lectures/21_survival-analysis.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](img/330-banner.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Lecture 21: Survival analysis \n",
    "\n",
    "UBC 2022-23\n",
    "\n",
    "Instructor: Varada Kolhatkar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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\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\"] = 12\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 November 29th 11:59pm. \n",
    "- Only one more homework left, which is going to be on communication, where you'll be writing a blog post. \n",
    "- Next lecture on Ethics is going to be a guest lecture by [Giulia Toti]( https://www.gtoti.com/), who is currently teaching [Computer and Society](https://www.cs.ubc.ca/course-section/cpsc-430-102-2022w) course and who has taught CPSC 330 before. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Learning objectives\n",
    "\n",
    "- Explain what is right-censored data. \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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Imagine that you are working for a subscription-based telecom company. \n",
    "- You want to come up with retention strategies for different customer segments. \n",
    "- So you want to model the \"time to churn\" to understand different factors affecting customer churn.   \n",
    "- Suppose you are given this dataset [Customer Churn Dataset](https://www.kaggle.com/blastchar/telco-customer-churn), which is collected at a fixed time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>"
      ],
      "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 are interested in predicting customer churn: the \"Churn\" column.  \n",
    "- How will you approach this problem with the approaches we have seen so far? \n",
    "- How about treating this as a binary classification problem where we want to predict `Churn` (yes/no) from these -other columns.\n",
    "- 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": 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": "-"
    }
   },
   "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)",
      "Cell \u001b[0;32mIn [10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpreprocessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py:657\u001b[0m, in \u001b[0;36mColumnTransformer.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[38;5;124;03m\"\"\"Fit all transformers using X.\u001b[39;00m\n\u001b[1;32m    640\u001b[0m \n\u001b[1;32m    641\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[38;5;124;03m    This estimator.\u001b[39;00m\n\u001b[1;32m    654\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    655\u001b[0m \u001b[38;5;66;03m# we use fit_transform to make sure to set sparse_output_ (for which we\u001b[39;00m\n\u001b[1;32m    656\u001b[0m \u001b[38;5;66;03m# need the transformed data) to have consistent output type in predict\u001b[39;00m\n\u001b[0;32m--> 657\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    658\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py:690\u001b[0m, in \u001b[0;36mColumnTransformer.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_column_callables(X)\n\u001b[1;32m    688\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_remainder(X)\n\u001b[0;32m--> 690\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_fit_transform_one\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    692\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result:\n\u001b[1;32m    693\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_fitted_transformers([])\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py:621\u001b[0m, in \u001b[0;36mColumnTransformer._fit_transform\u001b[0;34m(self, X, y, func, fitted, column_as_strings)\u001b[0m\n\u001b[1;32m    615\u001b[0m transformers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m    616\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter(\n\u001b[1;32m    617\u001b[0m         fitted\u001b[38;5;241m=\u001b[39mfitted, replace_strings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, column_as_strings\u001b[38;5;241m=\u001b[39mcolumn_as_strings\n\u001b[1;32m    618\u001b[0m     )\n\u001b[1;32m    619\u001b[0m )\n\u001b[1;32m    620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfitted\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m            \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_safe_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    625\u001b[0m \u001b[43m            \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    626\u001b[0m \u001b[43m            \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    627\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mColumnTransformer\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    628\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtransformers\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    629\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    630\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtransformers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    631\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    633\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(e):\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py:1043\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1034\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1035\u001b[0m     \u001b[38;5;66;03m# Only set self._iterating to True if at least a batch\u001b[39;00m\n\u001b[1;32m   1036\u001b[0m     \u001b[38;5;66;03m# was dispatched. In particular this covers the edge\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1040\u001b[0m     \u001b[38;5;66;03m# was very quick and its callback already dispatched all the\u001b[39;00m\n\u001b[1;32m   1041\u001b[0m     \u001b[38;5;66;03m# remaining jobs.\u001b[39;00m\n\u001b[1;32m   1042\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m-> 1043\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1044\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_iterator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1046\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch_one_batch(iterator):\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py:861\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    859\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    860\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 861\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    862\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py:779\u001b[0m, in \u001b[0;36mParallel._dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    777\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    778\u001b[0m     job_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs)\n\u001b[0;32m--> 779\u001b[0m     job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    780\u001b[0m     \u001b[38;5;66;03m# A job can complete so quickly than its callback is\u001b[39;00m\n\u001b[1;32m    781\u001b[0m     \u001b[38;5;66;03m# called before we get here, causing self._jobs to\u001b[39;00m\n\u001b[1;32m    782\u001b[0m     \u001b[38;5;66;03m# grow. To ensure correct results ordering, .insert is\u001b[39;00m\n\u001b[1;32m    783\u001b[0m     \u001b[38;5;66;03m# used (rather than .append) in the following line\u001b[39;00m\n\u001b[1;32m    784\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs\u001b[38;5;241m.\u001b[39minsert(job_idx, job)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/_parallel_backends.py:208\u001b[0m, in \u001b[0;36mSequentialBackend.apply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_async\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, callback\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;124;03m\"\"\"Schedule a func to be run\"\"\"\u001b[39;00m\n\u001b[0;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mImmediateResult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m callback:\n\u001b[1;32m    210\u001b[0m         callback(result)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/_parallel_backends.py:572\u001b[0m, in \u001b[0;36mImmediateResult.__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch):\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;66;03m# Don't delay the application, to avoid keeping the input\u001b[39;00m\n\u001b[1;32m    571\u001b[0m     \u001b[38;5;66;03m# arguments in memory\u001b[39;00m\n\u001b[0;32m--> 572\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults \u001b[38;5;241m=\u001b[39m \u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py:262\u001b[0m, in \u001b[0;36mBatchedCalls.__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[1;32m    261\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[0;32m--> 262\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    263\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py:262\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[1;32m    261\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[0;32m--> 262\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    263\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/fixes.py:117\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig):\n\u001b[0;32m--> 117\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/pipeline.py:870\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    868\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(transformer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 870\u001b[0m         res \u001b[38;5;241m=\u001b[39m \u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    872\u001b[0m         res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/base.py:867\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    863\u001b[0m \u001b[38;5;66;03m# non-optimized default implementation; override when a better\u001b[39;00m\n\u001b[1;32m    864\u001b[0m \u001b[38;5;66;03m# method is possible for a given clustering algorithm\u001b[39;00m\n\u001b[1;32m    865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    866\u001b[0m     \u001b[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001b[39;00m\n\u001b[0;32m--> 867\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:809\u001b[0m, in \u001b[0;36mStandardScaler.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[38;5;66;03m# Reset internal state before fitting\u001b[39;00m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[0;32m--> 809\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartial_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:844\u001b[0m, in \u001b[0;36mStandardScaler.partial_fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    812\u001b[0m \u001b[38;5;124;03m\"\"\"Online computation of mean and std on X for later scaling.\u001b[39;00m\n\u001b[1;32m    813\u001b[0m \n\u001b[1;32m    814\u001b[0m \u001b[38;5;124;03mAll of X is processed as a single batch. This is intended for cases\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    841\u001b[0m \u001b[38;5;124;03m    Fitted scaler.\u001b[39;00m\n\u001b[1;32m    842\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    843\u001b[0m first_call \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples_seen_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 844\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    846\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    847\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFLOAT_DTYPES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    848\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    849\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfirst_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    850\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    851\u001b[0m n_features \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    853\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/base.py:577\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    576\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 577\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    578\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[1;32m    579\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/validation.py:856\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, input_name)\u001b[0m\n\u001b[1;32m    854\u001b[0m         array \u001b[38;5;241m=\u001b[39m array\u001b[38;5;241m.\u001b[39mastype(dtype, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m\"\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 856\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[1;32m    858\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    859\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    860\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/generic.py:2064\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   2063\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m-> 2064\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\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)",
      "Cell \u001b[0;32mIn [12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTotalCharges\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTotalCharges\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/generic.py:5912\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5905\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   5906\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m   5907\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[1;32m   5908\u001b[0m     ]\n\u001b[1;32m   5910\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   5911\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 5912\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5913\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   5915\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/internals/managers.py:419\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mastype\u001b[39m(\u001b[38;5;28mself\u001b[39m: T, dtype, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[0;32m--> 419\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/internals/managers.py:304\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    303\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 304\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m):\n\u001b[1;32m    306\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ignore_failures:\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/internals/blocks.py:580\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    563\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m    564\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m    577\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    578\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 580\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m    583\u001b[0m newb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_block(new_values)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1292\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1289\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1292\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m   1294\u001b[0m     \u001b[38;5;66;03m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m   1295\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[1;32m   1296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1237\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1234\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m   1236\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1237\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m   1240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1181\u001b[0m, in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1177\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m   1179\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(arr\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(dtype):\n\u001b[1;32m   1180\u001b[0m     \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\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_out(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": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>TechSupport_No</th>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>SeniorCitizen</th>\n",
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       "  </thead>\n",
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       "<p>5 rows × 45 columns</p>\n",
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      ],
      "text/plain": [
       "        tenure  MonthlyCharges  TotalCharges  gender_Female  gender_Male  \\\n",
       "6464  0.707712        0.185175      0.513678            0.0          1.0   \n",
       "5707 -1.248999       -0.641538     -0.979562            1.0          0.0   \n",
       "3442 -0.148349        1.133562      0.226789            0.0          1.0   \n",
       "3932 -1.248999        0.458524     -0.950696            1.0          0.0   \n",
       "6124  0.993065       -0.183179      0.433814            1.0          0.0   \n",
       "\n",
       "      TechSupport_No  TechSupport_No internet service  TechSupport_Yes  \\\n",
       "6464             1.0                              0.0              0.0   \n",
       "5707             1.0                              0.0              0.0   \n",
       "3442             0.0                              0.0              1.0   \n",
       "3932             1.0                              0.0              0.0   \n",
       "6124             0.0                              0.0              1.0   \n",
       "\n",
       "      PaperlessBilling_No  PaperlessBilling_Yes  ...  DeviceProtection_Yes  \\\n",
       "6464                  0.0                   1.0  ...                   0.0   \n",
       "5707                  1.0                   0.0  ...                   0.0   \n",
       "3442                  0.0                   1.0  ...                   1.0   \n",
       "3932                  0.0                   1.0  ...                   1.0   \n",
       "6124                  0.0                   1.0  ...                   1.0   \n",
       "\n",
       "      OnlineSecurity_No  OnlineSecurity_No internet service  \\\n",
       "6464                0.0                                 0.0   \n",
       "5707                1.0                                 0.0   \n",
       "3442                1.0                                 0.0   \n",
       "3932                1.0                                 0.0   \n",
       "6124                0.0                                 0.0   \n",
       "\n",
       "      OnlineSecurity_Yes  Contract_Month-to-month  Contract_One year  \\\n",
       "6464                 1.0                      1.0                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                1.0   \n",
       "\n",
       "      Contract_Two year  Partner_No  Partner_Yes  SeniorCitizen  \n",
       "6464                0.0         1.0          0.0            1.0  \n",
       "5707                0.0         1.0          0.0            0.0  \n",
       "3442                0.0         1.0          0.0            0.0  \n",
       "3932                0.0         1.0          0.0            1.0  \n",
       "6124                0.0         0.0          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": "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.002 (+/- 0.001)</td>\n",
       "      <td>0.003 (+/- 0.003)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                fit_time         score_time         test_score  \\\n",
       "dummy  0.002 (+/- 0.001)  0.003 (+/- 0.003)  0.741 (+/- 0.000)   \n",
       "\n",
       "             train_score  \n",
       "dummy  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).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dummy model scores are pretty good because we have class imbalance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     3912\n",
       "Yes    1370\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = make_pipeline(preprocessor, LogisticRegression(max_iter=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.002 (+/- 0.001)</td>\n",
       "      <td>0.003 (+/- 0.003)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logistic regression</th>\n",
       "      <td>0.182 (+/- 0.038)</td>\n",
       "      <td>0.011 (+/- 0.008)</td>\n",
       "      <td>0.804 (+/- 0.013)</td>\n",
       "      <td>0.809 (+/- 0.002)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              fit_time         score_time         test_score  \\\n",
       "dummy                0.002 (+/- 0.001)  0.003 (+/- 0.003)  0.741 (+/- 0.000)   \n",
       "logistic regression  0.182 (+/- 0.038)  0.011 (+/- 0.008)  0.804 (+/- 0.013)   \n",
       "\n",
       "                           train_score  \n",
       "dummy                0.741 (+/- 0.000)  \n",
       "logistic regression  0.809 (+/- 0.002)  "
      ]
     },
     "execution_count": 29,
     "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).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3516,  396],\n",
       "       [ 637,  733]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, cross_val_predict(lr, X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Logistic regression beats the dummy model. \n",
    "- But it seems like we have many false negatives. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### RandomForestClassifier\n",
    "\n",
    "Let's try random forest model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = make_pipeline(preprocessor, RandomForestClassifier(n_estimators=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.002 (+/- 0.001)</td>\n",
       "      <td>0.003 (+/- 0.003)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logistic regression</th>\n",
       "      <td>0.182 (+/- 0.038)</td>\n",
       "      <td>0.011 (+/- 0.008)</td>\n",
       "      <td>0.804 (+/- 0.013)</td>\n",
       "      <td>0.809 (+/- 0.002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random forest</th>\n",
       "      <td>0.361 (+/- 0.032)</td>\n",
       "      <td>0.025 (+/- 0.006)</td>\n",
       "      <td>0.792 (+/- 0.012)</td>\n",
       "      <td>0.998 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              fit_time         score_time         test_score  \\\n",
       "dummy                0.002 (+/- 0.001)  0.003 (+/- 0.003)  0.741 (+/- 0.000)   \n",
       "logistic regression  0.182 (+/- 0.038)  0.011 (+/- 0.008)  0.804 (+/- 0.013)   \n",
       "random forest        0.361 (+/- 0.032)  0.025 (+/- 0.006)  0.792 (+/- 0.012)   \n",
       "\n",
       "                           train_score  \n",
       "dummy                0.741 (+/- 0.000)  \n",
       "logistic regression  0.809 (+/- 0.002)  \n",
       "random forest        0.998 (+/- 0.000)  "
      ]
     },
     "execution_count": 32,
     "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).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3525,  387],\n",
       "       [ 729,  641]])"
      ]
     },
     "execution_count": 33,
     "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": [
    "- Random forest is not improving the scores. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- We might decide to do hyperparamter optimization to further improve the score. \n",
    "- But after trying out all the usual things should we be happy with the scores?\n",
    "- Are we doing anything fundamentally wrong when we treat this problem as a binary classification? \n",
    "<br><br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the class is about what is wrong with what we just did!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Censoring and survival analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Time to event and censoring\n",
    "\n",
    "- When we treat the problem as a binary classification problem, we predict whether a customer would churn or not at a particular point in time, when the data was collected. \n",
    "- If a customer has not churned yet, wouldn't it be more useful to understand when they are likely to churn so that we can offer them promotions etc?  \n",
    "- Here we are actually interested in the time till the event of churn occurs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many situations where you want to analyze **the time until an event occurs**. For example,\n",
    "\n",
    "- the time until a customer leaves a subscription service (this dataset)\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 you wait for your turn to get a surgery"
   ]
  },
  {
   "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": {},
   "source": [
    "In our dataset there is a column called \"tenure\", which encodes this temporal aspect of the data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 34,
     "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. \n",
    "- But we only have information about this till the point we collected the data. "
   ]
  },
  {
   "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 (tenure in our case)? Take a minute to think about this.\n",
    "What could be possible scenarios for the duration column? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br><br>"
   ]
  },
  {
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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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       "      <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",
       "    </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": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 36,
     "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": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5282, 21)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1370, 21)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_churn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure', 'MonthlyCharges', 'TotalCharges']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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": 42,
   "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": 42,
     "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) from 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": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenure_lm.fit(train_df.drop(columns=[\"tenure\"]), train_df[\"tenure\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.400047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.220392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.332746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.825470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59.885968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.075453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.731498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.407862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>38.425365</td>\n",
       "    </tr>\n",
       "    <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": 45,
     "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": 46,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "\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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
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       "      <th>5707</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": 46,
     "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": 47,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
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       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>57</td>\n",
       "      <td>No</td>\n",
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       "</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": 47,
     "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"
   ]
  },
  {
   "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": 48,
   "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": 49,
   "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": 50,
   "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": 51,
   "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_out(categorical_features)\n",
    "    .tolist()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Churn</th>\n",
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       "      <th>gender_Male</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Churn  tenure  SeniorCitizen  MonthlyCharges  gender_Female  \\\n",
       "6464    0.0    50.0            1.0        0.185175            0.0   \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",
       "      gender_Male  TechSupport_No  TechSupport_No internet service  \\\n",
       "6464          1.0             1.0                              0.0   \n",
       "5707          0.0             1.0                              0.0   \n",
       "3442          1.0             0.0                              0.0   \n",
       "3932          0.0             1.0                              0.0   \n",
       "6124          0.0             0.0                              0.0   \n",
       "\n",
       "      TechSupport_Yes  PaperlessBilling_No  ...  \\\n",
       "6464              0.0                  0.0  ...   \n",
       "5707              0.0                  1.0  ...   \n",
       "3442              1.0                  0.0  ...   \n",
       "3932              0.0                  0.0  ...   \n",
       "6124              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",
       "      OnlineSecurity_No  OnlineSecurity_No internet service  \\\n",
       "6464                0.0                                 0.0   \n",
       "5707                1.0                                 0.0   \n",
       "3442                1.0                                 0.0   \n",
       "3932                1.0                                 0.0   \n",
       "6124                0.0                                 0.0   \n",
       "\n",
       "      OnlineSecurity_Yes  Contract_Month-to-month  Contract_One year  \\\n",
       "6464                 1.0                      1.0                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                1.0   \n",
       "\n",
       "      Contract_Two year  Partner_No  Partner_Yes  \n",
       "6464                0.0         1.0          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                0.0         0.0          1.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmf = lifelines.KaplanMeierFitter()\n",
    "kmf.fit(train_df_surv[\"tenure\"], train_df_surv[\"Churn\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "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 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "train_df_surv[train_df_surv['Churn'] == 0][\"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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
   ]
  },
  {
   "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": [],
   "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 to [this 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",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.812875</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>-0.351821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Two year</th>\n",
       "      <td>-0.776427</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                0.812875\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                     -0.351821\n",
       "Contract_Two year                     -0.776427\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": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>cmp to</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>-0.019556</td>\n",
       "      <td>0.980634</td>\n",
       "      <td>0.057254</td>\n",
       "      <td>-0.131773</td>\n",
       "      <td>0.092660</td>\n",
       "      <td>0.876540</td>\n",
       "      <td>1.097088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.341571</td>\n",
       "      <td>7.326741e-01</td>\n",
       "      <td>0.448757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>-0.003185</td>\n",
       "      <td>0.996820</td>\n",
       "      <td>0.040129</td>\n",
       "      <td>-0.081837</td>\n",
       "      <td>0.075467</td>\n",
       "      <td>0.921422</td>\n",
       "      <td>1.078387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.079377</td>\n",
       "      <td>9.367329e-01</td>\n",
       "      <td>0.094290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Female</th>\n",
       "      <td>0.018217</td>\n",
       "      <td>1.018384</td>\n",
       "      <td>0.066333</td>\n",
       "      <td>-0.111793</td>\n",
       "      <td>0.148227</td>\n",
       "      <td>0.894230</td>\n",
       "      <td>1.159776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.274631</td>\n",
       "      <td>7.835995e-01</td>\n",
       "      <td>0.351812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>-0.018217</td>\n",
       "      <td>0.981948</td>\n",
       "      <td>0.066333</td>\n",
       "      <td>-0.148227</td>\n",
       "      <td>0.111793</td>\n",
       "      <td>0.862236</td>\n",
       "      <td>1.118281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.274631</td>\n",
       "      <td>7.835995e-01</td>\n",
       "      <td>0.351812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport_No</th>\n",
       "      <td>0.212303</td>\n",
       "      <td>1.236522</td>\n",
       "      <td>0.069653</td>\n",
       "      <td>0.075786</td>\n",
       "      <td>0.348820</td>\n",
       "      <td>1.078732</td>\n",
       "      <td>1.417394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.048022</td>\n",
       "      <td>2.303533e-03</td>\n",
       "      <td>8.761936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <td>0.812875</td>\n",
       "      <td>2.254380</td>\n",
       "      <td>0.069188</td>\n",
       "      <td>0.677268</td>\n",
       "      <td>0.948482</td>\n",
       "      <td>1.968493</td>\n",
       "      <td>2.581787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.748725</td>\n",
       "      <td>7.169269e-32</td>\n",
       "      <td>103.459873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_One year</th>\n",
       "      <td>-0.351821</td>\n",
       "      <td>0.703406</td>\n",
       "      <td>0.077761</td>\n",
       "      <td>-0.504231</td>\n",
       "      <td>-0.199412</td>\n",
       "      <td>0.603970</td>\n",
       "      <td>0.819213</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.524364</td>\n",
       "      <td>6.057732e-06</td>\n",
       "      <td>17.332791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Two year</th>\n",
       "      <td>-0.776427</td>\n",
       "      <td>0.460047</td>\n",
       "      <td>0.081167</td>\n",
       "      <td>-0.935512</td>\n",
       "      <td>-0.617342</td>\n",
       "      <td>0.392385</td>\n",
       "      <td>0.539376</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.565746</td>\n",
       "      <td>1.113949e-21</td>\n",
       "      <td>69.604807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner_No</th>\n",
       "      <td>0.244814</td>\n",
       "      <td>1.277384</td>\n",
       "      <td>0.067499</td>\n",
       "      <td>0.112519</td>\n",
       "      <td>0.377109</td>\n",
       "      <td>1.119094</td>\n",
       "      <td>1.458063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.626946</td>\n",
       "      <td>2.867927e-04</td>\n",
       "      <td>11.767704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner_Yes</th>\n",
       "      <td>-0.244814</td>\n",
       "      <td>0.782850</td>\n",
       "      <td>0.067499</td>\n",
       "      <td>-0.377109</td>\n",
       "      <td>-0.112519</td>\n",
       "      <td>0.685841</td>\n",
       "      <td>0.893580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.626946</td>\n",
       "      <td>2.867927e-04</td>\n",
       "      <td>11.767704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             coef  exp(coef)  se(coef)  coef lower 95%  \\\n",
       "covariate                                                                \n",
       "SeniorCitizen           -0.019556   0.980634  0.057254       -0.131773   \n",
       "MonthlyCharges          -0.003185   0.996820  0.040129       -0.081837   \n",
       "gender_Female            0.018217   1.018384  0.066333       -0.111793   \n",
       "gender_Male             -0.018217   0.981948  0.066333       -0.148227   \n",
       "TechSupport_No           0.212303   1.236522  0.069653        0.075786   \n",
       "...                           ...        ...       ...             ...   \n",
       "Contract_Month-to-month  0.812875   2.254380  0.069188        0.677268   \n",
       "Contract_One year       -0.351821   0.703406  0.077761       -0.504231   \n",
       "Contract_Two year       -0.776427   0.460047  0.081167       -0.935512   \n",
       "Partner_No               0.244814   1.277384  0.067499        0.112519   \n",
       "Partner_Yes             -0.244814   0.782850  0.067499       -0.377109   \n",
       "\n",
       "                         coef upper 95%  exp(coef) lower 95%  \\\n",
       "covariate                                                      \n",
       "SeniorCitizen                  0.092660             0.876540   \n",
       "MonthlyCharges                 0.075467             0.921422   \n",
       "gender_Female                  0.148227             0.894230   \n",
       "gender_Male                    0.111793             0.862236   \n",
       "TechSupport_No                 0.348820             1.078732   \n",
       "...                                 ...                  ...   \n",
       "Contract_Month-to-month        0.948482             1.968493   \n",
       "Contract_One year             -0.199412             0.603970   \n",
       "Contract_Two year             -0.617342             0.392385   \n",
       "Partner_No                     0.377109             1.119094   \n",
       "Partner_Yes                   -0.112519             0.685841   \n",
       "\n",
       "                         exp(coef) upper 95%  cmp to          z             p  \\\n",
       "covariate                                                                       \n",
       "SeniorCitizen                       1.097088     0.0  -0.341571  7.326741e-01   \n",
       "MonthlyCharges                      1.078387     0.0  -0.079377  9.367329e-01   \n",
       "gender_Female                       1.159776     0.0   0.274631  7.835995e-01   \n",
       "gender_Male                         1.118281     0.0  -0.274631  7.835995e-01   \n",
       "TechSupport_No                      1.417394     0.0   3.048022  2.303533e-03   \n",
       "...                                      ...     ...        ...           ...   \n",
       "Contract_Month-to-month             2.581787     0.0  11.748725  7.169269e-32   \n",
       "Contract_One year                   0.819213     0.0  -4.524364  6.057732e-06   \n",
       "Contract_Two year                   0.539376     0.0  -9.565746  1.113949e-21   \n",
       "Partner_No                          1.458063     0.0   3.626946  2.867927e-04   \n",
       "Partner_Yes                         0.893580     0.0  -3.626946  2.867927e-04   \n",
       "\n",
       "                           -log2(p)  \n",
       "covariate                            \n",
       "SeniorCitizen              0.448757  \n",
       "MonthlyCharges             0.094290  \n",
       "gender_Female              0.351812  \n",
       "gender_Male                0.351812  \n",
       "TechSupport_No             8.761936  \n",
       "...                             ...  \n",
       "Contract_Month-to-month  103.459873  \n",
       "Contract_One year         17.332791  \n",
       "Contract_Two year         69.604807  \n",
       "Partner_No                11.767704  \n",
       "Partner_Yes               11.767704  \n",
       "\n",
       "[43 rows x 11 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Could we have gotten this type of information out of sklearn?\n",
    "- Yes, but the above approach is more appropriate for such problems. "
   ]
  },
  {
   "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",
       "        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>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\"]\n",
    "drop_features = [\"customerID\", \"tenure\", \"TotalCharges\"]\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_out(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>1.117063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <td>0.522513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <td>0.349206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <td>0.311261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup_No</th>\n",
       "      <td>0.253954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>-0.172977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <td>-0.242936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <td>-0.260652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <td>-0.416242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_Two year</th>\n",
       "      <td>-1.019200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Coefficient\n",
       "Contract_Month-to-month                   1.117063\n",
       "InternetService_Fiber optic               0.522513\n",
       "OnlineSecurity_No                         0.349206\n",
       "PaymentMethod_Electronic check            0.311261\n",
       "OnlineBackup_No                           0.253954\n",
       "...                                            ...\n",
       "MonthlyCharges                           -0.172977\n",
       "OnlineSecurity_Yes                       -0.242936\n",
       "PaymentMethod_Credit card (automatic)    -0.260652\n",
       "InternetService_DSL                      -0.416242\n",
       "Contract_Two year                        -1.019200\n",
       "\n",
       "[43 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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\n",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 12))\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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
   ]
  },
  {
   "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>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>TechSupport_No</th>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_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>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>...</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>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>1.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>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",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3684</th>\n",
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       "      <td>0.351852</td>\n",
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       "      <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>...</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>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>1.0</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>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen  MonthlyCharges  gender_Female  gender_Male  \\\n",
       "941             0.0       -1.154900            1.0          0.0   \n",
       "1404            0.0       -1.383246            1.0          0.0   \n",
       "5515            0.0       -1.514920            1.0          0.0   \n",
       "3684            0.0        0.351852            0.0          1.0   \n",
       "7017            0.0       -1.471584            1.0          0.0   \n",
       "\n",
       "      TechSupport_No  TechSupport_No internet service  TechSupport_Yes  \\\n",
       "941              1.0                              0.0              0.0   \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  \\\n",
       "941                   1.0                   0.0   \n",
       "1404                  1.0                   0.0   \n",
       "5515                  1.0                   0.0   \n",
       "3684                  0.0                   1.0   \n",
       "7017                  1.0                   0.0   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  ...  \\\n",
       "941                                       0.0  ...   \n",
       "1404                                      1.0  ...   \n",
       "5515                                      0.0  ...   \n",
       "3684                                      0.0  ...   \n",
       "7017                                      1.0  ...   \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",
       "      OnlineSecurity_No  OnlineSecurity_No internet service  \\\n",
       "941                 1.0                                 0.0   \n",
       "1404                0.0                                 1.0   \n",
       "5515                0.0                                 1.0   \n",
       "3684                1.0                                 0.0   \n",
       "7017                0.0                                 1.0   \n",
       "\n",
       "      OnlineSecurity_Yes  Contract_Month-to-month  Contract_One year  \\\n",
       "941                  0.0                      1.0                0.0   \n",
       "1404                 0.0                      0.0                0.0   \n",
       "5515                 0.0                      0.0                0.0   \n",
       "3684                 0.0                      1.0                0.0   \n",
       "7017                 0.0                      0.0                0.0   \n",
       "\n",
       "      Contract_Two year  Partner_No  Partner_Yes  \n",
       "941                 0.0         1.0          0.0  \n",
       "1404                1.0         1.0          0.0  \n",
       "5515                1.0         0.0          1.0  \n",
       "3684                0.0         1.0          0.0  \n",
       "7017                1.0         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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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>tenure</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>gender_Female</th>\n",
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       "      <th>Contract_One year</th>\n",
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       "      <th>Partner_No</th>\n",
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       "    <tr>\n",
       "      <th>3684</th>\n",
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       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Churn  tenure  SeniorCitizen  MonthlyCharges  gender_Female  \\\n",
       "941     0.0    13.0            0.0       -1.154900            1.0   \n",
       "1404    0.0    35.0            0.0       -1.383246            1.0   \n",
       "5515    0.0    18.0            0.0       -1.514920            1.0   \n",
       "3684    0.0    43.0            0.0        0.351852            0.0   \n",
       "7017    0.0    51.0            0.0       -1.471584            1.0   \n",
       "\n",
       "      gender_Male  TechSupport_No  TechSupport_No internet service  \\\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          1.0             1.0                              0.0   \n",
       "7017          0.0             0.0                              1.0   \n",
       "\n",
       "      TechSupport_Yes  PaperlessBilling_No  ...  \\\n",
       "941               0.0                  1.0  ...   \n",
       "1404              0.0                  1.0  ...   \n",
       "5515              0.0                  1.0  ...   \n",
       "3684              0.0                  0.0  ...   \n",
       "7017              0.0                  1.0  ...   \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",
       "      OnlineSecurity_No  OnlineSecurity_No internet service  \\\n",
       "941                 1.0                                 0.0   \n",
       "1404                0.0                                 1.0   \n",
       "5515                0.0                                 1.0   \n",
       "3684                1.0                                 0.0   \n",
       "7017                0.0                                 1.0   \n",
       "\n",
       "      OnlineSecurity_Yes  Contract_Month-to-month  Contract_One year  \\\n",
       "941                  0.0                      1.0                0.0   \n",
       "1404                 0.0                      0.0                0.0   \n",
       "5515                 0.0                      0.0                0.0   \n",
       "3684                 0.0                      1.0                0.0   \n",
       "7017                 0.0                      0.0                0.0   \n",
       "\n",
       "      Contract_Two year  Partner_No  Partner_Yes  \n",
       "941                 0.0         1.0          0.0  \n",
       "1404                1.0         1.0          0.0  \n",
       "5515                1.0         0.0          1.0  \n",
       "3684                0.0         1.0          0.0  \n",
       "7017                1.0         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     35.206724\n",
       "1404    69.023086\n",
       "5515    68.608565\n",
       "3684    27.565062\n",
       "7017    67.890933\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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iTVr0Rs+4L4OPFp3RE6BKg5SvGq1eg0qvlHT12XHoS5ZF0BVjVhVhdVswO8yYnebSZ4vTQoG9gAJbAYX2QgrsnmcFBZviwqa4yDq5NHXJQ6cCkwko/3utQUWAxkigWkcAGoJQE+xWCHY6CXbYCbabCbYWEWQ34+dW8Hc78DdnYSrOrFhoOhN1yZIIOh/wCSlpbSp59gnxPPyjwT+q5DnaE7qk+040MhJ+vMDQpjUAzQvS+ORwdo2v9yNEeVRqFVq9Bq2+evdlc7vcniUMzJ7B5CePg3LaPGOf7BbPrLzj3XfHu/LMhXbcTgUUSpdIqBw9EILOoMFg1OBfMnheb9SgN2ox+Okw+eowBekx+ukw+GpQjC6ceit2nYViCilyFJUGo9JnW4EnOJV8nm/Lp8BegM1lw4VCrstCrus/myirSsrRq8EvAP6zaINWpcZfYyJQYyBUbSAMDeFuhTCnk3CblVBbEQaXE73Lic7lQO9yoHM50Dnt6BR36VIIekVB7XaC2wkOM5izOCdaE/hHelqYTg5Kx4OTbxj4RXoe/pGeAeXy/1einpPw4wXGkhlfcYUZ5BWYZdyPaFDUGjVGX3WluvUURcHlcJc7K+9Ea9TxFcrdpQPPSxfiLHJgNTtKW5+OX1Ocf64tUCVfg1aFyVeP0S8af9+mhPvpMPl5gpLJX4cxTOd53d9zXGVyUewuIt+W73nY88mz5pFryyXXWvIo+fh4cCq0F+JSXDgVN7nOYnKdxST8t5Djw7fKdDWenkalRq/W4aMxEqz1JURjIFilIxg1IW4IcTqItpmJKs4jujADf3MuOC2Qm+B5nAudD/hFeLrojg8APz7u6fgCnD4ntzaVhCpZwkDUIRJ+vEAbE4Pazw9tURGxhZky7keIEirVidYo38DKb0njcrpLW55OHuvksHqClLW4JCiVLIVgKXJgLbRjLfasTu52Kp498yoQmvQmLaaScGTyDyc0oAlxAXp8Ag34Bp54Nvnr0WjVKIpS2gVXZC8i15ZLtiWbTEsmmZZMz8fmTHKsOdhcNhxuBw6XA4fbMyvP7rKXDh4v/boVNxaXDYvLRrY9/zSV4hkB7+OPrzaKaFMokTp/QtVGQtAQrECwy02I006I3UK4pZDQoix0RRlgL/K0KlUkLB2n0Zcd9H3yQPDAWAhrA2GtIbCpjF0SNU7CjxeoVCoMrVtj2bqV5jLuR4hqp9Gq8QnQ4xNQsdXJFUXBaXdjKbJjK3ZiKbJjLXZ49sgrdmAtLAlKJ4emkgU1jw8kz8+0nPV9dEbPgHejrw6jn+fZ5B9IVEgkrYI9Gxv7hRjw8dejUp++tURRFJxuJ3a3vXSpArvLTrGjmBxrTmmL0/GPsyxZpBenk16cTq4tl2JnMQcLizl4xm8mqAJVhEa1IdIUToQugAiNiTC1jkBFRaBbIcDlItDpIMBhJdBuJsBSgNqSC+YcsOSAy+55FB3zPM5Ea4TQ1p4gFNrSE4wCYyEwDgKaeNZvEqKKJPx4iaFNm5Lwk84PMu5HiDpBpVKVDiIn9NyuUdwKNovzxNilIs+zOd9GcYEdc77d83G+HUuBHbfbs3q5w+qiMNt6xnurtSp8Aw2eGXM6NRqdGo1WfdKyBCd9rC85R6tGb/IlIjCY5oEGfMP1+ATq0erKju2yOC2kF6eTVpzGseJj5FhzSkNSjq3k2ZpDliULp9tJliWLLEsWu87he6LRaAgOCybY2JEQQwgh+gBCtCaC1XqC0RKoQLDLRZDTSbDDSkh+OprsA5B9EJxWOLbT8yiPMcgThgKaQECM5znwpI+Dmnn2zhPiDCT8eMnxQc8tCtPJKrLLuB8h6imVWlXaihMcdeZzjwcla1FJS1Kxo/Rjc76dolwrRbk2CnOsFOfZcDuVswakc2Xw0eIXbCAgzERghA+B4SYCIwLpFB5F7ygj6tO0MLkVNznWHDLMGWSYMzhWfIxjZk9YOj5rLt+eXzr4u9hRjEtxlYalc2HUGGnVtBVtulxKG0Mord0a2liLCSpIh/xkyE/xPNvywZrneRz7t/ybqdSeABTWuqQFqZXnObi5Z9C2BCOBhB+vMZZsttq62NMELON+hGj4Tg5KZ+N2uT3jjvJsOO0uz1IEzrLLEpRZCNPhxul04yrZVLg43465wEZxnr10DJTN7CQ7pfiU91JrPC1MPoF6fIMM+Abo8Qky4BtowC/EQECoL21D2tEhtMNZ63a4HJ4WJFsuOZYccmw5nmdrDnm2PPJseeRac0s/zrPlYXVZ+Tf7X/7NLhtoQowhxAXH0SSuP7H+scQawohV6Yh1uYmwFqEpTIeCFE84Kkj1BCRHMeQe8TwO/HJqgT5hJ033jyppMYqBgFjPc2ATz6Bt0aBJ+PESQ2tPy09QYTY+DouM+xFClKHWqPEP8Yz/qQpFUbCZnZjz7RTmWinItJCfYSE/00x+poX8LIunhSnHSmHO6VuZVGoVfkEGAsKM+IeZ8As2YPLTlwzw1pcM9tZj9NUS6RtJpG/kOdXncrtIKkxif+7+Mo+UopTSrrjtmdtPuU6r8rxPrF8sMWGdifEbShPfGOJ1AbRwOPDJTYTsQ5B9ALIOeEKSy+5ZAsCcdfpuNQC9vycIla6HFHnq+kj+0dKKVI/Jru7lqMld3U92YNBgnOnpPNb/PjKatePvpy6WcT9CiFrldisU59k8j3wb5pLWpuICz3NhtpXCbCsup/vsN8Mzm90UoMc30OBpRTremlTSsuQZiG7AFKBDozn9rK4iexFHC4+SUphCclEyyYXJpBSlkFyYTGpxKk736deAUqEi1j+WVkGtaB3cmtZBrWnm35QotYEgmxlV0TEoTIPjLUcFqSXPKWDJPfdvnm9ESWtRSatRUDOIaO95+EfL1H4vkF3d6wFDm9Y409NpVXSM3UXxHMosplWEzGQQQtQetVp11hYmxa1gLrBTkGWhINtKQZbFM4C7ZJFKa1HJjDizA0WhZJC3nczEwjO+t9FPh0+AnuBIHyLiA4iKDyC8aQA6gwY/vR8dQzvSMbTjKde53C4yLZmkFqWSUpRCalEqqcWpJBcmczDvIDnWHJIKk0gqTGJV0qqy76kxEuUbRZRvFNG+0UTHtSXGbzBN/JrQxK8JEVpfNEUZUJAMhSUh6eSwVJgGBWngskFxhueRtq2cLy4QwttDRDvPmKPjW5X4hnqefcJAf7Zd90RNkfDjRcY2bSj+Yy3d3Tl8D6zZnynhRwhR56jUKk/rTZCB6FanP8/tcmMpcpxoPcq3ndSqZMdc4Jn5Zi70LA9gLVkqICe1mENbM0vfKyTGl8j4ACKa+hMc7UtIlC9GvxPjpDRqTWmA6RbZ7ZQ6si3ZHMo7xIG8AxzIPcDBvIMkFyaTbc3G6rKSUJBAQkFCuV/D8e60Jn5NSt8jKqIbUT6esBTlG4Wfztczjf94a9HxcUfZByFzr6e7zZoPSRs8j9PRmk4sFGnwP+njAAiIhqCmJY9mntYl2Qy32kj48SJDyUrPbc2eQc/vrDrI8AtiCTTJf+BCiPpHrVF7ursCDYQ3Pf0EDsWteGa4lXStZSUXcSyhgGNHCijOs5GdXER2chG7T7rG6KcjOMqH4ChfgqN8CIr0PAJCjaj/030Wagol1BRKz+ieZY7bXXaOFR8jrTiNdHM6aUVppBWnlWk9crqdpBSlkFKUctr6/fX+NPFrQoxvDDF+JY/IC2keMIrmAc3RuJ2ecUYZeyBzD+QcAXO2JzCZsz1jjlx2z+raRZazr30EnllsAU0gvC006wvN+kFMNxl3VEky5qcctTXmx7p3L0euvQ61vz/33fQqBzOLub1PM56/plONvacQQtRlRblWjh3xBKHslCJy0ospyrGd9ny1RkVguKk0DIXF+RHRLIDAcFOFx1Ae7047HoaOLwiZbk4vXROp0H6WrjyNkTYhbWgf0p52Ie1oH9qeVkGtMGhOWrFcUTyrZZuzwVoAtgKwFZ742JrnGYeUexTyEj0PVznfA60RYnt4glD8AGjap9Gvjn2uv78l/JSjtsKP225n3/ndwOUib+4ibv4uAbUKvr//Qjo1kamWQggBnj3a8o6ZyU0vJjfd85x3zEJehhmXo/yB2AYfLRHNA4ho5k9EswDCYv3wCzn9ekbnqthRTFpRGqnFqZ7WopPGHR3KP4TFWf4K3/56f0KNnhapUGMoYaYwQowhBBoCCdAH4K/3J8AQUPpxiDEEtaokyLjdnrFFuUchdSsc/ROOrvOEp5MFx0P3MdD1Vs+GtI2QhJ8qqK3wA3DoyiuxHzxE3Pvv8b8Uf37YkUa3pkEsmtC3yv9IhRCiIVPcCoW5VvKPWcgtCUeZiYVkJhXidp76q02tUeEfaiQw3IfACBOBYSYCI0yn7T6rKJfbRWJhIntz9rInew97cvawN2cveba8Ct/LoDEQ5x9HnH8czQKa0TSgKU39m9I6uDUhxhBP61HWfjj6FyT85VnTyFbguVijh/ZXQ/c7PV1kjWjWmYSfKqjN8JPy6KMUrPiR8McexTlyNBe9thqz3cWM4Z0Z0T2uRt9bCCEaIpfTTXZKERlHCzmWUEBGQgF5GeZyA9Fx/+0+C4osWQU73IRvoOGMe6ydiaIo5NvyybZmk23JLvOcY82hwFbgWSnbXkChvZACWwGFjjN3rbUPaU/fmL70jenL+RHno9PowF4M/y6GzZ94WoeOC2sDsT0hJB5CWpx4bqALOUr4qYLaDD9Z771H5qw3CbjqKprMeJUP/jjEiyv2Euqr5/fHBhHoI4OfhRCiqo6vZ5SfaSE/w7PAY0Gmp+ssL8Ny2u4zAI1OTUCokcBwEwHhJoIiTgQkv6DKB6PTcbqdpBWnkViQSGJhIokFiRwtOEpioef5ZCatiR5RPRjQZABXtLgCP70fpGyBf+bCzkXgMJf/Jj6h0OQCaD0EWl3iCUUNgISfKqjN8FP4++8k33sfhrZtafHdUhwuN8PeXMuBjCJu69OMqTL4WQghatTx7rO8Y2bPI91MXklAKsyxobhP/2tSq1MTGOFDUKSJ0CZ+RLUMJLJ5AHpjzUymzrJksT51PetS17EudR051pzS1/x1/oxoO4Jb299KhE+EZ7r9wd88U+9zDpc8jnjGD/1XaGtPEGp9iWcAtdZw6jn1gISfKqjN8GNPTubQJZeCTke7Lf+g0ulYdyiLWz7cKIOfhRDCy1wuN0U5VgoyreRneVqN8jJOtB65Xaf+ClWpVYTFeoJQdMtAIpoF4BdsQKOt3plYbsXN/tz9/JXyF0sPLi1du0ir1nJliyu5o+MdtAxqeeqFtkLPVPwja+DAb5C4HhTXiddNwXDBGOh5l2fl6npEwk8V1Gb4Udxu9nfvgdtspsWy70v3/Hrgi60s254qg5+FEKKOcrvcFOZYyTtmITe9mIyjhaQdyjvt1HxTgB6/IAN+wYbSRSN9/PUY/Tyb3Rr9dJj8dBh8dRX+/3y34mZ10mrm7ZrH1owTY34GxA6gV1QvWgS1ID4wnmjf6BOzyI6z5MHh1XDwVzjw64l1h9Ra6Hgd9L4Xmpy6mGRdJOGnCmoz/AAk3HQzlm3biHltJoFXXAFAer6Vi19bTbHdxas3dGZkDxn8LIQQ9UFRrpW0Q/mkHcon/VA+2alFZxxsfQoVBIQaCYnxIyTGl9AYX0Ji/AiO9EGjO3vr0baMbczbNY/fE39Hoez7GjVGmgc2Jz4gnvMjz+fiphd7usiOc7tg34+w4R3PTLLjmvaBPvdBuyvr9OwxCT9VUNvhJ+2558j78iuCRgwnetq00uMf/nGY6Sv2EGDU8ssjA4kKrNruzkIIIWqfoni28ijKtVFUst1HUa6V4jybZ1+0km0+rMUObOYzbNha0p3Wsls4LbtFEBRx5r3BEvITWH5kOYfyDnEk/wgJBQmnbAirQkWX8C5c0uwSLm12KTF+J3VzpW6DDe96ZpG5HZ5j542Aq2eDzlTZb0eNkvBTBbUdfsybN3N01GhUBgOtVq9CGxwMgMPl5oZ317EjOZ+BbcKZN6aH7PouhBAN2PH90fKOmclJLSY7tZic1CJyUotPCUahsX606hZBy27hBEf5nvXex7fuOJJ/hAO5B1idvJodmTvKnNMhtAPD4ocxos0IfHQl4aogDTa9D+tmg9sJ0V3gpoWe/cbqGAk/VVDb4UdRFI7ccAO23XsIf/RRwsbfVfrawYxChr31J3anm+nXdeLWXs1qvB4hhBB1i6IoFOXaSNyVzaGtmSTvzS0zCy001o+eV8QT3zWsQn8kpxenszJxJb8d/Y0tGVtwK54p/8GGYG7reBs3t7sZX11JsDqyFr653bOytG84jPwUmvWp1q+zqiT8VEFthx+AvG+XkjZ5MtqoKFr9+gsq3Yn1fT5ae5gXlu/BR6/hx4f60yz07AlfCCFEw2UtcnB4eyaHtmSQvCcXd0kQimoRSN8bWhHdsuKzhLMt2axMXMmC3QtK1xMKNARye4fbubndzZ41hHKPwpe3wrGdoNbBsBmeLTXqCAk/VeCN8OO22zk4+CJc2dk0ef01AoYNO/GaW+HmDzew8UgOPZoH8+X4Pmhk9pcQQgjAWuxg+8oktv2aiLNkscYW54fT59qWBEWeeVxQeZxuJz8e+ZEPdnxQOn0+0BDIbR1uY0zHMehcdlh6L+xe6rmg+1gY+nKd2GFewk8VeCP8AGTOnkPW229j6tqV5l9+Uea1pBwzl7+5liKbkyeHtWP8gHLWbhBCCNFoFeXa+PuHw+xZl4aigFqtokP/GFp2iyAs1g+jb8V2DHC5XfyY8CPvb3+/NAT1jenLG4PewEdrgj9fh5XTAAViusH1H0JYq+r/wipAwk8VeCv8ODMzOXDRxeBw0PybrzGdd16Z17/6O5EnFu9Er1Gz7IELaRvlX2u1CSGEqB+yU4vY8O0hEnaW3fXdL8RAWKw/YXF+hMf6E9M6CKPf2QORy+1ixZEVTNswDYvTwnlh5/H2xW8TbAyGfT/Bt+M9q0nrfOCyF+GCO7w2HV7CTxV4K/wApD7xBPnffV+619fJFEVh7PzN/L43g44xAXx7bz/01bxiqBBCiIYhZV8uO9ckk5lYSEGW9ZTX1VoVLbqE075fNHHtQs66R9mOzB3cu/Je8m35xAfG8/4l7xPtFw35KbB0Ahz5w3Nim8s90+H9wmviyzojCT9V4M3wY/l3FwnDh4NOR6uVv6GLiCjzekaBlSGz/iDP7ODBi1rx6JC2tVqfEEKI+sdmdpCdUkRmUhFZyUUcO1JAblpx6et+IQba94mmXd9oAkJPv4bP4bzDjP91PMfMx4j0ieT9S9/3bKHhdnsWRlz5PLjsntlg17wNbS6rjS+vlISfKvBm+AFIuOVWLFu2EHbvPYQ/+OApr/+wI5X7F25FrYLPxvWib8uwWq9RCCFE/ZaZWMiev1LZ//exE2sIqaBZp1D6Xt+KkOjyZxanF6cz/tfxHMk/QqAhkLcvfpsu4V1KXvwXltwFGbs9n/ccD0Om19pgaAk/VeDt8FPw08+kPPwwmpAQWq36HbXh1N11J36znUX/JBPmp+eHB/rL6s9CCCEqxWl3cXhbJrv/SiNlXy7gGSzd+aJYelwRj9506g71edY87lt5HzuydmDSmnh1wKsMihvkedFhhZVTYcPbns+b9oWR88Ev4pT7VDcJP1Xg7fCjOJ0cHDIEZ2oa0dOnE3TD9aecY7G7uP7ddexJK6Bb0yC+HN9Hxv8IIYSokrxjZv5afJCEHVkA+ATo6XN9S9r2jDplTJDZYebR1Y/yV6pnD7B7utzDhC4TTmycuu9HWHwX2AshoAnc9DnEnF+j9Z/r72/5bVkHqbRaQm65BYCcTz+lvHxq0mt4b1Q3/I1atiTm8dKPe2q7TCGEEA1MUKQPV9zbmSvv70JghAlzgZ2V8/awZOY/ZCYWljnXR+fD7Itmc1PbmwB4d/u7PPD7A+Tb8j0ntL0c7vodQltBQQp8MhR2fF3bX1K5JPzUUUHDh6MyGrHt3Yv577/LPadZqC+vj+wKwNy/Evh+e2otViiEEKKhatYplJun9KLPdS3RGjSkHy7gm5f+Zt/G9DLn6TQ6nur9FC/0ewGDxsAfyX9w8/Kb2Z+733NCeBtPAGp9GTitnvFAPz8FrtNv4FobJPzUUZqgIAKvuQaAnHnzT3vepR0iuXeQZ8HD/y3ewYFjhac9VwghhDhXGp2abpc149bnetPy/HAUBVbO31PaJXaya1pdw4LLFxDjG0NSYRKjVozixyM/el40BsLNX0L/iZ7P18+Bz28Ac04tfjVlSfipw0Juvx1UKop+/x3rntN3az02pC39WoVitruY8Nk/FNm8m6iFEEI0HH7BBi67qxNtekWiuBV++vBfUg/knXJeh9AOfHXlV/SJ7oPFaWHSH5N4ffPrns1S1Wq4eAqMmA86X8+aQOk7Tn2zWiLhpw4ztIgn4IorAMh8++3TnqdRq3jzpvOJCjByKLOYSYu2lztOSAghhKgMlVrFRbe1p/l5obgcbpa/vZ3MpFN7GoKMQbx7ybuMO28cAHN3zWXy2sk4XA7PCR2vhXG/wpWzoMWgWqv/vyT81HFh994DajVFv63Eunv36c/zM/D2rd3QaVSs2JnOiyv2SAASQghRbTQaNZfd1YnoVoHYrS6Wzd5OXob51PPUGh7q9hAvXvgiWpWWFUdWcN/K+yh2lCyqGNkRLri9lqsvS8JPHWdo0eKk1p93znjuBc2CmX6tZz+wD9ce4dWf90kAEkIIUW20eg1X3NuZ0Fg/LAV2vn9zG8V5tnLPvarlVcy5eA4mrYn1aeu58+c7ybZkl3tubZPwUw+E3TPB0/qz8sytPwAje8Qx9ZqOALy7+hBv/HagNkoUQgjRSBh8dFz9YFcCw00UZlv5/q1tWIsd5Z7br0k/PrnsE4INwezO3s3oH0eTVJBUyxWfSsJPPVCm9WfO6cf+HHdbn+ZMubIDAG+tPMDslRKAhBBCVB+fAD1XP9QV30A9OanFLH1jK+YCe7nndgrrxKfDPqWJXxPPTLAfR7En27tr00n4qSfC7ikZ+/P771h27Trr+WMvjGfy5e0AeO3X/by35lBNlyiEEKIRCQgzcdVDXTEF6MlOLmLJjH8oyLaUe26zgGZ8Nuwz2ga3Jceaw5ifx7Dl2JZarvgECT/1hKFFPAFXelp/ss4y9ue4uwe2ZOKQNgC8/ONePlp7uMbqE0II0fiExvhx/WPd8A8xkp9pYcmMLeSctFv8ycJMYcwdOpeeUT0J0AcQ6x9by9WeIHt7lcPbe3udju3IEQ5fcSW43TRftAhTp47ndN0bv+7nzZKur9G9m/HIpW0I8a2dHXaFEEI0fEW5Nr5/cyu56WaMvjquerALEc3K//1pd9nJsmQR4xdT7XXI3l4NkCE+nsCrrgQg6wzr/vzXw5e05v7BrQD4dMNRBs5YxYd/HMbmdNVInUIIIRoXv2AD103sRkQzf6zFDpa+vrV0h/j/0mv0NRJ8KkLCTz0TOqFk5teqVVh2/ntO16hUKiZe1paF43rRPjqAQquT6Sv2MOSNP/jp3zSZDi+EEKLKTH56rnnkfJq0DcJh86wDdKScrTDqAgk/9Yyn9ecqALLmzKnQtX1bhfHDAxfy6g2dCfc3cDTbzITPtnDj+xvYnVpQE+UKIYRoRPRGLVfe34X4LmG4nG5+en8nibvqxto+J5PwUw+F3TMBNBqK1qyheNOmCl2rUasY2SOO1RMH8eBFrTBo1WxKyOHad/7i67+9v/aCEEKI+k2r0zB0fCdadovA7VL48b2dpB7M83ZZZdS58GOz2XjiiSeIiYnBZDLRq1cvfv3113O69rfffmPw4MGEhYURFBREz549+fTTT2u44tqnb96c4BtHAnDsxZdQXBUfu+Nr0PLokLasmjiIi9pFYHe6mbR4B/9bvAOrQ8YCCSGEqDy1Rs2ld3agacdQnA43y+dsJzPx1L3AvKXOhZ877riD119/nVtvvZU333wTjUbDsGHD+PPPP8943ffff8+QIUOw2+0899xzTJ8+HZPJxG233cYbb7xRS9XXnrAHHkAdEIBt717yFi2u9H1igkx8dFt3Jg5pg0oFX/6dxIj31pOUc+p+LUIIIcS50mjVDL37xF5g37+17bTT4GtbnZrqvmnTJnr16sWMGTOYOHEiAFarlU6dOhEREcG6detOe+2QIUPYtWsXhw8fxmAwAOB0OmnXrh2+vr5s3779nOuoq1Pd/ytnwacce/FFNCEhtPz5JzT+/lW639oDmTz4xVZyzQ6CfHTMurErg9pGVFO1QgghGiO7xcnSN7aSmViIb5CB6yd2IyDMVCPvVS+nui9atAiNRsP48eNLjxmNRsaOHcv69etJSjr9mJSCggKCg4NLgw+AVqslLCwMk6lmvsneFnzzTehbtsSVk0PWO+9W+X79W4fzw4P96RIbSJ7ZwZh5f/Pmbwdwu+tMPhZCCFHP6E1arnqwC8HRvhTn2fhu1tbTboZaW+pU+Nm6dStt2rQ5Ja317NkTgG3btp322kGDBrFr1y6mTJnCwYMHOXToENOmTWPz5s1MmjTpjO9rs9koKCgo86gPVDodkf/7HwA5n36K7ciRKt+zSZCJryf04ZZeTVEUeOO3/dz/xRYsdhkHJIQQonJMfnqueagrAWFGCrJKNkMtKn8z1NpQp8JPWloa0dHRpxw/fiw1NfW0106ZMoWRI0cyffp0WrduTatWrXj55ZdZvHgx119//Rnf96WXXiIwMLD0ERcXV7UvpBb59b8Qv4EDwekk45VXq+WeBq2GF687j1dv6IxOo2LFznRGvr+e9HxrtdxfCCFE4+MbZOCah8/HN1BPXoaZzGTvDYCuU+HHYrGU6bY6zmg0lr5+OgaDgTZt2jB8+HC++OILPvvsM7p3786oUaPYsGHDGd938uTJ5Ofnlz7O1L1WF0U88QRotRStXk3R2jMPDK+IkT3i+Hxcb0J89exMyefqOX+yPSmv2u4vhBCicQkIM3H1Q+dz5f1diGsX4rU66lT4MZlM2Gyn9gNardbS10/n/vvvZ9myZXz55ZfcdNNN3Hrrrfz2229ER0fz0EMPnfF9DQYDAQEBZR71iaFFPCGjRgFw7OWXURzV15TYMz6E7+7rR5tIPzIKbYx8fz3Ltp++BU4IIYQ4k5AYX68GH6hj4Sc6Opq0tLRTjh8/FhNT/l4gdrudjz/+mCuuuAK1+sSXpNPpuPzyy9m8eTN2u71miq4jwu69B01wMPZDh8j98qtqvXdciA+L7+nLRe0isDndPPDFVl7/db8MhBZCCFEv1anw07VrV/bv33/KgOONGzeWvl6e7OxsnE4nrnIW+3M4HLjd7nJfa0g0AQGEP/wwAJmzZ+PMyanW+/sbdXx4W3fu6h8PwFsrDzD+083km703YE0IIYSojDoVfoYPH47L5eKDDz4oPWaz2Zg7dy69evUqHYicmJjI3r17S8+JiIggKCiIb7/9tkwLT1FREcuWLaNdu3YNdrr7yYKG34ChXTvcBQWkTX6y2jcs1ahVPHVFB14d3hm9Vs1vezK4cs5a/k3Jr9b3EUIIIWpSnQo/vXr1YsSIEUyePJlJkybxwQcfcNFFF5GQkMCrr56YyXTbbbfRvn370s81Gg0TJ05k//799O7dm1mzZvHaa6/Rs2dPkpOTefrpp73x5dQ6lUZDzCsvo9LrKVqzhtwFC2rkfUZ2j2PJPX2JDTaRlGPh+nfXyb5gQggh6o06tcIzeAY3T5kyhc8++4zc3Fw6d+7MtGnTuOyyy0rPGTRoEGvWrDmlZWPhwoW8+eab7N+/H5vNRufOnXn88ce54YYbKlRDfVnh+XRyv/iC9Oengk5H8y++wNSpY428T57ZzqNfb+f3vRkAjOwey9RrOmHUaWrk/YQQQogzOdff33Uu/NQF9T38KIpCyoMPUfjrr+iaNiV+yWI0fn418l5ut8K7aw7x2i/7cCvQITqAd0d1o1mob428nxBCCHE69XJ7C1E9VCoV0S9MQxsTjSMxkfTnnq/28T/HqdUq7hvcigV39iLEV8/utAKufOtPfvr31Fl7QgghRF0g4aeB0gQG0mTma6DRUPDDD+R/u7RG3+/C1mEsf/BCLmgWTKHNyYTPtvD8sl3Yne4afV8hhBCioiT8NGA+3c4n/MEHAUifNg3b4cM1+n7RgSa+HN+buwe0AGDuXwmMeH89STnmGn1fIYQQoiIk/DRwoXeNw7dvHxSLhZRHHsVdzgra1UmnUTN5WHs+uq07gSYd25PyuOKttfy6+1iNvq8QQghxriT8NHAqtZqYV15BExqKbd8+ksaOw3Eso8bf95IOkSx/8EK6xAVRYHVy14LNPPf9Lopszhp/byGEEOJMJPw0AtrwcJq8NhO1jw/mzZs5cu21FK1dW+PvGxvswzd39+HOfp5VoeetS+DS19fw8670Gn9vIYQQ4nQk/DQSvr1703zxIgzt2+PKzSXprvFkvPZ6tW6CWh69Vs0zV3Vg/p09iQsxkZZv5e5P/2Hc/M2k5Flq9L2FEEKI8sg6P+Wo7+v8nInbZiPjlVfIXfgFAKbzz6fJ66+hi46u8fe22F3M/v0AH/xxGKdbwUev4ZFL2jCmX3O0GsnhQgghqkYWOayChhx+jiv46WfSnn4ad1ERmsBAYmbOwK9//1p57/3HCnnq2538nZALeBZGnDGiMx1jAmvl/YUQQjRMssihOKOAoZcR/+0SjJ064crPJ+ne+yhctapW3rtNpD9fje/DKzecR6BJx+60Aq6Z8xdv/Lpf1gUSQghR4yT8NGL6uDiaL/wc/8uHgsNByoMPUbT2z1p5b7VaxY09mvLbowMZ2jEKp1vhzZUHuObtv9iVKrvECyGEqDkSfho5lV5Pk1dfxf/SS1EcDpLvv5/i9etr7f3D/Q28O6obb918PsE+OvaUtALN+k1agYQQQtSMSoWftDTZt6khUel0NHltJn6DB6PYbCTdcy/FGzfV3vurVFzdJYZfHhnIZR0jcboVZv3maQXak1ZQa3UIIYRoHCoVfuLi4hgyZAiffvopxcXF1V2T8AKVXk+TN2fhO3AAitVK0j33YP7nn1qtIdzfwHujLijTCnT1nD95e9VBnC5pBRJCCFE9KhV+pk6dSmpqKrfffjuRkZGMGjWKn376CbdbfkHVZ2q9nti33sK3Xz8Us5mku8Zj3rq1Vms4uRXokvaROFwKM37ex4j313MkS4K2EEKIqqvSVPetW7fy+eef8+WXX5KamkpERAQ333wzt956K927d6/OOmtVY5jqfiZuq5WkCfdg3rABtZ8fzT5dgLF9+1qvQ1EUFm9J4fnvd1Foc2LUqZl8eXtG926GWq2q9XqEEELUbbW6zo+iKPz+++8sXLiQxYsXU1hYSNu2bRk1ahSjRo2iadOmVX2LWtXYww+A22wmcfx4LJv/QRMWRvPPP0PfrJlXaknJszBp0Xb+OpgNQL9Wobx8fWfiQny8Uo8QQoi6qdYXObTb7SxbtowPP/yQX375BY1Gg0qlwu12c9111/HWW28RXQurCFcHCT8ersJCjo6+DdvevehiY2m28HN0ERFeqcXtVvh0w1Fe+nEPVocbg1bNfYNbMX5AC4w6jVdqEkIIUbfU2iKHq1atYty4cURGRjJy5EjS09OZOXMmycnJpKWl8fLLL7Ny5UpGjx5d1bcStUzj70/Tjz5E17QpjuRkksbdhSvfO2vwqNUqbu/bnBUP9qd3ixBsTjev/7qfIW/8wW+7jyELlQshhDhXlWr52b59O59//jlffPEFqampREVFccstt3Dbbbdx3nnnnXL+nDlzmDhxIlartVqKrmnS8lOWPTmZozffgjMzE9P559P0k49Rm0xeq0dRFH7Ykcb05XtIL/D8NzW4bTjPXNWR+DBfr9UlhBDCu2q020utVmMymbj22mu57bbbuPTSS1GrT9+ItGrVKqZOncqqWto+oaok/JzKum8/R0ePxl1QgO+A/sS9/TYqnc6rNRXbnMxZdZCP1h7G4VLQazxdYQ9c1EoGRAshRCNUo+Fn3rx5DB8+HD8/vyoVWVdJ+CmfectWEu+8E8VqJeDKK4l59RVUZwi9teVwZhHPL9vNmv2ZAFzSPoI3buyKv9G74UwIIUTtqtExP4mJiSQkJJz29V27djF16tTK3FrUYT7dzif2zVmg1VLwww+kPfMMSh1Y26lFuB/zxvTgtRFd0GvV/LYng+veWcfhzCJvlyaEEKIOqlT4ef7559mxY8dpX//33395/vnnK12UqLv8Bg4k5pWXQa0mf9Fi0qZMqRMBSKVSccMFsSya0IeoACMHM4q45u2/WL0vw9ulCSGEqGMqFX7O1lOWk5ODXq+vVEGi7gu84gpiXn3VE4AWL6kzAQigc2wQ3z/QjwuaBVNodTJm3t+8t+aQzAYTQghRSnuuJ/7xxx+sXr269PMlS5Zw8ODBU87Ly8vjq6++KnfWl2g4Aq+8AlSQ+vgk8hcvAQWiX5hWJ8YARfgbWXhXL577fhdfbEri5R/38m9KPi9c24kgHwnlQgjR2J3zgOfnn3++tCtLpVKd8S/pDh068PHHH9OrV6/qqbKWyYDnc1ewYgUpj08Cl4vA667zBCBN3Vh0UFEUPtuYyPPf78LpVgjz0zPlyg5c3SUGlUpmgwkhRENT7bO9LBYLZrMZRVGIiIjgvffe44Ybbih7M5UKHx8fjEZj1ar3Mgk/FVPw44+kTHzcE4CuvZbo6S/UmQAE8M/RXP63eAcHMjwDoPu3DuOFazvRLFTWBBJCiIakRqe6Hz16lPDwcHx8GubeShJ+Kq7gp59IeWwiuFwEXH0VMS++iEp7zr2qNc7udPPBH4d46/eD2J2e7TEeuqQ1d/VvgU7j/a46IYQQVVfre3s1JBJ+Kqfgp59JmTgRnE78hw6lyYxXvb4Q4n8lZBXz1NKdpZukto30Z+o1HenVItTLlQkhhKiqag0/8fHxqNVq9u7di06nIz4+/qxjJlQqFYcOHap45XWAhJ/KK1y5kpSHH0FxOPAbPJgms95AbTB4u6wyFEVh6bYUpv2wh5xiOwBXdI7myWHtaRLkvW07hBBCVM25/v4+p36JgQMHolKpSrewOP65EP/lf/HFxL7zNsn3P0DRqlUk33sfsXNme3UvsP9SqVRcd34sg9pE8Nqv+1i4MZHlO9JYuecYEwa2ZMLAlrJTvBBCNGDS7VUOafmpuuING0i6514UiwWfnj2Je/cd1L51c4Dx7tQCnlu2i01HcgBoEmTiqSvac3mnKAn5QghRj8iYnyqQ8FM9zFu2kHTXeNzFxZi6diXuww/Q+Pt7u6xyKYrC8p1pvLh8D6n5np3iu8QFce+gllzaPlI2ShVCiHqgWsPPH3/8UakiBgwYUKnrvE3CT/Wx7NhB4ri7cBcUYGjbltg5s9HHxXm7rNOy2F28t+YQ7/9xCKvDs2p16wg/JgxsydVdY2RmmBBC1GHVGn7UanWFmv8VRUGlUuFyuc75mrpEwk/1su7ZQ+K4u3BlZ6MODKTJzBn49e/v7bLOKKvIxty/jrBg/VEKrU7A0x12V/94buzRFJNexgQJIURdU63hZ82aNZUqYuDAgZW6ztsk/FQ/R3o6yQ89hHX7DlCpCH/oIULH31UntsM4kwKrg883JPLxn0fIKrIBEOanZ1z/Fozu3QxfQ91Zy0gIIRo7GfNTBRJ+aobbbufYC9PJ+/prAPwuuZiYl19G4+fn5crOzupwseifZN7/4xBJORYAgnx0jLswntv6NifAWLfWMxJCiMZIwk8VSPipWbnffMOxqdNQHA708fHEzpmNoWVLb5d1ThwuN99tS+XtVQc5klUMQIBRyx394rmzX3PZOFUIIbyoWsPPnXfeiUql4oMPPkCj0XDnnXeetQCVSsXHH39csarrCAk/Nc+yYwfJDz6EMz0dta8vTV5/Db961E3qciv8sCOV2b8f5GDJnmG+eg0jusdxe9/mxIfVzWn9QgjRkFVr+GnevDlqtZp9+/ah0+lo3rz5Oa3wfPjw4YpXXgdI+KkdzuxsUh5+BPPff4NaTeQTkwi+7bZ6tbaO263w06503lp5gL3phQCoVHBR2wjuvDCevi1D69XXI4QQ9Zl0e1WBhJ/ao9jtpE+bRt43iwAIuvFGop5+qs7tCXY2iqLw58Es5v6VwO97M0qPt430Z0y/5lzdNQYfvQyOFkKImiThpwok/NQuRVHImTefjFdfBUXBp09vYmfNQhMY6O3SKuVwZhHz1iWw6J9kzHbPcg++eg1Xdo5hRPdYLmgWLK1BQghRA2ol/Pz777+sWLGChIQEwNM9dvnll3PeeedV9pZ1goQf7yj8fRUpEyeimM3omzcn7r130Tdv7u2yKi3f4uDrv5P4bONRjmabS4+3CPNlePdYbugWS2SA0YsVCiFEw1Kj4cdms3H33Xfz6aefoihK6YanbrcblUrFrbfeykcffYReXz9nvkj48R7r3r0k3XMvzrQ01IGBxM5+C9+ePb1dVpUoisLfCbl8vTmJFTvTSluD1Cq4uksMjw9tJ7vJCyFENTjX39+VWmHuiSeeYMGCBdxzzz3s2bMHq9WKzWZjz549TJgwgc8++4xJkyZVunjReBnbtSP+668wdumMOz+fxLHjyFu61NtlVYlKpaJnfAgzR3Rh01OX8OoNnenRPBi3Aku3pXLRzNW89ss+im1Ob5cqhBCNQqVafsLCwrjiiiuYP39+ua+PHj2aH3/8kaysrCoX6A3S8uN9bquV1P9NpvCnnwAIvWcC4Q8+2KDGyvybks+0H3azsWQ3+XB/A48PacsNF8SikY1UhRCiwmq05cfhcNC7d+/Tvt63b1+cTvkrVlSe2mikyeuvEXr33QBkv/seqY9NxG2zebmy6tOpSSBfju/Ne6MuoFmoD5mFNiYt3sFVs//k193HsDrq5954QghR11Wq5WfkyJHY7XaWnqY74pprrsFgMPB1yTYG9Y20/NQteYuXkPbss+B0Yjr/fGLfnoM2JMTbZVUru9PNgvUJvLnyQOlGqkadmn4twxjcLoKL2kUQI+OChBDijKp1wHNOTk6ZzzMzMxk5ciQtW7bkvvvuo1WrVgAcOHCAt99+myNHjvDVV1/Rtm3bKn4Z3iHhp+4p3rCB5Acfwl1QgC4ujrh338FQ8t9dQ5JTbOedVQdZvjONtHxrmdfaRflzWccoJgxsKbvKCyFEOao1/KjV6lPGWhy/7HTH1Wp1ve36kvBTN9kOHybp7gk4kpJQmUxETZlC4HXXNqhxQMcpisKetEJW7cvg970ZbE3MxV3yL7VDdADvj76AuBAf7xYphBB1TLWGn+eee65Sv2CeffbZCl9TF0j4qbucOTmkTpxI8br1AARcfRVRzzyLxq9h76WVW2xn5d4MXlqxh+xiO4EmHW/dfD4D24R7uzQhhKgzZIXnKpDwU7cpbjfZH3xI5uzZ4HKhb9aMJm+8jrFDB2+XVuNS8yzc8/kWtifloVLBxCFtuWdgS9QyO0wIIWp2tldNstlsPPHEE8TExGAymejVqxe//vrrOV//1Vdf0adPH3x9fQkKCqJv3778/vvvNVixqG0qtZqwCXfT7NMFaKOjsR89SsKNN5Hz2ec09CwfE2Ti67t7c3PPOBQFZvy8jwmf/UOh1eHt0oQQot6oUsvPX3/9xZYtW8jPz8ftdpe9sUrFlClTKnzPm2++mUWLFvHwww/TunVr5s2bx99//82qVau48MILz3jtc889x9SpUxk+fDgXX3wxDoeDf//9l379+jF69OhzrkFafuoPV14eqU8+RVFJwA0YdjkxL7+Mqp6uLl4RX2xK5NnvdmF3uWkR7stbN51Ppyb1cz80IYSoDjXa7ZWTk8MVV1zBpk2bUBQFlUpVZgD08WMuV8XWKdm0aRO9evVixowZTJw4EQCr1UqnTp2IiIhg3bp1p712w4YN9O3bl9dee41HHnmkol9SGRJ+6hdFUcj99DOOzZgBDgf+l15Ck9deaxQBaFtSHvd89g9p+VbUKrijbzyPDmmDn0F2kBdCND412u31+OOPs2PHDhYuXMjhw4dRFIWff/6Z/fv3M2HCBLp27UpqamqF77to0SI0Gg3jx48vPWY0Ghk7dizr168nKSnptNfOmjWLqKgoHnroIRRFoaioqDJfmqiHVCoVIbeNJu6dt1Hp9RT++hspjz2GYrd7u7Qa1zUuiGUPXMiVnaNxK/DJX0e49PU1/PRveoPvAhRCiMqqVPhZsWIFd999NzfeeCP+/v6eG6nVtGrVirfffpvmzZvz8MMPV/i+W7dupU2bNqektZ4lG1tu27bttNeuXLmSHj168NZbbxEeHo6/vz/R0dHMmTPnrO9rs9koKCgo8xD1j1///sS+PafRBaAwPwNzbunGvDE9aBriQ1q+lQmf/cNdCzaTnGs++w2EEKKRqVT4ycvLo2PHjgD4+fkBlGlpGTJkCD///HOF75uWlkZ0dPQpx48fO11rUm5uLllZWfz1119MmTKF//3vf3z11Vd07dqVBx54gPfff/+M7/vSSy8RGBhY+oiLi6tw7aJuaKwBCGBQ2wh+eWQA9w1uiU6j4rc9GVz6+h/M+f2AbJoqhBAnqVT4iYmJIT09HQCDwUBERATbt28vfT0lJaVS6wJZLBYMBsMpx41GY+nr5TkevLKzs/noo4+YOHEiI0eOZPny5XTo0IEXXnjhjO87efJk8vPzSx9n6l4TdV9jDkBGnYbHL2vHigf707N5CBaHi5m/7GfAq6v4aO1h2S9MCCGoZPgZMGBAmennN954I6+++irTp09n2rRpzJo1i8GDB1f4viaTCVs5G1dardbS1093HYBOp2P48OGlx9VqNTfeeCPJyckkJiae9n0NBgMBAQFlHqJ++28ASn70UdynCc8NUetIf74c35tZN3aleagP2cV2Xli+hwGvruLT9QnYne6z30QIIRqoSoWfRx99lKuvvro0qDz33HP07t2bKVOm8Oyzz3LBBRcwe/bsCt83OjqatLS0U44fPxYTE1PudSEhIRiNRkJDQ9Foyu55FBERAXi6xkTjcnIAKvptJQk33oTtyBFvl1Vr1GoV157fhN8eHcirN3SmSZCJjEIbU77bxeCZq5m/LoGUvMYTCIUQ4rhKhZ/zzjuPRx99tLSLKjg4mN9++42cnBzy8/NZvXp1uWN3zqZr167s37//lAHHGzduLH29PGq1mq5du5KZmYn9P90bx8cJhYfLNgCNkV///sR99CGasDBs+/eTMHwEBT9VfDxafabVqBnZI47fJw5k2jUdifA3kJJn4dnvd9Hv5d+57I0/eOnHPWw4nI3DJS1CQoiGr05tb7Fx40Z69+5dZp0fm81Gp06dCA0NZcOGDQAkJiZiNptp165d6bWzZs3ikUce4YMPPuCuu+4CPN1lHTt2xGg0smvXrnOuQ9b5aXgcGRmkPvoY5s2bAQi5/TYiHnusUawF9F9Wh4uFGxNZvjOtzIapAP5GLQPahHNDtyYMaB2OVlPnFoEXQojTqvG9vTIzM3nllVdYsWIFCQkJADRv3pxhw4bx+OOPExkZWanCR44cybfffssjjzxCq1atmD9/Pps2bWLlypUMGDAAgEGDBrFmzZoy65hYLBZ69OjB/v37eeihh2jatCmffvopW7ZsYdmyZVx++eXnXIOEn4ZJcTrJnDWL7I8+BsDUtStNZr2BLirKy5V5T26xnT8OZLJ6XyZr9meSU3yi5TQywMAN3WIZ0T2O+LCGvXGsEKJhqNHws2vXLi6++GIyMjLo1asXbdq0AWD//v1s3LiR8PBwVq5cSadOnSpcuNVqZcqUKXz22Wfk5ubSuXNnpk2bxmWXXVZ6TnnhByAjI4NJkyaxbNkyiouL6dq1K88//3yZa8+FhJ+GrXDlSlL/Nxl3YSGa4GCiX3oR/0GDvF2W17ncCtuT81i+I41vt6aUCUI9m4cwskccV5wXjUmvOcNdhBDCe2o0/AwaNIhdu3axYsUKevToUea1TZs2MWzYMM477zxWrVpV8crrAAk/DZ89KYnkhx7CtnsPAMG33EzE44+jPs2MwsbG7nSzcs8xvtqcxB/7M0u7xoJ8dNzcsym39WlGdKB8r4QQdUuNhh8fHx+efPJJnn766XJfnzZtGi+99BJmc/1cXVbCT+PgttnIfP11cuYvAEDfogVNZs7A2KGDlyurW9LyLSzZksIXmxJJzvXMDtOoVQztFMWd/eLp1jSoUut6CSFEdavRvb0iIiJKFx4sj9FoLJ1iLkRdpTYYiJw8mbiPPkITHob98GGO3HgT2R99hOKWWU/HRQeauG9wK9Y8Ppj3R19A7xYhuNwKy3ekccO767j27b9YsTNN9hITQtQblWr5mTVrFrNnz+avv/4i6j+DRVNTU7nwwgt58MEHK7W/V10gLT+NjzM3l7QpUyj6bSUAPj17EvPSi+iaNPFyZXXT7tQC5q07wtJtqaULJnaJC2Ly5e3o3SLUy9UJIRqrau32ev311085Nn/+fA4dOsR1111Hq1atADhw4ABLly6lVatW3H777TzyyCNV+BK8R8JP46QoCnmLFnHsxZdQLBZUBgMhd44hbNw41L4y26k82UU25q9L4KM/j2C2e7bOuKhdBJOGtqVdlPzbEULUrmoNP2p1xXvHVCoVLlf93EdIwk/jZk9IIO2ZZzFv2gSANjyc8EcfJfCaq1FV4t9CY5BRaOWtlQf4YlMSLreCSgU3dIvl0UvbEBMkA6OFELWjWsPP0aNHK1VEs2bNKnWdt0n4EYqiUPjbb2S8OgNHyUa3xo4diXxyMj4XXODl6uquw5lFzPxlHyt2ejY+1qhVDGwTzvALYrm4fQQGrUyTF0LUnBpf5LAhk/AjjnPb7eR++ilZ77yLu7gYAP/LhxL5+OPoTrPXnICtibm8+tM+1h/OLj0WaNJxdZcYhl8QS+fYQJkhJoSodrUSfoqLi1mzZk1py1CzZs0YOHAgvvV8fISEH/FfzqwsMt+aTd6iReB2ozIaCR1/F6Fjx6Iu2eNOnOpQZhGL/0lmyZYU0guspcfbRPox9ZpOMjhaCFGtajz8zJ49m6effpqioqIyU1z9/f2ZPn06999/f2VuWydI+BGnY927l/QXXsCy+R8AdLGxRP7vCfwuvlhaMs7A5VZYdyiLRf8k89O/6dicblQquHdQSx6+pA062UNMCFENajT8LFiwgDvuuIM+ffrw4IMP0r59ewD27NnD7NmzWb9+PfPmzWP06NGV/wq8SMKPOBNFUShYsYKMV2fgPHYMAN9+/Yh8cjKGli29XF3dl29xMH35br7enAxA59hAZt3YlRbhfl6uTAhR39Vo+OnatStBQUGsXLkSjabsAEaXy8XFF19MXl4e27Ztq3DhdYGEH3Eu3MXFZH3wITmffILicIBGQ9D11xF2zz0yHugc/Lgzjf8t2Um+xYFJp+HZqzpwY484aUETQlRaja7wvG/fPkaMGHFK8AHQaDSMGDGCffv2VebWQtQbal9fIh55mBbLf8DvoovA5SLvm0Ucumwo6dNfxJmV5e0S67TLz4vmp4f707dlKBaHi/8t2cmEz/4ho9B69ouFEKIKKhV+AgMDSUhIOO3rCQkJ0mIiGg1906bEvfM2zRYuxKdnTxSHg9xPP+XgpUPIeO11XHl53i6xzooONPHZ2F48OawdOo2Kn3cdY8Crq5i6bDfHCiQECSFqRqXCzxVXXMHs2bP58ssvT3ntq6++Ys6cOVx11VVVLk6I+sSn2/k0nT+Ppp98jLFzZxSLhewPP+TgJZeS8drrOI5leLvEOkmtVjF+QEu+vbcfXeOCsDrcfPLXEfq/uopnvvuX1DyLt0sUQjQwlRrzk5mZycCBA9m3bx9RUVG0bt0a8GxvkZ6eTrt27VizZg1hYWHVXnBtkDE/oqoURaFo1SoyZ72Jbf9+z0GdjsArriBkzBiMbdt4t8A6SlEU/jyYxVsrD/B3Qi4AOo2KEd3juKt/C+LD6vcyGkKImlXjU92tVivvv/8+P/74Y5l1foYNG8b48ePPuOt7XSfhR1QXxe2maNUqsufOLZ0eD57ZYSF3jsG3b18Z4FsORVHYcDiHt1YeKLNQYte4IK7pGsMVnaOJ8K+//x8jhKgZNRZ+LBYLTz31FIMHD26wXVsSfkRNsOzYQfbcuRT+/Au4PTuh+3TvTvSL09E3berl6uquTUdyeHf1Qdbsz8Rd8v9WahX0axXGNV2bcFnHSPyNOu8WKYSoE2q05cfX15c333yTcePGVanIukrCj6hJ9uRkchYsIO+bRZ7d400mIh59lOBbb5GNU88gs9DG8h2pfLc9la2JeaXH/Qxa7r+oFWP6NZe9w4Ro5Go0/AwYMIBu3boxa9asqtRYZ0n4EbXBnpxM2pNPle4eL61A5+5odjHfb0vl220pHM707LnWNMSHJ4e147KOUdKVKEQjVaPhZ8uWLQwbNowXXniBO+64A61WW6Vi6xoJP6K2KG43uV9+ScbM11DMZmkFqiC3W+HbrSm88tNeMgptAPSKD2HKlR3o1CTQy9UJIWpbjYafzp07k5WVxbFjxzAYDDRp0gSTyVT2xioV27dvr3jldYCEH1Hb/tsKZOjQnqBrr8X/sqHoIiO8XF3dV2xz8v6aQ7z/x+HSfcNGXBDLfYNb0SxUZogJ0VjUaPgZNGjQOTUrr1q1qqK3rhMk/Ahv+G8rEAAqFT49ehAwbBj+lw1BGxzs3SLruJQ8C6/8uJfvt6cCoFLBRW0jGNMvnn6tQqU7TIgGrsanujdkEn6ENzmzsij48ScKVqzAsnXriRc0GvwGDCDq+efQRUhr0Jn8czSH2b8fZPW+zNJjbSL9uKNvPNed3wSTXgZGC9EQSfipAgk/oq5wpKRQ8NNPFCxfgXX3bgB0cXE0/fgjGRh9Dg5lFrFgXQLf/JOM2e4CINCkY8LAloy9MB69VsZVCdGQ1Hj4sdlsfPjhh6xYsaJ0n6/mzZszbNgwxo0bJ4scClHNrPv3k3z/AzgSE9GEhdH0ow8xtmvn7bLqhQKrg6//TmL++gSScjzbZbQM92XaNZ3o26p+rkQvhDhVjYaf5ORkLr30Uvbt20d0dDStWrUC4ODBg6SlpdGmTRt+++03YmNjK/8VeJGEH1FXOTMzSbxrPLa9e1H7+xP37jv4dO/u7bLqDZdb4bttKby4Yi9ZRZ7ZYVd1ieHpK9oTGVB//2ATQnic6+/vSrX53nfffRw9epSvv/6alJQU1qxZw5o1a0hJSeGrr74iMTGR++67r9LFCyHKpw0Pp9mC+Zi6X4C7sJDEseMo/L1+TizwBo1axfXdYln52EDu6NsctQqWbU/l4tfW8NHawzhdbm+XKISoBZVq+fHz8+Ohhx5i+vTp5b4+efJkZs+eTVFRUZUL9AZp+RF1ndtqJeWRRylatQo0GqKnv0DQtdd6u6x659+UfJ5e+i/bkvIAaBvpz5QrO3Bha+kKE6I+qtGWH39/fyLOMNskKioKf3//ytxaCHEO1EYjsbPfIvDaa8HlIu1/k8mYNQvF4fB2afVKpyaBLLmnLy9ffx7BPjr2HStk1McbGTf/b45kFXu7PCFEDalU+BkzZgzz5s3DfHwtkpMUFRUxd+5cxo4dW+XihBCnp9JqiX5xOiFjxgCQ/d77JIwahT0x0cuV1S9qtYqbejZl1cRBjOnXHK1axW97Mhjyxhpe+GE3+RYJlEI0NJXq9vr666+ZPn06ubm53H777aUDng8cOMCCBQsICQnhySefPGXbi+uvv756qq5h0u0l6puCFStIe/Y53IWFqH18iHr2GQKuvloW9auEgxlFTF++m1UlawSF+Op59NI23NyzKRq1fD+FqMtqdLaX+hz2HFKpVJx8a5VKhcvlquhbeYWEH1EfOVJSSHniCSyb/wEg4IoriHruWTTSBV0pq/dlMO2H3Rwq2Ti1fXQAz1/dkZ7xIV6uTAhxOjUaftasWVOpogYOHFip62qbhB9RXykuF9kffEDmnLfB5UIXE0P0Sy/h26unt0urlxwuN59tOMobv+6nwOoE4OouMUwe1o7oQNNZrhZC1DZZ4bkKJPyI+s6ybRspj0/CkZQEeFqBIiY9ji4y0suV1U/ZRTZe+3U/X2xKRFHApNNw/0WtGHthPEadbJUhRF0h4acKJPyIhsBVVETGzJnkffU1KAoqHx/CJkwg5I7bUev13i6vXvo3JZ/nvt/F5qO5ADQL9eHZqzpwUTsJlULUBRJ+qkDCj2hILLt2ceyF6aWbpOqbNSPyycn41ZNu6LpGURS+357Kiyv2cKzAs0r0Je0jefaqDsSF+Hi5OiEaNwk/VSDhRzQ0iqJQ8P33HJs5E1dmFgA+vXsTeNVV+F98EZqgIO8WWA8V25zM/v2gZ2Vot4JBq+beQa24e2AL6QoTwksk/FSBhB/RULmKish6511yFiwAp2cAL1otvr1743/ZEPwvuQRtcLB3i6xnDmYU8sx3u1h3KBvwdIU9d1VHBrc7/UKwQoiaIeGnCiT8iIbOnpREwQ8/UPDTz9j27TvxgkaDb69eBN18E/4XXYRKIy0Y50JRFJbvTGPaD7tLu8KGnRfFC9eeR4ivjK8SorZI+KkCCT+iMbEdOULhL79S+PPPWHfvLj2ua9KE4FGjCBp+g6wVdI6KbE5mrzzAx38ewelWCPMz8PL153FJBxkQLURtqNbwM3Xq1AoXoFKpmDJlSoWvqwsk/IjGyp6YSN6ixeR99RWu/HwAVD4+BF13HcGjbsUQH+/lCuuHf1PyefTrbew/5tncecQFsTxzVQf8jTovVyZEw1at4edcVnQ+5cb1aEXn/5LwIxo7t8VC/rJl5CxYgP3godLj/pdeQtg992Ds0MGL1dUPVoeLN37dzwdrD6Mo0CTIxIwRnenbUnaMF6KmSLdXFUj4EcJDURTM69eTM38BRSet7O43aBBh996DqXNnL1ZXP2w6ksPEb7aTmOPZCHpU76Y8eHFrIvyNXq5MiIZHwk8VSPgR4lS2AwfIeu99Cn78EdxuAHz79SPs3nvwueACL1dXtxXbnLy4Yg+fb0wEwKhTc3vf5tw9oKUMiBaiGkn4qQIJP0Kcnu3IEbLf/4D8ZcugpGvb98ILiXr6KfTNm3u3uDpu3aEsZvy8j62JeQD46jWMvTCesf1bEGiS8UBCVFWNh58dO3Ywe/ZstmzZQn5+Pu6SvwRLb6xScejQodNcXbdJ+BHi7OxJSWR/8CF5S5eCw4FKpyNk3FjC7r4btVG6dE5HURRW7cvgtV/2syu1AIAAo5a7B7Zk/IAW6DQVH2MphPA419/flfpXtnr1anr27MkPP/xATEwMhw8fpkWLFsTExHD06FH8/PwYMGBApYsXQtR9+rg4oqdNpeWy7/G98EIUh4Psd9/j8JVXUbhqlbfLq7NUKhUXtYvkhwcu5L1R3WgT6UeB1cmMn/dxy4cbyCiwertEIRq8SrX8DBgwgKysLDZs2IDdbiciIoLffvuNiy66iI0bN3L55Zfz+eefc/nll9dEzTVOWn6EqBhFUSj85VeOvfQSzvR0APwuuojIJ59EH9vEy9XVbS63wtKtKTz3/S4KbU4i/A28c2s3ujcP8XZpQtQ7Ndrys2XLFsaOHUtAQACakhVgj09r79WrF3fffXe9XeNHCFFxKpWKgMuG0HL5D4SOGwtaLUW//86hoUNJfuQRzH//jQwvLJ9GreKGC2L57v5+tI7wI6PQxk0fbGDuX0fkeyZEDalU+NFqtfiXrPgaFBSETqcjIyOj9PUWLVqw+6SVYoUQjYPa15eIiRNpsfRbfPr0BqeTwh9/4ujo2zhy9dXkLFyIq6jI22XWSS3C/Vh6Xz+u7ByN063w/LLdPPzVNsx2p7dLE6LBqVT4adWqFQcOHAA8f/G1a9eOb7/9tvT15cuXExUVVT0VCiHqHUOrVjSbO5f4b5cQNHIkKpMJ24GDHJs6jYMDBpI+dSquvDxvl1nn+Bq0zL75fKZc2QGNWsV321K5/p11fLbhKDuS87A56+fCsULUNZUa8/PMM8/wySefkJCQgFarZf78+YwZM4aWLVsCcOjQIV566SWeeOKJai+4NsiYHyGql6uwkPyl35H7xRfYDx8GPHuHxc5+S1aLPo2Nh7O5b+FWsopspcd0GhVto/w5r0kQnWMDOa9JIG2j/GWGmBAlanSqu8PhoKCggJCQEFQqFQCfffYZixcvRqPRcOWVV3LHHXdUunhvk/AjRM1QFAXzhg2kPfMsjqQkVAYDUc89R9B113q7tDopo8DKgvVH2Z6cx86UfPLMjlPOMWjVdIgJoEusJxB1jg2iRZgvarXKCxUL4V2yyGEVSPgRoma58vNJnfRE6ZYZQTfdSOSTT6LWy2rHp6MoCsm5Fnam5LMjOZ+dKXnsSM6n0HrqmKCmIT5MvaYjg9pGeKFSIbynRmd7TZo0ia1bt1a6uDOx2Ww88cQTxMTEYDKZ6NWrF7/++muF73PppZeiUqm4//77a6BKIURVaAIDiX33HcIeuB9UKvK+/Iqjo0fjKJkmL06lUqmIC/Fh2HnR/O/ydnw+rjfbnxnC748NZNaNXRnTrzkXNAvGqFOTmGPmjrl/8+AXW8kstJ395kI0MpUKP7Nnz6Z79+60bt2aKVOmsHPnzmor6I477uD111/n1ltv5c0330Sj0TBs2DD+/PPPc77HkiVLWL9+fbXVJISofiq1mvD77iPu/fdQBwZi3b6DI9ffQMHPv8gU73OkVqtoEe7Htec34dmrOrL4nr788/SljL0wHrUKvt+eyiWvr+Hrv5PkeyrESSrV7VVYWMi3337LV199xW+//YbT6aRdu3bcdNNNjBw5krZt21aqmE2bNtGrVy9mzJjBxIkTAbBarXTq1ImIiAjWrVt31ntYrVbat2/PnXfeyTPPPMN9993HnDlzKlSHdHsJUbvsSUkkP/gQtj17ADB26UzEo4/h26unlyurv3Yk5/G/xTvZnebZQqN3ixBevO48WoT7ebkyIWpOjXZ7+fv7c9ttt7F8+XKOHTvGBx98QGxsLNOmTaNDhw507dqVl19+ucL3XbRoERqNhvHjx5ceMxqNjB07lvXr15OUlHTWe7z66qu43e7S8CSEqPv0cXE0/2IhofdMQGUyYd2+g8TbbyfxrvFYSwKRqJjOsUF8f38/nhzWDqNOzYbDOQx9cy3fbUvxdmlCeF2V50cGBQUxduxYfv75Z9LS0njttdc4cuQITz31VIXvtXXrVtq0aXNKWuvZ0/PX37Zt2854fWJiIi+//DKvvPIKJpPpnN/XZrNRUFBQ5iGEqF1qo5GIhx6i1S8/E3zLzaDVUrx2LUeuu56UiY9jO3LE2yXWO1qNmvEDWvLrIwPp3zoMu9PNQ19u4+1VB6UbTDRq1bI4hMPh4Pvvv+fBBx/kmWeeobCwkNjY2ArfJy0tjejo6FOOHz+Wmpp6xusfe+wxzj//fG666aYKve9LL71EYGBg6SMuLq5C1wshqo82PJyoZ56h5fIfCBjm2R+w4IcfOHz5MI6OGk3et0txm81errJ+iQvxYd6Ynoy9MB6AGT/v48lvd+J0ub1cmRDeUenw43Q6WbFiBbfffjvh4eFce+21rF69mjFjxvDnn39y9OjRCt/TYrFgMBhOOW40GktfP51Vq1axePFiZs2aVeH3nTx5Mvn5+aWPc+leE0LULH2zZjR5/XWaL1qE36BBoFZj3ryZtMmTOdB/AGlTpmDeulVaMM6RRq1iypUdePaqDqhU8MWmJMbO30yRTbbPEI2PtjIXjR07lqVLl5Kbm0tYWBg333wzN910EwMGDChd9LAyTCYTNtup0zKtVmvp6+VxOp08+OCDjB49mh49elT4fQ0GQ7mhSwjhfaZOHYl7710c6enkL11K3uIlOJKSyPtmEXnfLMLQti2RTz2Jb08ZHH0uxvSLJybIxENfbmXN/kxGvreeuWN6EBlg9HZpQtSaSrX8LF26lOuuu650nM+7777LwIEDqxR8wNO9lZaWdsrx48diYmLKvW7BggXs27ePu+++m4SEhNIHeGamJSQkYJZmciHqNV1UFGETJtDy559oumA+gddcg8poxLZvH4m33U7qE//DmZ3t7TLrhcs6RvHl+D6E+enZnVbAdW//xZbEXG+XJUStqdRUd6fTiVZbqUajM3r88cd54403yMnJKTPo+cUXX+Spp54iMTGx3PE4zz33HM8///wZ7/3tt99y7bXXnlMdFZnq7nK5cDhOXXJe1C6dTodGo/F2GaKWufLzyZg1i7wvvwJFQR0YSMQjjxA0cgQqtex3dTaJ2WbumLeJw5nFANzYPY5JQ9sS6ict4aJ+qpfbW2zcuJHevXuXWefHZrPRqVMnQkND2bBhA+CZ1WU2m2nXrh0Ae/fuZe/evafc77rrrmPYsGHcdddd9OrVq9zB1OU5l2+eoiikp6eTJztT1xlBQUFERUVVuQVS1D+W7dtJe/55bLtPrBMU/dxzGNu393JldV+e2c60H/aweEsyAAFGLRMva8utvZqhkf3BRD1TreEnPj4etVrN3r170el0xMfHn/UXjEql4tChQxUufOTIkXz77bc88sgjtGrVivnz57Np0yZWrlzJgAEDABg0aBBr1qw560BHlUpVY4scpqWlkZeXR0REBD4+PvIL14sURcFsNpORkUFQUNA5h1zRsChOJ7kLvyDzzTdxFxeDWk3A0MsIHTdOdo4/B5sTcnjmu12liyJ2iA5g2rUduaBZiJcrE+LcnWv4Oae+q+PjedQlzcjVMb7ndBYsWMCUKVP49NNPyc3NpXPnzvzwww+lwacucLlcpcEnNDTU2+UITgyGz8jIICIiQrrAGiGVVkvIbaPxv+wyjr38EoU//kTBih8pWPEjvn37EjpuLD59+sgfKqfRvXkIyx64kM83HmXmz/vYnVbADe+u5+aeTXnmyg6Y9PJvSjQcdarbq644W3K0Wq0cOXKE5s2bV2gxRVGzLBYLCQkJxMfHly6PIBov6549ZH/8CQU//gguFwDGDh0IHTcW/yFDUNXAuMWGIqvIxqs/7eXrzZ6usLaR/rx9azdaRcjWGKJuq9HtLfLz8ytdWEMif0HWLfLzECcztm9Pk5kzaPnzzwSPHu3ZNmP3blIefYwjI0di2bXL2yXWWWF+Bl4d3oXPx/UizE/PvmOFXD3nT5Zula0xRMNQqfATERHBNddcw8KFCykqKqrumoQQotroY5sQ9dSTtPp9JWEP3I86IADb7j0kjLyRjJkzcZ9h8dTGrl+rMFY82J8+LUIx2108/NU2/rd4B1aHy9ulCVEllQo/jz76KLt27WLUqFFEREQwfPhwvvnmmzOuwCyEEN6kDQ4m/L77aLliuWfbDJeL7I8+5vA111JcMpNUnCoiwMhn43rx0MWtUangy7+TuPbtvziYIX/4ivqrSmN+/v77b7766isWLVpEYmIivr6+XHnlldx4440MGzYMvV5fnbXWmnMd8yNjS+oW+bmIiij8/XfSn5+K89gxAAJvuJ7ISZPQBAZ6ubK666+DWTz05VayiuzoNWqu79aE8QNa0CJcxgKJuqFGx/wc16NHD2bOnElCQgJ//fUXY8eOZe3atdxwww1ERkZW5dbCi/755x+GDh1KQEAA/v7+DBkyhG3btp3xmuOz31QqFYsWLSrzWlFREc8++yxDhw4lJCQElUrFvHnzau4LEOIc+F90ES2W/+DZQR7IX7yEQ8OuIG/Jtyhu2fCzPMe7wQa0CcfucvPl30lc/Poa7vnsH7Yn5Xm7PCHOWbUtgdqnTx/uu+8+7rrrLvz8/CgoKKiuW4tatGXLFi688EIOHz7Ms88+yzPPPMOBAwcYOHAg+/btO+11zzzzzGm3EMnKymLq1Kns2bOHLl261FTpQlSYxs+PqGeeodnnn6Fv0QJXdjZpTz7J0VtHYd2zx9vl1UkRAUYW3NmTxff04ZL2kSgK/PhvOte8/Re3frSBPw9kyWazos6rcvg5cuQIL7/8Mt26daNdu3a88MIL9OrViw8++KA66hO1bMqUKZhMJtavX89jjz3G448/zrp163C73Tz55JPlXvPvv//y7rvv8sQTT5T7+vE9244ePcqMGTNqsnwhKsXnggtosfRbIiY+hsrHB8vWrRy5YTjpL0zHJX/IleuCZiF8dHt3fnlkANd3a4JWreKvg9mM+ngjoz7eyO5U+b6JuqtS4ScpKYnXXnuNnj170qpVK55++mkCAwN5++23SU1N5ZdffmHs2LHVXauoBWvXruWSSy4ps3hjdHQ0AwcO5Icffih3dt9DDz3EddddR//+/cu9p8FgICoqqsZqFqI6qPR6QseNo+WK5fhfPhTcbnI/+4xDlw8j79ul0ppxGm0i/Xl9ZFdWPz6IO/o2R69R89fBbK6YvZb/Ld5BRqHV2yUKcYpKhZ9mzZoxadIkdDodb7zxBklJSaxatYoJEyYQHh5e3TWKWmSz2cpduNHHxwe73c6///5b5vg333zDunXrePXVV2urRCFqlC4qitg33qDpJx+jj4/3dIVNnszRUaOx7t/v7fLqrNhgH567uiMrHxvIFZ2jURTPzLDBM1bz9qqDMj1e1CmVWuJ0xowZjBw5stwd1hszRVGw1KF/4CadpsIL/7Vt25YNGzbgcrlKt4iw2+1s3LgRgJSUE4ucWSwWJk6cyCOPPELz5s1JSEiottqF8Dbfvn1p8d1SsufPJ+udd7H88w9HrruekNtvJ/y+e1H7+nq7xDopLsSHt2/pxp39cpj6wx62J+Ux4+d9fL7hKA9f2obrz2+CVlNtw02FqJQKhx+z2czChQvx9fVlwoQJNVFTvWVxuOjwzM/eLqPU7qmX4aOv2I/43nvv5Z577mHs2LFMmjQJt9vNCy+8QFpaGkCZtZxefvllHA7HaccCCVHfqfR6wu66i8ArruDYSy9R+Otv5HzyCQUrVhA5eTL+Qy6VlcVP44JmIXx7T1+W7UjllR/3kppvZdKiHby96iAPXNSaa7vGSAgSXlPh//J8fHw4cuSI/INvoCZMmMCTTz7JwoUL6dixI+eddx6HDh1i0qRJAPj5edbzSEhIYMaMGUyfPr30mBANlS4mhtjZs4l97110sbE409NJeeghksbfjT0x0dvl1VlqtYprujbh94mDeHJYO0J99RzNNjPxm+1c8voaFv+TjNMlywqI2lepbq+hQ4fy888/c/fdd1d3PfWaSadh99TLvF1GKZOucrswT58+nYkTJ7Jr1y4CAwM577zzSlt32rRpA3imtjdp0oRBgwaVdnelp6cDkJmZSUJCAk2bNkWtlr/sRMPhP2gQvr17k/3Bh2R/+CHFa9dy+KqrCZtwNyFjx6Kupwu71jSjTsP4AS0Z1bsZC9Yf5YM/DpOQbeaxb7YzZ9VBnr6iPRe3l7XhRO2p1ArPe/bsYcSIEZx//vncfffdxMfHlztINiQkpFqKrG2ywvOpevbsWTpdXa1WM2jQINasWXPGa3JzcwkKCipzbPPmzfTo0YO5c+dyxx13VGuNjfHnIrzHduQIx6ZNo3jdegD0LVoQ9eyz+Pbq6eXK6r5im7MkBB0i1+xAo1bx4W0XcFE7CUCias51hedKtfx07NgRgN27d7Nw4cLTnudy1Z3Bv6LyvvrqK/7++29mzpxZ2pLzwgsvkJWVVea8f//9lylTpjBp0iT69OmDrwwIFQ2YIT6euI8/pmD5Co69/DL2w4dJvP12Aq+5mohJk9CetFyEKMvXoOWeQS0Z3acZT3+7k6XbUrn38y0svKs33ZoGe7s80QhUKvw888wzMuangfrjjz+YOnUqQ4YMITQ0lA0bNjB37lyGDh3KQw89VHrehRdeeMq1x1t5evTowbXXXlvmtTlz5pCXl0dqaioAy5YtIzk5GYAHHniAQNlPSdRDKpWKwCuvwG9AfzJnzSL3iy/J/+57ClevIXLSJAKvv07+v/IM/AxaZozoQp7Fwep9mdw5728WTehLqwgZRyhqVpU2Nm2oGnO316FDh7j33nvZsmULhYWFxMfHc/vtt/Poo4+edaPa1atXM3jwYL755huGDx9e5rXmzZtz9OjRcq87cuQIzZs3r3LtDfnnIuoHy44dpD33HLbdnq0x/C65mOipU9HW0yEAtcVsd3LzhxvZnpRHkyATi+/pS1Sg/BsWFXeu3V4SfsrRmMNPfSY/F1EXKE4n2XPnkvnWbHA40ISGEv3CNPwHD/Z2aXVaTrGd4e+u43BWMW0j/fl6Qh8CTTpvlyXqmRoNP1OnTj3rOSqViilTplT01nWChJ/6SX4uoi6x7tlD6qRJ2A4cBCBo5Egin5gkiyOeQVKOmRveXUdGoY2e8SEsuLMnxkrOWhWNU42GnzNNX1apVCiKgkqlqrcDniX81E/ycxF1jdtmI/ONWeTMmweArmlTYl56EZ8LLvBuYXXYnrQCRr63nkKbkyEdInnr5vMlAIlzdq7hp1KLsLjd7lMeTqeTQ4cO8cgjj9C9e3cyMjIqXbwQQjQEaoOByP89QdN5c9FGR+NITOToraNIuvc+rPv2ebu8Oql9dAAf3NYdvUbNL7uPMeK99STlmL1dlmhgqm0FOrVaTXx8PDNnzqR169Y88MAD1XVrIYSo13x796bFd0sJHH4DqNUU/f47R665lpRHH8V2+Ii3y6tz+rQM5ZM7ehDso2NnSj5Xzv6TVXvlD2pRfWpk+d0BAwawYsWKmri1EELUS5qAAGJeeIEWPywjYNjlABSs+JHDV15J6uQnsZcs/SA8Lmwdxg8P9qdLXBD5Fgdj5v3N67/sw+WWOTqi6mok/GzevFm2NRBCiHIYWrSgyeuvE7/0W/wGDwa3m/xvv+Xw5cPI/fJLZALuCU2CTHx9d29G924GwFu/H+SOuZvIKbZ7uTJR31VqkcMFCxaUezwvL48//viDJUuWMG7cuCoVJoQQDZmxXTvi3n0Hy/btZLz+BuaNG0l/7nks27YT9ewzqMvZMqgxMmg1TLu2Exc0C2bykp2sPZDFlW+t5c2bz6dHc1k/SVROtc/2CgsLY9y4cTzzzDP1dsaNzPaqn+TnIuorRVHI/ugjMt+YBW43hnbtiH3rTfRNm3q7tDplX3oh93z2D4ezilGr4J5BLXn4kjboNNLTIDxqdKp7eSv1qlQqgoOD8ff3r+jt6hwJP/WT/FxEfVe8YQMpjz6GKycHtb8/Ma++Iosj/keh1cFz3+9m8RbPGKnOsYHMurErLcJlSwwhKzxXiYSf+kl+LqIhcKSnk/LQw1i2bwcgdMLdhI4Zg0b2vytj+Y40nvx2J/kWByadhilXduDmnnGyl1ojV6vhZ+/evXzzzTekpaXRtm1bxowZc8Y3resk/NRP8nMRDYVit3PslVfJ/fzz0mO6uDiMHTpg7NgRY8cOmDp2RFOymXBjlZZv4bGvt7PuUDYAl7SP5JUbziPUz+DlyoS3VHv4mTNnDm+99Rbr1q0jLCys9PiyZcsYMWIEdvuJ0fctWrRgw4YNZc6rTyT81E/ycxENTf6yH8icMxvH0cRyX9fFxmLs1AlTp44YO3XC2KEDmnr8h2dluN0KH/95hBk/78PuctM81IdPx/YiLsTH26UJL6j28DNkyBA0Gg0//vhj6TGn00mTJk0oKirinXfeoXv37ixfvpynnnqK+++/nzfeeKPqX4kXSPipn+TnIhoqV14e1j17sO7ahWXXLqy7duNILD8Q6Zs1w7d/f8LuuxdtcHAtV+o9u1MLGP/pZpJzLUQFGPl0bE9aR9b/MaiiYqp9e4vdu3fTu3fvMsdWrVpFZmYmjzzyCLfffjsdO3Zk0qRJjBw5UhY5rMeKiop49tlnGTp0KCEhIahUKuaV7E10Og6Hgw4dOqBSqZg5c+Ypr7vdbl599dXSYNK5c2e++OKLKt1TiMZCExSEb58+hI4bR+wbb9Dql59ps2kjTefNJWLiY/gPHYouNhYA+9Gj5H72GYcvH0be4sUobreXq68dHWICWDShL60j/EgvsDLi/fVsS8rzdlmijjrn8JOdnU1cXFyZYytXrkSlUnHdddeVOd6vXz8ST/NXiaj7srKymDp1Knv27KFLly7ndM3s2bPP+DN/6qmneOKJJ7j00kuZPXs2TZs25ZZbbuHLL7+s9D2FaMw0AQH49u7tCUSz3qDVb7/Sev06Yt95G0ObNrjy8kh76mmOjr4N6/793i63VkQFGvn67j50iQsiz+zglg838NfBLG+XJeqgcw4/kZGRpKenlzm2du1afHx8TvkFqdfr0ev11VOhqHXR0dGkpaVx9OhRZsyYcdbzMzIymDp1Kk888US5r6ekpPDaa69x33338cEHH3DXXXexbNky+vfvz+OPP47L5arwPYUQp9IGB+N/0UXEL15ExKRJqHx8sPzzD0euv4FjM2bgNjf8DUKDffV8Pq4X/VqFYra7GDP3b376N/3sF4pG5ZzDT/fu3Zk/fz6FhYUA7Nq1i02bNnHZZZeh1ZZdKHrv3r3EljTBivrHYDAQFRV1zuf/73//o23btowaNarc17/77jscDgf33ntv6TGVSsU999xDcnIy69evr/A9hRCnp9LpCL1zDC2X/4D/pZeA00nOx59w6MorsezY4e3yapyfQcsnd/RgaMco7C43937+D5+uT8At+4KJEuccfp599lmOHj1K69atufjii+nXrx8qlYrJkyefcu63335L3759q7XQekFRwF5cdx61sITTpk2bmD9/PrNmzTrt+hpbt27F19eX9u3blznes2fP0tcrek8hxNnpoqOJnT2b2HffQdekCc7UNI6Ovo385cu9XVqNM2g1zLnlfEZ2j8WtwJTvdjH8vXX8m5Lv7dJEHXDOe3udd955/P7770yfPp3Dhw/Tu3dvJk6cyAUXXFDmvNWrV+Pj48OIESOqvdg6z2GGF2O8XcUJT6aC3rfGbq8oCg888AA33ngjffr0ISEhodzz0tLSiIyMPCXIREdHA5Camlrhewohzp3/4MH49OhJ6sSJFK1eTepjE7EfOkzY/fehasCbUGs1al65oTOtIvyY9dsBtiTmcdWcP7mpR1Mev6wtIb4yPKOxqtDGpn379mX5Wf5iGDRoEDt37qxSUaJ+mDdvHjt37mTRokVnPM9isWAwnLro2PHp6BaLpcL3FEJUjMbPl9i355Dx+uvkfPwJWe+8g+3QIWJefqlBb6KqUqkYP6AlV3dpwks/7uG7bal8sSmRFTvTeGxIG27p2RSt7A3W6FRqV3dxGjofT2tLXaGruUW+CgoKmDx5Mo8//vgpswD/y2QyYbPZTjlutVpLX6/oPYUQFafSaIh8/HEMLVqS9txzFP78M0eTk4l95210kZHeLq9GRQUaefOm87m1VzOe/X4Xe9IKeOa7XXyxKYkXSnaNF42HxN3qpFJ5upnqyqMGx8vMnDkTu93OjTfeSEJCAgkJCSQnezYazM3NJSEhoXTV7+joaNLT0/nvepppaWkAxMTEVPieQojKC7rheprN/QRNcDDWXbtIGD6Cor/+8nZZtaJnfAjL7u/HtGs6EmjSsSetgOHvrfPsE2Z2eLs8UUsk/IhKSUxMJDc3l44dOxIfH098fDz9+/cH4MUXXyQ+Pp7du3cD0LVrV8xmM3v27Clzj40bN5a+XtF7CiGqxqd7d5p/8zWG1q1wZmaSNHYcSXdPwHbokLdLq3FajZrRfZqzauIgRlwQi6LAwo2JXPz6apZuTTnlDzXR8Miu7uWQ7S1O2Lx5Mz169GDu3Lnccccdpce3bNlyygKEGRkZ3H333dxxxx1cc801DB48mMDAQJKTk2nRogXjx49nzpw5gGdg88CBAzl8+DBHjx5Fo9FU6J7laUw/FyGqi6uoiMw33yL3iy/A6QSNhuAbbyTs/vvQhoR4u7xasfFwNk8t/ZeDGUUA9GsVyrRrOtEi3M/LlYmKOtftLWTMjyjXnDlzyMvLK52JtWzZstIuqAceeIBu3brRrVu3Mtccn5nVsWNHrr322tLjsbGxPPzww8yYMQOHw0GPHj1YunQpa9eu5fPPP0ej0QBU6J5CiOqh8fMj6qknCb7lZjJmvkbRypXkLlxI/vffE3bPBIJHj0bdwBet7dUilBUP9ufDtYd5a+UB/jqYzdBZa7m9bzPuHdSKYJkV1uBI+BHlmjlzJkePHi39fMmSJSxZsgSAUaNGnbb15XRefvllgoODef/995k3bx6tW7fms88+45ZbbqnWuoUQlWOIjyfu7TkUb9jIsVdewbZnDxkzZpK3eAkxM17F1LGjt0usUXqtmvsGt+KqzjFM+e5f1uzP5MO1R/jy7yQmDGzJnf3iMek13i5TVBPp9iqHdHvVT/JzEaJ6KC4X+d99T8Ybr+PKzAKtlvD77yd03FhU2ob/N7OiKKzen8krP+5lb7pnV4MIfwMPX9KGkd1jZWp8HVbtu7oLIYRoHFQaDUHXX0eL77/Hf8gQcDrJnDWLo6Nvw94INhtWqVQMbhvBigf788aNXYgNNpFRaOPJb3cy5I0/+OnfNBkUXc9J+BFCCFEubXAwTd6cRcwrL6P288OydStHrr2OvEWLGsUvf7VaxXXnx7LysYE8e1UHQnz1HM4qZsJnW7jx/Q3sSM7zdomikiT8CCGEOC2VSkXgNdfQ4rul+HTvjttsJu3pKSSNvxvz3383ihBk0GoY0y+ePyYN5oGLWmHUqdmUkMPVc/7i0a+2kZpnOftNRJ0i4UcIIcRZ6Zo0oen8eUQ8/jgqnY7itWs5Ovo2jlx3PXmLF+MuWbG9IfMzaHlsSFt+f2wQ15/fBIAlW1MYPHM1r/2yj2Kb08sVinMlA57LIQOe6yf5uQhRO2yHj5Azfz75332HUhJ6NEFBBI0YQfDNN6GLqUMbPNegHcl5vLB8D5uO5ADQJMjEh7d1p0PM6Qfaipp1rgOeJfyUQ8JP/SQ/FyFqlysvj7zFS8j9/HMcJWuCodEQeO01hN1zL/rYJt4tsBYoisLPu47xwvLdJOdaMOk0vHFjF4Z2ivZ2aY2SzPYSQghRozRBQYSOvZOWv/5C7Ntz8OnVC1wu8hcv4dDll5P23HM4Svbwa6hUKhVDO0Wx/IH+9G8dhsXhYsJnW3jztwONYjxUfSXhRwghRJWoNBr8L76YZvPn0eyLhfj27QMOB3lffsWhIZeR/sJ0HBkZ3i6zRgX66Jh7Rw/G9GsOwBu/7ef+hVsx22UcUF0k4UcIIUS18Tn/fJp+8gnNPl2AT48eKA4HuZ99xqFLh5A+dVqDXidIq1Hz7FUdeeWG89BpVCzfmcaI99aTIrPB6pw6F35sNhtPPPEEMTExmEwmevXqxa+//nrW65YsWcKNN95IixYt8PHxoW3btjz22GPk5eXVfNFCCCHK8OnRg6YL5tN07ieYunZFsdnIXbiQQ5cNJfmBBzBv2dJgu4Vu7NGUhXf1JtRXz67UAq54ay3Tl+9mb3qBt0sTJercgOebb76ZRYsW8fDDD9O6dWvmzZvH33//zapVq7jwwgtPe11YWBgxMTFce+21NG3alJ07d/Lee+/RokULtmzZgslkOucaZMBz/SQ/FyHqJkVRMG/cSPbcuRSv+aP0uLFLZ0LHjMH/kksa5LYZyblmxi/4h91pJ0JPh+gAru/WhGu6NiHc3+DF6hqmejnba9OmTfTq1YsZM2YwceJEwPMLrVOnTkRERLBu3brTXrt69WoGDRpU5tiCBQu4/fbb+fDDDxk3btw51yHhp36Sn4sQdZ/t4EGy582j4LvvURwOAIydO9P0g/fRBAV5t7ga4HC5Wb0vk8X/JLNy7zEcLs+vXI1axYDWYVzULoI+LcNoGe6LSqXycrX1X72c7bVo0SI0Gg3jx48vPWY0Ghk7dizr168nKSnptNf+N/gAXHfddQDs2bOn2mttyFavXo1KpSr3sWHDhtLzBg0aVO45Q4cOLXO/oqIinn32WYYOHUpISAgqlYp58+aV+9533HFHufds165dTX7JQohaYmjVipgXXqDVqt8Ju/de1AEBWHfs4OgdY3Dm5Hi7vGqn06i5tEMk742+gE1PXsK0azrSNS4Il1th1b5Mpny3i0teX0Pvl1byyFfb+HpzEsm5Zm+X3eDVqXbGrVu30qZNm1PSWs+ePQHYtm0bcXFx53y/9PR0wNMldiY2mw2bzVb6eUGB9MsCPPjgg/To0aPMsVatWpX5PDY2lpdeeqnMsZj/LHCWlZXF1KlTadq0KV26dGH16tVnfF+DwcBHH31U5lhgYGAFqxdC1GXasDDCH3yAgMuHcnTMndj27uXobbfRbO5ctOHh3i6vRgT76hndpzmj+zTnUGYRP+5MY92hbDYfzeVYgY1vt6bw7dYUAHrGhzD58nac3zTYy1U3THUq/KSlpREdferCUMePpR5fROscvfLKK2g0GoYPH37G81566SWef/75Ct27Mejfv/9Zv3eBgYGMGjXqjOdER0eTlpZGVFQUmzdvPiVQ/ZdWqz3rPYUQDYOhdWuafbqAxDvGYD94iKOjb6Pp/HnoIiO9XVqNahnux/0Xteb+i1pjdbjYcjSXdYeyWXcoi+3J+Ww6ksN176zjis7RTLqsLc1Cfb1dcoNSp7q9LBYLBsOpA8COj9+wWM59uuDChQv5+OOPeeyxx2jduvUZz508eTL5+fmljzN1rzU2hYWFOJ1nXqfC6XRSVFR02tcNBgNRUVEVel+XyyUtcEI0Eob4eJp9ugBtTDT2hASOjhqNIyXF22XVGqNOQ99WYUy8rC1L7u3H2kmDuaFbLCoVLN+RxiWvr+H5ZbvILbZ7u9QGo06FH5PJVKb76Thryd4x5zpja+3atYwdO5bLLruM6dOnn/V8g8FAQEBAmUdlKIqC2WGuM4+qjmUfM2YMAQEBGI1GBg8ezObNm085Z//+/fj6+uLv709UVBRTpkzBUTKIsbLMZjMBAQEEBgYSEhLCfffdd8ZwJYSo//RNm9L800/RxcXhSEoiYfToBr0m0JnEBJl4bWQXlj/QnwFtwnG4FOb+lcCAGav45M8jDXaJgNpUp7q9oqOjSSkn7aeVLI/+37Ek5dm+fTtXX301nTp1YtGiRWhrcfqkxWmh18JetfZ+Z7Pxlo346HwqfJ1er+eGG25g2LBhhIWFsXv3bmbOnEn//v1Zt24d559/PgAtW7Zk8ODBnHfeeRQXF7No0SJeeOEF9u/fz1dffVWpmqOjo5k0aRLdunXD7Xbz008/8c4777B9+3ZWr15dqz9PIUTt0jVpQrPPPiXx9juwJySQcOutxEyfjt+AAd4uzSs6xASw4M6erD2QyUsr9rI7rYCpP+zmWIGV/13eTmaHVUGd+k3StWtXVq1aRUFBQZnWl40bN5a+fiaHDh1i6NChREREsGLFCvz8/Gqy3Aarb9++9O3bt/Tzq6++muHDh9O5c2cmT57MTz/9BMDHH39c5rrRo0czfvx4PvzwQx555BF69+5d4ff+7+Dpm266iTZt2vDUU0+xaNEibrrppkp8RUKI+kIXGekZA3TnWGwHDpA0/m4Cr7uOyP89gaaRTnzo3zqcfg+E8fGfR5i+Yg/v/3EYp1vh6SvaSwCqpDq1zs/GjRvp3bt3mXV+bDYbnTp1IjQ0tHSadWJiImazucz05/T0dPr164fVauWvv/6iefPmla6jsuv8KIqCxVl3ljE3aU3V+g/j5ptvZsmSJZjNZjQaTbnn7Nu3j3bt2jFt2jSefvrpU14/PuB57ty53HHHHef0vhaLBT8/P8aMGXPKLLCTyTo/QjQcbrOZzDffJGfBp6AoaMPDiXr+efwvGuzt0rzqsw1HeXrpvwDc0bc5z17VQQLQSc51nZ861fLTq1cvRowYweTJk8nIyKBVq1bMnz+fhISEMq0Mt912G2vWrCnT7zl06FAOHz7MpEmT+PPPP/nzzz9LX4uMjOTSSy+t8fpVKlWlupnqi7i4OOx2O8XFxaf9j+r4UgQ51bheh8lkIjQ0tFrvKYSo29Q+PkROnoz/ZZeR9uRT2BMSSL73XgKuvoqoJ59skAsinotRvZuhVauY/O1O5q1LwOl2M/XqTqjVEoAqok6FH/CsyjxlyhQ+/fRTcnP/3959R0dVbQ8c/076hJAJgSQktIQWSqTXCAnSRXpJQJCqFCEIiAq+hyBNqYKiCPoTEILSBATpPoIIhCIgCI8iECANCKT3cn5/YOY5JqFO2mR/1pq1zLnn3rv3THB2zj33nmjq1avHzp078XnMNd/ff/8dgPnz5+fY5uvrWyDFj6m7fv06NjY2j7yceP36dQCcjPicjvj4eKKioox6TCFE8WDbqBEe27Zy79PPeLB6NXE/7iDx6DFc3nsX+65dS+SoR/9mlTE30/DulnOsC75FZpZiTs8XpAB6CkXqbi94eFv7ggULiIiIICUlhRMnTtCpUyeDPkFBQTlmuyul8nw97qF6wtC9e/dytP3+++/8+OOPdOzYETMzM+Li4nLcmaeUYvbs2QA5PrMnkZKSQnx8fI72WbNmoZTK8eRoIUTJYGZjg8u77+D+3XqsqlUjMyqK8Hfe5eZrr5Fy6VJhh1co+jWpxGK/+php4LsTt3lvyzmysorMLJYir8iN/IjC5+/vj1arxdvbG2dnZy5evMjKlSuxtbXl448/BuD06dMMGDCAAQMGUL16dZKTk9m6dStHjhxh5MiRNGrUyOCYy5YtIyYmRv+gyh07dhAaGgpAQEAAOp2OyMhIGjZsyIABA/Tzufbu3cuuXbvo3LkzPXr0KMB3QQhR1Gjr18dj6w88+GYVUStWkHzqN2707kOZ/v1xGh9Q4i6F9WpYETONhokbzrLpt1CqOdsx2rdaYYdVLBSpCc9FRUlf2PTTTz8lMDCQP//8k7i4OJycnGjXrh3Tp0/XL29x48YN3nvvPU6ePElkZCRmZmbUrl2bN954g5EjR+YYinZ3d+fmzZu5nu/GjRu4u7sTExNDQEAAwcHBhIeHk5mZSfXq1Rk4cCCTJ0/G0tLykXGb+ucihPif9IgI7syfT/zuh3efmjs44DRpIg59+qDJ44YMU7X++C3e33oeS3MN28e2oo7bsz2rzhQUy1Xdi4qSXvwUV/K5CFHyJAYHEzl7Nml/XgPAtnlzKixcYLLrg+VGKcUb3/7Ggf/eoVb50mwf9yLWFiWrAMxWLFd1F0IIIZ5GqRYtqLp1Ky5Tp6CxtSXp+HGu9+5N4vEThR1agdFoNHzc5wXKlrLiUmQ8i/ddKeyQijwpfoQQQhRrGktLHIcMwWPzJqxrVCfzXhS3hg0j6ssVqKyswg6vQJSzs+bjPvUAWHn4OsHX7xdyREWbFD9CCCFMgnXVqrhv2ICuZ0/IyuLekiXcHj2ajOjowg6tQHSo44J/k0ooBW9v/J24lOdbZ9GUSfEjhBDCZJjZ2uL28Ue4zpmNxtqaxF8Oc6N3H5JOnizs0ArEtG51qOSoJSwmmQ9/vFjY4RRZUvwIIYQwOQ59+uC+cQNWVaqQERHBzdcGc3PIUBKOHDHpVdHtrC1Y7NcAjQa2nA5lzx8RhR1SkSTFjxBCCJNk4+mJ+5bNOPTrBxYWJB0/zu0RrxPStx9xe/ehMjMLO8R80dTdUf+8n6k/nOf2g6RCjqjokeJHCCGEyTK3s8N11kyq79tLmcGvobGxIeXCBcLeeovrXbsRs2ULKi2tsMM0uonta1Lb1Z7opHTaLgpi0oazXAiPLeywigx5zk8u5Dk/xZN8LkKIx8mIjiZ67VoerAskKy4OAAs3V8q98Qa63r0xs7Yu5AiN5+b9RN7ZdI4TIf9bFLpl1bK83tqDlzydTXItMHnI4XOQ4qd4ks9FCPGkMhMSidmwgfurVpEZFQWAhbMzZUcMx8HPDzOttpAjNJ7fb8fwf7/e4KfzEWT+tf5X1XKleKeTJy+/4FrI0RmXFD/PQYqf4kk+FyHE08pKSSFm8xbuf/01GZGRAJg7OuI4bChl/Pww1+kKOULjCY9JZs3RENafuEV8SgYA49vVYGL7GjmWJCqupPh5DlL8FE/yuQghnpVKSyNm2zbur/yK9L8WXdbY2GD/ShfKDHgVrVfdQo7QeBJSM1h64ApfHb4BQNd6rizsVx8by+K/JIYsbyGEEEI8IY2VFWX8/Ki2exeuH3+Edc2aqJQUYrf8QEjfvtzw8ydm6zayUlIKO9TnZmdtwb9eqcP8vvWwNNew81wE/iuDuRtf/HN7UlL8iBwuXLhAv379qFq1Kra2tpQrVw4fHx927NiRo29WVhbLly+nQYMGaLVaypYtS9u2bfn9998N+kVERDBy5Eg8PDzQarVUq1aNSZMmcf9+3o9gT09Pp06dOmg0GhYuXGj0PIUQ4p80lpY49OyJx/ZtVFkfiH3XrmBpScq5c0RMncqfvm24M38BaX+NDhVnfk0qsXZEcxxsLfn9dgw9lx3hYnhcYYdVIKT4ETncvHmT+Ph4hgwZwtKlS5k2bRoA3bt3Z+XKlQZ9hw8fzvjx42ncuDGfffYZH3zwAZUrV+bu3bv6PgkJCbRs2ZKtW7cyePBgPvvsM7p06cKyZcto3749WXmsvfPZZ59x69at/EtUCCHyoNFosG3UiAoLF1Aj6CBOEydi6eZGZmwsD775hmsdOnL7zbHF/qGJLaqWZdubL1LVqRThsSn0/fIo+y/eKeyw8p8SOcTGxipAxcbG5ro9OTlZXbx4USUnJxdwZIUnIyND1a9fX3l6eurbNmzYoAD1ww8/PHLfwMBABaidO3catH/wwQcKUKdPn86xz507d5ROp1MzZ85UgFqwYMFjYyyJn4sQouBkZWSouJ//o24OH6EuetbSv/58uYu6v3adyohPKOwQn1lMYpoa+FWwqvLeTlV16k/qwMXIwg7pmTzu+zubjPyIJ2Jubk6lSpWIiYnRty1evJhmzZrRq1cvsrKySExMzHXfuL+epeHi4mLQ7ur68BZLbS63lE6ZMgVPT08GDRpkpAyEEOL5aMzNKd32JSr/39dU3fUTZQYNwqxUKdKuX+fO7Nlc69CBmC0/FMuV5HW2lqwa1pTeDSuQmaUYu/40p2+Z7oKwUvyIPCUmJhIVFcW1a9f45JNP2L17N+3atQMeFjQnTpygadOmvP/+++h0Ouzs7KhatSobN240OI6Pjw9mZma89dZbBAcHExoayq5du5gzZw49e/akVq1aBv1PnDjBmjVrWLJkicncfimEMC3WVatS/t//ovqhIFym/RurKlXIjI4m4l//4uag10i5fLmwQ3xqluZmzOtbjzaeTqSkZzFi9Umu3Uso7LDyhUVhB2BKlFKo5OTCDkNPo9U+V/Hw9ttvs2LFCgDMzMzo3bs3y5YtA+DatWsopfj++++xsLBg/vz56HQ6li5dSv/+/bG3t6dz584A1KlTh5UrVzJ58mRatmypP/6QIUP4+uuvDc6plCIgIAB/f39atmxJSEjIM8cvhBD5zdzODseBAynj58eDb9dy7/PPST59mhu9++A4aBDlAsZhbmdX2GE+MUtzM74Y2IgBK4P5PTSWwf93gh/e9MbF3rQeHyLFjxGp5GQuN2pc2GHoeZ7+DY2t7TPvP2HCBPr27Ut4eDgbN24kMzOTtL/WwElIePjXwP379wkODqZ58+bAw0nRHh4ezJ49W1/8AFSoUIFmzZrRpUsXqlSpwuHDh/n0008pV66cwZ1cq1ev5vz582zevPmZ4xZCiIKmsbSk7Ijh2L/ShTsffUz83r08WLOGuF27cHp7EqXbd8DcrlRhh/lEbK0s+GZoU/p+eYwbUYkMXXWSDaNaYG9jWdihGY0UPyJPtWrV0l+SGjx4MB07dqRbt24cP35cP0/Hw8NDX/gA2NnZ0a1bN9atW0dGRgYWFhYcOXKErl27EhwcTJMmTQDo2bMn9vb2fPjhhwwfPpw6deoQFxfH1KlTeeedd6hUqVLBJyyEEM/Jsnx5Ki5dQsLhX4mcPYv0m7eImDKVCItpaOvVo1TLlpTybom2Xj00lkW3mChrZ82aYc3ovfwo/42IY/Ta31g1rCnWFsX/QYggxY9RabRaPE//Vthh6GmMvDZN3759GTVqFFeuXMHNzQ3IOYkZwNnZmfT0dBITE9HpdKxYsQIXFxd94ZOte/fuzJgxg6NHj1KnTh0WLlxIWloa/v7++stdoX89SyM6OpqQkBDc3NywsrIyal5CCGFsdq1bUfXHH3mwahUxP2wl/dYtkk+fJvn0aaI+/xyNrS22TRqj9fLC2rMWNrU8saxUCY1Z0ZmKW7msLauHNcV/xTGOXrvP2xt/59P+DU1iQVQpfoxIo9E812Wmoi75r/lMsbGxeHp6Ur58ecLCwnL0Cw8Px8bGhtKlSwNw584dMjMzc/RLT08HICPj4Rozt27dIjo6mrp1cz5Gfu7cucydO5czZ87QoEEDY6UkhBD5xszamnKjR1Nu9GjSQkNJPHaMpGPHSDwWTGZ0NIm/HCbxl8P/629ri7WnJ9a1PLFr1Qo7H59CHx3yqqDjy9caM3z1SXaei6BR5TIMb+VRqDEZgxQ/Ioe7d+/i7Oxs0Jaens63336LVqulTp06APj7+7N06VL2799Phw4dAIiKimL79u20bdsWs7/+gqlZsyb79u0jKCiINm3a6I/53XffAdCwYUMAxo8fT8+ePXPEMmrUKIYOHUqPHj3w8Cj+/+iEECWPVcWKWPXrR5l+/VBZWaRevkzSyVOkXLpE6qVLpP75J1lJSSSfOUPymTPEfPc95uXKoevRHYc+fbCuWrXQYm9dw4kPutVl2rY/WLTvMp29yuPmULxXvZeFTXNR0hc27dWrF3Fxcfj4+FChQgUiIyMJDAzk0qVLLFq0iEmTJgEPR3QaNmxIQkICkyZNQqfT8eWXX3L79m2OHTtG/fr1Abh8+TKNGzdGo9EQEBBAlSpVOHToEN999x0dOnRg3759ecYSEhKCh4cHCxYsYPLkyY+M29Q/FyGE6VIZGaTduEHKpcsknztH3K5dZP5t+R9tgwY49O1D6c4vF8rE6awsRb8Vx/jtZjQd67iwcnCTx+9UCGRhU/HM/P39MTMzY/ny5YwZM4bFixdTsWJFtm/fri984OF8n19//ZX27dvzySef8K9//YuKFSty6NAhfeED4OnpyW+//Ubnzp1Zt24dAQEBHD16lMmTJ7Nt27ZCyFAIIYoWjYUF1jVqoOvWlfL/ep8aQQep+Pky7F56CczNST57loh/T+Nahw7E7dlT4PGZmWmY2+sFLMw07Lt4h70XIgs8BmOSkZ9clPSRn+JKPhchhClKv3uXuB9/JHrjJtL/Wu+wdKdOlP9gGhZlyxZoLPP3XOKLoGu46mzYP8kXO+uiNXtGRn6EEEIIE2Dp7EzZ11+n2s4dlHvzTbCwIH7vXq537Ubc7t0FGktA2xpUdrQlIjaFxfuuFOi5jUmKHyGEEKIY0FhZ4TQ+AI+NG7D29CQzOpqwiZMIfWsCGX+bH5SftFbmzOrpBcDqozc4HxpbIOc1Nil+hBBCiGLEpk4dPDZtzDEKlHT6dIGc37emE93ru5GlYOrWc2RkFr+FXKX4EUIIIYqZ3EaBbg0fQcKhQwVy/n93rU1pGwv+CIvj22M3C+ScxiTFjxBCCFFM2dSpg/v331HK1weVksLtseOI3bEj38/rXNqGKS8/XP5o0b7LhMcUnUW9n4QUP0IIIUQxZqbVUmnZMuy7d4OMDMLfeZcH367N9/MOaFqZRpUdSEzLZMy634hLSc/3cxqLFD9CCCFEMaextMTt448pM/g1AO7MncvdpUvJz6fZmJlpmN+3PmVsLfk9NJYh35wgvpgUQFL8CCGEECZAY2aGy9SpOE14C4D7y78kcsaHqPT8K0iqO9ux7vXm6LSWnLkVw9BVJ0lIzci38xmLFD9CCCGEidBoNJQbPZryM6aDRkPMhg1cad6C26PH8GDtOlKvXzf6aFBdNx2BrzfH3saC325GM2zVCRKLeAEkxY8QQghhYsr070+FTz7BvGxZspKSSAgK4s6cOVzv8gp/vtSW8Pf/ReLRo0YrhLwq6Fg7ojmlbSw4GRLN8NUnSUorugWQFD9CCCGECbLv3Ikah3/BfctmnN6ehG3LFmisrMiIjCT2hx+4NXwEIf37k3DokFGKoPqVHPh2eDPsrC04fuMBr685RXJaphEyMT5Z2ysXsrZX8SSfixBCPFpWcjJJv50m4T8/E7PlB1RqKgA2detSbuyb2L30EhqN5rnO8dvNBwz+vxMkpmXSoqojKwY1QWdraYzwH0vW9hJCCCGEATOtFrtWL1L+gw+ofmA/jsOHo9FqSblwgdA3x3Kjdx/iDxx4rpGgxlUcWT28GaWszAm+/oDey49w836iEbN4flL8iByGDh2KRqPJ8xUWFqbve/ToUVq1aoWtrS3ly5dn/PjxJCQkGBwvISGB6dOn07lzZxwdHdFoNKxevTrXcz/qvB06dMjPtIUQokSxcHLC5d13qP7zAcqOHImZrS2p//0voeMCuP36G6SFhDzzsZu6O7JxdEtcdTZcu5dIz8+PcDLkgfGCf05Fay16USSMGjWK9u3bG7QppRg9ejTu7u5UqFABgLNnz9KuXTtq167N4sWLCQ0NZeHChVy9epXdf1tpOCoqipkzZ1K5cmXq169PUFBQnudeuzbng7lOnTrF0qVL6dixo3ESFEIIoWfh6IjzpImUHT6M+6tW8+Cbb0g8coTr3bpT9o03KDvyDcyeYSpBXTcd28e+yIg1pzgfFsvAr44zr+8L9GpYMR+yeDpS/IgcWrZsScuWLQ3afv31V5KSkhg4cKC+7f3336dMmTIEBQXpr626u7vzxhtvsG/fPn2x4urqSkREBOXLl+fUqVM0bdo0z3MPGjQoR1tQUBAajYYBAwYYIz0hhBC5MHdwwHniBBx69SRy9hwSf/2VqC++IHbHDspP+zd2Pj5PfUxnexs2jGrBxA1n2XvhDhM3/M6NqCQmtq/x3HOLnodc9hJPZP369Wg0Gl599VXg4aSy/fv3M2jQIINJZYMHD8bOzo6NGzfq26ytrSlfvvwznTc1NZUtW7bg6+tLxYqF/9eCEEKYOit3dyp9tZIKS5Zg4eJC+u3b3B45itCA8WTcv//Ux7O1smD5wMaM8q0KwKc/X+Wt78+Skl54d4JJ8WNESinSUzOLzMtYN/Klp6ezceNGvL29cXd3B+D8+fNkZGTQpEkTg75WVlY0aNCAM2fOGOXcu3btIiYmxmDESQghRP7SaDTYd+5E1Z9+wnH4cDA3J37/fm707kPy2bNPfTwzMw1TX67NvD4vYGGm4cffwzl8Ncr4gT8huexlRBlpWax861Bhh6E3cqkvltbmz32cvXv3cv/+fYMCJCIiAnh4SeufXF1dOXz48HOfFyAwMBBra2v69u1rlOMJIYR4cuZ2pXB59x10PboTNmEiaTduEPLaYFymTqHMgAFPfenKv2llKpWx5fStaDrUccmnqB9PRn7EY61fvx5LS0v8/Pz0bcnJycDDS1r/ZGNjo9/+POLi4vjpp5/o0qULDg4Oz308IYQQz8bG0xP3TRsp3bEjpKdzZ+YsIqZMIesZ/l/vXb0c49rWyIcon5yM/BiRhZUZI5f6FnYYehZWz1/bJiQksH37djp16kTZsmX17VqtFng4J+efUlJS9Nufx5YtW0hJSZFLXkIIUQSY29lRYekSHnyziruLFxO7/UdSLl2m4qdLsapSpbDDeypS/BiRRqMxymWmomTbtm057vKC/13uyr789XcRERG4ubk997kDAwPR6XR07dr1uY8lhBDi+Wk0GsqOGI6NlxdhkyaRevkyN/r2w2ncWHS9e2NeunRhh/hE5LKXeKTAwEDs7Ozo3r27QbuXlxcWFhacOnXKoD0tLY2zZ8/SoEGD5zpvREQEBw8epE+fPrleWhNCCFF4SjVvhscPW9A2bEhWfDx3PvqYP33bEDlzFqnXrxd2eI8lxY/I07179zhw4AC9evXC1tbWYJtOp6N9+/asW7eO+Ph4ffvatWtJSEigX79+z3Xu77//nqysLLnkJYQQRZSliwtV1qym/IzpWFWrRlZSEtHr13O9yyvcGj6C+IMHUZlFc2FTuewl8rRhwwYyMjLyLEDmzJmDt7c3vr6+jBw5ktDQUBYtWkTHjh3p3LmzQd9ly5YRExNDeHg4ADt27CA0NBSAgIAAdDqdQf/AwEDc3Nxo06aN8RMTQghhFBorK8r074+Dvz9JwcE8WLuOhIMHSTx6lMSjRzF3Kkfpl9pSul1bbFu0wKyojOSrIiYlJUW9++67ytXVVdnY2KhmzZqpffv2PdG+oaGhql+/fkqn06nSpUur7t27q2vXrj11DLGxsQpQsbGxuW5PTk5WFy9eVMnJyU997OKkRYsWytnZWWVkZOTZ5/Dhw8rb21vZ2NgoJycnNXbsWBUXF5ejX5UqVRSQ6+vGjRsGfS9duqQANWnSpKeKt6R8LkIIUZSl3r6tIufNV5eaNVcXPWvpX5caNlK3x7+lYrZvVxkxMfly7sd9f2fTKGWkJ+EZyYABA9i8eTMTJkygRo0arF69mpMnT3Lw4EFatWqV534JCQk0atSI2NhY3n77bSwtLfnkk09QSnH27FmDO5UeJy4uDp1OR2xsrMHTi7OlpKRw48YNPDw8sHmG9U5E/pDPRQghio6stDSSjp8g/ucDJPznIBl37/5vo7k5lb/5hlLNmxn1nI/7/s5WpIqfEydO0Lx5cxYsWMDkyZOBh19oXl5eODs7c/To0Tz3nT9/Pu+99x4nTpzQrx116dIlvLy8ePfdd5k7d+4TxyHFT/Ekn4sQQhRNKiuLlAsXiP/5ZxJ+/pm0m7eocfQo5naljHqeJy1+itSE582bN2Nubs7IkSP1bTY2NowYMYJjx45x+/btR+7btGlTg0Uza9WqRbt27QzWmRJCCCFEwdKYmaF94QWcJ0yg6o4dVP/5gNELn6dRpIqfM2fOULNmzRzVWrNmD4fFzuaxnkhWVhbnzp3Lsc5U9r7Xrl0zuCPpn1JTU4mLizN4CSGEECJ/WDg5Fer5i1TxExERkedaUYD+TqF/evDgAampqc+0L8BHH32ETqfTvypVqvQs4QshhBCiGChSxU9ycnKea0Vlb89rP8h7nalH7QswdepUYmNj9a9HXV4TQgghRPFWpJ7zo9Vq81wrKnt7XvtB3utMPWpfeFg0PctThIvQXHGBfB5CCCGeTJEa+XF1dc1zrSggz/WiHB0dsba2fqZ9n4WlpSUASUlJRjumeH7Zn0f25yOEEELkpkiN/DRo0ICDBw8SFxdnMOn5+PHj+u25MTMz44UXXsixzlT2vlWrVqW0ERdbMzc3x8HBgbt/PbPA1tYWjUZjtOOLp6OUIikpibt37+Lg4IC5uWktLiuEEMK4ilTx07dvXxYuXMjKlSv1z/lJTU1l1apVNG/eXD8R+datWyQlJVGrVi2DfadMmcKpU6f0d31dvnyZ//znP/pjGVP58uUB9AWQKHwODg76z0UIIYTIS5F6yCGAn58fW7duZeLEiVSvXp01a9Zw4sQJfv75Z3x8fABo06YNhw4dMpjjER8fT8OGDYmPj2fy5MlYWlqyePFiMjMzOXv2LE5PcVvdkz4kCSAzM5P09PRnS1YYjaWlpYz4CCFECfek399FauQH4Ntvv2XatGmsXbuW6Oho6tWrx86dO/WFT15Kly5NUFAQEydOZPbs2WRlZdGmTRs++eSTpyp8npa5ubl86QohhBDFSJEb+SkKnmbkRwghhBBFQ7Fc3kIIIYQQIr9J8SOEEEKIEkWKHyGEEEKUKFL8CCGEEKJEKXJ3exUF2XPAZXV3IYQQovjI/t5+3L1cUvzkIj4+HkBWdxdCCCGKofj4eHQ6XZ7b5Vb3XGRlZREeHk7p0qWNumxFXFwclSpV4vbt2yXuFvqSmntJzRsk95KYe0nNGyT3opK7Uor4+Hjc3NwwM8t7Zo+M/OTCzMyMihUr5tvx7e3tC/0XpLCU1NxLat4guZfE3Etq3iC5F4XcHzXik00mPAshhBCiRJHiRwghhBAlihQ/Bcja2prp06djbW1d2KEUuJKae0nNGyT3kph7Sc0bJPfilrtMeBZCCCFEiSIjP0IIIYQoUaT4EUIIIUSJIsWPEEIIIUoUKX6EEEIIUaJI8VMAUlNTee+993Bzc0Or1dK8eXP2799f2GEZVUJCAtOnT6dz5844Ojqi0WhYvXp1rn3/+9//0rlzZ+zs7HB0dOS1117j3r17BRuwkZw8eZJx48ZRt25dSpUqReXKlfHz8+PKlSs5+ppS3gAXLlygX79+VK1aFVtbW8qVK4ePjw87duzI0dfUcv+nOXPmoNFo8PLyyrHt6NGjtGrVCltbW8qXL8/48eNJSEgohCifX1BQEBqNJtdXcHCwQV9TyvvvTp8+Tffu3XF0dMTW1hYvLy8+/fRTgz6mlPvQoUPz/Mw1Gg1hYWH6vsUpb3nCcwEYOnQomzdvZsKECdSoUYPVq1fTpUsXDh48SKtWrQo7PKOIiopi5syZVK5cmfr16xMUFJRrv9DQUHx8fNDpdMydO5eEhAQWLlzI+fPnOXHiBFZWVgUb+HOaN28eR44coV+/ftSrV4/IyEiWLVtGo0aNCA4O1n8ZmlreADdv3iQ+Pp4hQ4bg5uZGUlISW7ZsoXv37qxYsYKRI0cCppn734WGhjJ37lxKlSqVY9vZs2dp164dtWvXZvHixYSGhrJw4UKuXr3K7t27CyFa4xg/fjxNmzY1aKtevbr+v00173379tGtWzcaNmzItGnTsLOz49q1a4SGhur7mFruo0aNon379gZtSilGjx6Nu7s7FSpUAIph3krkq+PHjytALViwQN+WnJysqlWrplq2bFmIkRlXSkqKioiIUEopdfLkSQWoVatW5eg3ZswYpdVq1c2bN/Vt+/fvV4BasWJFQYVrNEeOHFGpqakGbVeuXFHW1tZq4MCB+jZTyzsvGRkZqn79+srT01PfZuq5+/v7q7Zt2ypfX19Vt25dg20vv/yycnV1VbGxsfq2r776SgFq7969BR3qczt48KAC1KZNmx7Zz9TyVkqp2NhY5eLionr16qUyMzPz7GeKuf/T4cOHFaDmzJmjbytueUvxk8/eeecdZW5ubvALoZRSc+fOVYC6detWIUWWfx5V/Dg7O6t+/frlaK9Zs6Zq165dAURXMBo1aqQaNWqk/7mk5K2UUl27dlUuLi76n00590OHDilzc3N17ty5HMVPbGyssrCwUO+8847BPqmpqcrOzk6NGDGioMN9bn8vfuLi4lR6enqOPqaYt1JKLV++XAHq4sWLSimlEhISchRBppr7P40ZM0ZpNBp148YNpVTxzFvm/OSzM2fOULNmzRyLvTVr1gx4OFRYUoSFhXH37l2aNGmSY1uzZs04c+ZMIURlfEop7ty5Q7ly5QDTzzsxMZGoqCiuXbvGJ598wu7du2nXrh1g2rlnZmYSEBDA66+/zgsvvJBj+/nz58nIyMiRu5WVFQ0aNCjWuQ8bNgx7e3tsbGx46aWXOHXqlH6bqeZ94MAB7O3tCQsLw9PTEzs7O+zt7RkzZgwpKSmA6eb+d+np6WzcuBFvb2/c3d2B4pm3FD/5LCIiAldX1xzt2W3h4eEFHVKhiYiIAMjz/Xjw4AGpqakFHZbRBQYGEhYWhr+/P2D6eb/99ts4OTlRvXp1Jk+eTK9evVi2bBlg2rl/+eWX3Lx5k1mzZuW6/XG5F8d/+1ZWVvTp04elS5eyfft2Zs+ezfnz52ndurX+C84U8wa4evUqGRkZ9OjRg06dOrFlyxaGDx/Ol19+ybBhwwDTzf3v9u7dy/379xk4cKC+rTjmLROe81lycnKu653Y2Njot5cU2bk+7v0oTuvD/NOlS5cYO3YsLVu2ZMiQIYDp5z1hwgT69u1LeHg4GzduJDMzk7S0NMB0c79//z4ffPAB06ZNw8nJKdc+j8u9OP7b9/b2xtvbW/9z9+7d6du3L/Xq1WPq1Kns2bPHJPOGh3e0JiUlMXr0aP3dXb179yYtLY0VK1Ywc+ZMk83979avX4+lpSV+fn76tuKYt4z85DOtVpvrX7bZw6RarbagQyo02bma6vsRGRnJK6+8gk6nY/PmzZibmwOmn3etWrVo3749gwcPZufOnSQkJNCtWzeUUiab+7///W8cHR0JCAjIs8/jci+OeeemevXq9OjRg4MHD5KZmWmyeWfHPWDAAIP2V199FYBjx46ZbO7ZEhIS2L59O506daJs2bL69uKYtxQ/+czV1VU/JPh32W1ubm4FHVKhyR4Szev9cHR0LHYjANliY2N5+eWXiYmJYc+ePQafqynnnZu+ffty8uRJrly5YpK5X716lZUrVzJ+/HjCw8MJCQkhJCSElJQU0tPTCQkJ4cGDB4/N3ZT+7VeqVIm0tDQSExNNNu/suF1cXAzanZ2dAYiOjjbZ3LNt27aNpKQkg0te8Pj/xxXFvKX4yWcNGjTgypUrxMXFGbQfP35cv72kqFChAk5OTgaTI7OdOHGi2L4XKSkpdOvWjStXrrBz507q1KljsN1U885L9hB3bGysSeYeFhZGVlYW48ePx8PDQ/86fvw4V65cwcPDg5kzZ+Ll5YWFhUWO3NPS0jh79myxzD0v169fx8bGBjs7O5PNu3HjxgAGD/WD/83bdHJyMtncswUGBmJnZ0f37t0N2otl3oV9u5mpCw4OzvGcn5SUFFW9enXVvHnzQows/zzqVvfRo0crrVZrcIv/gQMHFKCWL19egFEaR0ZGhurevbuysLBQP/30U579TC1vpZS6c+dOjra0tDTVqFEjpdVqVXx8vFLK9HK/d++e2rp1a45X3bp1VeXKldXWrVvVuXPnlFJKde7cWbm6uqq4uDj9/l9//bUC1O7duwsrhWd29+7dHG1nz55VlpaWqnv37vo2U8tbKaVOnz6tAPXqq68atA8YMEBZWFiosLAwpZRp5q7Uw8/ewsJCvfbaa7luL255a5RSqrAKr5LCz8+PrVu3MnHiRKpXr86aNWs4ceIEP//8Mz4+PoUdntEsW7aMmJgYwsPDWb58Ob1796Zhw4YABAQEoNPpuH37Ng0bNsTBwYG33nqLhIQEFixYQMWKFTl58mSxuwQyYcIEli5dSrdu3QwmAGYbNGgQgMnlDdCrVy/i4uLw8fGhQoUKREZGEhgYyKVLl1i0aBGTJk0CTDP33LRp04aoqCj++OMPfdvp06fx9vamTp06jBw5ktDQUBYtWoSPjw979+4txGifTdu2bdFqtXh7e+Ps7MzFixdZuXIllpaWHDt2jNq1awOml3e2ESNG8M033+Dn54evry9BQUFs2rSJqVOnMnfuXMB0c1+2bBkBAQHs2bOHTp065dhe7PIu7OqrJEhOTlaTJ09W5cuXV9bW1qpp06Zqz549hR2W0VWpUkUBub6yH4allFJ//PGH6tixo7K1tVUODg5q4MCBKjIysvACfw6+vr555vzPf16mlLdSSn333Xeqffv2ysXFRVlYWKgyZcqo9u3bq+3bt+foa2q55ya3Jzwr9fBpuN7e3srGxkY5OTmpsWPHGvx1XJwsXbpUNWvWTDk6OioLCwvl6uqqBg0apK5evZqjrynlnS0tLU3NmDFDValSRVlaWqrq1aurTz75JEc/U8y9RYsWytnZWWVkZOTZpzjlLSM/QgghhChRZMKzEEIIIUoUKX6EEEIIUaJI8SOEEEKIEkWKHyGEEEKUKFL8CCGEEKJEkeJHCCGEECWKFD9CCCGEKFGk+BFCCCFEiSLFjxBCCCFKFCl+hMgnQ4cOxd3dvbDDeG5Pk8fQoUOxs7PL34AKkEajYcaMGQV+3qysLLy8vJgzZ06Bn9sYQkJC0Gg0LFy40CjHa9GiBe+++65RjiUESPEjxFPRaDRP9AoKCirsUPNNUlISM2bMMOkcC9t3333H7du3GTduXGGH8ki7du0qkOLwvffe4/PPPycyMjLfzyVKBovCDkCI4mTt2rUGP3/77bfs378/R3vt2rX56quvyMrKKsjw8sU/80hKSuLDDz8EHq5kbsqSk5OxsCj4/00uWLCA/v37o9PpCvzcT2PXrl18/vnn+V4A9ejRA3t7e7744gtmzpyZr+cSJYMUP0I8hUGDBhn8HBwczP79+3O0mxJLS8vCDsEolFKkpKSg1WqfeB8bG5t8jCh3Z86c4ffff2fRokUFfu6iyszMjL59+/Ltt9/y4YcfotFoCjskUczJZS8h8sk/58r8fR7E559/TtWqVbG1taVjx47cvn0bpRSzZs2iYsWKaLVaevTowYMHD3Icd/fu3bRu3ZpSpUpRunRpXnnlFS5cuPDIWGJiYjA3N+fTTz/Vt0VFRWFmZkbZsmVRSunbx4wZQ/ny5XPNIyQkBCcnJwD9l1Bu82LCwsLo2bMndnZ2ODk5MXnyZDIzMx/7np06dYpOnTpRrlw5tFotHh4eDB8+3KBPVlYWS5YsoW7dutjY2ODi4sKoUaOIjo426Ofu7k7Xrl3Zu3cvTZo0QavVsmLFCry8vHjppZdynDsrK4sKFSrQt29ffVteuY0YMQI3Nzesra3x8PBgzJgxpKWl6fvExMQwYcIEKlWqhLW1NdWrV2fevHlPNBK4bds2rKys8PHxMWifMWMGGo2GK1euMGjQIHQ6HU5OTkybNg2lFLdv39aPkJQvXz7X4unu3buMGDECFxcXbGxsqF+/PmvWrDHo8/ff05UrV1KtWjWsra1p2rQpJ0+e1PcbOnQon3/+uf59yn7906OOARAZGcmwYcOoWLEi1tbWuLq60qNHD0JCQgz6dejQgZs3b3L27NnHvodCPI6M/AhRwAIDA0lLSyMgIIAHDx4wf/58/Pz8aNu2LUFBQbz33nv8+eeffPbZZ0yePJlvvvlGv+/atWsZMmQInTp1Yt68eSQlJbF8+XJatWrFmTNn8pyY7ODggJeXF7/88gvjx48H4Ndff0Wj0fDgwQMuXrxI3bp1ATh8+DCtW7fO9ThOTk4sX76cMWPG0KtXL3r37g1AvXr19H0yMzPp1KkTzZs3Z+HChRw4cIBFixZRrVo1xowZk+f7cvfuXTp27IiTkxNTpkzBwcGBkJAQfvjhB4N+o0aNYvXq1QwbNozx48dz48YNli1bxpkzZzhy5IjBSNXly5cZMGAAo0aN4o033sDT0xN/f39mzJhBZGSkQZH366+/Eh4eTv/+/fOMMTw8nGbNmhETE8PIkSOpVasWYWFhbN68maSkJKysrEhKSsLX15ewsDBGjRpF5cqVOXr0KFOnTiUiIoIlS5bkeXyAo0eP4uXlleeIm7+/P7Vr1+bjjz/mp59+Yvbs2Tg6OrJixQratm3LvHnzCAwMZPLkyTRt2lRfRCUnJ9OmTRv+/PNPxo0bh4eHB5s2bWLo0KHExMTw1ltvGZxn/fr1xMfHM2rUKDQaDfPnz6d3795cv34dS0tLRo0aRXh4eK6XfZ/0GAB9+vThwoULBAQE4O7uzt27d9m/fz+3bt0y+H1u3LgxAEeOHKFhw4aPfA+FeCwlhHhmY8eOVXn9MxoyZIiqUqWK/ucbN24oQDk5OamYmBh9+9SpUxWg6tevr9LT0/XtAwYMUFZWViolJUUppVR8fLxycHBQb7zxhsF5IiMjlU6ny9GeW6wuLi76nydNmqR8fHyUs7OzWr58uVJKqfv37yuNRqOWLl2aZx737t1TgJo+fXquOQNq5syZBu0NGzZUjRs3fmR8W7duVYA6efJknn0OHz6sABUYGGjQvmfPnhztVapUUYDas2ePQd/Lly8rQH322WcG7W+++aays7NTSUlJ+rZ/5jl48GBlZmaWa4xZWVlKKaVmzZqlSpUqpa5cuWKwfcqUKcrc3FzdunUrz/yUUqpixYqqT58+OdqnT5+uADVy5Eh9W0ZGhqpYsaLSaDTq448/1rdHR0crrVarhgwZom9bsmSJAtS6dev0bWlpaaply5bKzs5OxcXFKaX+93tatmxZ9eDBA33f7du3K0Dt2LFD35bX7/+THiM6OloBasGCBY98T7JZWVmpMWPGPFFfIR5FLnsJUcD69etnMJG1efPmwMP5RH+fXNu8eXPS0tIICwsDYP/+/cTExDBgwACioqL0L3Nzc5o3b87Bgwcfed7WrVtz584dLl++DDwc4fHx8aF169YcPnwYeDj6oZTKc+TnSY0ePTrHua9fv/7IfRwcHADYuXMn6enpufbZtGkTOp2ODh06GLwHjRs3xs7OLsd74OHhQadOnQzaatasSYMGDdiwYYO+LTMzk82bN9OtW7c85wRlZWWxbds2unXrRpMmTXJsz77ks2nTJlq3bk2ZMmUMYmzfvj2ZmZn88ssvj3wf7t+/T5kyZfLc/vrrr+v/29zcnCZNmqCUYsSIEfp2BwcHPD09Dd7zXbt2Ub58eQYMGKBvs7S0ZPz48SQkJHDo0CGD8/j7+xvEkf078bjP8WmOodVqsbKyIigoKMdly9xkv6dCPC8pfoQoYJUrVzb4ObsQqlSpUq7t2V8KV69eBaBt27Y4OTkZvPbt28fdu3cfed7sL57Dhw+TmJjImTNnaN26NT4+Pvri5/Dhw9jb21O/fv1nzs/GxkY/LyhbmTJlHvvl5uvrS58+ffjwww8pV64cPXr0YNWqVaSmpur7XL16ldjYWJydnXO8BwkJCTneAw8Pj1zP5e/vz5EjR/SFZVBQEHfv3sXf3z/P+O7du0dcXBxeXl6PzOPq1avs2bMnR3zt27cHeOznBBjMwfqn3H5/bGxsKFeuXI72v7/nN2/epEaNGpiZGf5vv3bt2vrtjzpPdhHzJEXKkx7D2tqaefPmsXv3blxcXPDx8WH+/Pl53tKulJLJzsIoZM6PEAXM3Nz8qdqzvwizJ8uuXbvWYK5Ktsfdku3m5oaHhwe//PIL7u7uKKVo2bIlTk5OvPXWW9y8eZPDhw/j7e2d4wvyaeSVx+NoNBo2b95McHAwO3bsYO/evQwfPpxFixYRHByMnZ0dWVlZODs7ExgYmOsx/ll05TWK4+/vz9SpU9m0aRMTJkxg48aN6HQ6Onfu/Eyx/11WVhYdOnTI86F8NWvWfOT+ZcuWfWSBkdv7+7jfnWdhjGM+yTEmTJhAt27d2LZtG3v37mXatGl89NFH/Oc//8kxtycmJiZHkSfEs5DiR4hiolq1agA4OzvrRxGeVuvWrfnll1/w8PCgQYMGlC5dmvr166PT6dizZw+nT5/WP8MnL/n9l3eLFi1o0aIFc+bMYf369QwcOJDvv/+e119/nWrVqnHgwAFefPHFp7pl/Z88PDxo1qwZGzZsYNy4cfzwww/07NkTa2vrPPdxcnLC3t6eP/7445HHrlatGgkJCc/8GdWqVYsbN248076PUqVKFc6dO0dWVpZBcXvp0iX99qdlrN+FatWq8fbbb/P2229z9epVGjRowKJFi1i3bp2+T1hYGGlpafqRKiGeh1z2EqKY6NSpE/b29sydOzfXOTH37t177DFat25NSEgIGzZs0F8GMzMzw9vbm8WLF5Oenv7Y+T62trbAw7/CjSk6OjrHqEKDBg0A9Je+/Pz8yMzMZNasWTn2z8jIeKqY/P39CQ4O5ptvviEqKuqRl7zg4fvUs2dPduzYwalTp3Jsz47dz8+PY8eOsXfv3hx9YmJiyMjIeOR5WrZsyR9//GFwuc8YunTpQmRkpMFcp4yMDD777DPs7Ozw9fV96mOWKlUKePbfhaSkJFJSUgzaqlWrRunSpXPk/9tvvwHg7e39TOcS4u9k5EeIYsLe3p7ly5fz2muv0ahRI/r374+TkxO3bt3ip59+4sUXX2TZsmWPPEZ2YXP58mXmzp2rb/fx8WH37t36Z7E8ilarpU6dOmzYsIGaNWvi6OiIl5fXY+fCPM6aNWv44osv6NWrF9WqVSM+Pp6vvvoKe3t7unTpAjycFzRq1Cg++ugjzp49S8eOHbG0tOTq1ats2rSJpUuXGjyn51H8/PyYPHkykydPxtHR8YlGaubOncu+ffvw9fVl5MiR1K5dm4iICDZt2sSvv/6Kg4MD77zzDj/++CNdu3Zl6NChNG7cmMTERM6fP8/mzZsJCQl55KWbHj16MGvWLA4dOkTHjh2f7M17AiNHjmTFihUMHTqU3377DXd3dzZv3syRI0dYsmQJpUuXfupjZt9+Pn78eDp16oS5ufkjHxXwT1euXKFdu3b4+flRp04dLCws2Lp1K3fu3MlxnP3791O5cmW5zV0YhRQ/QhQjr776Km5ubnz88ccsWLCA1NRUKlSoQOvWrRk2bNhj9/f09MTZ2Zm7d+/SqlUrfXt2UdSsWbNHXvrJ9vXXXxMQEMDEiRNJS0tj+vTpz138+Pr6cuLECb7//nvu3LmDTqejWbNmBAYGGkxc/vLLL2ncuDErVqzg/fffx8LCAnd3dwYNGsSLL774xOerWLEi3t7eHDlyhNdff/2JnmRdoUIFjh8/zrRp0wgMDCQuLo4KFSrw8ssv60fEbG1tOXToEHPnzmXTpk18++232NvbU7NmTT788MPHLlnRuHFj6tWrx8aNG41a/Gi1WoKCgpgyZQpr1qwhLi4OT09PVq1axdChQ5/pmL179yYgIIDvv/+edevWoZR6quKnUqVKDBgwgJ9//pm1a9diYWFBrVq12LhxI3369NH3y8rKYsuWLYwYMUImPAuj0KjnmREnhBDC6NauXcvYsWO5deuW/hEAJdm2bdt49dVXuXbtGq6uroUdjjABMudHCCGKmIEDB1K5cmX98hEl3bx58xg3bpwUPsJoZORHCCGEECWKjPwIIYQQokSR4kcIIYQQJYoUP0IIIYQoUaT4EUIIIUSJIsWPEEIIIUoUKX6EEEIIUaJI8SOEEEKIEkWKHyGEEEKUKFL8CCGEEKJEkeJHCCGEECXK/wOeRBGGY5tDpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 84,
   "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": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6464</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.996788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.991966</td>\n",
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       "    <tr>\n",
       "      <th>3.0</th>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.982570</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>68.0</th>\n",
       "      <td>0.429634</td>\n",
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       "    <tr>\n",
       "      <th>69.0</th>\n",
       "      <td>0.429634</td>\n",
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       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>0.429634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.0</th>\n",
       "      <td>0.429634</td>\n",
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       "      <th>72.0</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 1 columns</p>\n",
       "</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",
       "4.0   0.982570\n",
       "...        ...\n",
       "68.0  0.429634\n",
       "69.0  0.429634\n",
       "70.0  0.429634\n",
       "71.0  0.429634\n",
       "72.0  0.429634\n",
       "\n",
       "[73 rows x 1 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.predict_survival_function(train_df_surv_not_churned[:1], conditional_after=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Evaluation \n",
    "\n",
    "By default score returns \"partial log likelihood\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.864186433729249"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(train_df_surv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7277854625841886"
      ]
     },
     "execution_count": 89,
     "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625888648969532"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.concordance_index_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625888648969532"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(train_df_surv, scoring_method=\"concordance_index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8546143543902771"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph.log_likelihood_ratio_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph.check_assumptions(df_train_surv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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..."
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    "### 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 reasons the data might be censored at random times, e.g. the person died?\n",
    "- Left censoring\n",
    "- Interval censoring"
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    "## 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"
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    "## 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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