lectures/mehrdad/20_survival-analysis.ipynb

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    "![](../img/330-banner.png)"
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   "source": [
    "# Lecture 20: Survival analysis \n",
    "\n",
    "UBC 2024 Summer\n",
    "\n",
    "Instructor: Mehrdad Oveisi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Imports"
   ]
  },
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\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",
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.append(os.path.join(os.path.abspath(\"../\"), \"code\"))\n",
    "from utils import *\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 12\n",
    "\n",
    "# does lifelines try to mess with this?\n",
    "pd.options.display.max_rows = 10\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "import lifelines"
   ]
  },
  {
   "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": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Customer churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customer churn, also known as customer attrition, refers to the phenomenon where customers or subscribers stop doing business with a company or service.\n",
    "- The bar-chart below is showing the monthly subscriber churn rates for various streaming services. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../img/subscriber-churn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Imagine that you are working for a subscription-based telecom company. \n",
    "- They want to predict when a specific customer will churn so that they can come up with \n",
    "retention strategies for different customer segments.\n",
    "- We want to model \"time to churn\" to understand different factors affecting customer churn.   \n",
    "- Is it possible to use machine learning to predict whether a specific customer will churn?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's work with this dataset [Customer Churn Dataset](https://www.kaggle.com/blastchar/telco-customer-churn), which is collected at a fixed time. "
   ]
  },
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    {
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       "  <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",
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       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>70.35</td>\n",
       "      <td>3454.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>4537-DKTAL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>45.55</td>\n",
       "      <td>84.4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0468-YRPXN</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>98.80</td>\n",
       "      <td>2807.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>1304-NECVQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.55</td>\n",
       "      <td>149.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>7153-CHRBV</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>57</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>59.30</td>\n",
       "      <td>3274.35</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
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      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "6464  4726-DLWQN    Male              1      No         No      50   \n",
       "5707  4537-DKTAL  Female              0      No         No       2   \n",
       "3442  0468-YRPXN    Male              0      No         No      29   \n",
       "3932  1304-NECVQ  Female              1      No         No       2   \n",
       "6124  7153-CHRBV  Female              0     Yes        Yes      57   \n",
       "\n",
       "     PhoneService MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "6464          Yes           Yes             DSL            Yes  ...   \n",
       "5707          Yes            No             DSL             No  ...   \n",
       "3442          Yes            No     Fiber optic             No  ...   \n",
       "3932          Yes           Yes     Fiber optic             No  ...   \n",
       "6124          Yes            No             DSL            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "6464               No          No         Yes              No  Month-to-month   \n",
       "5707               No          No          No              No  Month-to-month   \n",
       "3442              Yes         Yes         Yes             Yes  Month-to-month   \n",
       "3932              Yes          No          No              No  Month-to-month   \n",
       "6124              Yes         Yes          No              No        One year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "6464              Yes  Bank transfer (automatic)          70.35        3454.6   \n",
       "5707               No           Electronic check          45.55          84.4   \n",
       "3442              Yes    Credit card (automatic)          98.80        2807.1   \n",
       "3932              Yes           Electronic check          78.55        149.55   \n",
       "6124              Yes               Mailed check          59.30       3274.35   \n",
       "\n",
       "     Churn  \n",
       "6464    No  \n",
       "5707    No  \n",
       "3442    No  \n",
       "3932   Yes  \n",
       "6124    No  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "train_df, test_df = train_test_split(df, random_state=123)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We 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,
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    {
     "data": {
      "text/plain": [
       "(5282, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
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   "execution_count": 5,
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    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "No     3912\n",
       "Yes    1370\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Churn\"].value_counts()"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeniorCitizen\n",
       "0    4430\n",
       "1     852\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"SeniorCitizen\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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   },
   "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",
    ")"
   ]
  },
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     "iopub.status.idle": "2024-04-02T19:41:30.385093Z",
     "shell.execute_reply": "2024-04-02T19:41:30.383485Z",
     "shell.execute_reply.started": "2024-04-02T19:41:30.375951Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:30.387878Z",
     "iopub.status.busy": "2024-04-02T19:41:30.387279Z",
     "iopub.status.idle": "2024-04-02T19:41:30.777798Z",
     "shell.execute_reply": "2024-04-02T19:41:30.775908Z",
     "shell.execute_reply.started": "2024-04-02T19:41:30.387845Z"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ' '",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_9045/2961559258.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreprocessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    713\u001b[0m             \u001b[0mThis\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m         \"\"\"\n\u001b[1;32m    715\u001b[0m         \u001b[0;31m# we use fit_transform to make sure to set sparse_output_ (for which we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m         \u001b[0;31m# need the transformed data) to have consistent output type in predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    718\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m             return_tuple = (\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 skip_parameter_validation=(\n\u001b[1;32m   1149\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 )\n\u001b[1;32m   1151\u001b[0m             ):\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    750\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_transformers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_column_callables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_remainder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_fit_transform_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fitted_transformers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, func, fitted, column_as_strings)\u001b[0m\n\u001b[1;32m    692\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m\"Expected 2D array, got 1D array instead\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ERR_MSG_1DCOLUMN\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m         iterable_with_config = (\n\u001b[1;32m     62\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0m_with_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         )\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1069\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_terminate_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1073\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pickle_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1074\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1075\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0;31m# No more tasks available in the iterator: tell caller to stop.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 864\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    865\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    778\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;31m# grow. To ensure correct results ordering, .insert is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# Set the default nested backend to self._backend but do not set the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    264\u001b[0m                     for func, args, kwargs in self.items]\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    264\u001b[0m         \u001b[0;31m# Set the default nested backend to self._backend but do not set the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m                 \u001b[0mUserWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             )\n\u001b[1;32m    125\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    953\u001b[0m     \u001b[0mbe\u001b[0m \u001b[0mmultiplied\u001b[0m \u001b[0mby\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m     \"\"\"\n\u001b[1;32m    955\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    956\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"fit_transform\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 957\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    958\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    959\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m             return_tuple = (\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0;31m# non-optimized default implementation; override when a better\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m         \u001b[0;31m# method is possible for a given clustering algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    835\u001b[0m             \u001b[0mFitted\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m         \"\"\"\n\u001b[1;32m    837\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 839\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 skip_parameter_validation=(\n\u001b[1;32m   1149\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 )\n\u001b[1;32m   1151\u001b[0m             ):\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0mself\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0mFitted\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         \"\"\"\n\u001b[1;32m    874\u001b[0m         \u001b[0mfirst_call\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"n_samples_seen_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 875\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m    876\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m             \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    601\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\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    912\u001b[0m                         )\n\u001b[1;32m    913\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m                 raise ValueError(\n\u001b[1;32m    918\u001b[0m                     \u001b[0;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m                 ) from complex_warning\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m         \u001b[0;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1996\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         if (\n\u001b[1;32m   2000\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2001\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:30.779656Z",
     "iopub.status.busy": "2024-04-02T19:41:30.779175Z",
     "iopub.status.idle": "2024-04-02T19:41:30.812700Z",
     "shell.execute_reply": "2024-04-02T19:41:30.810363Z",
     "shell.execute_reply.started": "2024-04-02T19:41:30.779600Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:30.815320Z",
     "iopub.status.busy": "2024-04-02T19:41:30.814800Z",
     "iopub.status.idle": "2024-04-02T19:41:32.396632Z",
     "shell.execute_reply": "2024-04-02T19:41:32.394868Z",
     "shell.execute_reply.started": "2024-04-02T19:41:30.815281Z"
    },
    "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~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/generic.py:6324\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   6317\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   6318\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   6319\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   6320\u001b[0m     ]\n\u001b[1;32m   6322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6323\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6324\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   6325\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   6327\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/internals/managers.py:451\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m    449\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 451\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\n\u001b[1;32m    452\u001b[0m \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\n\u001b[1;32m    453\u001b[0m \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    454\u001b[0m \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    455\u001b[0m \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    456\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/internals/managers.py:352\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    350\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    351\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 352\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    353\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m    355\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
      "File \u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/internals/blocks.py:511\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    492\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m    493\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m    508\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    509\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--> 511\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    513\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m    515\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:242\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    239\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 242\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    243\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    244\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    245\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[1;32m    246\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~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:187\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    184\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    186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_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    189\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    190\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~/miniconda3/envs/cpsc330/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:138\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m    136\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    137\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--> 138\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    140\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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.398928Z",
     "iopub.status.busy": "2024-04-02T19:41:32.398247Z",
     "iopub.status.idle": "2024-04-02T19:41:32.413891Z",
     "shell.execute_reply": "2024-04-02T19:41:32.412031Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.398862Z"
    },
    "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.417212Z",
     "iopub.status.busy": "2024-04-02T19:41:32.416199Z",
     "iopub.status.idle": "2024-04-02T19:41:32.430466Z",
     "shell.execute_reply": "2024-04-02T19:41:32.429115Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.417129Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.433963Z",
     "iopub.status.busy": "2024-04-02T19:41:32.432232Z",
     "iopub.status.idle": "2024-04-02T19:41:32.450820Z",
     "shell.execute_reply": "2024-04-02T19:41:32.448797Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.433893Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.453893Z",
     "iopub.status.busy": "2024-04-02T19:41:32.453410Z",
     "iopub.status.idle": "2024-04-02T19:41:32.480781Z",
     "shell.execute_reply": "2024-04-02T19:41:32.478786Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.453841Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.484327Z",
     "iopub.status.busy": "2024-04-02T19:41:32.483173Z",
     "iopub.status.idle": "2024-04-02T19:41:32.496199Z",
     "shell.execute_reply": "2024-04-02T19:41:32.494108Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.484286Z"
    },
    "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.499249Z",
     "iopub.status.busy": "2024-04-02T19:41:32.498098Z",
     "iopub.status.idle": "2024-04-02T19:41:32.610425Z",
     "shell.execute_reply": "2024-04-02T19:41:32.606638Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.499187Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.614751Z",
     "iopub.status.busy": "2024-04-02T19:41:32.613733Z",
     "iopub.status.idle": "2024-04-02T19:41:32.623310Z",
     "shell.execute_reply": "2024-04-02T19:41:32.621336Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.614678Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.626953Z",
     "iopub.status.busy": "2024-04-02T19:41:32.625458Z",
     "iopub.status.idle": "2024-04-02T19:41:32.737791Z",
     "shell.execute_reply": "2024-04-02T19:41:32.736179Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.626884Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.739580Z",
     "iopub.status.busy": "2024-04-02T19:41:32.739216Z",
     "iopub.status.idle": "2024-04-02T19:41:32.784695Z",
     "shell.execute_reply": "2024-04-02T19:41:32.782361Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.739549Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tenure</th>\n",
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       "      <th>gender_Female</th>\n",
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       "      <th>TechSupport_No</th>\n",
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       "      <th>StreamingMovies_No</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "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",
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       "6124  0.993065       -0.183179      0.433814            1.0          0.0   \n",
       "\n",
       "      PaperlessBilling_No  PaperlessBilling_Yes  Contract_Month-to-month  \\\n",
       "6464                  0.0                   1.0                      1.0   \n",
       "5707                  1.0                   0.0                      1.0   \n",
       "3442                  0.0                   1.0                      1.0   \n",
       "3932                  0.0                   1.0                      1.0   \n",
       "6124                  0.0                   1.0                      0.0   \n",
       "\n",
       "      Contract_One year  Contract_Two year  ...  TechSupport_No  \\\n",
       "6464                0.0                0.0  ...             1.0   \n",
       "5707                0.0                0.0  ...             1.0   \n",
       "3442                0.0                0.0  ...             0.0   \n",
       "3932                0.0                0.0  ...             1.0   \n",
       "6124                1.0                0.0  ...             0.0   \n",
       "\n",
       "      TechSupport_No internet service  TechSupport_Yes  StreamingMovies_No  \\\n",
       "6464                              0.0              0.0                 1.0   \n",
       "5707                              0.0              0.0                 1.0   \n",
       "3442                              0.0              1.0                 0.0   \n",
       "3932                              0.0              0.0                 1.0   \n",
       "6124                              0.0              1.0                 1.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "6464                                  0.0                  0.0   \n",
       "5707                                  0.0                  0.0   \n",
       "3442                                  0.0                  1.0   \n",
       "3932                                  0.0                  0.0   \n",
       "6124                                  0.0                  0.0   \n",
       "\n",
       "      MultipleLines_No  MultipleLines_No phone service  MultipleLines_Yes  \\\n",
       "6464               0.0                             0.0                1.0   \n",
       "5707               1.0                             0.0                0.0   \n",
       "3442               1.0                             0.0                0.0   \n",
       "3932               0.0                             0.0                1.0   \n",
       "6124               1.0                             0.0                0.0   \n",
       "\n",
       "      SeniorCitizen  \n",
       "6464            1.0  \n",
       "5707            0.0  \n",
       "3442            0.0  \n",
       "3932            1.0  \n",
       "6124            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": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.794296Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:32.794235Z"
    }
   },
   "outputs": [],
   "source": [
    "results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.810606Z",
     "iopub.status.busy": "2024-04-02T19:41:32.808355Z",
     "iopub.status.idle": "2024-04-02T19:41:32.825713Z",
     "shell.execute_reply": "2024-04-02T19:41:32.823102Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.810521Z"
    },
    "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": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.829466Z",
     "iopub.status.busy": "2024-04-02T19:41:32.827701Z",
     "iopub.status.idle": "2024-04-02T19:41:32.836519Z",
     "shell.execute_reply": "2024-04-02T19:41:32.834687Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.829427Z"
    }
   },
   "outputs": [],
   "source": [
    "dc = DummyClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:32.840591Z",
     "iopub.status.busy": "2024-04-02T19:41:32.839554Z",
     "iopub.status.idle": "2024-04-02T19:41:33.025369Z",
     "shell.execute_reply": "2024-04-02T19:41:33.023061Z",
     "shell.execute_reply.started": "2024-04-02T19:41:32.840525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>dummy</th>\n",
       "      <td>0.008 (+/- 0.001)</td>\n",
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       "      <td>0.741 (+/- 0.000)</td>\n",
       "      <td>0.741 (+/- 0.000)</td>\n",
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      "text/plain": [
       "                fit_time         score_time         test_score  \\\n",
       "dummy  0.008 (+/- 0.001)  0.004 (+/- 0.001)  0.741 (+/- 0.000)   \n",
       "\n",
       "             train_score  \n",
       "dummy  0.741 (+/- 0.000)  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
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   ],
   "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": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:33.027837Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:33.027800Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>count</th>\n",
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       "      <th>Churn</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1370</td>\n",
       "      <td>0.259371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count  proportion\n",
       "Churn                   \n",
       "No      3912    0.740629\n",
       "Yes     1370    0.259371"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts().to_frame().join(y_train.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:33.052388Z",
     "iopub.status.busy": "2024-04-02T19:41:33.051297Z",
     "iopub.status.idle": "2024-04-02T19:41:33.063932Z",
     "shell.execute_reply": "2024-04-02T19:41:33.059116Z",
     "shell.execute_reply.started": "2024-04-02T19:41:33.052319Z"
    }
   },
   "outputs": [],
   "source": [
    "lr = make_pipeline(preprocessor, LogisticRegression(max_iter=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:33.069980Z",
     "iopub.status.busy": "2024-04-02T19:41:33.068541Z",
     "iopub.status.idle": "2024-04-02T19:41:38.957377Z",
     "shell.execute_reply": "2024-04-02T19:41:38.954584Z",
     "shell.execute_reply.started": "2024-04-02T19:41:33.069939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.008 (+/- 0.001)</td>\n",
       "      <td>0.004 (+/- 0.001)</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>0.900 (+/- 0.467)</td>\n",
       "      <td>0.081 (+/- 0.012)</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                0.008 (+/- 0.001)  0.004 (+/- 0.001)  0.741 (+/- 0.000)   \n",
       "logistic regression  0.900 (+/- 0.467)  0.081 (+/- 0.012)  0.804 (+/- 0.013)   \n",
       "\n",
       "                           train_score  \n",
       "dummy                0.741 (+/- 0.000)  \n",
       "logistic regression  0.809 (+/- 0.002)  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"logistic regression\"] = mean_std_cross_val_scores(\n",
    "    lr, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:38.962351Z",
     "iopub.status.busy": "2024-04-02T19:41:38.960364Z",
     "iopub.status.idle": "2024-04-02T19:41:44.311580Z",
     "shell.execute_reply": "2024-04-02T19:41:44.309828Z",
     "shell.execute_reply.started": "2024-04-02T19:41:38.962295Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3516,  396],\n",
       "       [ 637,  733]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, cross_val_predict(lr, X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:44.314635Z",
     "iopub.status.busy": "2024-04-02T19:41:44.313640Z",
     "iopub.status.idle": "2024-04-02T19:41:44.327040Z",
     "shell.execute_reply": "2024-04-02T19:41:44.322053Z",
     "shell.execute_reply.started": "2024-04-02T19:41:44.314570Z"
    }
   },
   "outputs": [],
   "source": [
    "rf = make_pipeline(preprocessor, RandomForestClassifier(n_estimators=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:44.332874Z",
     "iopub.status.busy": "2024-04-02T19:41:44.331251Z",
     "iopub.status.idle": "2024-04-02T19:41:49.507099Z",
     "shell.execute_reply": "2024-04-02T19:41:49.505189Z",
     "shell.execute_reply.started": "2024-04-02T19:41:44.332752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.008 (+/- 0.001)</td>\n",
       "      <td>0.004 (+/- 0.001)</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>0.900 (+/- 0.467)</td>\n",
       "      <td>0.081 (+/- 0.012)</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>0.816 (+/- 0.164)</td>\n",
       "      <td>0.066 (+/- 0.024)</td>\n",
       "      <td>0.789 (+/- 0.013)</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                0.008 (+/- 0.001)  0.004 (+/- 0.001)  0.741 (+/- 0.000)   \n",
       "logistic regression  0.900 (+/- 0.467)  0.081 (+/- 0.012)  0.804 (+/- 0.013)   \n",
       "random forest        0.816 (+/- 0.164)  0.066 (+/- 0.024)  0.789 (+/- 0.013)   \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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"random forest\"] = mean_std_cross_val_scores(\n",
    "    rf, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:49.509482Z",
     "iopub.status.busy": "2024-04-02T19:41:49.508932Z",
     "iopub.status.idle": "2024-04-02T19:41:53.073964Z",
     "shell.execute_reply": "2024-04-02T19:41:53.072231Z",
     "shell.execute_reply.started": "2024-04-02T19:41:49.509448Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3532,  380],\n",
       "       [ 728,  642]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, cross_val_predict(rf, X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- 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": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.075828Z",
     "iopub.status.busy": "2024-04-02T19:41:53.075295Z",
     "iopub.status.idle": "2024-04-02T19:41:53.088388Z",
     "shell.execute_reply": "2024-04-02T19:41:53.087045Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.075796Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tenure\n",
       "6464      50\n",
       "5707       2\n",
       "3442      29\n",
       "3932       2\n",
       "6124      57"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The tenure column is the number of months the customer has stayed with the company. \n",
    "- But we only have information about this till the point we collected the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### ❓❓ Questions for you\n",
    "\n",
    "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": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.090593Z",
     "iopub.status.busy": "2024-04-02T19:41:53.089648Z",
     "iopub.status.idle": "2024-04-02T19:41:53.104641Z",
     "shell.execute_reply": "2024-04-02T19:41:53.103219Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.090561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>tenure</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6464</th>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>57</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tenure Churn\n",
       "6464      50    No\n",
       "5707       2    No\n",
       "3442      29    No\n",
       "3932       2   Yes\n",
       "6124      57    No"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What this means is that we **don't have correct target values** to train or test our model.\n",
    "- This is a problem!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider some approaches to deal with this censoring issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Approach 1: Only consider the examples where \"Churn\"=Yes\n",
    "\n",
    "Let's just consider the cases _for which we have the time_, to obtain the average subscription length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.106536Z",
     "iopub.status.busy": "2024-04-02T19:41:53.106037Z",
     "iopub.status.idle": "2024-04-02T19:41:53.145716Z",
     "shell.execute_reply": "2024-04-02T19:41:53.144094Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.106489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>1304-NECVQ</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.55</td>\n",
       "      <td>149.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>8098-LLAZX</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>95.45</td>\n",
       "      <td>396.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5540</th>\n",
       "      <td>3803-KMQFW</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.55</td>\n",
       "      <td>20.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4084</th>\n",
       "      <td>2777-PHDEI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>78.05</td>\n",
       "      <td>78.05</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>6772-KSATR</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>81.70</td>\n",
       "      <td>81.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "3932  1304-NECVQ  Female              1      No         No       2   \n",
       "301   8098-LLAZX  Female              1      No         No       4   \n",
       "5540  3803-KMQFW  Female              0     Yes        Yes       1   \n",
       "4084  2777-PHDEI  Female              0      No         No       1   \n",
       "3272  6772-KSATR    Male              0      No         No       1   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "3932          Yes           Yes     Fiber optic                   No  ...   \n",
       "301           Yes           Yes     Fiber optic                   No  ...   \n",
       "5540          Yes            No              No  No internet service  ...   \n",
       "4084          Yes            No     Fiber optic                   No  ...   \n",
       "3272          Yes           Yes     Fiber optic                  Yes  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "3932                  Yes                   No                   No   \n",
       "301                    No                   No                  Yes   \n",
       "5540  No internet service  No internet service  No internet service   \n",
       "4084                   No                   No                  Yes   \n",
       "3272                   No                   No                   No   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling     PaymentMethod  \\\n",
       "3932                   No  Month-to-month              Yes  Electronic check   \n",
       "301                   Yes  Month-to-month              Yes  Electronic check   \n",
       "5540  No internet service  Month-to-month               No      Mailed check   \n",
       "4084                   No  Month-to-month               No  Electronic check   \n",
       "3272                   No  Month-to-month              Yes  Electronic check   \n",
       "\n",
       "     MonthlyCharges  TotalCharges  Churn  \n",
       "3932          78.55        149.55    Yes  \n",
       "301           95.45        396.10    Yes  \n",
       "5540          20.55         20.55    Yes  \n",
       "4084          78.05         78.05    Yes  \n",
       "3272          81.70         81.70    Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_churn = train_df.query(\n",
    "    \"Churn == 'Yes'\"\n",
    ")  # Consider only examples where the customers churned.\n",
    "test_df_churn = test_df.query(\n",
    "    \"Churn == 'Yes'\"\n",
    ")  # Consider only examples where the customers churned.\n",
    "train_df_churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.148366Z",
     "iopub.status.busy": "2024-04-02T19:41:53.147061Z",
     "iopub.status.idle": "2024-04-02T19:41:53.157614Z",
     "shell.execute_reply": "2024-04-02T19:41:53.155643Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.148307Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5282, 21)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.160953Z",
     "iopub.status.busy": "2024-04-02T19:41:53.160023Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:53.160855Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1370, 21)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_churn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.180643Z",
     "iopub.status.busy": "2024-04-02T19:41:53.179183Z",
     "iopub.status.idle": "2024-04-02T19:41:53.191051Z",
     "shell.execute_reply": "2024-04-02T19:41:53.188918Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.180587Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure', 'MonthlyCharges', 'TotalCharges']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.194233Z",
     "iopub.status.busy": "2024-04-02T19:41:53.193554Z",
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     "shell.execute_reply": "2024-04-02T19:41:53.205733Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.194160Z"
    }
   },
   "outputs": [],
   "source": [
    "preprocessing_notenure = make_column_transformer(\n",
    "    (\n",
    "        make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler()),\n",
    "        numeric_features[1:],  # Getting rid of the tenure column\n",
    "    ),\n",
    "    (OneHotEncoder(handle_unknown=\"ignore\"), categorical_features),\n",
    "    (\"passthrough\", passthrough_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.214459Z",
     "iopub.status.busy": "2024-04-02T19:41:53.212440Z",
     "iopub.status.idle": "2024-04-02T19:41:53.272138Z",
     "shell.execute_reply": "2024-04-02T19:41:53.270036Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.214415Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "tenure_lm = make_pipeline(preprocessing_notenure, Ridge())\n",
    "\n",
    "tenure_lm.fit(train_df_churn.drop(columns=[\"tenure\"]), train_df_churn[\"tenure\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.286019Z",
     "iopub.status.busy": "2024-04-02T19:41:53.274289Z",
     "iopub.status.idle": "2024-04-02T19:41:53.412421Z",
     "shell.execute_reply": "2024-04-02T19:41:53.410868Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.285912Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.859455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.865562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.154842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.757932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.932070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.720893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.818041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.263541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure_predictions\n",
       "0            5.062449\n",
       "1           13.198645\n",
       "2           11.859455\n",
       "3            5.865562\n",
       "4           58.154842\n",
       "5            3.757932\n",
       "6           18.932070\n",
       "7            7.720893\n",
       "8           36.818041\n",
       "9            7.263541"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    tenure_lm.predict(test_df_churn.drop(columns=[\"tenure\"]))[:10],\n",
    "    columns=[\"tenure_predictions\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ❓❓ Questions for you\n",
    "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": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.421688Z",
     "iopub.status.busy": "2024-04-02T19:41:53.420572Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:53.421640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>6464</th>\n",
       "      <td>50</td>\n",
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       "      <th>5707</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
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       "      <th>3442</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
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       "      <th>3932</th>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
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       "    <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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.477458Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:53.477387Z"
    }
   },
   "outputs": [],
   "source": [
    "tenure_lm.fit(train_df.drop(columns=[\"tenure\"]), train_df[\"tenure\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.616691Z",
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       "      <th>0</th>\n",
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       "      <td>20.220392</td>\n",
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       "      <td>22.332746</td>\n",
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       "    <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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    tenure_lm.predict(test_df_churn.drop(columns=[\"tenure\"]))[:10],\n",
    "    columns=[\"tenure_predictions\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ❓❓ Questions for you\n",
    "\n",
    "What will be wrong with our estimated survival time?\n",
    "<br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.785910Z",
     "iopub.status.busy": "2024-04-02T19:41:53.785251Z",
     "iopub.status.idle": "2024-04-02T19:41:53.841483Z",
     "shell.execute_reply": "2024-04-02T19:41:53.839629Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.785855Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
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       "      <th>6464</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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will be an **underestimate** again. For those still subscribed, while we did not remove them, we recorded a total tenure shorter than in reality, because they will keep going for some amount of time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Approach 3: Survival analysis\n",
    "\n",
    "Deal with this properly using [survival analysis](https://en.wikipedia.org/wiki/Survival_analysis).\n",
    "\n",
    "- You may learn about this in a statistics course.\n",
    "- We will use the `lifelines` package in Python and will not go into the math/stats of how it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.847132Z",
     "iopub.status.busy": "2024-04-02T19:41:53.843035Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:53.847055Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tenure</th>\n",
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       "      tenure Churn\n",
       "6464      50    No\n",
       "5707       2    No\n",
       "3442      29    No\n",
       "3932       2   Yes\n",
       "6124      57    No"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"tenure\", \"Churn\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Types of questions we might want to answer:\n",
    "\n",
    "1. How long do customers stay with the service? \n",
    "2. For a particular customer, can we predict how long they might stay with the service?\n",
    "3. What factors influence a customer's churn time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](../img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### (iClicker) Exercise 20.1\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "- (A) Right censoring occurs when the endpoint of event has not been observed for all study subjects by the end of the study period.\n",
    "- (B) Right censoring implies that the data is missing completely at random.\n",
    "- (C) In the presence of right-censored data, binary classification models can be applied directly without any modifications or special considerations.\n",
    "- (D) If we apply the `Ridge` regression model to predict tenure in right censored data, we are likely to underestimate it because the tenure observed in our data is shorter than what it would be in reality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Kaplan-Meier survival curve\n",
    "\n",
    "- 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": "markdown",
   "metadata": {},
   "source": [
    "But 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 not going to scale the `tenure` column, since it will be convenient to keep it in its original units of months. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Just for our sanity, I'm redefining the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.870635Z",
     "iopub.status.busy": "2024-04-02T19:41:53.869295Z",
     "iopub.status.idle": "2024-04-02T19:41:53.877748Z",
     "shell.execute_reply": "2024-04-02T19:41:53.875777Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.870594Z"
    }
   },
   "outputs": [],
   "source": [
    "numeric_features = [\"MonthlyCharges\"]\n",
    "drop_features = [\"customerID\", \"TotalCharges\"]\n",
    "passthrough_features = [\"tenure\", \"SeniorCitizen\"]  # don't want to scale tenure\n",
    "target_column = [\"Churn\"]\n",
    "# the rest are categorical\n",
    "categorical_features = list(\n",
    "    set(train_df.columns)\n",
    "    - set(numeric_features)\n",
    "    - set(passthrough_features)\n",
    "    - set(drop_features)\n",
    "    - set(target_column)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.881413Z",
     "iopub.status.busy": "2024-04-02T19:41:53.880381Z",
     "iopub.status.idle": "2024-04-02T19:41:53.891738Z",
     "shell.execute_reply": "2024-04-02T19:41:53.889830Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.881342Z"
    },
    "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_output=False), categorical_features),\n",
    "    (\"drop\", drop_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.896499Z",
     "iopub.status.busy": "2024-04-02T19:41:53.895605Z",
     "iopub.status.idle": "2024-04-02T19:41:53.984474Z",
     "shell.execute_reply": "2024-04-02T19:41:53.982060Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.896347Z"
    }
   },
   "outputs": [],
   "source": [
    "preprocessing_final.fit(train_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's get the column names of the columns created by our column transformer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.987561Z",
     "iopub.status.busy": "2024-04-02T19:41:53.986471Z",
     "iopub.status.idle": "2024-04-02T19:41:53.995288Z",
     "shell.execute_reply": "2024-04-02T19:41:53.992993Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.987521Z"
    },
    "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": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:53.998640Z",
     "iopub.status.busy": "2024-04-02T19:41:53.997894Z",
     "iopub.status.idle": "2024-04-02T19:41:54.082307Z",
     "shell.execute_reply": "2024-04-02T19:41:54.080001Z",
     "shell.execute_reply.started": "2024-04-02T19:41:53.998592Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df_surv = pd.DataFrame(\n",
    "    preprocessing_final.transform(train_df), index=train_df.index, columns=new_columns\n",
    ")\n",
    "test_df_surv = pd.DataFrame(\n",
    "    preprocessing_final.transform(test_df), index=test_df.index, columns=new_columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.084523Z",
     "iopub.status.busy": "2024-04-02T19:41:54.084096Z",
     "iopub.status.idle": "2024-04-02T19:41:54.151160Z",
     "shell.execute_reply": "2024-04-02T19:41:54.148830Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.084479Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.183179</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</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  PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
       "6464          1.0                  0.0                   1.0   \n",
       "5707          0.0                  1.0                   0.0   \n",
       "3442          1.0                  0.0                   1.0   \n",
       "3932          0.0                  0.0                   1.0   \n",
       "6124          0.0                  0.0                   1.0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  ...  StreamingTV_Yes  \\\n",
       "6464                      1.0                0.0  ...              1.0   \n",
       "5707                      1.0                0.0  ...              0.0   \n",
       "3442                      1.0                0.0  ...              1.0   \n",
       "3932                      1.0                0.0  ...              0.0   \n",
       "6124                      0.0                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",
       "      StreamingMovies_No  StreamingMovies_No internet service  \\\n",
       "6464                 1.0                                  0.0   \n",
       "5707                 1.0                                  0.0   \n",
       "3442                 0.0                                  0.0   \n",
       "3932                 1.0                                  0.0   \n",
       "6124                 1.0                                  0.0   \n",
       "\n",
       "      StreamingMovies_Yes  MultipleLines_No  MultipleLines_No phone service  \\\n",
       "6464                  0.0               0.0                             0.0   \n",
       "5707                  0.0               1.0                             0.0   \n",
       "3442                  1.0               1.0                             0.0   \n",
       "3932                  0.0               0.0                             0.0   \n",
       "6124                  0.0               1.0                             0.0   \n",
       "\n",
       "      MultipleLines_Yes  \n",
       "6464                1.0  \n",
       "5707                0.0  \n",
       "3442                0.0  \n",
       "3932                1.0  \n",
       "6124                0.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_surv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's visualize the Kaplan-Meier survival curve.\n",
    "- This is a non-sklearn tool but the syntax is similar to `sklearn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.154503Z",
     "iopub.status.busy": "2024-04-02T19:41:54.153440Z",
     "iopub.status.idle": "2024-04-02T19:41:54.194396Z",
     "shell.execute_reply": "2024-04-02T19:41:54.192857Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.154433Z"
    }
   },
   "outputs": [],
   "source": [
    "kmf = lifelines.KaplanMeierFitter()\n",
    "kmf.fit(train_df_surv[\"tenure\"], train_df_surv[\"Churn\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.198525Z",
     "iopub.status.busy": "2024-04-02T19:41:54.196869Z",
     "iopub.status.idle": "2024-04-02T19:41:54.696241Z",
     "shell.execute_reply": "2024-04-02T19:41:54.694300Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.198435Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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",
    "  - That is, the probability that a customer **remains with the service at least 20 months 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": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.698935Z",
     "iopub.status.busy": "2024-04-02T19:41:54.698448Z",
     "iopub.status.idle": "2024-04-02T19:41:54.709430Z",
     "shell.execute_reply": "2024-04-02T19:41:54.708001Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.698901Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.6391518364256"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.711391Z",
     "iopub.status.busy": "2024-04-02T19:41:54.710986Z",
     "iopub.status.idle": "2024-04-02T19:41:54.729248Z",
     "shell.execute_reply": "2024-04-02T19:41:54.727658Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.711361Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.854744525547446"
      ]
     },
     "execution_count": 56,
     "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": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.732035Z",
     "iopub.status.busy": "2024-04-02T19:41:54.731574Z",
     "iopub.status.idle": "2024-04-02T19:41:54.753386Z",
     "shell.execute_reply": "2024-04-02T19:41:54.751221Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.731981Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.816717791411044"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:54.756558Z",
     "iopub.status.busy": "2024-04-02T19:41:54.756154Z",
     "iopub.status.idle": "2024-04-02T19:41:55.069151Z",
     "shell.execute_reply": "2024-04-02T19:41:55.066680Z",
     "shell.execute_reply.started": "2024-04-02T19:41:54.756527Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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(bins=20, 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": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:55.072852Z",
     "iopub.status.busy": "2024-04-02T19:41:55.072236Z",
     "iopub.status.idle": "2024-04-02T19:41:55.438455Z",
     "shell.execute_reply": "2024-04-02T19:41:55.437108Z",
     "shell.execute_reply.started": "2024-04-02T19:41:55.072809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.plot_survival_function()\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",
    "- By the way, the [original paper by Kaplan and Meier](https://web.stanford.edu/~lutian/coursepdf/KMpaper.pdf) has been cited over 65000 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": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:55.441177Z",
     "iopub.status.busy": "2024-04-02T19:41:55.440598Z",
     "iopub.status.idle": "2024-04-02T19:41:55.449167Z",
     "shell.execute_reply": "2024-04-02T19:41:55.447544Z",
     "shell.execute_reply.started": "2024-04-02T19:41:55.441129Z"
    }
   },
   "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": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:55.452909Z",
     "iopub.status.busy": "2024-04-02T19:41:55.451827Z",
     "iopub.status.idle": "2024-04-02T19:41:55.858993Z",
     "shell.execute_reply": "2024-04-02T19:41:55.857617Z",
     "shell.execute_reply.started": "2024-04-02T19:41:55.452834Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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_survival_function(ax=ax)\n",
    "\n",
    "kmf.fit(T[~senior], event_observed=E[~senior], label=\"Non-Senior Citizens\")\n",
    "kmf.plot_survival_function(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": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:55.861242Z",
     "iopub.status.busy": "2024-04-02T19:41:55.860714Z",
     "iopub.status.idle": "2024-04-02T19:41:55.868873Z",
     "shell.execute_reply": "2024-04-02T19:41:55.866772Z",
     "shell.execute_reply.started": "2024-04-02T19:41:55.861196Z"
    },
    "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": {},
   "source": [
    "If you uncomment the line above, you'll get the following **error message**:\n",
    "\n",
    "```\n",
    "ConvergenceError: Convergence halted due to matrix inversion problems. Suspicion is high collinearity. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-modelMatrix is singular.\n",
    "```"
   ]
  },
  {
   "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": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:55.872410Z",
     "iopub.status.busy": "2024-04-02T19:41:55.870810Z",
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     "shell.execute_reply": "2024-04-02T19:41:56.827914Z",
     "shell.execute_reply.started": "2024-04-02T19:41:55.872343Z"
    },
    "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": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:56.875167Z",
     "iopub.status.busy": "2024-04-02T19:41:56.860699Z",
     "iopub.status.idle": "2024-04-02T19:41:56.941547Z",
     "shell.execute_reply": "2024-04-02T19:41:56.940467Z",
     "shell.execute_reply.started": "2024-04-02T19:41:56.875085Z"
    },
    "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": 64,
     "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": [
    "How to interpret these coefficients In the Cox Proportional Hazards model? \n",
    "- A positive coefficient indicates that higher values of the feature are associated with higher hazard rates, meaning they are associated with worse survival.\n",
    "- A negative coefficient indicates that higher values of the feature are associated with lower hazard rates, meaning they are associated with better survival.\n",
    "- In our example, it looks like `Contract_Month-to-month` has a positive coefficient \n",
    "    - If the contract is month-to-month, it leads to more churn / worse survival (😔)\n",
    "- In our example, it looks like `Contract_Two year` has a negative coefficient \n",
    "    - If the contract is two-year contract, it leads to less churn / better survival (😊)\n",
    "    \n",
    "This makes sense!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:56.947086Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:56.947052Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>baseline hazard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.020883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.009180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.008321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.007563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68.0</th>\n",
       "      <td>0.032779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69.0</th>\n",
       "      <td>0.050586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>0.094493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.0</th>\n",
       "      <td>0.083124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72.0</th>\n",
       "      <td>0.096863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      baseline hazard\n",
       "0.0          0.000000\n",
       "1.0          0.020883\n",
       "2.0          0.009180\n",
       "3.0          0.008321\n",
       "4.0          0.007563\n",
       "...               ...\n",
       "68.0         0.032779\n",
       "69.0         0.050586\n",
       "70.0         0.094493\n",
       "71.0         0.083124\n",
       "72.0         0.096863\n",
       "\n",
       "[73 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.baseline_hazard_ # baseline hazard for different durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:56.976452Z",
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     "shell.execute_reply.started": "2024-04-02T19:41:56.976415Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
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       "<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>0.732674</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>0.936733</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>0.783599</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>0.783599</td>\n",
       "      <td>0.351812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <td>-0.075063</td>\n",
       "      <td>0.927685</td>\n",
       "      <td>0.068817</td>\n",
       "      <td>-0.209943</td>\n",
       "      <td>0.059816</td>\n",
       "      <td>0.810631</td>\n",
       "      <td>1.061641</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.090765</td>\n",
       "      <td>0.275376</td>\n",
       "      <td>1.860523</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>StreamingMovies_No internet service</th>\n",
       "      <td>-0.057267</td>\n",
       "      <td>0.944342</td>\n",
       "      <td>0.100970</td>\n",
       "      <td>-0.255165</td>\n",
       "      <td>0.140631</td>\n",
       "      <td>0.774789</td>\n",
       "      <td>1.150999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.567169</td>\n",
       "      <td>0.570599</td>\n",
       "      <td>0.809450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <td>-0.070898</td>\n",
       "      <td>0.931556</td>\n",
       "      <td>0.068689</td>\n",
       "      <td>-0.205527</td>\n",
       "      <td>0.063730</td>\n",
       "      <td>0.814218</td>\n",
       "      <td>1.065805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.032161</td>\n",
       "      <td>0.301997</td>\n",
       "      <td>1.727394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_No</th>\n",
       "      <td>0.162904</td>\n",
       "      <td>1.176924</td>\n",
       "      <td>0.066166</td>\n",
       "      <td>0.033222</td>\n",
       "      <td>0.292587</td>\n",
       "      <td>1.033780</td>\n",
       "      <td>1.339889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.462060</td>\n",
       "      <td>0.013814</td>\n",
       "      <td>6.177709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <td>0.014650</td>\n",
       "      <td>1.014758</td>\n",
       "      <td>0.127087</td>\n",
       "      <td>-0.234436</td>\n",
       "      <td>0.263736</td>\n",
       "      <td>0.791017</td>\n",
       "      <td>1.301784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115276</td>\n",
       "      <td>0.908227</td>\n",
       "      <td>0.138876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <td>-0.171899</td>\n",
       "      <td>0.842064</td>\n",
       "      <td>0.066810</td>\n",
       "      <td>-0.302844</td>\n",
       "      <td>-0.040954</td>\n",
       "      <td>0.738715</td>\n",
       "      <td>0.959873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.572962</td>\n",
       "      <td>0.010083</td>\n",
       "      <td>6.631899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         coef  exp(coef)  se(coef)  \\\n",
       "covariate                                                            \n",
       "SeniorCitizen                       -0.019556   0.980634  0.057254   \n",
       "MonthlyCharges                      -0.003185   0.996820  0.040129   \n",
       "gender_Female                        0.018217   1.018384  0.066333   \n",
       "gender_Male                         -0.018217   0.981948  0.066333   \n",
       "PaperlessBilling_No                 -0.075063   0.927685  0.068817   \n",
       "...                                       ...        ...       ...   \n",
       "StreamingMovies_No internet service -0.057267   0.944342  0.100970   \n",
       "StreamingMovies_Yes                 -0.070898   0.931556  0.068689   \n",
       "MultipleLines_No                     0.162904   1.176924  0.066166   \n",
       "MultipleLines_No phone service       0.014650   1.014758  0.127087   \n",
       "MultipleLines_Yes                   -0.171899   0.842064  0.066810   \n",
       "\n",
       "                                     coef lower 95%  coef upper 95%  \\\n",
       "covariate                                                             \n",
       "SeniorCitizen                             -0.131773        0.092660   \n",
       "MonthlyCharges                            -0.081837        0.075467   \n",
       "gender_Female                             -0.111793        0.148227   \n",
       "gender_Male                               -0.148227        0.111793   \n",
       "PaperlessBilling_No                       -0.209943        0.059816   \n",
       "...                                             ...             ...   \n",
       "StreamingMovies_No internet service       -0.255165        0.140631   \n",
       "StreamingMovies_Yes                       -0.205527        0.063730   \n",
       "MultipleLines_No                           0.033222        0.292587   \n",
       "MultipleLines_No phone service            -0.234436        0.263736   \n",
       "MultipleLines_Yes                         -0.302844       -0.040954   \n",
       "\n",
       "                                     exp(coef) lower 95%  exp(coef) upper 95%  \\\n",
       "covariate                                                                       \n",
       "SeniorCitizen                                   0.876540             1.097088   \n",
       "MonthlyCharges                                  0.921422             1.078387   \n",
       "gender_Female                                   0.894230             1.159776   \n",
       "gender_Male                                     0.862236             1.118281   \n",
       "PaperlessBilling_No                             0.810631             1.061641   \n",
       "...                                                  ...                  ...   \n",
       "StreamingMovies_No internet service             0.774789             1.150999   \n",
       "StreamingMovies_Yes                             0.814218             1.065805   \n",
       "MultipleLines_No                                1.033780             1.339889   \n",
       "MultipleLines_No phone service                  0.791017             1.301784   \n",
       "MultipleLines_Yes                               0.738715             0.959873   \n",
       "\n",
       "                                     cmp to         z         p  -log2(p)  \n",
       "covariate                                                                  \n",
       "SeniorCitizen                           0.0 -0.341571  0.732674  0.448757  \n",
       "MonthlyCharges                          0.0 -0.079377  0.936733  0.094290  \n",
       "gender_Female                           0.0  0.274631  0.783599  0.351812  \n",
       "gender_Male                             0.0 -0.274631  0.783599  0.351812  \n",
       "PaperlessBilling_No                     0.0 -1.090765  0.275376  1.860523  \n",
       "...                                     ...       ...       ...       ...  \n",
       "StreamingMovies_No internet service     0.0 -0.567169  0.570599  0.809450  \n",
       "StreamingMovies_Yes                     0.0 -1.032161  0.301997  1.727394  \n",
       "MultipleLines_No                        0.0  2.462060  0.013814  6.177709  \n",
       "MultipleLines_No phone service          0.0  0.115276  0.908227  0.138876  \n",
       "MultipleLines_Yes                       0.0 -2.572962  0.010083  6.631899  \n",
       "\n",
       "[43 rows x 11 columns]"
      ]
     },
     "execution_count": 66,
     "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, let's try it out! \n",
    "- But remember that using survival analysis approach is more appropriate for such problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.108207Z",
     "iopub.status.busy": "2024-04-02T19:41:57.105367Z",
     "iopub.status.idle": "2024-04-02T19:41:57.126430Z",
     "shell.execute_reply": "2024-04-02T19:41:57.122961Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.107972Z"
    }
   },
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.133025Z",
     "iopub.status.busy": "2024-04-02T19:41:57.131277Z",
     "iopub.status.idle": "2024-04-02T19:41:57.194899Z",
     "shell.execute_reply": "2024-04-02T19:41:57.193163Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.132887Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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",
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       "      <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",
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       "      <td>Yes</td>\n",
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       "      <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": 68,
     "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": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.197830Z",
     "iopub.status.busy": "2024-04-02T19:41:57.196665Z",
     "iopub.status.idle": "2024-04-02T19:41:57.207461Z",
     "shell.execute_reply": "2024-04-02T19:41:57.205142Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.197789Z"
    },
    "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": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.210246Z",
     "iopub.status.busy": "2024-04-02T19:41:57.209718Z",
     "iopub.status.idle": "2024-04-02T19:41:57.222926Z",
     "shell.execute_reply": "2024-04-02T19:41:57.220661Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.210199Z"
    }
   },
   "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": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.227600Z",
     "iopub.status.busy": "2024-04-02T19:41:57.226167Z",
     "iopub.status.idle": "2024-04-02T19:41:57.314454Z",
     "shell.execute_reply": "2024-04-02T19:41:57.312576Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.227521Z"
    }
   },
   "outputs": [],
   "source": [
    "preprocessor.fit(X_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.317873Z",
     "iopub.status.busy": "2024-04-02T19:41:57.316592Z",
     "iopub.status.idle": "2024-04-02T19:41:57.326209Z",
     "shell.execute_reply": "2024-04-02T19:41:57.324336Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.317812Z"
    },
    "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": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:57.330462Z",
     "iopub.status.busy": "2024-04-02T19:41:57.329713Z",
     "iopub.status.idle": "2024-04-02T19:41:58.205413Z",
     "shell.execute_reply": "2024-04-02T19:41:58.193924Z",
     "shell.execute_reply.started": "2024-04-02T19:41:57.330397Z"
    }
   },
   "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": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:58.209308Z",
     "iopub.status.busy": "2024-04-02T19:41:58.207976Z",
     "iopub.status.idle": "2024-04-02T19:41:58.279272Z",
     "shell.execute_reply": "2024-04-02T19:41:58.277351Z",
     "shell.execute_reply.started": "2024-04-02T19:41:58.209210Z"
    },
    "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": 74,
     "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": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:58.294251Z",
     "iopub.status.busy": "2024-04-02T19:41:58.281612Z",
     "iopub.status.idle": "2024-04-02T19:41:59.300579Z",
     "shell.execute_reply": "2024-04-02T19:41:59.298955Z",
     "shell.execute_reply.started": "2024-04-02T19:41:58.294158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": {},
   "source": [
    "### Survival 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": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:59.304657Z",
     "iopub.status.busy": "2024-04-02T19:41:59.304233Z",
     "iopub.status.idle": "2024-04-02T19:41:59.777235Z",
     "shell.execute_reply": "2024-04-02T19:41:59.773814Z",
     "shell.execute_reply.started": "2024-04-02T19:41:59.304623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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]);\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\");"
   ]
  },
  {
   "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": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:41:59.780556Z",
     "iopub.status.busy": "2024-04-02T19:41:59.779869Z",
     "iopub.status.idle": "2024-04-02T19:42:00.300875Z",
     "shell.execute_reply": "2024-04-02T19:42:00.299103Z",
     "shell.execute_reply.started": "2024-04-02T19:41:59.780494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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]);\n",
    "plt.xlabel(\"Time with service (months)\")\n",
    "plt.ylabel(\"Survival probability\");"
   ]
  },
  {
   "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": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.304254Z",
     "iopub.status.busy": "2024-04-02T19:42:00.302577Z",
     "iopub.status.idle": "2024-04-02T19:42:00.314830Z",
     "shell.execute_reply": "2024-04-02T19:42:00.313434Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.304188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coef   -0.003185\n",
       "Name: MonthlyCharges, dtype: float64"
      ]
     },
     "execution_count": 78,
     "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": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.316592Z",
     "iopub.status.busy": "2024-04-02T19:42:00.316124Z",
     "iopub.status.idle": "2024-04-02T19:42:00.326350Z",
     "shell.execute_reply": "2024-04-02T19:42:00.324239Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.316544Z"
    }
   },
   "outputs": [],
   "source": [
    "test_X = test_df_surv.drop(columns=[\"tenure\", \"Churn\"])"
   ]
  },
  {
   "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": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.329115Z",
     "iopub.status.busy": "2024-04-02T19:42:00.328609Z",
     "iopub.status.idle": "2024-04-02T19:42:00.406382Z",
     "shell.execute_reply": "2024-04-02T19:42:00.405028Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.329058Z"
    },
    "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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.predict_expectation(test_X).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": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.413775Z",
     "iopub.status.busy": "2024-04-02T19:42:00.412625Z",
     "iopub.status.idle": "2024-04-02T19:42:00.923503Z",
     "shell.execute_reply": "2024-04-02T19:42:00.921460Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.413714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.predict_survival_function(test_X[: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.\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": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.926526Z",
     "iopub.status.busy": "2024-04-02T19:42:00.925891Z",
     "iopub.status.idle": "2024-04-02T19:42:00.940276Z",
     "shell.execute_reply": "2024-04-02T19:42:00.936375Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.926476Z"
    },
    "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": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.944326Z",
     "iopub.status.busy": "2024-04-02T19:42:00.943449Z",
     "iopub.status.idle": "2024-04-02T19:42:00.983749Z",
     "shell.execute_reply": "2024-04-02T19:42:00.981982Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.944283Z"
    }
   },
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.989443</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68.0</th>\n",
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       "      <th>69.0</th>\n",
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       "      <th>70.0</th>\n",
       "      <td>0.429634</td>\n",
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       "    <tr>\n",
       "      <th>71.0</th>\n",
       "      <td>0.429634</td>\n",
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       "    <tr>\n",
       "      <th>72.0</th>\n",
       "      <td>0.429634</td>\n",
       "    </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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.predict_survival_function(train_df_surv_not_churned[:1], conditional_after=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:00.987851Z",
     "iopub.status.busy": "2024-04-02T19:42:00.986039Z",
     "iopub.status.idle": "2024-04-02T19:42:01.401447Z",
     "shell.execute_reply": "2024-04-02T19:42:01.400032Z",
     "shell.execute_reply.started": "2024-04-02T19:42:00.987785Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:01.403791Z",
     "iopub.status.busy": "2024-04-02T19:42:01.403297Z",
     "iopub.status.idle": "2024-04-02T19:42:01.798762Z",
     "shell.execute_reply": "2024-04-02T19:42:01.797282Z",
     "shell.execute_reply.started": "2024-04-02T19:42:01.403755Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": [
    "## (Optional) Evaluation \n",
    "\n",
    "By default score returns \"partial log likelihood\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:01.800821Z",
     "iopub.status.busy": "2024-04-02T19:42:01.800435Z",
     "iopub.status.idle": "2024-04-02T19:42:01.964729Z",
     "shell.execute_reply": "2024-04-02T19:42:01.962958Z",
     "shell.execute_reply.started": "2024-04-02T19:42:01.800787Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.8641864337292489"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(train_df_surv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:01.969036Z",
     "iopub.status.busy": "2024-04-02T19:42:01.967445Z",
     "iopub.status.idle": "2024-04-02T19:42:02.172524Z",
     "shell.execute_reply": "2024-04-02T19:42:02.169683Z",
     "shell.execute_reply.started": "2024-04-02T19:42:01.968943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7277854625841886"
      ]
     },
     "execution_count": 87,
     "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": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:02.196970Z",
     "iopub.status.busy": "2024-04-02T19:42:02.180324Z",
     "iopub.status.idle": "2024-04-02T19:42:02.349482Z",
     "shell.execute_reply": "2024-04-02T19:42:02.347308Z",
     "shell.execute_reply.started": "2024-04-02T19:42:02.196839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625888648969532"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.concordance_index_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:02.352715Z",
     "iopub.status.busy": "2024-04-02T19:42:02.351472Z",
     "iopub.status.idle": "2024-04-02T19:42:02.563765Z",
     "shell.execute_reply": "2024-04-02T19:42:02.560716Z",
     "shell.execute_reply.started": "2024-04-02T19:42:02.352655Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625888648969532"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.score(train_df_surv, scoring_method=\"concordance_index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:02.568788Z",
     "iopub.status.busy": "2024-04-02T19:42:02.567299Z",
     "iopub.status.idle": "2024-04-02T19:42:02.745926Z",
     "shell.execute_reply": "2024-04-02T19:42:02.743669Z",
     "shell.execute_reply.started": "2024-04-02T19:42:02.568679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8546143543902771"
      ]
     },
     "execution_count": 90,
     "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": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:02.756210Z",
     "iopub.status.busy": "2024-04-02T19:42:02.754999Z",
     "iopub.status.idle": "2024-04-02T19:42:03.044562Z",
     "shell.execute_reply": "2024-04-02T19:42:03.042174Z",
     "shell.execute_reply.started": "2024-04-02T19:42:02.756142Z"
    }
   },
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_freedom</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>log-likelihood ratio test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2206.68</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 2206.68 & 0.00 & inf \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: log-likelihood ratio test>\n",
       "null_distribution = chi squared\n",
       "  degrees_freedom = 43\n",
       "        test_name = log-likelihood ratio test\n",
       "\n",
       "---\n",
       " test_statistic      p  -log2(p)\n",
       "        2206.68 <0.005       inf"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.log_likelihood_ratio_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T19:42:03.049317Z",
     "iopub.status.busy": "2024-04-02T19:42:03.048408Z",
     "iopub.status.idle": "2024-04-02T19:42:04.494689Z",
     "shell.execute_reply": "2024-04-02T19:42:04.491733Z",
     "shell.execute_reply.started": "2024-04-02T19:42:03.049250Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ``p_value_threshold`` is set at 0.01. Even under the null hypothesis of no violations, some\n",
      "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
      "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
      "assumption will be flagged.\n",
      "\n",
      "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
      "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
      "and looking for non-constant lines. See link [A] below for a full example.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>&lt;lifelines.CoxPHFitter: fitted with 5282 total...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Contract_Month-to-month</th>\n",
       "      <th>km</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Contract_One year</th>\n",
       "      <th>km</th>\n",
       "      <td>14.52</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>12.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>10.11</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Contract_Two year</th>\n",
       "      <th>km</th>\n",
       "      <td>8.86</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>8.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>7.69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dependents_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dependents_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DeviceProtection_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DeviceProtection_No internet service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DeviceProtection_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">InternetService_DSL</th>\n",
       "      <th>km</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">InternetService_Fiber optic</th>\n",
       "      <th>km</th>\n",
       "      <td>1.02</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">InternetService_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MonthlyCharges</th>\n",
       "      <th>km</th>\n",
       "      <td>1.65</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.72</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MultipleLines_No</th>\n",
       "      <th>km</th>\n",
       "      <td>1.57</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.87</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MultipleLines_No phone service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MultipleLines_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>1.92</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>2.35</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineBackup_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineBackup_No internet service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineBackup_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineSecurity_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineSecurity_No internet service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">OnlineSecurity_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaperlessBilling_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaperlessBilling_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Partner_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Partner_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>km</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>km</th>\n",
       "      <td>1.46</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.70</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaymentMethod_Electronic check</th>\n",
       "      <th>km</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PaymentMethod_Mailed check</th>\n",
       "      <th>km</th>\n",
       "      <td>2.36</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>2.85</td>\n",
       "      <td>0.09</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PhoneService_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PhoneService_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SeniorCitizen</th>\n",
       "      <th>km</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">StreamingMovies_No</th>\n",
       "      <th>km</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">StreamingMovies_No internet service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">StreamingMovies_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>2.45</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>2.73</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">StreamingTV_No</th>\n",
       "      <th>km</th>\n",
       "      <td>1.09</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">StreamingTV_No internet service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">StreamingTV_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>2.48</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>2.23</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TechSupport_No</th>\n",
       "      <th>km</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TechSupport_No internet service</th>\n",
       "      <th>km</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TechSupport_Yes</th>\n",
       "      <th>km</th>\n",
       "      <td>1.92</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>2.01</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gender_Female</th>\n",
       "      <th>km</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gender_Male</th>\n",
       "      <th>km</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrr}\n",
       " &  & test_statistic & p & -log2(p) \\\\\n",
       "\\multirow[c]{2}{*}{Contract_Month-to-month} & km & 0.07 & 0.80 & 0.33 \\\\\n",
       " & rank & 0.00 & 0.97 & 0.04 \\\\\n",
       "\\multirow[c]{2}{*}{Contract_One year} & km & 14.52 & 0.00 & 12.81 \\\\\n",
       " & rank & 10.11 & 0.00 & 9.41 \\\\\n",
       "\\multirow[c]{2}{*}{Contract_Two year} & km & 8.86 & 0.00 & 8.42 \\\\\n",
       " & rank & 7.69 & 0.01 & 7.49 \\\\\n",
       "\\multirow[c]{2}{*}{Dependents_No} & km & 0.07 & 0.79 & 0.34 \\\\\n",
       " & rank & 0.07 & 0.79 & 0.34 \\\\\n",
       "\\multirow[c]{2}{*}{Dependents_Yes} & km & 0.07 & 0.79 & 0.34 \\\\\n",
       " & rank & 0.07 & 0.79 & 0.34 \\\\\n",
       "\\multirow[c]{2}{*}{DeviceProtection_No} & km & 0.07 & 0.79 & 0.34 \\\\\n",
       " & rank & 0.08 & 0.77 & 0.37 \\\\\n",
       "\\multirow[c]{2}{*}{DeviceProtection_No internet service} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{DeviceProtection_Yes} & km & 0.70 & 0.40 & 1.31 \\\\\n",
       " & rank & 0.76 & 0.38 & 1.38 \\\\\n",
       "\\multirow[c]{2}{*}{InternetService_DSL} & km & 0.32 & 0.57 & 0.81 \\\\\n",
       " & rank & 0.28 & 0.59 & 0.75 \\\\\n",
       "\\multirow[c]{2}{*}{InternetService_Fiber optic} & km & 1.02 & 0.31 & 1.68 \\\\\n",
       " & rank & 0.98 & 0.32 & 1.64 \\\\\n",
       "\\multirow[c]{2}{*}{InternetService_No} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{MonthlyCharges} & km & 1.65 & 0.20 & 2.33 \\\\\n",
       " & rank & 1.72 & 0.19 & 2.40 \\\\\n",
       "\\multirow[c]{2}{*}{MultipleLines_No} & km & 1.57 & 0.21 & 2.25 \\\\\n",
       " & rank & 1.87 & 0.17 & 2.55 \\\\\n",
       "\\multirow[c]{2}{*}{MultipleLines_No phone service} & km & 0.03 & 0.86 & 0.21 \\\\\n",
       " & rank & 0.05 & 0.83 & 0.27 \\\\\n",
       "\\multirow[c]{2}{*}{MultipleLines_Yes} & km & 1.92 & 0.17 & 2.59 \\\\\n",
       " & rank & 2.35 & 0.13 & 3.00 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineBackup_No} & km & 0.29 & 0.59 & 0.76 \\\\\n",
       " & rank & 0.24 & 0.63 & 0.68 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineBackup_No internet service} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineBackup_Yes} & km & 1.25 & 0.26 & 1.92 \\\\\n",
       " & rank & 1.17 & 0.28 & 1.84 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineSecurity_No} & km & 0.02 & 0.88 & 0.19 \\\\\n",
       " & rank & 0.09 & 0.77 & 0.38 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineSecurity_No internet service} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{OnlineSecurity_Yes} & km & 0.56 & 0.46 & 1.13 \\\\\n",
       " & rank & 0.85 & 0.36 & 1.49 \\\\\n",
       "\\multirow[c]{2}{*}{PaperlessBilling_No} & km & 0.03 & 0.86 & 0.21 \\\\\n",
       " & rank & 0.02 & 0.90 & 0.16 \\\\\n",
       "\\multirow[c]{2}{*}{PaperlessBilling_Yes} & km & 0.03 & 0.86 & 0.21 \\\\\n",
       " & rank & 0.02 & 0.90 & 0.16 \\\\\n",
       "\\multirow[c]{2}{*}{Partner_No} & km & 0.27 & 0.60 & 0.73 \\\\\n",
       " & rank & 0.37 & 0.54 & 0.88 \\\\\n",
       "\\multirow[c]{2}{*}{Partner_Yes} & km & 0.27 & 0.60 & 0.73 \\\\\n",
       " & rank & 0.37 & 0.54 & 0.88 \\\\\n",
       "\\multirow[c]{2}{*}{PaymentMethod_Bank transfer (automatic)} & km & 0.44 & 0.51 & 0.98 \\\\\n",
       " & rank & 0.51 & 0.48 & 1.07 \\\\\n",
       "\\multirow[c]{2}{*}{PaymentMethod_Credit card (automatic)} & km & 1.46 & 0.23 & 2.14 \\\\\n",
       " & rank & 1.70 & 0.19 & 2.38 \\\\\n",
       "\\multirow[c]{2}{*}{PaymentMethod_Electronic check} & km & 0.06 & 0.81 & 0.30 \\\\\n",
       " & rank & 0.05 & 0.82 & 0.29 \\\\\n",
       "\\multirow[c]{2}{*}{PaymentMethod_Mailed check} & km & 2.36 & 0.12 & 3.01 \\\\\n",
       " & rank & 2.85 & 0.09 & 3.45 \\\\\n",
       "\\multirow[c]{2}{*}{PhoneService_No} & km & 0.03 & 0.86 & 0.21 \\\\\n",
       " & rank & 0.05 & 0.83 & 0.27 \\\\\n",
       "\\multirow[c]{2}{*}{PhoneService_Yes} & km & 0.03 & 0.86 & 0.21 \\\\\n",
       " & rank & 0.05 & 0.83 & 0.27 \\\\\n",
       "\\multirow[c]{2}{*}{SeniorCitizen} & km & 0.00 & 0.95 & 0.08 \\\\\n",
       " & rank & 0.00 & 0.95 & 0.08 \\\\\n",
       "\\multirow[c]{2}{*}{StreamingMovies_No} & km & 1.10 & 0.30 & 1.76 \\\\\n",
       " & rank & 1.25 & 0.26 & 1.93 \\\\\n",
       "\\multirow[c]{2}{*}{StreamingMovies_No internet service} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{StreamingMovies_Yes} & km & 2.45 & 0.12 & 3.09 \\\\\n",
       " & rank & 2.73 & 0.10 & 3.35 \\\\\n",
       "\\multirow[c]{2}{*}{StreamingTV_No} & km & 1.09 & 0.30 & 1.76 \\\\\n",
       " & rank & 0.90 & 0.34 & 1.55 \\\\\n",
       "\\multirow[c]{2}{*}{StreamingTV_No internet service} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{StreamingTV_Yes} & km & 2.48 & 0.12 & 3.12 \\\\\n",
       " & rank & 2.23 & 0.14 & 2.89 \\\\\n",
       "\\multirow[c]{2}{*}{TechSupport_No} & km & 0.49 & 0.49 & 1.04 \\\\\n",
       " & rank & 0.50 & 0.48 & 1.07 \\\\\n",
       "\\multirow[c]{2}{*}{TechSupport_No internet service} & km & 0.25 & 0.62 & 0.69 \\\\\n",
       " & rank & 0.26 & 0.61 & 0.72 \\\\\n",
       "\\multirow[c]{2}{*}{TechSupport_Yes} & km & 1.92 & 0.17 & 2.59 \\\\\n",
       " & rank & 2.01 & 0.16 & 2.68 \\\\\n",
       "\\multirow[c]{2}{*}{gender_Female} & km & 0.22 & 0.64 & 0.65 \\\\\n",
       " & rank & 0.08 & 0.78 & 0.35 \\\\\n",
       "\\multirow[c]{2}{*}{gender_Male} & km & 0.22 & 0.64 & 0.65 \\\\\n",
       " & rank & 0.08 & 0.78 & 0.35 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: proportional_hazard_test>\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "             model = <lifelines.CoxPHFitter: fitted with 5282 total observations, 3912 right-censored observations>\n",
       "         test_name = proportional_hazard_test\n",
       "\n",
       "---\n",
       "                                              test_statistic      p  -log2(p)\n",
       "Contract_Month-to-month                 km              0.07   0.80      0.33\n",
       "                                        rank            0.00   0.97      0.04\n",
       "Contract_One year                       km             14.52 <0.005     12.81\n",
       "                                        rank           10.11 <0.005      9.41\n",
       "Contract_Two year                       km              8.86 <0.005      8.42\n",
       "                                        rank            7.69   0.01      7.49\n",
       "Dependents_No                           km              0.07   0.79      0.34\n",
       "                                        rank            0.07   0.79      0.34\n",
       "Dependents_Yes                          km              0.07   0.79      0.34\n",
       "                                        rank            0.07   0.79      0.34\n",
       "DeviceProtection_No                     km              0.07   0.79      0.34\n",
       "                                        rank            0.08   0.77      0.37\n",
       "DeviceProtection_No internet service    km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "DeviceProtection_Yes                    km              0.70   0.40      1.31\n",
       "                                        rank            0.76   0.38      1.38\n",
       "InternetService_DSL                     km              0.32   0.57      0.81\n",
       "                                        rank            0.28   0.59      0.75\n",
       "InternetService_Fiber optic             km              1.02   0.31      1.68\n",
       "                                        rank            0.98   0.32      1.64\n",
       "InternetService_No                      km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "MonthlyCharges                          km              1.65   0.20      2.33\n",
       "                                        rank            1.72   0.19      2.40\n",
       "MultipleLines_No                        km              1.57   0.21      2.25\n",
       "                                        rank            1.87   0.17      2.55\n",
       "MultipleLines_No phone service          km              0.03   0.86      0.21\n",
       "                                        rank            0.05   0.83      0.27\n",
       "MultipleLines_Yes                       km              1.92   0.17      2.59\n",
       "                                        rank            2.35   0.13      3.00\n",
       "OnlineBackup_No                         km              0.29   0.59      0.76\n",
       "                                        rank            0.24   0.63      0.68\n",
       "OnlineBackup_No internet service        km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "OnlineBackup_Yes                        km              1.25   0.26      1.92\n",
       "                                        rank            1.17   0.28      1.84\n",
       "OnlineSecurity_No                       km              0.02   0.88      0.19\n",
       "                                        rank            0.09   0.77      0.38\n",
       "OnlineSecurity_No internet service      km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "OnlineSecurity_Yes                      km              0.56   0.46      1.13\n",
       "                                        rank            0.85   0.36      1.49\n",
       "PaperlessBilling_No                     km              0.03   0.86      0.21\n",
       "                                        rank            0.02   0.90      0.16\n",
       "PaperlessBilling_Yes                    km              0.03   0.86      0.21\n",
       "                                        rank            0.02   0.90      0.16\n",
       "Partner_No                              km              0.27   0.60      0.73\n",
       "                                        rank            0.37   0.54      0.88\n",
       "Partner_Yes                             km              0.27   0.60      0.73\n",
       "                                        rank            0.37   0.54      0.88\n",
       "PaymentMethod_Bank transfer (automatic) km              0.44   0.51      0.98\n",
       "                                        rank            0.51   0.48      1.07\n",
       "PaymentMethod_Credit card (automatic)   km              1.46   0.23      2.14\n",
       "                                        rank            1.70   0.19      2.38\n",
       "PaymentMethod_Electronic check          km              0.06   0.81      0.30\n",
       "                                        rank            0.05   0.82      0.29\n",
       "PaymentMethod_Mailed check              km              2.36   0.12      3.01\n",
       "                                        rank            2.85   0.09      3.45\n",
       "PhoneService_No                         km              0.03   0.86      0.21\n",
       "                                        rank            0.05   0.83      0.27\n",
       "PhoneService_Yes                        km              0.03   0.86      0.21\n",
       "                                        rank            0.05   0.83      0.27\n",
       "SeniorCitizen                           km              0.00   0.95      0.08\n",
       "                                        rank            0.00   0.95      0.08\n",
       "StreamingMovies_No                      km              1.10   0.30      1.76\n",
       "                                        rank            1.25   0.26      1.93\n",
       "StreamingMovies_No internet service     km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "StreamingMovies_Yes                     km              2.45   0.12      3.09\n",
       "                                        rank            2.73   0.10      3.35\n",
       "StreamingTV_No                          km              1.09   0.30      1.76\n",
       "                                        rank            0.90   0.34      1.55\n",
       "StreamingTV_No internet service         km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "StreamingTV_Yes                         km              2.48   0.12      3.12\n",
       "                                        rank            2.23   0.14      2.89\n",
       "TechSupport_No                          km              0.49   0.49      1.04\n",
       "                                        rank            0.50   0.48      1.07\n",
       "TechSupport_No internet service         km              0.25   0.62      0.69\n",
       "                                        rank            0.26   0.61      0.72\n",
       "TechSupport_Yes                         km              1.92   0.17      2.59\n",
       "                                        rank            2.01   0.16      2.68\n",
       "gender_Female                           km              0.22   0.64      0.65\n",
       "                                        rank            0.08   0.78      0.35\n",
       "gender_Male                             km              0.22   0.64      0.65\n",
       "                                        rank            0.08   0.78      0.35"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "1. Variable 'Contract_One year' failed the non-proportional test: p-value is 0.0001.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['Contract_One year', ...]`\n",
      "in the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "2. Variable 'Contract_Two year' failed the non-proportional test: p-value is 0.0029.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['Contract_Two year', ...]`\n",
      "in the call in `.fit`. See documentation in link [E] below.\n",
      "\n",
      "---\n",
      "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
      "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
      "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
      "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
      "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph.check_assumptions(train_df_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?\n",
    "\n",
    "There are many other approaches to modelling in survival analysis:\n",
    "\n",
    "- **Time-varying** proportional hazards.\n",
    "  - What if some of the features change over time, e.g. **plan type**, **number of lines**, etc.\n",
    "- Approaches **based on deep learning**, e.g. the [pysurvival](https://square.github.io/pysurvival/) package.\n",
    "- **Random survival forests**.\n",
    "- And more..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Types of censoring\n",
    "There are also various types and sub-types of censoring we didn't cover:\n",
    "\n",
    "- What we did **today** is called \"**right censoring**\"\n",
    "- **Sub-types** within right censoring\n",
    "  - Did **everyone join** at the **same time**?\n",
    "  - Are there **other reasons** the data might be censored at random times, e.g. the **person died**?\n",
    "- **Left** censoring\n",
    "- **Interval** censoring\n",
    "\n",
    "[From Wikipedia](https://en.wikipedia.org/wiki/Censoring_(statistics)):\n",
    "> - Left censoring – a data point is below a certain value but it is unknown by how much.\n",
    "> - Interval censoring – a data point is somewhere on an interval between two values.\n",
    "> - Right censoring – a data point is above a certain value but it is unknown by how much."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Summary\n",
    "\n",
    "- Censoring and incorrect approaches to handling it\n",
    "  - Throw away people who haven't churned\n",
    "  - Assume everyone churns today\n",
    "- Predicting tenure vs. churned\n",
    "- Survival analysis encompasses both of these, and deals with censoring\n",
    "- And it can make rich and interesting predictions!\n",
    "- KM model -> doesn't look at features\n",
    "- CPH model -> like linear regression, does look at the features"
   ]
  },
  {
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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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