coxph_trainer/coxph_trainer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Coxph trainer - Survival analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function provides both [Cox proprotional hazards modelling](https://en.wikipedia.org/wiki/Proportional_hazards_model)\n",
    "and [Kaplan-Meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Basics of the Cox proportional hazards model**\n",
    "The purpose of the model is to evaluate simultaneously the effect of several factors on survival.<br> In other words, it allows us to examine how specified factors influence the rate of a particular event happening (e.g., infection, death) at a particular point in time.<br> This rate is commonly referred as the hazard rate ([link](http://www.sthda.com/english/wiki/cox-proportional-hazards-model)).\n",
    "\n",
    "### **Kaplan-Meier survival estimate**\n",
    "The Kaplan-Meier (KM) method is a non-parametric method used to estimate the survival probability from observed survival times (Kaplan and Meier, 1958)([link](http://www.sthda.com/english/wiki/survival-analysis-basics))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **The function**\n",
    "train models to predict the timing of events.<br>\n",
    "Although identical in structure to other training functions, this one\n",
    "requires generating a 'Y' that represents the age/duration/tenure of\n",
    "the obervation, designated 'tenure' here, and a binary labels columns that\n",
    "represents the event of interest, churned/not-churned.<br>\n",
    "In addition, there is a strata_cols parameter, representing a list of\n",
    "stratification (aka grouping) variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example covers:\n",
    "\n",
    "- [Data exploration](#Data-exploration)\n",
    "- [Training Cox proprotional hazards and Kaplan-Meier model](#Training-Cox-proprotional-hazards-and-Kaplan-Meier-model)\n",
    "- - [Importing the function](#Importing-the-function)\n",
    "- - [Setup function parameters](#Setup-function-parameters)\n",
    "- - [Running the function locally](#Running-the-function-locally)\n",
    "- [A peek at a pickled kaplan-meier model](#A-peek-at-a-pickled-kaplan-meier-model)\n",
    "- [A peek at a pickeld cox hazards default model](#A-peek-at-a-pickeld-cox-hazards-default-model)\n",
    "- [Some potential default analyses of coxph](#Some-potential-default-analyses-of-coxph)\n",
    "- - [Running the function remotely](#Running-the-function-remotely)\n",
    "\n",
    "We will train on [Telco Customer Churn dataset](https://www.kaggle.com/blastchar/telco-customer-churn) from kaggle, click the link for context.<br>\n",
    "The dataset is transformed using [one-hot-encoding](https://en.wikipedia.org/wiki/One-hot), check out [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) in the clean_data section for further information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure the following libraries are installed \n",
    "# !pip install lifelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data exploration**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw dataset\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>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>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</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>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</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>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</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>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>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</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>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</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>Fiber optic</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>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</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 PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting raw data as - downloaded from kaggle\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "print('raw dataset')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoded dataset\n"
     ]
    },
    {
     "data": {
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       "      <th>gender</th>\n",
       "      <th>senior</th>\n",
       "      <th>partner</th>\n",
       "      <th>deps</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>labels</th>\n",
       "      <th>tenure_map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   gender  senior  partner  deps  tenure  PhoneService  MultipleLines  \\\n",
       "0       0       0        1     0       1             0              0   \n",
       "1       1       0        0     0      34             1              0   \n",
       "2       1       0        0     0       2             1              0   \n",
       "3       1       0        0     0      45             0              0   \n",
       "4       0       0        0     0       2             1              0   \n",
       "\n",
       "   InternetService  OnlineSecurity  OnlineBackup  DeviceProtection  \\\n",
       "0                0               0             1                 0   \n",
       "1                0               1             0                 1   \n",
       "2                0               1             1                 0   \n",
       "3                0               1             0                 1   \n",
       "4                1               0             0                 0   \n",
       "\n",
       "   TechSupport  StreamingTV  StreamingMovies  Contract  PaperlessBilling  \\\n",
       "0            0            0                0         0                 1   \n",
       "1            0            0                0         1                 0   \n",
       "2            0            0                0         0                 1   \n",
       "3            1            0                0         1                 0   \n",
       "4            0            0                0         0                 1   \n",
       "\n",
       "   PaymentMethod  MonthlyCharges  labels  tenure_map  \n",
       "0              2           29.85       0         0.0  \n",
       "1              3           56.95       0         2.0  \n",
       "2              3           53.85       1         0.0  \n",
       "3              0           42.30       0         3.0  \n",
       "4              2           70.70       1         0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\")\n",
    "print('encoded dataset')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Training Cox proprotional hazards and Kaplan-Meier model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Importing the function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 13:22:07,678 [info] loaded project function-marketplace from MLRun DB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7f77c03c33d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mlrun\n",
    "mlrun.set_environment(project='function-marketplace')\n",
    "\n",
    "fn = mlrun.import_function(\"hub://coxph_trainer\")\n",
    "fn.image='mlrun/mlrun'\n",
    "fn.apply(mlrun.auto_mount())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Setup function parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = mlrun.new_task(name = \"tasks-survive-trainer\",\n",
    "                      params = {\"event_column\" : \"labels\", \n",
    "                                \"strata_cols\"  : ['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService'],\n",
    "                                \"p_value\"      : 0.005,\n",
    "                                \"encode_cols\"  : {\"Contract\"       : \"Contract\",\n",
    "                                                  \"PaymentMethod\"  : \"Payment\"},\n",
    "                                \"models_dest\"  : 'models/cox',\n",
    "                                \"file_ext\" : \"csv\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function locally**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 13:18:36,975 [info] starting run tasks-survive-trainer uid=c525c7402c7e4188b0e22996e8fc682c DB=http://mlrun-api:8080\n"
     ]
    },
    {
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       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
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       "      <td>default</td>\n",
       "      <td><div title=\"c525c7402c7e4188b0e22996e8fc682c\"><a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/default/jobs/monitor/c525c7402c7e4188b0e22996e8fc682c/overview\" target=\"_blank\" >...e8fc682c</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 13 13:18:37</td>\n",
       "      <td>completed</td>\n",
       "      <td>tasks-survive-trainer</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=dani</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=dani</div><div class=\"dictlist\">host=jupyter-dani-5bbd9959b7-tsgh8</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\">dataset</div></td>\n",
       "      <td><div class=\"dictlist\">event_column=labels</div><div class=\"dictlist\">strata_cols=['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService']</div><div class=\"dictlist\">p_value=0.005</div><div class=\"dictlist\">encode_cols={'Contract': 'Contract', 'PaymentMethod': 'Payment'}</div><div class=\"dictlist\">models_dest=models/cox</div><div class=\"dictlist\">file_ext=csv</div></td>\n",
       "      <td></td>\n",
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      "\n"
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    },
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     "data": {
      "text/html": [
       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/default/jobs/monitor/c525c7402c7e4188b0e22996e8fc682c/overview\" target=\"_blank\">click here</a> to open in UI</b>"
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     "text": [
      "> 2021-10-13 13:18:41,277 [info] run executed, status=completed\n"
     ]
    },
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# running the function with the task provided\n",
    "coxph_run = fn.run(task,\n",
    "                   local=True,\n",
    "                   inputs={\"dataset\"  : \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **A peek at a pickled kaplan-meier model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the model trained\n",
    "from mlrun.artifacts import get_model\n",
    "import pickle\n",
    "model_file, model_obj, _ = get_model(coxph_run.artifact('km-model'))\n",
    "model = pickle.load(open(model_file,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0.969027\n",
       "10     0.869452\n",
       "30     0.781377\n",
       "100    0.668167\n",
       "200    0.668167\n",
       "Name: KM_estimate, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([1,10,30,100,200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = model.plot(figsize=(11,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **A peek at a pickeld cox hazards default model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the model trained\n",
    "from mlrun.artifacts import get_model\n",
    "import pickle\n",
    "model_file, model_obj, _ = get_model(coxph_run.artifact('cx-model'))\n",
    "model = pickle.load(open(model_file,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'tenure'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'labels'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[InternetService, StreamingMovies, StreamingTV...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-102.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2021-10-13 13:18:38 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.71</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partner</th>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.43</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.57</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deps</th>\n",
       "      <td>0.62</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.70</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>-0.79</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.09</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.83</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5.93</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.03</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.08</td>\n",
       "      <td>5.54</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>0.51</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.17</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.17</td>\n",
       "      <td>16.43</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.35</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.64</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_1</th>\n",
       "      <td>-2.19</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract_2</th>\n",
       "      <td>-19.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3478.68</td>\n",
       "      <td>-6838.04</td>\n",
       "      <td>6798.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_1</th>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_2</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.66</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.36</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>237.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>106.72 on 16 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>48.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "covariate        &        &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "gender           &   0.71 &       2.04 &      0.34 &            0.04 &            1.39 &                 1.04 &                 4.00 &  2.08 & 0.04 &      4.72 \\\\\n",
       "senior           &  -0.33 &       0.72 &      0.44 &           -1.20 &            0.54 &                 0.30 &                 1.72 & -0.74 & 0.46 &      1.13 \\\\\n",
       "partner          &  -0.39 &       0.67 &      0.43 &           -1.24 &            0.45 &                 0.29 &                 1.57 & -0.91 & 0.36 &      1.47 \\\\\n",
       "deps             &   0.62 &       1.85 &      0.50 &           -0.36 &            1.59 &                 0.70 &                 4.93 &  1.24 & 0.22 &      2.21 \\\\\n",
       "MultipleLines    &  -0.79 &       0.45 &      1.09 &           -2.92 &            1.34 &                 0.05 &                 3.83 & -0.72 & 0.47 &      1.09 \\\\\n",
       "OnlineSecurity   &  -0.77 &       0.46 &      1.30 &           -3.31 &            1.78 &                 0.04 &                 5.93 & -0.59 & 0.56 &      0.85 \\\\\n",
       "OnlineBackup     &  -0.47 &       0.63 &      0.95 &           -2.33 &            1.39 &                 0.10 &                 4.03 & -0.49 & 0.62 &      0.68 \\\\\n",
       "DeviceProtection &  -0.41 &       0.66 &      1.08 &           -2.54 &            1.71 &                 0.08 &                 5.54 & -0.38 & 0.70 &      0.51 \\\\\n",
       "TechSupport      &   0.51 &       1.66 &      1.17 &           -1.78 &            2.80 &                 0.17 &                16.43 &  0.44 & 0.66 &      0.59 \\\\\n",
       "PaperlessBilling &   0.35 &       1.42 &      0.41 &           -0.45 &            1.15 &                 0.64 &                 3.16 &  0.86 & 0.39 &      1.35 \\\\\n",
       "MonthlyCharges   &  -0.08 &       0.92 &      0.19 &           -0.46 &            0.30 &                 0.63 &                 1.35 & -0.40 & 0.69 &      0.54 \\\\\n",
       "Contract\\_1       &  -2.19 &       0.11 &      0.71 &           -3.58 &           -0.79 &                 0.03 &                 0.45 & -3.07 & 0.00 &      8.88 \\\\\n",
       "Contract\\_2       & -19.94 &       0.00 &   3478.68 &        -6838.04 &         6798.16 &                 0.00 &                  inf & -0.01 & 1.00 &      0.01 \\\\\n",
       "Payment\\_1        &  -0.87 &       0.42 &      0.62 &           -2.07 &            0.34 &                 0.13 &                 1.40 & -1.41 & 0.16 &      2.65 \\\\\n",
       "Payment\\_2        &   0.46 &       1.58 &      0.45 &           -0.42 &            1.33 &                 0.66 &                 3.80 &  1.03 & 0.31 &      1.71 \\\\\n",
       "Payment\\_3        &   0.23 &       1.26 &      0.64 &           -1.02 &            1.49 &                 0.36 &                 4.43 &  0.36 & 0.72 &      0.48 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 226 total observations, 171 right-censored observations>\n",
       "             duration col = 'tenure'\n",
       "                event col = 'labels'\n",
       "                   strata = ['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService']\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 226\n",
       "number of events observed = 55\n",
       "   partial log-likelihood = -102.57\n",
       "         time fit was run = 2021-10-13 13:18:38 UTC\n",
       "\n",
       "---\n",
       "                   coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                \n",
       "gender             0.71       2.04       0.34             0.04             1.39                 1.04                 4.00\n",
       "senior            -0.33       0.72       0.44            -1.20             0.54                 0.30                 1.72\n",
       "partner           -0.39       0.67       0.43            -1.24             0.45                 0.29                 1.57\n",
       "deps               0.62       1.85       0.50            -0.36             1.59                 0.70                 4.93\n",
       "MultipleLines     -0.79       0.45       1.09            -2.92             1.34                 0.05                 3.83\n",
       "OnlineSecurity    -0.77       0.46       1.30            -3.31             1.78                 0.04                 5.93\n",
       "OnlineBackup      -0.47       0.63       0.95            -2.33             1.39                 0.10                 4.03\n",
       "DeviceProtection  -0.41       0.66       1.08            -2.54             1.71                 0.08                 5.54\n",
       "TechSupport        0.51       1.66       1.17            -1.78             2.80                 0.17                16.43\n",
       "PaperlessBilling   0.35       1.42       0.41            -0.45             1.15                 0.64                 3.16\n",
       "MonthlyCharges    -0.08       0.92       0.19            -0.46             0.30                 0.63                 1.35\n",
       "Contract_1        -2.19       0.11       0.71            -3.58            -0.79                 0.03                 0.45\n",
       "Contract_2       -19.94       0.00    3478.68         -6838.04          6798.16                 0.00                  inf\n",
       "Payment_1         -0.87       0.42       0.62            -2.07             0.34                 0.13                 1.40\n",
       "Payment_2          0.46       1.58       0.45            -0.42             1.33                 0.66                 3.80\n",
       "Payment_3          0.23       1.26       0.64            -1.02             1.49                 0.36                 4.43\n",
       "\n",
       "                     z      p   -log2(p)\n",
       "covariate                               \n",
       "gender            2.08   0.04       4.72\n",
       "senior           -0.74   0.46       1.13\n",
       "partner          -0.91   0.36       1.47\n",
       "deps              1.24   0.22       2.21\n",
       "MultipleLines    -0.72   0.47       1.09\n",
       "OnlineSecurity   -0.59   0.56       0.85\n",
       "OnlineBackup     -0.49   0.62       0.68\n",
       "DeviceProtection -0.38   0.70       0.51\n",
       "TechSupport       0.44   0.66       0.59\n",
       "PaperlessBilling  0.86   0.39       1.35\n",
       "MonthlyCharges   -0.40   0.69       0.54\n",
       "Contract_1       -3.07 <0.005       8.88\n",
       "Contract_2       -0.01   1.00       0.01\n",
       "Payment_1        -1.41   0.16       2.65\n",
       "Payment_2         1.03   0.31       1.71\n",
       "Payment_3         0.36   0.72       0.48\n",
       "---\n",
       "Concordance = 0.88\n",
       "Partial AIC = 237.14\n",
       "log-likelihood ratio test = 106.72 on 16 df\n",
       "-log2(p) of ll-ratio test = 48.92"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Some potential default analyses of coxph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77c03141d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.baseline_survival_.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* run the following for each of the lines that passes some test (p < 0.005,for example):<br>\n",
    " `model.plot_covariate_groups('Contract_1', values=[0, 1]);`<br>\n",
    " the plot needs to have the strata decoded\n",
    " \n",
    " In the train_model above, set param `plot_cov_groups=True` and produce the following set of artifacts by selecting only those covariates whose p-values\n",
    " are below some threshold `p_value`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function remotely**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 13:18:42,095 [info] Started building image: .mlrun/func-default-coxph-trainer:latest\n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0000] Built cross stage deps: map[]                \n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0001] Executing 0 build triggers                   \n",
      "\u001b[36mINFO\u001b[0m[0001] Unpacking rootfs as cmd RUN pip install lifelines requires it. \n",
      "\u001b[36mINFO\u001b[0m[0015] RUN pip install lifelines                    \n",
      "\u001b[36mINFO\u001b[0m[0015] Taking snapshot of full filesystem...        \n",
      "\u001b[36mINFO\u001b[0m[0026] cmd: /bin/sh                                 \n",
      "\u001b[36mINFO\u001b[0m[0026] args: [-c pip install lifelines]             \n",
      "\u001b[36mINFO\u001b[0m[0026] Running: [/bin/sh -c pip install lifelines]  \n",
      "Collecting lifelines\n",
      "  Downloading lifelines-0.26.3-py3-none-any.whl (348 kB)\n",
      "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/site-packages (from lifelines) (1.19.5)\n",
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      "  Downloading interface_meta-1.2.4-py2.py3-none-any.whl (14 kB)\n",
      "Requirement already satisfied: wrapt in /usr/local/lib/python3.7/site-packages (from formulaic<0.3,>=0.2.2->lifelines) (1.12.1)\n",
      "Collecting astor\n",
      "  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas>=0.23.0->lifelines) (2021.1)\n",
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      "Building wheels for collected packages: autograd-gamma, autograd\n",
      "  Building wheel for autograd-gamma (setup.py): started\n",
      "  Building wheel for autograd-gamma (setup.py): finished with status 'done'\n",
      "  Created wheel for autograd-gamma: filename=autograd_gamma-0.5.0-py3-none-any.whl size=4034 sha256=5211d5dddff0a9102583375dc2c468962b66b424d3fc0c75437b2d2f0d7e2576\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-vbqa_7jp/wheels/9f/01/ee/1331593abb5725ff7d8c1333aee93a50a1c29d6ddda9665c9f\n",
      "  Building wheel for autograd (setup.py): started\n",
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      "  Created wheel for autograd: filename=autograd-1.3-py3-none-any.whl size=47989 sha256=f7fbf41d442f597b0969c5314cbdbda143f3bd2da827efc46f58d03ca6373e55\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-vbqa_7jp/wheels/ef/32/31/0e87227cd0ca1d99ad51fbe4b54c6fa02afccf7e483d045e04\n",
      "Successfully built autograd-gamma autograd\n",
      "Installing collected packages: autograd, autograd-gamma, interface-meta, astor, formulaic, lifelines\n",
      "Successfully installed astor-0.8.1 autograd-1.3 autograd-gamma-0.5.0 formulaic-0.2.4 interface-meta-1.2.4 lifelines-0.26.3\n",
      "WARNING: You are using pip version 20.2.4; however, version 21.3 is available.\n",
      "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\n",
      "\u001b[36mINFO\u001b[0m[0029] Taking snapshot of full filesystem...        \n"
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    "fn.deploy(with_mlrun=False)"
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     "name": "stdout",
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     "text": [
      "> 2021-10-13 13:19:41,275 [info] starting run tasks-survive-trainer uid=8b4ee122120645be9eb29a646ef6e562 DB=http://mlrun-api:8080\n",
      "> 2021-10-13 13:19:41,455 [info] Job is running in the background, pod: tasks-survive-trainer-swjkk\n",
      "> 2021-10-13 13:19:50,461 [info] run executed, status=completed\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "Column Contract_2 have very low variance when conditioned on death event present or not. This may harm convergence. This could be a form of 'complete separation'. For example, try the following code:\n",
      "\n",
      ">>> events = df['labels'].astype(bool)\n",
      ">>> print(df.loc[events, 'Contract_2'].var())\n",
      ">>> print(df.loc[~events, 'Contract_2'].var())\n",
      "\n",
      "A very low variance means that the column Contract_2 completely determines whether a subject dies or not. See https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression.\n",
      "\n",
      "Newton-Rhaphson convergence completed successfully but norm(delta) is still high, 0.443. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?\n",
      "\n",
      "final state: completed\n"
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       "      <td><div title=\"8b4ee122120645be9eb29a646ef6e562\"><a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/default/jobs/monitor/8b4ee122120645be9eb29a646ef6e562/overview\" target=\"_blank\" >...6ef6e562</a></div></td>\n",
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       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/default/jobs/monitor/8b4ee122120645be9eb29a646ef6e562/overview\" target=\"_blank\">click here</a> to open in UI</b>"
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      "> 2021-10-13 13:19:50,645 [info] run executed, status=completed\n"
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   "source": [
    "coxph_run = fn.run(task,\n",
    "                   local=False,\n",
    "                   inputs={\"dataset\"  : \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\"})"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to the top](#Coxph-trainer---Survival-analysis)"
   ]
  }
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