQ4izEVls1+k9fJBLpAijzEjISibQjQZzymawYZyv1PQZYIk9sNJRLrgEOwT0wr5hZI9FKSYsVK4Pc2ZG7wJYENsXFR7cxs9FN7GMo7s1az8w+krQYbjB1BzY0s/nFjm8pffv2tZdffrktTxFZiKmrq2PIkCEMHDiwXcKvEvHN2trGqW8VFRVMmDChXQyall73PvvsE4sHRCIdhKRXzKxvc46NAp2RSCdDUm9Jt0uaJmm+pPGS8oyODSTdJ2mGpC8lvSPplJx2W0saJ2mupDck7ZppUol7MhqRNmokVUlq9CZE0rBgoCW/e0kySUdKukzS1HDuh4JBlT52oqThkg6RNEHSvDDWLXPOs6+k10Kb6ZJuk/T9Av0dKOltPOdoB9yoAQ/3s/Dpn3fNOfwFr/KWhOUdg3vUDjSz+ZKWlXShpBpJX4Xv04IBlIxreUlXSZoU7ulnkkZLWrfMMUQizaK9BSk7UnwzTRTijEQWTWIoWiTSiQgily8CU3Gl+ml41ax7Je1qZg+Edr8CxuC5I8cDk4G1gZ9luvwRcAUecjYdX6TfA/y/VJuXgAMkfQCMMLN3W+lyTsGrq/0ZL4t8HvC4pPVClbCELYCNqA/jOgl4VNIGZvZOuN5D8XyUf4V+fxD6+7WkX5hZutzylsCGwGB8HqfjIWDXUB/uB/BmRnumEWb2jZnNknQY8JCkU/EQtCvN7Plw/GPAT4Cz8ZycjfHcm0p8vsHD+3YOx76H51D9Bvf6RCJtTluLbmbP0xHimx1xnkgk0rmIhk0k0rmoAgRsYWZJSePHgsFzFvBA2HYJnteysZnNDduezOlvZaCfmb0HIGkcrt/yGzNT2PY0buxcBFwkaQaeV3KzmZVMsi/CF8AuqYpr7wLP4Po3afHP1cJ4JoV2T+CV4U4H9pO0OG40jDGzvZODgkemGs9tuTLVXwWwUUpEk1DqGeAtM3shbBsA3FziGgRgZg9Luh3PiXkfN8LAS3Zvht+vp8O2J0IRiEGSLjSzqXhu1e1mlr7u+wqeNAp0RlqZziB6CZ1nHKX4+c9/3tFDiEQizSAaNpFI52I74BFgVsab8BhwsaRueGW13wAXp4yaQryXGDUAZjZV0lSgZ2rbu5J+HvrcFvc47AbsLekMMzunmddyTzqUzcyelTQZX+SnF/gvJEZNaPeFpIepL7SwDu7xOS11DGb2jKQPcY9P2rB5IW3UFOFBvGhDuZwF/Am4LDXv2+FG2HOZ+/U4cA4+lw/gXqIBkqaHfa+a2beFThQFOiOtTVuLbiZ0pPhmU8ZRihNPPLEVRxOJRNqLaNhEIp2LVXGPxv4F9q+E540shoeflWJmzrb5wPfSG8Ii++nwQdIP8DLGgyRdEzRgmspnBbat0cR2SabxJzntPqWxcGleu0YEj9iMkg3r+SrzDX6/fgh83bg54PcLPC/nU9y7dC4wU9KtwGllGKeRSItpb9HLri6+efHFF0fjJhLpgkTDJhLpXMzAw6suLLB/CrA4rpWSNRBaDTObEqqBXYHn7ryE68SglHBoYKWcLsBDzPK2jS+z3cfh34lxtnpOu9WBbMmw9vRwzMCry+1VYP9E8NLReG7QKZJ+iJfYvgA3kk5q+2FGFlXaW5CyI8U307T0ul999dXWHVAkEmkXSlZFkzQgVUXIJH0RKhMdXSrxdmFA0q6STsjZ3is1J4fm7F8uzJVJanIoT5Hz9g99bt3UPpt4/twKWCWOGZN5Vr6U9LakMyQt3VZjTZ37mWYct2G41s4iqDASLwDwPzN7OfkAbwHH4Totc3CvxL6SlmnpCUP+Th6JQl0S1vVh+F6gPyOpO15KOY/fZyqD/QboATwffvfCk+c3To9B0gp4JbPnw6Z3cA/Ogvya0G5T3FsyttC1pUhKMjd5vsJ/A7Oio0mOzh9wUdDZ6fuV+kzPHPN9/F5W44UGWqTlE4mUIhGkbC/DJhHfTFdESz7tVeoZ2v+6I5FI56AphsmeeOhLt/Dvq/AwjDPbYFydiV2BrYHLCuz/AlcyvyGzfQ9a9ua41Hk7K/8FDgv/Xhboh4shroqH43Q2NsTHN5z8sK325kzcO/K0pKvxN/4VwOF4/ssA4F1cFPNfwPOSLsX/NtfCdVWaOs9vSHoKT2avwf/G9wZ+CoxK5b88CswCbpQ0CBcA/Rswu3GXAKwA3C/p+jDe8/GKYLeG/b2AFfHKb49LqqK+KtpyeMEAzOxbSWcC10sajt+rNfCQrvcoXQAAfM6+AQ6UNDOc5x0z+6KMYwfg/63MFmd4GC8ccD5eMOBS4DVgKbwa3c7ArmY2V9LzeK7N63gO1V24QZQnaBqJdGk6u/hmJBJZeGmKYTPezCaEfz8uqQ8wkIXfsCnFv4H9JfU2s5rU9v1xpfgBHTKqjuOLpOpU4MnwrOxF5zRsyiZU55KZfdNW5zCzSZL64tXRzsMNghl46NlMM0uMgsQDchb+kmFp3KNSziIfGnprTwJ+F/paDfgWmIsbSzumxlYnaUe8dPFdYf9ZuAHeP+cc5wN9gGG4ofIUcHSm1DO4MfzPcL09gDeB7dNlp83sBklzgROBEbgx9Qjwt0yp51zMbIako8O1jsXD+bbES2Y3CzObBkyTtC1wMl7FrDfuUXsfN3ySkL2n8b+Bk3HB0GWAS83siuaePxLpSGbOnJmrSdOtW7eFwqiJ4pyRSNekJQKd/wFWkLSqpF9KukfSZNULBZ6XDpORdLVclG7JdCdy4bovJJ0ffiehVrtKul7STEn/v73zDrOjrP7450snSMxGQKQmiIKC1KCAlIAIKKI0pQsoUkVD8YcYIaGDtCBdghQpKiBNSqgJoRPpIEggoQhIyoaWENr5/XHeSWYnc9vu3d1scj7PM8/NnXnnrXM375nTWiWdIWne1NZ9kj6Q9KykLYodk7SxpLtSvR9IGiFp1UKZkameiskLJV0C7AEsnTOvGl9o7j7gZWC33H3L4JumyyhBNRIw1tlurzSnE1M9lyezoHw7vVOZN1I7L0g6WPJYtLlya0oaLU98+F9JR5LC3DaJd/HNXL7NnSTdnfr+vqTHJe1RvDGN/ThJv5InPnxP0ihJq9RqVG4C95GkXStc35OZgsCLubnul2v7eEm/lTQO36R+Q9JC6Xl8JvX9LUk3qZBsUTPNONdN6/1uWos/SloIwMz64dGyjpX0kqQPcR+UlYEdzWwB3I9kKeALuT4ONLPH8RwxV+Hapv7AXkqmkWY20Mw2yPVjI0lX4+ZfM/LdmNn5ZvZDM1vezBbCnfHnw6N/zfClSX3eHt+Uf4ZrdnYETkrjyBiUPj8ys0PMbHEz64VrZu5JdQ1kZtLM7wAX4hqnLcxsTWB0WvfxaQ3HpzkZYGYLmtkX8Fwxb0jaT9KJkt4i+fvIk2aumH7770saC3xoZiuY2Xwp1PXr8iSf4+R/t16WdJ5mhoZG0kg86tq3cW0WpN96Nq/AkmY21MxWNrMFceFp3vQ5QdIoPD/Qmmb2+TQXT6f5C4KmMGXKFIYOHdolCSknT57MiiuuSP/+/Wc5VlxxRSZP7nzld2eP9+67y6LnB0Ewu9MRwaY//mb3fTx07BO4ucyWuMPxz2j79vhc3BypmEF9V/xt7oWF88PwN587Amfjm6VhuLDwZ2A7fDP3D0mLZTdJ2gq4K/VrN2AX3CRmtGb1JciSF56e6nsTuEauYQA3hbkF35Ctl45ZMsDjpjG7577vhr/NHlksqJkJGFfHEyv+EHgMT8D4wwbaPRM3ddsFf2u+fTqXtTMP/sZ4L+A0YGvcf+N03IQnK7cYbmKzGC5MHYiv4Sw+BfUiab509Ja/4d8VN5vKswKeO2VX3OzuJmC4pP1KqtwN97n4dRrPcsANquDjJWkeSefiG8utzeyKCl29GQ/JC25emc11PqrWnqntw9LnG7h2ZNF071bA/niUsYcklTm4/wV/g78dcB4+x0fkrh+OPwt/BLZIY7yLmdG+1sPDPb+V6+Nj8tDP96c+DE2fNwHnSSrTjl2Bb853wDUHlVgXF35GF843Ou5qPIbPA7jz/4xxpXOXpj5ehmuNLsbnqcx0K0vYuQeuQd4ROB83rbsZ/+08BVxcEIiXwn+ng/B5PwYXsm7JlTkAeDzdn/XxgEqDknQqbpb6GK6h2Q3X1hST0dyL/86CoClMmTKFo48+uksEm3fffZfW1lZGjx7dxodm9OjRtLa2lmpymk1nj3f48OGdUm8QBJ1LI6Zo86aN5KL4f9jbATelUKXXZoWSNuB+/C39ZZIONLNJZvZcenO5L27GkrEvcLuZvVxo724zy5zn70gCyy+BDc3svtTWm7hN+1bM3PCcCYwysx/l+nQPrlU5lJlvk6Fy8sKfACeY2UuSJuBvnvPmVUUuw8PirpvK7Q5cbmZWUI5AHQkY62z33pwvxe2SVgL2lrSnmRluWrQBsJeZXZIrtwhwqKTTk2PzwbhguUUuQeIdzHQUb5RvM2vo25tSOzMwsxOyfychbCTwJXyzfH7h/o+BH2QmTGlOrwa+CTyQLygPUnAl7tuzqZk9UqmjZjZB0kvpa97Usk2VwOZmNq1wfu8ZBdxEbQTu4L4zbqqV50ozG5L+faekb6Vy2bn18N9A3izpplw/H5LnP5mefx6SZm154Bu5XDV3yjV3QySdVzCbu8bM/q9kjEXWxYXmp/InzeydBsddETN7V9Jz6es7hXGtmuo72syGptO3S/oUOFbSSWaW79tLZpZp+0ZI2hD/De5uZpenOsfgLxF2AJ5NZe+j7fPzMC74jZSbAz6e/m69C8xX428A6YXIwcAZub9d4MJVkceBAyUtZWZvVKs3CBqhGGa5M9uoFLq5K/sQBEGQpxHB5vncvz/D3/4OAjd5wpPn7YA7xObNjr7CzFwR5wJ/lfQVM3tR0jrAmriQVOTWkva/mgk1hT4tm/rxFVwLc0Lhbf5UPMLSRoU6ayYvrAcze1nS/XiW9E+Ar1cYE9SRgNHM6nndVdwsPY2/Uf8i/mZ/I3ydriqUuxz4Ob6Zvil9FhMkfiDpJtrnH/QkMze/C+IO6EOBqyX9MAld2Vodk/q5JDO1h9OZlTsKfhlPp8/laLsxXRRPfrg8sIGZvdCO/he5rUSoQdJPcEF5JdwBPmOlkjrK1iof1e5RPAzw8fhz/0ghnHIltiRtxkuepb3x5zAvAFTMdF9gKeDdsj7UOe4pAGZW7yvPkwrfs9/p5YXzl+PazI1pO66yvxXg80DqS2v6bee1tkcDv6/Qp0dx4b5ftY4X2Ax/jouBRMqYkD6XwrWAbUjmhPsALLdcQ3+OgrmcjiSlnJP6EATB3Ekjgs22uNnGe8ArZvZh7trF+H/qR+EmaR/gb9PPoW0iwOvwTfe+uGnPfvh/6jcxK8WEgB+RNkwZZvZRenuftbFE+ryItpnNM14tfK8reWGdXIY7P8+Lb0wrbarrScBYj2BT7HsmEGR974s7mxcFhbdy18G1JM+U1F+WNLEe3k/hiTPulzQJ19JtCdwq6XPAHbjA+VvcTOsjXFtTZgJXa6wZywGrABc2SaiBkmSPkrbGTesuxTfHE3Eh8paSPkF5//Phr0/Ac8TsBvwOeF/SNcBviuGCCyyBO+fXSg5ZcSwVWIgSAbMd424vlRJyFp/djLK/FZXO5/u5OB4p7UJcUJqKz+kp+PiupTGy+a7nVXImLJeGnzazP5EEpAEDBnRlXp6ghzN69GiWWWaZTm3j9ddfryq8zA596CiHHXZYp9UdBEHn0Yhg80yZqU5yKP4RMDRvSiPpG8WyZvaxPOnfAZL+gIeUPa2JUaYyzdARwJ0l1+t5C95e/o6bwf0C+FWVcvUkYGwGk4G+mjWZYuYLkc3Vm1ROkNgsMtOf1fC36+vhWpUN8xq4Sj4zDbZzDvAXSdMK5kDtpWxTuRMw1sz2zE7Ig2K0KxRQ0kadDJycfFV+gPtC9cL9RSoxCXgb9z0qoyjc1btBnoSHmC5S77gbTeRZJJ+Q86Xc+eKz21G2Bi41sxk+M5I2Sf8cb2ZPl99WkUwIXZpZ575INmfVBNcgaJhK5mGdQdEcrJaJWk+if//+3d2FIAjaQTMSbC6IaymKb433rFD+AlzwuDrdWwwa0BFewPN+rGJmRfOW9jKdOpL6pVC4J+KmdX+tUvQ2fGP/bJmJU6PtVmEUHhr3x7jZYMauuICX+Qs8CPxG0rJm9hp4clF809cssghcmflNr/Q545lJkah+RAcxs6uSOeCVkuYxs0E1bmlP4sZe+Jv+PLvjv4MOYWZv4UEUvk/t5I234SG0XzWztzvado7ngfklHUzbPEqfAJ/Kwyafn15IlI07n8jzMWiTyDOfNyYzWV2Fti8isoSbO5ELdIE/u+CO982gF7P+3dor+0fSLD6NB1Io+gDCzOd1Vfzvzp24Bmsf3FyvGv3x3+G4GuWCoC769OnDkCFDuiQhZe/evWlpaSnVmLS0tNC7d+cH/Ovs8R544IER8jkIeiAdFmzM7B1JD+EO6W/ibyB/hr+1LCv/3+S/sS0efOC1jvYhV7dJOhCPmLUArkWZiGsf1sc3gI0mvHwO13zsj0dv+rDSm1wzO6aO+iolYFwVWMHMMlOsututwK24c/T5khbHtRnfx30vTsyZOJ2BR3nKJ0j8DTNNZRplUUnrpn8vgAs1RwL/xXP+gPvFvAucI0/0uAju6zCRtn4b7cLMrpb0GXBVEm6qadAyB/YDJV2Kb3SfquHjchuwjaQzgH8Ca+Nauint6a+kG3DfpMdw86k1cbO9C2rcegau0Rmd+vICPpcr49qw9gqKmeCwQvrMkvPuhGuHzgK+KekZysddbyLPTNDdIzn3Z0kzn5V0FTA0afEewF8GHAlcVQgc0BFuS20/DYzF/eLWzy6a2fuSfoGbTa4maUdcg/QeLsxlOX6eSeVfSutwiKRF8YScn+Jmuc+bWT4y4LeARwsmvUHQbvr06cPQoUO7pK2+ffsyduzYbs1j05XjDYKg59AMjQ14BKPzcDOgabhA8Wt801fG1bhgU2vj1jBmdoukjfBgBsPxN/Fv4RqKYsjhehiOR4k6AX9z26hDcbF/lRIwPkPbULYdatfMPkuR5E7Aw+R+AReiDsHDZmflJkr6Dm5Gd2nqy/n4s9Ge5Kur4VogcCHhNeB64Fgzm5LanCDP23MaHvL5jdR+X2ZGCusQZnZtcnT/W4re9csscEGh3JNJoNsHNyOcB3+bPr5K9RfiTug/w/3FHsU1XPU65xe5FxceDsS1CK8Cf6CttmIW0kuF9fF1Ohx/mTAFF3Aa9Q/J1zte0iPAGunUE2Y2Np17Hxd8d0/9nmXcVkcizxS9bkq6ZVlmTZq5B64l+Rku9L6Bm+sd3d5xlXAoHvUum+db8L9lMyLpmdmdkq7EIyVehAuOo3BhbQLuo0au/GHynDkHpDF8gPvv3J6Vkef3+g7uTxUEQRAEQbMwsy4/cNOo8cA83dF+HHHEUf3ATUmn4n45Kxau/SGd/xYumL6Ov9B4ARekFy6UH4lrD7fGwxxPx8MiW8mxZ+GezXBN1lRc+N+mpK+r49qR1tSP+3GNVb7MJamf6+EaoGnAmXXOxefTvTen77/GNTHrpe+9cKErS+I6Dn+xMk+ujs/hmq5XcYH/szTGlWu1v/baa1sQ1ENra6sNGTLEWltbO7WdSZMmWUtLS9nv11paWmzSpEmd1nZXjfHCCy/s1PqDIKgMMMbauX/pSILOhpFnYN8PN5853cw+68r2gyCom78wa1SxjCw574rUTsyb8VU8AelZeDLMu5kZEv1EZia/zIfGrpVAF0lr4YJKX1zjtj2udbxT0tqFPnwe93+7Cvgenu+oJub5e/YFvi/pd7iG549m9mAylcvCa5+Z6h2Om82dkqvmDFzrczQu+PwNNzHtU08fgqAeuipJZ3cm6OyqMe699961CwVBMNvRLFO0enkQN2W5FM9pE/QAUgLNakKwmdmnXdWfoPMxs08l/Rk3A6uUnHdGUAqpPDFvrsrF8ESnT+TueSf982UrT35ZNYFuKnMKrgXZ1JJflKQRuHbnSGCbXH2fA3YzsxsanA7M7GZJV+BCzUu4RgbcdG0DPOFu5pt0VwpDP0TSyeaBHdbDNdU3A8sAp5gnNw6CptPZySu7M0FnVyXmHDx4MMcfX9UaOAiC2ZAuFWzMTF3ZXtA0/oz7C1RiFMl3IpijyEItlybnbSAxL3j45CcabL9qAt3kq7IxLuR8VggXficzo6hlfEJlv796OCbVeXpOKNkS9397oND+7cBxuJ/cjbgf1p54gIybKU9EO4NI0Bl0hO5OkNnd7TeDceMiYGEQ9ES6WmMT9EyGAmdXuf5elWtBz6c0Oa+ka6kvMS/Unxw0T60Eun3xgANHpmMWUlS8zOT17Q5qFj8qfIIn9Fye2klSD8KDmPwM1/pMlnQZMLhMc2ORoDPoAJ2dILM7E3R2dmLOIAh6NiHYBDUxs/FUjxIWzNnMkpy3kcS8ic7YnE/BNUjnAJeVNtrWj68z+jAJ95n5SYXr41M/3sfzdx0haXlcy3USLiQd3gn9CuZiuipB5pycoLMr8gEFQdB8QrAJgqA9NJqYt4z2JEedgZl9IGk0HhXtsW4KRnIbHrDgfTN7vlZhADN7BThN0q7UTsIaBHXTVUk6uzNBZ1eN8dxzww04CHoiIdgEQZORtDkezvibeN6TV/FcLyeZWaVIY9XqGwlgZgPT94HAPcAmZjayCV0ua3MoM3MKvSjpUzzJ6p3AUeaJdutOzFuB/+Eaj50kPYWbsj1Kjfw9BQ7B8+mMkHQRbvK2GLAWMK+Z/baBumZB0jXApsDXSq4NxP3PXsYDBpyGJ1pdAI/o9kM8PPVUSQ/ivjZP4wFUNsYFskuL9QZBe+mqpJXdmaCzq8Z47bXXsv3223d6O0EQNJcQbIKgieTCAV+PhwCeDKyNmxttL2kTM3utg808hkfZeq6D9dTLj3Eh5Ot4uOK1U5jlRhPztsE8iezeuPP/nbTj75GZPSZpHVwI+yMe0nkCPkfnN1pfCQcCz+I+Zr/JTqbABRfikR43w9d3HzwU9gd44IWbmemPcy9urvZbfJwvAweb2R+b0McgaDqTJ0+uKrhkx5xKCDZB0DMJwSYImoSkTfBIWMPM7ODcpVGSrgP+hfuCbNKRdszsXaAsPHJnML+ZfZL+PTppbi4EVjKzf+N5W4q0iX6YaZrKMLPrcSHQb5Ss2j1m1q/k3L+BnSoPAcxsz2rXq9z3P0mD8Lw+V2SRHSWdjIdt/oGZTcMDbAytUs/hhC9N0EGmTJnCsGHDGDRoUKeaYk2ePJkVV1yR1tZZFcwtLS2MHTu2U4WarhpnEARzHl2aoDMI5nD+D9fQHFG8YGbjcGfxgZK+Bb6Jl3ScpF9JGifpPUmjJK1SrRFJA9O9A3PnRkq6T9Jmkh6TNFXSM5K2Kbl/dUk3SmqVNE3S/ZLqDTOUvcKdEdpZ0jqSrpH0eqrvBUknJK1Gse1tU3vvS3pX0iOSflhlrL0k3STpzdTvWcaeyu2ZzvfLnRsv6XJJv5A0VtKHaW4aEizN7HJcC3WupD5JW3UIHjjhhdTWLyQ9mdqYKOkiSW12fpJ+LenfaY5aJY2RtG0jfQnmbuaGBJzQdeMMgmDOIzQ2QdAEUg6TjYEbsnDIJdwInIz7bDyczu0GvICbcC2AJ5y8QdLKOU1JvXwZOBM4Efd5ORS4JtU1NvVzLWA08DjwC2AqsB9wp6T1zexfhTrnTckm58dN0X6Hm2Y9kyuzHB7u+RI8JPQqeAjoFchpUiQdhJuLXY/nRXof94XpVzaYJBj8E/eZWd/MxhUFmjrYGDcFHIwHKzgcuFXS6sCL1J94dj983GcAa+DjPTX18yR8rv+Im6stjWvuVk1z+mkKFHAangtnNB4wYTU8ZHUQNMScnICzK+qvh0jOGQQ9kxBsgqA5fAHfrI6vUia7tmzu3Me4OdPHAEmIuBoPPPBAg31YDNgoS2op6THcmf4nuB8LuOD0KrCpmX2Uyo3ABZUjgW0KdRaFtOdTf2dEIDOza7N/ywdwP67ZuUzSgWY2SZ7M8wTgOjPbLlffiLKBSFouXXsf+LaZTahrBmbli+n+V1O9d+EJNX+P+8EMqXLvKyShKwVLOAw3w/sYWDsJLP1wYeZoMzsm1///APcBW+OC3HrAU/kywC2VGlYk6Ayq0N15XLq7/SAIgkqEYBMEzUG1i5RyRybUJJ5On8vRuGDzYibUAJjZ25LeTnVlDu8b4wLGZ0nLlHEnsGtJnesCn+KajeVxjcftSRPxv1Rvb1wjsgMutM2fu/8reOSz9YHPkZJO1uDr+NifA7ZLOWDay0OZUANgZu9JuhkXNA6neqCD6fkvZjZc0jHAfWaWrdN38bm5ojCfD+PC3Ua4YPMocICks4AbgAfKEnPm2ooEnUFF5uQEnPW03xUMHjyYK6+8slv7EARB44RgEwTNYSIeGaxflTLZtXxUtMmFMtlmeqF29KFYV1ZfVldfPPfMkemYBUnzFPLB/CtnEveIpHtxLdAhzHSGvxiPDHYUbqL1Aa5xOifX9hfSZz02Jhul8od2UKgBj+ZWdm5pM3sDeKPB+j5iZqQzgCXS59iSsjBz3Jfhc/Fz4ADgY0m3AIekBLhBUDeRgDMIgqCcEGyCoAmY2Sdp0/9dSQtV8LPJnOTv7sKu5ZkCfIYLHJeVFaiV5DJFCZuI+4cgaSHgR7gj/ZlZOUnfKNw6MX0uTVv/nDIuwMM2Xy7pk7ypGzNN4xYo3PMFyvlihXP/rdGHepmUPjcHynIUTQJ31sHHdYGkllT+NOBvwLea1JdgDmduSMAJXTfOIAjmPLolKlouglF2vJciCv2yYM4xRyJpG0mHlJzvl5uTfUquL5LmyiQd18R2s0hTmzVaZ4PtD1UK59vgffNLOiBF05oiabo8itifkzN8pyKPODYy970sKtkgPKfJF5jpz5Kvoz+u4bjXzB4uXu8KzOwDXFt0EPCYmY0pHiW3vSZpRjIHSV/CfXkyn5cFcS3Qx4X79ix8fwD3l2nzXFd4Js3MfokLYH+V9OPctVfS56qFe75f0neAdSXN8GmStCiwFb5WdZEioQ2t8KztmT6XK5vPFA2v7J7BeN6f4jiCoCJZcsrO3vBnCTjzEdGyo7NDPUPXjbMakcMmCHom3S1E/Bg3Temd/n0WbtpxVHd2qgvYBjfdOb3C9feA3ZnVH2F7oCP29rXane2QtAhwK7AOnnDxBHyDvCIeUewuoKWLu1WWIHMQ7ix+FHBMciq/DH+LvxaemPEdfF27kxG4cDFC0kW4WdlieB/nNbPfFspPBf4raX3cx+Y3uM/N+QBm9o6kh4BDJb2Ja2Z+hmtmZpB8W44AzpJ0LXAF/pwPxtdylmfSzAbJ8+ZcmUzk/mZmb0oaBRyRNEdv48/BlyuM93+4T9BQZkZFWwQ4tr7pAqAPHmSgzIzuZ/jany1pJWAUrlVaFve/GW5m90j6Uxrvg7gf1fJ4AtfbG+hHEHQZc3oCzlqEYBMEPZPuFmyeyMLQ4puPFfFNwpwu2NTiH8BPJfUvvPH9KXAts74Nn5M5EzfVGWhm+bfso4CLauUBkbSgmU2vVqZRqiXINLNjJT0KHIz7nvTCo5BdBpxoZmV+ME2lxpjfTJ+T8PDEn8e1L4+RhJXEvOlzBXwzbsBbeJLR/czskVzZnYHzcA3LNFwT8WsKjvlmdrakt3Dh6ApcyzMdF5RKMbNDJX2CO+fPY2ZX4YLMean/HwJ/xsMrX1hSxShgJC4QL4MLo98zs/9UarMRzOw5YB9Jo4ED02G4ZuwuPKQ0eKS4vXDB9vP4PH2Ch70Ogm5l8uTJpblpevfuPdcKNwcccADnnntud3cjCIJGMbMuP/CNuQErFs7/IZ1fAn9Dfw3+lnQanuvjBGDhXPmz8Tey8xfq+Rz+dvTE9H1gqncb3M59Mv4m/Qx8A7cO/rb9AzxXxRYlfd4Y36i8l8qNAFYtlBmZ6tkM3yhOxf0JtsmVuST1JX+MT9f6pe97487IR+buWwbfAG6SyhxXaLs/vlmcgG8WnwC2rbPdbH5+mOZ0YqrncqBPoZ3eqcwbqZ0X8E28CuXWxPN1fIj7MxwJHE1yN6jzOfkSvvk7u87y2fxvjedpmQ4cXM/85OrYCQ9pPD09C9umekfmymTzNTB9H18yt5fU6Gt/PJv9W6mtl4Ezc9drPv91jLndawDsmJ63xQrn6+3X+LI5SHMztNYzma6vBFyH+wZNw4XJLQv1DU33rYz/Jj/ABcm9Uh/uT+v5PnAP8OWS9b47PRfvpzncI3e9X0kfDdgzN/8jC3UuDpyLCzfT0+dfgAVzZdZK9axfay3WXnttC+YuWltbbciQIdba2trpbU2aNMlaWlrKnnFraWmxSZMmdXofzLp2zPWw8847d3cXgmCuBRhjDcgV+aO7NTZF+uObqfepL+nfufgb0m3xt8QZu+LmJsU3uMNwbciOeOSl3+Naq83w/B7/Tef+IWl5M5sIIGkrPETrzfjbYnCTltGSVjOzfJSrWkkSj8U3Pusw05m87O365fjb3cxkZjd8MzmyWDD5EDyMm+UcjG/SdgSulbSNmd1YZ7tn4m/Zd8E3lX/A12OP1M48aQ7Wwtfiadxf4fRU9+9SucXwzeJb6d7p+Fv6RhNybIILnjc2cM9X8Tf5x+LCwuQ654fkY3RlGuOhaUxn4uGLX6jS5rZ4TpIn8Y02zPRBmYXkb/MILvgOwd/qL4s7lGfUlfSyypg7ugZbAv/OfgPt7FctKj6TkpbCBbb3gF/iZnwHAjdL+oGZ3Vqo62r8934qHnXsz+meRfBEpPPja3klbZ31V8AFtZPwwAobAcMlLWxm5+Maru3wvxsnMvNZfKlsQCkwwAN4BLrjgKfwFzU/wgMeZL+5J/Bw0FvSeFjvYA5nypQpHH300ey5556d7mfy7rvv0traOksI5yzk8rvvvtslWpuuHHMQBHMu3S3YzJuCBSyKJxHcDrjJPL9DzaR/ZvZcsrffl7aCzb7A7Wb2cqG9u80sc1S+IwksvwQ2NLP7Ultv4hvUrYBLU9kzgVFm9qNcn+7BN5GH4uZzGVWTJJrZS5ImAB+ZWak5U+IyYIikdVO53YHLzcx8OtowFM+jsrGZZVGaRqQN/THAjXW2e6+ZHZT+fXvyGdhb0p5Jgv4+sAGwl5ldkiu3CO5jcXraCB+Mbyi3sJmJEe9gpuN3vWRO343ctxiwuZk9kZ1IviRV5yedOxp/u/8jS9HBJP0b1xRUFGzM7HFJ04GJNdY042g8mefq5iGHM7LnDasj6WWNMR9Px9ZgXVzr2IYG+1WVGs/kIbjv1HrphQDy8MjPAcfjfld5TjGzy1K5MbgGa1HgGjO7Pp3/EnBmemnxSur/H3J1CNdwLQXsL2m4mU2X9Hi6/nId63swLiwNMLPHc+evKoz9M0lP4fMcBKUUwy13ZhuVQjh3RR+6sp166d+/f3d3IQiCdtDdgs3zuX9/hpsKDYK6k/6Ba23+KukrZvaipHVwE5x8dvOM4mboeeCrmVBT6NOyqR9fwbUwJ6htxLapuO/BRoU6qyZJrBcze1nS/cDuycfg6xXGBP7W9xbgnUIfRwCnSOpt7hdSi5sL35/Go159EX/zvxG+TlcVyl2O5+dYD7gpfRYTI34g6SY63z9ofH6Dn6g5P7gJ0zrASZYLeWxmD0sa3+Q+bg78syDUtKGB5x/Kx9zRNVgKn5+O9KsjbIT3f0Z+GDP7VNJVwFElz/StuXKt6Tf3uJntmCuT/22/gpuX3lOlDx8n7VojbA48WhBqKjEB17bNgjwq4j4Ayy3XqKIzmFPo7iSVs0sfuoPjjz++u7sQBEE76G7BZlvcvOo94BVrm/ujnqR/4Db4b+FamsOA/XD/j5tK2ivmmfgIt9+fgZl9lDQiWRtZAr6L0lHk1cL3WkkSG+Ey3H9hXuARM6ukNVgCDyzw0wrXv4C/Va9FrWSRfYHJNqtj+lu56+C+MWW5SsqSJVYjM/FbnuqmYHneLDlXz/wsjG/SKyV0bCZfoHaiynqffygfc0fXYCHKTSQb6VdH6Iv7uxR5C9estND2mS77bZedg5n9fB6fuw9x07rX8YAGO+Cmcevgf0uWaqDfX8A1vvUwDX/uZsHM/kSKijhgwICOREIMejBF87DOIDM5684+1NOPrmb48OHsvffe3d2NIAgapLsFm2fyb2QzVH/SP8zsY0nDgQMk/QG38z/NZmZL7yjZG+gjgDtLrn9Ucq5Z/B03g/sF8Ksq5SbhJjQnV7jeaHb1SkwG+kpawMzy414y1w/wzWKlxIiNMBL38dma+sPilm0C65mfT/BNbaV+N2pGV42JFMIh52nk+U+UjbmjazCJQhjtBvv1IYUkmpIaMdSfzMznKs+S+HibEV3uG7gAOMMUFWb4WmEpr0+J6Wc1qq5tgb7MTFwaBLNQyTysMyiagtUyUZvTufvuu0OwCYIeSHcLNpWoN+lfxgW44HF1urcs7Gt7eQGPrrSKmZ3UpDqnU+FNbR4zmyLpRNy07q9Vit6Gmx49a2bTOtpuFUbhDug/xs0GM3bFBbzM/+BB4DeSls0CKyQ/nK0baczM3pB0CR5O90prG+6ZVO82mQ9FFeqanxSmeQdJQ3M+Nt/CI2PVEmwamdvbge0kfcnMyrQtjT7/ZXR0DZ7HfUXa269XmDX55A9KylWat1HAIEn9zGw8gKR58aAPj5vZe9U6Xye90ueM8STn/x8VymWaq3rW93bg95JWN7NampssiEQQtKFPnz4MGTKkS5zoe/fuTUtLS6m2pKWlhd69e3d6H6BrxxwEwZzLbCnYWJ1J/3Ll/5t8B7bFgw+8VlaunX0xSQcCN0haANeiTMTffK8PvGpmjSa8fA7XfOwPjAE+NLOnK7R/TB31HYVvkO6VdDYuiLXgG8sVzOxnjbZbgVvxSFXnS1ocD4f8fTw89Ym5CFpn4JGp8okRf4Ob3jTKINwP4S5J5+Nas/fxTfeuwADg+hp11Ds/Q/CN6fWSLsAjdh3NTFO7IifLcy8tgm+Ol5G0Ix4xa2K2IS9hCB6c4gFJJ+Chvc8F+prZF9Pz/xxwkqTP43Ne8fmvQM01kEe52wPYH0+SuWAa66NpDD+W5475DBr+Xf4V+LOkM/BIe6tTLgBVeibPSOXvkDQENzs7AH8WJkq6xMzy9R2VF0jr5IFU7zmpjUXwqIgT8VwzSLoG2BTXEO2UHP4/AMbhGp+N8fXLOAOPKninpONwP7XFcGFpv0wgk9QnjeXUBvobzCX06dOHoUOHdklbffv2ZezYsd2ex6YrxxwEwZzLbCnYJOpK+pfjalywuaDZHTGzWyRthDtND8ff3L6Fayj+1o4qh+PRkE7As5q/gmsF2tu/VyUNwKOjnYBvyCfhPhaX5op2qN0UyWmrdP/huD/BeDyC1bBcuYmSvoOb0V2a+nI+/rw1lHzVzN5Pde2DCzJ74z4S/8XzCh1aRx11zY+Z3Slp11TuH/iGdRD+3OXZNX1+lPozGfgePg9X4T4gl1JBw2Jm45Mm6Dg8hPCieFCGvFnSjriPxUGpjVrPf7GNetbgVNzEcVga88f4ZnvbNPbPAxvi2pOMen+Xl+JO+j/H/d9G5+rNU/pMJm3dBrj54Hm40PUELhD+j1l9xo7EI9zVLdiY2QR5gtfT8JDPb+Dz1RcXPsFDTD+La25bcMF6Pvx5/L/Uj//m6pwi6dv42v4W/438Dw+9nTff3Cp9v67e/gZBR6mWiHNuNDerxjnnnNPdXQiCoB3Io/j2fCRdAXwbfwPfyFvbIKgbSZvgAtWZZnZw4Vp/4F/Ak2a2SYP1jgQws4HN6WnN9hbGA2ecWxxHuj4Pvhkfa2azhaG5pAVLAleQNFJD8ES9zfKty9e/G55gc9tc6OiTcaFwjSpBParVeSuu1du9VtkBAwbYmDFjGm0i6AFMmTKFYcOGMWjQoE43wZo8eTIrrrgira3FmBpucjZ27Nguy1fTVWPuCI899hhrrbVWd3cjCOZKJP3LzAa05955mt2ZrkbSupL2w99wnx5CTdDJ/B+uoTmieMHMxuGJHgcmjQySTNJxkn4laZyk9ySNkrRKtUYkDUz3DsydGynpPkmbSXpM0lRJz0japuT+1SXdKKlV0jRJ90vKG9Evgjv3l5rZpd/RYGBXSUvXUV/W7saS7pD0jqQPJD0p6ee565YEkfw9/dL5PXPnLpH0uqT1JD0gaRop54yk8cn3Ki/UgIdntnQsKGlCMoUr9nHPVGblsrGXzMXluEbqXEl9JK2Fa+eGZkKNpF+ksX4oaaKki1QIliDp15L+LelDPAT5GkljFMylZEkpp0yZ0ult5RNxjhs3bsYxevRoWltbSzU5nUFXjrkjnHpqWIkGQU9kdjZFq5cHcZ+LS3E/haAHkDQC1QRrM7NPu6o/9SDPgbMxcEMhNHmeG3HzqU2Bh9O53XBTpl/jwsQpuM/Wyu3QMHwZN5c6ETddOxS4JtWVJbJcCzf9ehyPqDcVD4N+p6T1zexfyVRtHHCYpHeAW/I5bwDM7H5JBwPfxU3PKtaX2v0Rnlj3ftz8bCKwCh6uuz18HvfVORX4HeU+WsOBZXCTtw3wKHqkxJoX4wlmjyis1754wt3nZ6mtMvvhJmlnAGvgZnGnAkg6CV+HP+J+TEvjpmirpvn5NJk4noaby32Im6i1MjNEejAXE4k4gyAImkOPF2zMrKFYrMFsw59xx/VKjAIGdk1X6ibLdzO+Spns2rK5cx8DPzCzj2FG+OCr8fwvDzTYh8WAjbIksJIew0M7/wT3UwEXnF4FNs3CcksagfsUHQlsk8rtggsO56Uyb+AR5C4ws0cAzOx8SXfVqk8+qDPxDf8mOc1pWYj0evkcsJuZ3VCpgJm9LinbKT1cEBTPwwWOH+OmZEhaDffp2bmRjqQAJYfhERc/BtZOAks/XJg5Oh/oQ9J/8KAPW+PBLdYDnqozGEgk6JzLmB3yt8wOfQiCIOgoPV6wCXosQ4Gzq1xvRjjfZtNeIfqOTKhJZJHolqNxwebFTKgBMLO3Jb2d6sp8ZzbGhZzPkpYp405mBj7AzB6StBKwCZ50cz1c2NxT0l5mdlkD9a2Ea2ZOaqI56CfUGSyhDDMblwSwfUmCTfr3BDw4RKP1DZd0DHBfLprgd3HN4xWFuXkYDyywES7YPIrn2joLuAF4wMymVmkrEnTORUQiztmPyGETBD2TEGyCbiGFQh7fzd1olIm4OVS/KmWya/mQ48VkkpkD/ELt6ENZYsrpubr64rlmjkzHLKhtCOfpuJbmtnTtq8A9wOnAZfXWh2uzAJppZ/J2E8wRzwVukrQqHqJ5N+D8QoLZRviIttHNlkifsyQaTmTzchm+Rj/Hw1Z/LOkW4JAqYcGDuYRIxDn7semmm3Z3F4IgaAch2ARBnZjZJ5LuBb4raaEKfjY/TJ93d2HX8kzBQx6fg2+mZ6GaRsXM/iPpb8DBkpaotz5JWajqWrl2puN+Rnm+UFYQaIam4hZcgN4XeBIPrf2nJtSbMSl9bo77zJReNw8/eQFwgTwJ6Oa4z83fgG81sT9BDyIScc6+7LLLLlx55ZXd3Y0gCBokBJsgaIxTcBOsiyXNizusL4abzj0HrAnca2YPV66i3fTBk1nOU0k4MbMPJI3GE2I+VqmcpPnxhKD/K7m8Mq6Zeic54Wf1bQi8YmZlZlz/wQWIvSX9yUriyEtaB/dPOUTSobhv0C14wsuOkGnAFqZgwpiErgvwnDIbAnea2UsdbC/PHbjgt5yZ3VHPDWbWCvxNHjlv3yb2JehhRCLOIAiC5hKCTRA0gJndJc9BshPuq3EF8DIeKWtn3NyoVLPRBPrgQQnmoXoiykOAe4ERki7CBYjFgLWAec3st3jEsVcl/R0XLl7HNSc74clG/5DLGZPV901gjKRJxfrMzCQNwn1X7pZ0Pj4/X8PNtcYCF6d2lgMuwqMZ7gF09FXxc+nz0LQ2n5pZPvHLRbhP1+rA9h1sqw1m9pI8p83ZyV9pFB71bFnc/2a4md0j6U+40PUg8DaeCHV34PZm9icIqtG3b98uE2CCIAi6gxBsgqABJG2E5yC5AdcQ7An0wqOGnQNchTu9dxtm9pik9fA8NH/EhZgJwGPA+anYu8Dv8c33Kbjw8SEe0nhfPPpXvr51gDHAOvhmvFgfZnaDpO/ivjgXpdMv4ZHXLsSd6HfHc/38BI96dicwAA/ZPG87h/xP3JfmAOAoPMjDjEAPZjZB0ijgG3g47qZiZr+T9G/gwHQY7mN1F5AFergf2Asf/+eBN4DLgSGqkHg0CDrK5MmTu11D01NZc801u7sLQRC0BzOLI4446jxw7cZEYKE6y38T37y/j5tc3QV8s1DmElyTsSaef2YqviHeL1dmKL5hbnOka/3S9wPwJJZv4BqdFmBx3LfjP6ne14ArgaVL+ro6cB3uFzINz71zRLo2vqT9S+qcg/NwE7QvVbj+k1Tfjrlz4/GN/07Av9PcjQE2KLl/4zSv76VyI4BVc9db0rVjc+cOw03YFi/UJVwDd1XuXC88N9E4PHDAOFxonCdXZiE8x80zaa3fAm4CVi7Uv2ca60Z4yO8pwBPV5m/ttde2oOfS2tpqQ4YMsdbW1i5td9KkSdbS0jLL3wzAWlpabNKkSV3an+6ahyAIeh7AGGvnPq1agsQgCHIkn5qBwO1WOUFnvvxquGlSC76h/SludjVK0uqF4r1xgeNy4Ed4eODzJG2Srg9nphZkAzw083qFOgbjJk77ANviGpi+6fMIXNP0G+ArwP2SZkRlk/RN3Ezqy8DBwFZ4ZLQs/uu2+GZ9RK7tY2vNQeI7+B+pNytcvxkXxIphiDbE89AcCeyIa3T+KalPrt9b4ULN+3jEs13wAAGjJa0uaQM8WMA8tE3g++fU5l6FNjcH+uPCYJaUdQSwN56n53v4WhyJa7oyFkztHofP3f64sPOQpCVLxnwFLiDtgPv/BHMoU6ZM4eijj2bKlCld2u67775La2sro0ePZty4cTOO0aNH09raWqrJ6Uy6ax7ayymnnFK7UBAEsx1hihYE9bMYbn72Sp3lj8K1At8xsykAku7AtRFDgO1yZRcFDjCze1K5e/FN9s7APVY9EWXG/4Bt09uOjBeAX2dfknB2P2469z1cQwNwKq6pWddm5leZEdnNzB6XNB2YaGYP1Tn+jGWBf1W6aB7wYAJtk5qCC3trmDvbI+ktXOD7Pi4Eggsbo8zsR7kx3oNrXU7BTe1eBfbIC1ZmNjlFf9tH0um5NvfD5+y+JNTsjAuSG5vZvanMXSnJ6hBJJ5vZ22b2Di78ZH2YFxeI/pfqOKMwtmvM7P8qzUkw51EMs9xV7VUK49xd/ekpPP74493dhSAI2kEINkHQeWwE/DMTagDM7F1JN+IZ6fNMzYSaVG66pBdJiTfr5PqCUAOApP3xDfuXgUVyl1ZK13sB3wZOsSpJIzuZsuSnD2ZCTSKf2BRJX8HHdEIhOeZUXPu0tJlVS6r6EB684OOSa9m5G3BB9oFCG7fj2pl1SX47kn6Ca5hWwv1oMlYqqf+6knMzkLQPrnljueUaeQSC2ZXZLSnl7NafIAiCZhCCTRDUT+Z7snyd5fviEcmKvIWbp+Upy4GST7xZD7O0JekgPIDA6bgZWitulvVQru6WdK6zXqm+TpWkppIWwbVhrxUutUlGmoQ9mNnvLDnmRcw008vzao1+XQEchK/H4XjyzL1wjVBmp3MSvt5lwg+kHDyStsZz0lwKHI37YX2G+2SVrWElszwAzOxPpHw7AwYMaEY+n6CbGT16NMsss0ztgk3i9ddfryq8zG79CYIgaAYh2ARBnZgn6ByJJ+isJ5LVZKDMv2JJCpv2JlG2Ad4JuMvMDs1OSOpfKNOKb8JrJddsL3cBP5f0pQp+NlvhglWjSU2z5JhH4AEainxU7WYze0/Sabg/zZu4QPN3M5vRj5R4dBwe4KCM8elzJ2Csme2Zu3d+XLgtbb5a34I5j0omYZ1N0QSslola4ERyziDomYRgEwSNcRIwEvff+FXxYhIaFjWzp/DAAVtJWtTM3kvXF8XN0Ea2o+2KiSir0IuZ2oeMNg7zZjZV0n3AbpKOMbNpVdpfuN7O5jgztXmWpJ9YLmmopL7ACXiem7LEn9V4ARcsVjGzk9rRL/Dw3KfiPjvLkQtfnbgNz33zvpk9X6WeXswa5nt32h/COphD6NOnD0OGDKFPnz5d2m7v3r1paWkp1ZK0tLTQu3dH00c1RnfNQ3u5++672XTTYjyTIAhmd0KwCYIGMLN7JR0CnC7pa8B9eLjh1fEEmvMAT0o6Azge+AHubH4y/pb+cHwTfEw7ms8SUZ4l6WPgT2b2aI17bgMOl/Q74BE88tgOJeUOwwWxB5MW43VgBdx5/6Bc+xumZJMvAleb2fhanTazf0t6DxcQPk3mZGV8RLmvTaV6TdKBwA2SFgD+jpuAfRFYH3jVzE6vUcc0SZfgkeCeNrMHCkWuwIWyu9K8PAksgPv2/BDYJvkl3QZsk9b9n8DauOA7pd7xBHMmffr0YejQoV3ebt++fRk7duxsk8emu+ahvQwfPjwEmyDogYRgEwQNYmbDJD2Chw8eigssU/HIX/fhoX/Pw6NhDcQFnEvxTftDeIStJ9vRdJaIck9cONqb2oLAMbjAdTDu6zEK2AKPGpYf06OSvp3Kn5XG8Apwca7YEXiizazdVVJf6uG7wFq4FmN13Ln+kzSmS4C366ynDWZ2S0qaOhgPw7ww7jPzEO7zUg9X4/NzQUn9H0vaAg/JvA8eCvoDPPHozcw0d7sQj+r2MzzB6aO4Zq5qkIC5jUgY2bX07ds35jUIgrkKlQRRCoKgBmkzPRI428zKTNK+DCySTNKa3fZQPFz0/BXCPufLzg98UhYtrQPtjwfuM7PdOlDHSGA+M9ugWf3qQF+Ox0NiL2VmXZrcQ66+mt/MKvoDDRgwwMaMGdOU9qZMmcKwYcMYNGhQl5sETZ48mRVXXJHW1lnjZLS0tDB27Nhu2YR355wEsy+77LJL+NkEQTch6V9mNqA990aCziBoH7/FAwCU5iIxs5fyQo2kb0q6U9L7kj6QdFdKikmuzCWSXpe0pqTRkqZKelHSfrkyQ3GhBuBjSSbJ0rV+6fsBkv4g6Q3cL6aPpMUlXSDpP6ne1yRdKWmWgAEpseV1kiZJmibpBUlHpGvj8Shhu2ZtJ1OuDiHpGUnDc98/L+nTXO6e7Pz9kv6e+95b0tmS3pA0PfX1YFWxd5O0oKQJki6XtBMu1PwpheLeM41p5Vz5jdN6vZfWboSkVQt1bi7pFklvpvl9RtKh8nw2+XLjU7s/k/Q8rvHZqr3z1ijdmSRxdksYmdHTEkcGXcNhhx3W3V0IgqAdhClaEDRI2qwOxPPGfFhH+dVwE7DncNMtwwWjUZLWLZil9cYd2YfhZmF7AedJeiHluRkOLIOHJt4A+LSkycG4KdQ+uPP6h7hj/Ie4OdkEYCk858r9klbOxpGErZG4M//BuK/NV4DVUt3b4iGMn8TN8OYFJqhtjpc8n9apLbob90fKGIgLZUtL+qqZ/UceFnod4PLU13lwc7C18GSoT+NCwunA4sDvyhpKYaMvTuPfHk+kmQmL++IJP59PbWyF57K5Gcg0VIcDoyWtZmZZiOoV8OhvZ+HzPACfn8Xxtc6zCbAGHhb6bWZGVusyuiNZ4uyWMLK72w1mb/r3LwaPDIKgR2BmccQRRwMH7pxuwIl1lr8GdyLvkzvXG9f4/CN37pJU7ya5cwviDvF/yp0bmsrNV2inXzr/GMnMtEqf5sV9QgzYNnf+XjyfTK8q947HhYusvWrHwAp1jMTN2bLv26byy6fvw/DEly8C+6ZzW6YyK6fvP0jf9yzUPRwXiharMob+uFC4e+7caqm+nXLnxuLhsvP39k5rMqxC3cJfGg0m5Q0qzN1UYMka67MPMAYYs9xyy1mzGDduXK316vRj3Lhxs12fyvoVzN3svPPO3d2FIJhrAcZYO/doobEJgs5nI+CfZjYlO2Fu9nQj7mCeZ6q5ZiYrN13Si7jGpV6uT38Y2iBpf2A/PKLXIrlLK6XrvYBvA6eYR/qqxRu4BqUaL9TVY9dofYZHbbs4ff4Zzy+zKe7Yvynwps0Mu7xRuueqQl2X4xqt9YCbyhozs3GSRuAamr+k0/vi2qx/AEj6Cj5XJxQ0UlOBB1P7pLJfwgXOLXFtWL78EnhAg4yHzCz/vax/nZqgs6uTM8LslzAyIxJHBkEQzDmEYBMEjTMJmIb7mtRDX8ozzb8FtBTOzepZ7dqHsuz1lZilLUkHAX/EzbR+k9qZB48eltXdks7VZZtj7vDeFK92M5ss6UlgE0k3AasC9+BzdGYqtkk6l9EXmGyzJkp9K3e9GucCNyV/mXG4qdn5NtORf4n0eVE6irwKM0zibsQFmqHA8/jzsQ2utSmuXdmz0KV0Z3LGSBgZBEEQdBYh2ARBg5jZJymq13clLViysS4yGViy5PyS6VqzKXvDvxNuUnVodkKeTDRPK64BmSWgQBdxD7AjLsBMAp7ChYAlUijqNWkbknky0FfSAtY2qlg215NqtHcLbhq2L+4ztChJS1K4/wjgzpL7sza/jPvU7G5ml2cXJRW1cRlN18DUS3cmSZzdEkZm9LTEkUHXEDlsgqBnEoJNELSPk3A/kVPwRIxtSELDouaR0UYBW0la1MzeS9cXxc3QRraj7UyQWhh4r857egHFsFN75b+Y2VRJ9wG7STrGzKZVaX/hejvbAPcAh+CCxshkTve2pGdxR/t58SADGaNw7dOP8USaGbviQsdD1Rozs88kXYA7928I3GlmL+WKvIALPquY2UlVquqVPj/OTqQw27tWa7876M4kibNbwsiMnpY4Muga9t577+7uQhAE7SAEmyBoB2Z2r6RDgNMlfQ13/H8VN+f6Dp7Echdc63As7uh+l6ST8Tf2h+Mb4mPa0fxz6fNQSbfikcdqmYTdBhwu6XfAI7i/yg4l5Q7DBYYHJZ2Gm6WtAKxhZgfl2t9Q0g9ws6+JZja+HeMoci/u0P8d4MDc+XuAXwKvmlk+seiteELU8yUtDjwLfB+f+xPNbGIdbV6Em4+tjkdIm4GZmaQDgRskLQD8HQ8a8EVg/dSf04F/48lMj5f0KS7gHNzAuOcaImFk0FMYPHgwxx9/fHd3IwiCBok8NkHQTsxsGB5yeQpwKq5NuAT4Gq51uCmVewoPX/wucCnurP4+sLG1DfVcL//E/UMOwJ3YH63jnmNwM66DgevwCGBblIzpUTyAwGt46OJbcK1I3jHiCFyb8ffU9tB2jGEWzJNj/it9zWtmsn/fUyj/GR7e+VJcULw5fT8E922pp80JuCD3Ju4nU7x+Cx4kYBE82toI4A+4uduDqcxHuD/NW8BlwDm4kFZNyxMEwWzMuHHjursLQRC0A5UETwqCIJgrkNSCa9qGmdmR3d2fMgYMGGBjxjQlRkMQBHWyyy67cOWVV3Z3N4JgrkTSv8xsQHvuDVO0IAjmOpLp2krAr3HN9bnd26MgCGYnIphEEPRMQrAJgqDDFPK8lPFpWW6dbmQrPF/Oq8AeZtbtIZiDIJh9OPfceNcRBD2R8LEJgqBDSOqHO8xXOzburv6VYWaXmJnMbHkzu6a7+xMEwezFtdde291dCIKgHYTGJgiCjvIGsE6NMi90RUeCIAiawbXXXsv2229fu2AQBLMVIdgEQdAhUlSw8G4PgiAIgqBbCVO0IAiCIAiCIAh6PCHYBEEQBEEQ5IjknEHQMwnBJgiCIAiCIAiCHk8INkEQBEEQBDkGDx7c3V0IgqAdhGATBEEQBEEQBEGPJwSbIAiCIAiCIAh6PCHYBEEQBEEQ5IgcNkHQM5GZdXcfgiAIggpImgC8UnJpMWBiF3enK4hx9SxiXD2HOXFMEOPqadQzruXNbPH2VB6CTRAEQQ9E0hgzG9Dd/Wg2Ma6eRYyr5zAnjgliXD2Nzh5XmKIFQRAEQRAEQdDjCcEmCIIgCIIgCIIeTwg2QRAEPZM/dXcHOokYV88ixtVzmBPHBDGunkanjit8bIIgCIIgCIIg6PGExiYIgiAIgiAIgh5PCDZBEASzGZIOkXSTpDclmaShDd6/jaTHJX0o6RVJv5c0b0m5DSQ9IGmapLcknS5p4aYNpG1b80g6QtL41K8nJdVMFiKpX5qDSsdOubJDK5S5vjPG1JFxpXsvqdDfYSVlu2ytUnvtXa/eko5KfZ0kaUr69zYlZTttvSQtK+kaSe9IelfSPyQtV+e9C0k6Jf3+pkl6UNJGJeXavfbtpb3jkjRA0p8kPS9pqqRXJV0hqX9J2fEV1mWbThkUHV6vSn8b1iiU69L16sBaVfpdmKQPC2W7Y62WkXRW+l1MTe31q/PeTv9tzdfgeIIgCILO5xfAu8D1wH6N3ChpC+Ba4CLgEGBN4ARgUeDwXLnVgDuAEcAPgP7AKcDSwI4dHUAJxwKHAYOBfwE7AVdL+oGZ3VLlvjeB9UrOHwdsANxecm0D4NPc98nt6nF9tHdcGROAHxbOvZn/0g1rBe0f13LAAcDFqY7PgJ2B6yT90szOKbmnqeslqRdwNzAd2AMw/Hm5R9JqZvZBjSouArYCfgO8DBwIjJC0npk9kSvX0bVviA6OaydgFeCPwLP4s3MkMEbSGmb2WqH8CGBo4dwLHR5ECU1YL4BLgAsK5/5T+N5l69XBMQ0HbiucWySdu7GkfJetVWJF4Cf4HI4GNm/g3s7/bZlZHHHEEUccs9EBzJM+58P/QxzawL2PA6MK544CPgKWzJ27DngRmD937qepvbWaPJ4l8P/gjy6cvwt4qh319cIFv6sL54em/s/XRevUoXHhm7HX6yjXZWvV0XHhG7BeJefvAl7tivUCfo0LSivmzvUHPgEOqXHv6qlPe+XOzYdvFG9s1tp3w7gWLzm3PC54HlM4Px64vDPG0OxxpbIGHFejTJeuV0fHVFLf7mmcW3XnWqU258n9e+/Ur3513Nclv60wRQuCIJjNMLPP2nOfpGWBNYDLC5f+AswPfC+Vmx/YEvi7mX2cK/d3XAD6UXvar8IWwAIl/boc+EaZOUwNtsM1UJc2oW8dodnjmoVuWCvowLjM7AMzm1pyaQywVPO6WJUfAg+Z2dhcv8YB91N7vn4IfAz8LXfvJ8BfgS0kLZhOd/raV+hbu8ZlZhNKzr2CawyXbnI/G6Uj61UvXb1ezR7THsD/cO1Mt9Le/5/oot9WCDZBEARzDqukz2fyJ9N/qFOBr6dTXwYWKin3IfBSrlwz+zUdGFs4/2z6bLS9PYC3mdVcI+M1SZ/K/YtOVuf5ojRjXEtImijpE0n/kXS42vpDdfVaQfPXC2Aj4PkK15q9XqtQmK/Es9Tu+yrAuBLh7Fl8s7Virlyz56gWHRnXLEj6Gv52/N8ll7dO/hPTJT3UmT4bNGdc+6e+TpV0t6QNS9royvVq2lpJWgbYBLgiCQJFunKtOkKX/LbCxyYIgmDOoW/6bC251pq7Xq3c5Nz1ZvZriiV7gkJb+f7URNLSwKbAmSX/yY8Ffoub4xlu+30wsBbw3Xb0uxYdHdcTuP34s7jwsi1wIvAV3MQjX0dXrVXWZlPWC0DSPsC6wG6FS521Xn2pPF8tHbg3u559Nm2O6qQj42qDpPmA83GNzUWFyzcBjwLjgC8Cv8R9pHY3s+Jb9GbQ0XFdDvwTeAM3r/sNcLek75rZyFwbXbleTVsr3AxtHso11F29Vh2hS35bIdgEQRB0IpI2wx2/azHKzAZ2tLn0WZagTO0oV95I42NSe9sqoeJ/8iX/kd8h6XVgmKTNzOzOahV39bjMbFjh1C2S3gcGSTrZzF6kg2sF3btekgbiDut/MbMr8tc6ul41aG//6x17M5/pRmhWm2cD6+M+G202m2Z2UJvKpeuAh3Chu7M2yx35He2e+zpa0g24tiQLMJLV1dXr1az2fgo8bmZPzdJA96xVe+mS31YINkEQBJ3LA8DX6ihX5pfQKNXeaPXJXa9WroWZKv9KNDqmyUCLJBXewrXkrtfLT4EnzOzJOstfBQwD1gFqbZS7c1wZVwGDgAF4wICOrhV007gkrYNHcbob+Hk999DYelUir53M00L5G+M8k/HIbmX3Ztezz2avfS06Mq4ZSDoR2AfYw8zKogq2wcw+lXQ1cLKkL5nZm7XuaZCmjCvDzN6TdDNtn7muXq9mrdU3gZXxvwk16YK16ghd8tsKwSYIgqATSfbElXwLmk22yV0FeDA7mXIM9AKeS6dewm2YV8nfLGkhYAXg6mqNtGNMzwIL4v4iebvpzFb6uVnuKCFtlL+Gmys1StkbwLYFumlcBYoamg6tFXTPuCR9A3d0fgLYvhD4oB5qrlcVnqUwX4mvU7vvzwLbSupV8AX4Oh6sYWyuXLPXvhYdGRcAkgbj5n+/MrO/NNB2Nc1hR+nwuEoovvXv6vVq1pj2wCOpXdnAPZ25Vh2hS35bETwgCIJgDsHMXgWeBHYtXNoNj0Zzayr3Ee54/5Nka5+xA/4fSlmuhI5wG/4fV1m/nknBDeqhPf/JZ20+3MA99dKsceXZBd+QPArdslbQwXFJ+gpu+vYy8AMzm9ZA281YrxuBdSWtkOtTP+Db1J6vG/EIgj/O3Tsfni/odjObnk53xtrXoiPjQtKvcPOswWZ2Vr2NpvH/GA/X/Vajna6DDo2riKTeeK6U/DPU1evV4TFJWgDP33JLWVS7Cvd09lp1hK75bdWKBx1HHHHEEUfXHrgZ0g54EjTDQ/vukI5euXJ3AWML934fz01xATAQ1258CJxSKLcGMA34B/Ad3GxjMoXcME0c00mpH4ekfp2X+rl1odwsY0rn5wcmkst3UFLm8TTe7+OhrU8nCXSduFbtGhfu5Hwvnsxyc2Br4M/p3vO6c606OK4l8Nwak/HN5bqFY8HOXi88l85Y4Gk8tO4PcYH/ZeBzhTX4BDiqcP9fcXOhvdN8X5PmYq32zFET16Td48I3yJ/hLzeKa/L1XLmd0/h/ikfi2glPwmjATrPhuA4DLsRfCAzEX348jW+MN+yu9eroM5iubZfmfbsKbXT5WuXazv4/Oi+1t3/6vnF3/7Y6bdBxxBFHHHG078ATN1qFo1+u3EhgfMn926X/RKcDr+IJOuctKbcRbrL2IZ4jYRglyRWbNKZ5gd8Dr6R+PQXsUFKu0pi2TePfvkobf8VNt6amNp7Ds6sv2KxxNGtcuP399em+D3HB5TE8qtE8Jfd32Vp1cFwDqzy7xee309YLt+W/Fk/k+l6a636FMv0oSYALLIwLWW+l+X4YGNjeOWryurRrXFT/mzIyV25d3Cfqf7iQ+Q7u67TFbDqurfHcMBNTfyfhmoFvdvd6deQZTNduSONZoEL93bJWqe2qz1J3/raUKgiCIAiCIAiCIOixhI9NEARBEARBEAQ9nhBsgiAIgiAIgiDo8YRgEwRBEARBEARBjycEmyAIgiAIgiAIejwh2ARBEARBEARB0OMJwSYIgiAIgiAIgh5PCDZBEARBEMxA0lBJnZ4LQtLSkj6QNCB37hJJr1cov5kkkzSw2NfcMV3Sc5J+I2mewv0HS3qqeL5GH9eWNFXS0rlz80o6UtK41N6LkgaV3FvsW3ZcXyi3pqRHJL0n6U5JyxeuzyfpSUm/qbff6b7lJJ2d+vehpPclPSppsKTP58qZpONy37eV9JakzzXSXhDMDoRgEwRBEARBd3AscI+ZjWlCXRsA6+GJXJ8B/gAcXChzPrAEnp2+Xk4B/mxm/82dOxdPHngR8APgauBUSb+v0bfs+L/sgqT58OzrL+GJdecFLi3cf1A6f0a9nZa0EZ7UcHPgj8D3Uv03AQcCQ6vcfj2eQLEhQSoIZgfm6+4OBEEQBEEwdyHpi8BuuCDSDB42s09S3bcBqwG/AE7LCpjZNEmXAYcBF9fRx7WATXDBIju3HLA3cKyZZVqOOyT1BgZLOtfMJlfqWwkrASsA65nZ25LeAx6Q1MvMpkpaChdCtq5SR7HfLbiw9G9gMzP7IHf5dkmnAetXut/MTNKfgGMlnWhmH9bTbhDMDoTGJgiCIAiCqkjqncya3kjmVy8k0y4Vyq0laXQyfXpN0u8kHV1i2rYn8B4wotl9NbPPgCeB5Uou/xX4uqSKG/scvwCeMrNnc+e+ie+dbi2UvQ1YCNeMNMIC6XNa+vwAELBg+n46cJ2Z3dtAnb8AFgcOKgg1AJjZB2Z2R406/g70wbU8QdBjCMEmCIIgCIKKJJ+Um4G9cA3I1vhG/nTg+Fy5xYC7gL7AT3FNxxa4EFNkS+DBSlqI5FfS5qCxPUs/3LyryBPAu6n9WmwJjC6c+zR9flQ4Pz19rlpSz2uSPpX0iqSTJS2cu/YCMAUYlDQtvwb+Y2atkr6Dm5I1ahK2GfBWR0z8zGwirvGpZ56CYLYhTNGCIAiCIKjG93E/kb3M7JJ07nZJiwCHSjo9bYQPARYBtjCz1wEkjQDG5ytLWp5vUdlnZGng4wb7OG9SHrXgpmJrAzsUC5nZZ5KeAtatVlkyleuHa37yvJA+1wUez51fL332zZ0bC/w2lTNcSDkYWAv4burPVEn74aZxxwCTgG0lLQCcAxxhZhOq9bWEZSnMeTt5nBrzFASzGyHYBEEQBEFQjY2Az4CrCucvB36Ob+pvwjfBD2ZCDczwa8m0PRl9gIWBShv2t4GtSs5/E9/sl1H0A/k/M7u+QtkJwFcrXMtYKld2Bmb2nKQ7gKMlvQw8jPvhDEpFPsuVvbxQ5x0p4tswSZuZ2Z2p3N8k3YSbzr1sZh9J+h3wDnChpDWAs4FVgOeA/c3sqRr9bwYTmDkPQdAjCFO0IAiCIAiq0ReYbGbTC+ffyl0H+BIulBT5X+H7QumzWF/Gx2Y2pngA/6nSx3VxwWdb4DHgpHxY6ALTcMGqGtX6uBcuYNwGtAKXAEeka2/WqDcTDtfJnzSzqWb2fBJqlsc1Pfvj0dCuw32RlgJuB66TNH+VNl7DtU0dZRoz5yEIegQh2ARBEARBUI3JQN9kHpVnyfQ5KX2+iYdTLvLFwvesfEtzugfAv8zs0aSl2RwXOM6qkLOmLzCxRn0V+2hm/zWzgbjJ3DfweXgiXb6vzv5WyxP0R+ASM3sMWBkXUoaZ2TTcr2kFqmuc7gSWlLR2nX2pRF9mzkMQ9AhCsAmCIAiCoBqj8P3Cjwvnd8Wd6B9K3x8C1pO0TFYgOcq3MSszs4+AcfgGvemY2STcX2VVYPuSIv2Z6StTifG4eVvFPprZG2b2TCo3CHgeGFmj3l3T58NlFyVthWuejixcWiR9ZkkzRWWG44Lb2ckPqthGL0mb1egn1DdPQTBbET42QRAEQRBU41ZcE3G+pMWBZ/GAAnsDJ6bAAeDahP2BEZKOxs24DkmfRQ3FvfgGvrO4AI8m9ntJ15iZAUjqg2s7Tq12czIJe7isj5L2x4WZcbi2Zg88uMJ3UqjprNzjwGW4cGB4wICDgNvM7J6SehcCzgIONbN30ukXcNOysyRdgM/vK1QxyzOzyZK2B24EHpN0Fp60dP40nv3wPDd3VqojBXhYBzivUpkgmB0JjU0QBEEQBBVJm/WtgEuBw/HQz1vhQsvgXLmJwHdwM7DLgHPxzfN1uCN8nr8Bq0rq10l9ng4ciyfq3CZ3aStcy3RdHdX8Ddi0ROsxL+4Dcxs+xmnAumb2SKHcC8AvcSHiejx08jGF/uT5HTDOzK7MjeMjXOu0PHADHvFs+3S+IinvzerAHXgkthH4mLfBAzAMqXY/nsCzL573Jwh6DEovMYIgCIIgCJqKpHlxZ/6JZvad3Pl5gBeBi83suC7sz62pL7vXUbY38DpwQEmEszkaSecBq5rZht3dlyBohBBsgiAIgiBoCpKOxfO3vAJ8ATdX2xL4vpndWii7K26+1t/MpnZB39bA/YBWNbOxdd4zGNgRWN3mkg2TpCWBl4Etk+YnCHoM4WMTBEEQBEGzMOAoPDSxAU8B2xSFmsSVeGSxfnj45M5mSTzJaF1CTeJ03PTsS8AbndKr2Y9+uJ9PCDVBjyM0NkEQBEEQBEEQ9HgieEAQBEEQBEEQBD2eEGyCIAiCIAiCIOjxhGATBEEQBEEQBEGPJwSbIAiCIAiCIAh6PCHYBEEQBEEQBEHQ4wnBJgiCIAiCIAiCHs//A0GAP4zyKs4NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "cph.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- (We could probably get the same for logistic regression if using `statsmodels` instead of sklearn.)\n",
    "- However, in general, I would be careful with all of this.\n",
    "- Ideally we would have more statistical training when using `lifelines` - there is a lot that can go wrong.\n",
    "  - It comes with various diagnostics as well.\n",
    "- But I think it's very useful to know about survival analysis and the availability of software to deal with it.\n",
    "- Oh, and there are lots of other nice plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's look at the survival plots for the people with \n",
    "    - two-year contract (Contract_Two year = 1) and \n",
    "    - people without two-year contract (Contract_Two year = 0)\n",
    "- As expected, the former survive longer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot_partial_effects_on_outcome(\"Contract_Two year\", [0, 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now let's look at the survival plots for the people with different MonthlyCharges. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot_partial_effects_on_outcome(\"MonthlyCharges\", [10, 100, 1000, 10_000]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- That's the thing with linear models, they can't stop the growth.\n",
    "- We have a negative coefficient associated with `MonthlyCharges`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coef   -0.003185\n",
       "Name: MonthlyCharges, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph_params.loc[\"MonthlyCharges\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your monthly charges are huge, it takes this to the extreme and thinks you'll basically never churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Prediction (10 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can use survival analysis to make predictions as well.\n",
    "- Here is the expected number of months to churn for the first 5 customers in the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>StreamingMovies_No</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>PhoneService_No</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>OnlineBackup_No</th>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>DeviceProtection_No</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.154900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>1404</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.383246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>5515</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.514920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7017</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.471584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen  MonthlyCharges  StreamingMovies_No  \\\n",
       "941             0.0       -1.154900                 1.0   \n",
       "1404            0.0       -1.383246                 0.0   \n",
       "5515            0.0       -1.514920                 0.0   \n",
       "3684            0.0        0.351852                 1.0   \n",
       "7017            0.0       -1.471584                 0.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  Partner_No  \\\n",
       "941                                   0.0                  0.0         1.0   \n",
       "1404                                  1.0                  0.0         1.0   \n",
       "5515                                  1.0                  0.0         0.0   \n",
       "3684                                  0.0                  0.0         1.0   \n",
       "7017                                  1.0                  0.0         1.0   \n",
       "\n",
       "      Partner_Yes  OnlineSecurity_No  OnlineSecurity_No internet service  \\\n",
       "941           0.0                1.0                                 0.0   \n",
       "1404          0.0                0.0                                 1.0   \n",
       "5515          1.0                0.0                                 1.0   \n",
       "3684          0.0                1.0                                 0.0   \n",
       "7017          0.0                0.0                                 1.0   \n",
       "\n",
       "      OnlineSecurity_Yes  ...  PhoneService_No  PhoneService_Yes  \\\n",
       "941                  0.0  ...              1.0               0.0   \n",
       "1404                 0.0  ...              0.0               1.0   \n",
       "5515                 0.0  ...              0.0               1.0   \n",
       "3684                 0.0  ...              0.0               1.0   \n",
       "7017                 0.0  ...              0.0               1.0   \n",
       "\n",
       "      OnlineBackup_No  OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
       "941               0.0                               0.0               1.0   \n",
       "1404              0.0                               1.0               0.0   \n",
       "5515              0.0                               1.0               0.0   \n",
       "3684              1.0                               0.0               0.0   \n",
       "7017              0.0                               1.0               0.0   \n",
       "\n",
       "      PaperlessBilling_No  PaperlessBilling_Yes  DeviceProtection_No  \\\n",
       "941                   1.0                   0.0                  1.0   \n",
       "1404                  1.0                   0.0                  0.0   \n",
       "5515                  1.0                   0.0                  0.0   \n",
       "3684                  0.0                   1.0                  1.0   \n",
       "7017                  1.0                   0.0                  0.0   \n",
       "\n",
       "      DeviceProtection_No internet service  DeviceProtection_Yes  \n",
       "941                                    0.0                   0.0  \n",
       "1404                                   1.0                   0.0  \n",
       "5515                                   1.0                   0.0  \n",
       "3684                                   0.0                   0.0  \n",
       "7017                                   1.0                   0.0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_surv.drop(columns=[\"tenure\", \"Churn\"]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>tenure</th>\n",
       "      <th>SeniorCitizen</th>\n",
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       "<p>5 rows × 45 columns</p>\n",
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      "text/plain": [
       "      Churn  tenure  SeniorCitizen  MonthlyCharges  StreamingMovies_No  \\\n",
       "941     0.0    13.0            0.0       -1.154900                 1.0   \n",
       "1404    0.0    35.0            0.0       -1.383246                 0.0   \n",
       "5515    0.0    18.0            0.0       -1.514920                 0.0   \n",
       "3684    0.0    43.0            0.0        0.351852                 1.0   \n",
       "7017    0.0    51.0            0.0       -1.471584                 0.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  Partner_No  \\\n",
       "941                                   0.0                  0.0         1.0   \n",
       "1404                                  1.0                  0.0         1.0   \n",
       "5515                                  1.0                  0.0         0.0   \n",
       "3684                                  0.0                  0.0         1.0   \n",
       "7017                                  1.0                  0.0         1.0   \n",
       "\n",
       "      Partner_Yes  OnlineSecurity_No  ...  PhoneService_No  PhoneService_Yes  \\\n",
       "941           0.0                1.0  ...              1.0               0.0   \n",
       "1404          0.0                0.0  ...              0.0               1.0   \n",
       "5515          1.0                0.0  ...              0.0               1.0   \n",
       "3684          0.0                1.0  ...              0.0               1.0   \n",
       "7017          0.0                0.0  ...              0.0               1.0   \n",
       "\n",
       "      OnlineBackup_No  OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
       "941               0.0                               0.0               1.0   \n",
       "1404              0.0                               1.0               0.0   \n",
       "5515              0.0                               1.0               0.0   \n",
       "3684              1.0                               0.0               0.0   \n",
       "7017              0.0                               1.0               0.0   \n",
       "\n",
       "      PaperlessBilling_No  PaperlessBilling_Yes  DeviceProtection_No  \\\n",
       "941                   1.0                   0.0                  1.0   \n",
       "1404                  1.0                   0.0                  0.0   \n",
       "5515                  1.0                   0.0                  0.0   \n",
       "3684                  0.0                   1.0                  1.0   \n",
       "7017                  1.0                   0.0                  0.0   \n",
       "\n",
       "      DeviceProtection_No internet service  DeviceProtection_Yes  \n",
       "941                                    0.0                   0.0  \n",
       "1404                                   1.0                   0.0  \n",
       "5515                                   1.0                   0.0  \n",
       "3684                                   0.0                   0.0  \n",
       "7017                                   1.0                   0.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_surv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "How long each non-churned customer is likely to stay according to the model assuming that they just joined right now?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "941     35.206731\n",
       "1404    69.023078\n",
       "5515    68.608555\n",
       "3684    27.565069\n",
       "7017    67.890922\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.predict_expectation(test_df_surv).head()  # assumes they just joined right now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Survival curves for first 5 customers in the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.predict_survival_function(test_df_surv[:5]).plot()\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From `predict_survival_function` documentation:\n",
    "\n",
    "> Predict the survival function for individuals, given their covariates. This assumes that the individual just entered the study (that is, we do not condition on how long they have already lived for.) \n",
    "\n",
    "So these curves are \"starting now\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- There's no probability prerequisite for this course, so this is optional material.\n",
    "- But you can do some interesting stuff here with conditional probabilities.\n",
    "- \"Given that a customer has been here 5 months, what's the outlook?\"\n",
    "  - It will be different than for a new customer. \n",
    "  - Thus, we might still want to predict for the non-churned customers in the training set!\n",
    "  - Not something we really thought about with our traditional supervised learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's get the customers who have not churned yet.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "train_df_surv_not_churned = train_df_surv[train_df_surv[\"Churn\"] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can _condition_ on the person having been around for 20 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
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       "    <tr>\n",
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       "      <th>4.0</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>68.0</th>\n",
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       "      <th>69.0</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "          6464\n",
       "0.0   1.000000\n",
       "1.0   0.996788\n",
       "2.0   0.991966\n",
       "3.0   0.989443\n",
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       "...        ...\n",
       "68.0  0.429635\n",
       "69.0  0.429635\n",
       "70.0  0.429635\n",
       "71.0  0.429635\n",
       "72.0  0.429635\n",
       "\n",
       "[73 rows x 1 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.predict_survival_function(train_df_surv_not_churned[:1], conditional_after=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "cph.predict_survival_function(train_df_surv_not_churned[:1]).plot(ax=plt.gca())\n",
    "preds = cph.predict_survival_function(\n",
    "    train_df_surv_not_churned[:1], conditional_after=20\n",
    ")\n",
    "plt.plot(preds.index[20:], preds.values[:-20])\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\")\n",
    "plt.legend([\"Starting now\", \"Given 20 more months of service\"])\n",
    "plt.ylim([0, 1])\n",
    "plt.xlim([1, 50]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Look at how the survival function (and expected lifetime) is much longer _given_ that the customer has already lasted 20 months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- How long each non-churned customer is likely to stay according to the model assuming that they have been here for the tenure time? \n",
    "- So, we can set this to their actual tenure so far to get a prediciton of what will happen going forward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.predict_survival_function(\n",
    "    train_df_surv_not_churned[:1],\n",
    "    conditional_after=train_df_surv_not_churned[:1][\"tenure\"],\n",
    ").plot()\n",
    "plt.xlabel(\"Time into the future (months)\")\n",
    "plt.ylabel(\"Survival probability\")\n",
    "plt.ylim([0, 1])\n",
    "plt.xlim([0, 20]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Another useful application: you could ask what is the [customer lifetime value](https://en.wikipedia.org/wiki/Customer_lifetime_value).\n",
    "  - Basically, how much money do you expect to make off this customer between now and when they churn?\n",
    "- With regular supervised learning, tenure was a feature and we could only predict whether or not they had churned by then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Evaluation \n",
    "\n",
    "By default score returns \"partial log likelihood\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.8641864810547806"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(train_df_surv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7277855153568582"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(test_df_surv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can look at the \"concordance index\" which is more interpretable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625886620148505"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.concordance_index_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625886620148505"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(train_df_surv, scoring_method=\"concordance_index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8546143543902771"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(test_df_surv, scoring_method=\"concordance_index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "From the documentation [here](https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html#model-selection-and-calibration-in-survival-regression):\n",
    "\n",
    "> Another censoring-sensitive measure is the concordance-index, also known as the c-index. This measure evaluates the accuracy of the ranking of predicted time. It is in fact a generalization of AUC, another common loss function, and is interpreted similarly:\n",
    "> \n",
    "> - 0.5 is the expected result from random predictions,\n",
    "> - 1.0 is perfect concordance and,\n",
    "> - 0.0 is perfect anti-concordance (multiply predictions with -1 to get 1.0)\n",
    "> \n",
    "> [Here](https://stats.stackexchange.com/a/478305/11867) is an excellent introduction & description of the c-index for new users.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph.log_likelihood_ratio_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph.check_assumptions(df_train_surv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other approaches / what did we not cover? (5 min)\n",
    "\n",
    "There are many other approaches to modelling in survival analysis:\n",
    "\n",
    "- Time-varying proportional hazards.\n",
    "  - What if some of the features change over time, e.g. plan type, number of lines, etc.\n",
    "- Approaches based on deep learning, e.g. the [pysurvival](https://square.github.io/pysurvival/) package.\n",
    "- Random survival forests.\n",
    "- And more..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "### Types of censoring\n",
    "There are also various types and sub-types of censoring we didn't cover:\n",
    "\n",
    "- What we did today is called \"right censoring\"\n",
    "- Sub-types within right censoring\n",
    "  - Did everyone join at the same time?\n",
    "  - Other there other reasons the data might be censored at random times, e.g. the person died?\n",
    "- Left censoring\n",
    "- Interval censoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Summary\n",
    "\n",
    "- Censoring and incorrect approaches to handling it\n",
    "  - Throw away people who haven't churned\n",
    "  - Assume everyone churns today\n",
    "- Predicting tenure vs. churned\n",
    "- Survival analysis encompasses both of these, and deals with censoring\n",
    "- And it can make rich and interesting predictions!\n",
    "- KM model -> doesn't look at features\n",
    "- CPH model -> like linear regression, does look at the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## True/False questions\n",
    "\n",
    "1. If all customers joined a service at the same time (hypothetically), then censoring would not be an issue.\n",
    "3. The Cox proportional hazards model (`cph` above) assumes the effect of a feature is the same for all customers and over all time.\n",
    "4. Survival analysis can be useful even without a \"deployment\" stage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## References\n",
    "\n",
    "Some people working with this same dataset:\n",
    "\n",
    "- https://medium.com/@zachary.james.angell/applying-survival-analysis-to-customer-churn-40b5a809b05a\n",
    "- https://towardsdatascience.com/churn-prediction-and-prevention-in-python-2d454e5fd9a5 (Cox)\n",
    "- https://towardsdatascience.com/survival-analysis-in-python-a-model-for-customer-churn-e737c5242822\n",
    "- https://towardsdatascience.com/survival-analysis-intuition-implementation-in-python-504fde4fcf8e\n",
    "\n",
    "lifelines documentation: \n",
    "- https://lifelines.readthedocs.io/en/latest/Survival%20analysis%20with%20lifelines.html\n",
    "- https://lifelines.readthedocs.io/en/latest/Survival%20Analysis%20intro.html#introduction-to-survival-analysis"
   ]
  }
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