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survival-analysis-for-data-analysis-introduction_2024-06.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
},
"colab": {
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/1599bf39c72bda16beac7b03bec05e11/survival-analysis-for-data-analysis-introduction_2024-06.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aEj365e4uTvD"
},
"source": [
"# Survival Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "0Z1wEfUFuTvG"
},
"source": [
"* Historically, survival analysis was developed and used by actuaries\n",
"and medical researchers to measure the lifetime of populations.\n",
"* What's the expected lifetime of patients that were given drug A? drug B?\n",
"* What's the life-expectancy of a baby born today in France?\n",
"\n",
"These researchers wanted to measure the duration between *Birth* and *Death*\n",
"\n",
"\n",
"Source: [Lifelines: Survival Analysis in Python](https://www.youtube.com/watch?v=XQfxndJH4UA)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JxSbfi7QuTvI"
},
"source": [
"# Survival function and hazard function"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fXfTmltSuTvK"
},
"source": [
"**Definition:** Let $T$ be a random variable called failure time.\n",
"\n",
"- $f(t)$ be its probability density function\n",
"- $F(t):=\\mathcal{P}(T\\le t)$ its cumulative distribution function\n",
"\n",
"Then we define\n",
"\n",
"- The *survival function* $S(t):=\\mathcal{P}(T>t)=1-F(t)$.\n",
"- The *hazard function* (probability of failure between $t$ and $t+\\delta t$ knowing that it was working at time $t$):\n",
"$$\n",
"h(t):=\\lim_{\\delta t\\to0}\\frac{\\mathcal{P}(T<t+\\delta t|T>t)}{\\delta t}=\n",
"\\lim_{\\delta t\\to0}\\frac{F(t+\\delta t)-F(t)}{\\delta t}\\times\\frac{1}{1-F(t)}=\\frac{f(t)}{1-F(t)}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZBY8J3sxuTvM"
},
"source": [
"**Properties:**\n",
"- $S(t)=\\exp(-\\int_0^t h(s)\\,ds)$.\n",
"- $h(t)=-\\frac{d}{dt}\\ln(S(t))$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1VYNZWpmuTvO"
},
"source": [
"# Right censoring\n",
"\n",
"By the end of the study, the event of interest (for example, in medicine \"death of a patient\" or \"churn of a customer\") has only occurred for a subset of the observations.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vao5BhTYuTvP"
},
"source": [
"# Modern Survival Analysis\n",
"\n",
"+ **Birth:** Customer joins Netflix \n",
"**Death:** Customer leaves Netflix \n",
"**Censorship:** At the current time, I cannot see all cancelations \n",
" \n",
" \n",
"+ **Birth:** Leader forms government \n",
"**Death:** Government dissolves \n",
"**Censorship:** Death of leader or current time do not allow me to see all dissolvements \n",
"\n",
"\n",
"+ **Birth:** Couple starts dating \n",
"**Death:** Couple breaks-up \n",
"**Censorship:** Some couples never break-up (partner's death comes first) "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EVUDoqXOuTvU"
},
"source": [
"First, let's take a dataset from lifelines to see what does it mean in practice."
]
},
{
"cell_type": "code",
"metadata": {
"id": "IcqsnDunuZ3x",
"outputId": "7f914600-d7a4-4858-cb08-9ca330d3349b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"%pip install --quiet --upgrade lifelines"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m349.3/349.3 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.7/115.7 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:10.331529Z",
"start_time": "2020-01-09T22:37:03.811697Z"
},
"id": "A5qmUxSxuTvW"
},
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"plt.style.use('seaborn-v0_8-bright')"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-Hz_oM2IuTve"
},
"source": [
"from lifelines.datasets import load_dd\n",
"\n",
"df = load_dd()\n",
"df = df[['ctryname', 'un_region_name', 'un_continent_name', 'ehead',\\\n",
" 'democracy', 'regime', 'start_year', 'duration', 'observed']]"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "xCN-nZXDuTvk"
},
"source": [
"# Democracy and dictatorship\n",
"\n",
"This dataset contains a classification of political regimes as democracy and dictatorship.\n",
"* Classification of democracies as\n",
" + parliamentary,\n",
" + semi-presidential (mixed), and\n",
" + presidential.\n",
" \n",
"* Classification of dictatorships as\n",
" + military,\n",
" + civilian, and\n",
" + royal.\n",
" \n",
"Coverage: 202 countries, from 1946 or year of independence to 2008.\n",
"\n",
"**References**\n",
"\n",
"José Antonio Cheibub, Jennifer Gandhi, and James Raymond Vreeland. [\"Democracy and Dictatorship Revisited.\"](https://doi.org/10.1007/s11127-009-9491-2) Public Choice, vol. 143, no. 2-1, pp. 67-101, 2010."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:10.515360Z",
"start_time": "2020-01-09T22:37:10.466697Z"
},
"id": "9b51kQDjuTvm",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 362
},
"outputId": "8c792bc1-8cd5-4a9e-90b0-18aaca702d84"
},
"source": [
"df.tail(10).style.hide(axis=\"index\")"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7bee4f504110>"
],
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_b98e3\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_b98e3_level0_col0\" class=\"col_heading level0 col0\" >ctryname</th>\n",
" <th id=\"T_b98e3_level0_col1\" class=\"col_heading level0 col1\" >un_region_name</th>\n",
" <th id=\"T_b98e3_level0_col2\" class=\"col_heading level0 col2\" >un_continent_name</th>\n",
" <th id=\"T_b98e3_level0_col3\" class=\"col_heading level0 col3\" >ehead</th>\n",
" <th id=\"T_b98e3_level0_col4\" class=\"col_heading level0 col4\" >democracy</th>\n",
" <th id=\"T_b98e3_level0_col5\" class=\"col_heading level0 col5\" >regime</th>\n",
" <th id=\"T_b98e3_level0_col6\" class=\"col_heading level0 col6\" >start_year</th>\n",
" <th id=\"T_b98e3_level0_col7\" class=\"col_heading level0 col7\" >duration</th>\n",
" <th id=\"T_b98e3_level0_col8\" class=\"col_heading level0 col8\" >observed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_b98e3_row0_col0\" class=\"data row0 col0\" >Yugoslavia</td>\n",
" <td id=\"T_b98e3_row0_col1\" class=\"data row0 col1\" >Southern Europe</td>\n",
" <td id=\"T_b98e3_row0_col2\" class=\"data row0 col2\" >Europe</td>\n",
" <td id=\"T_b98e3_row0_col3\" class=\"data row0 col3\" >Stipe Suvar</td>\n",
" <td id=\"T_b98e3_row0_col4\" class=\"data row0 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row0_col5\" class=\"data row0 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row0_col6\" class=\"data row0 col6\" >1988</td>\n",
" <td id=\"T_b98e3_row0_col7\" class=\"data row0 col7\" >1</td>\n",
" <td id=\"T_b98e3_row0_col8\" class=\"data row0 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row1_col0\" class=\"data row1 col0\" >Yugoslavia</td>\n",
" <td id=\"T_b98e3_row1_col1\" class=\"data row1 col1\" >Southern Europe</td>\n",
" <td id=\"T_b98e3_row1_col2\" class=\"data row1 col2\" >Europe</td>\n",
" <td id=\"T_b98e3_row1_col3\" class=\"data row1 col3\" >Milan Pancevski</td>\n",
" <td id=\"T_b98e3_row1_col4\" class=\"data row1 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row1_col5\" class=\"data row1 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row1_col6\" class=\"data row1 col6\" >1989</td>\n",
" <td id=\"T_b98e3_row1_col7\" class=\"data row1 col7\" >1</td>\n",
" <td id=\"T_b98e3_row1_col8\" class=\"data row1 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row2_col0\" class=\"data row2 col0\" >Yugoslavia</td>\n",
" <td id=\"T_b98e3_row2_col1\" class=\"data row2 col1\" >Southern Europe</td>\n",
" <td id=\"T_b98e3_row2_col2\" class=\"data row2 col2\" >Europe</td>\n",
" <td id=\"T_b98e3_row2_col3\" class=\"data row2 col3\" >Borisav Jovic</td>\n",
" <td id=\"T_b98e3_row2_col4\" class=\"data row2 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row2_col5\" class=\"data row2 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row2_col6\" class=\"data row2 col6\" >1990</td>\n",
" <td id=\"T_b98e3_row2_col7\" class=\"data row2 col7\" >1</td>\n",
" <td id=\"T_b98e3_row2_col8\" class=\"data row2 col8\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row3_col0\" class=\"data row3 col0\" >Zambia</td>\n",
" <td id=\"T_b98e3_row3_col1\" class=\"data row3 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row3_col2\" class=\"data row3 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row3_col3\" class=\"data row3 col3\" >Kenneth Kaunda</td>\n",
" <td id=\"T_b98e3_row3_col4\" class=\"data row3 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row3_col5\" class=\"data row3 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row3_col6\" class=\"data row3 col6\" >1964</td>\n",
" <td id=\"T_b98e3_row3_col7\" class=\"data row3 col7\" >27</td>\n",
" <td id=\"T_b98e3_row3_col8\" class=\"data row3 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row4_col0\" class=\"data row4 col0\" >Zambia</td>\n",
" <td id=\"T_b98e3_row4_col1\" class=\"data row4 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row4_col2\" class=\"data row4 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row4_col3\" class=\"data row4 col3\" >Frederick Chiluba</td>\n",
" <td id=\"T_b98e3_row4_col4\" class=\"data row4 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row4_col5\" class=\"data row4 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row4_col6\" class=\"data row4 col6\" >1991</td>\n",
" <td id=\"T_b98e3_row4_col7\" class=\"data row4 col7\" >11</td>\n",
" <td id=\"T_b98e3_row4_col8\" class=\"data row4 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row5_col0\" class=\"data row5 col0\" >Zambia</td>\n",
" <td id=\"T_b98e3_row5_col1\" class=\"data row5 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row5_col2\" class=\"data row5 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row5_col3\" class=\"data row5 col3\" >Levy Patrick Mwanawasa</td>\n",
" <td id=\"T_b98e3_row5_col4\" class=\"data row5 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row5_col5\" class=\"data row5 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row5_col6\" class=\"data row5 col6\" >2002</td>\n",
" <td id=\"T_b98e3_row5_col7\" class=\"data row5 col7\" >6</td>\n",
" <td id=\"T_b98e3_row5_col8\" class=\"data row5 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row6_col0\" class=\"data row6 col0\" >Zambia</td>\n",
" <td id=\"T_b98e3_row6_col1\" class=\"data row6 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row6_col2\" class=\"data row6 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row6_col3\" class=\"data row6 col3\" >Rupiah Bwezani Banda</td>\n",
" <td id=\"T_b98e3_row6_col4\" class=\"data row6 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row6_col5\" class=\"data row6 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row6_col6\" class=\"data row6 col6\" >2008</td>\n",
" <td id=\"T_b98e3_row6_col7\" class=\"data row6 col7\" >1</td>\n",
" <td id=\"T_b98e3_row6_col8\" class=\"data row6 col8\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row7_col0\" class=\"data row7 col0\" >Zimbabwe</td>\n",
" <td id=\"T_b98e3_row7_col1\" class=\"data row7 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row7_col2\" class=\"data row7 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row7_col3\" class=\"data row7 col3\" >Ian Smith</td>\n",
" <td id=\"T_b98e3_row7_col4\" class=\"data row7 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row7_col5\" class=\"data row7 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row7_col6\" class=\"data row7 col6\" >1965</td>\n",
" <td id=\"T_b98e3_row7_col7\" class=\"data row7 col7\" >14</td>\n",
" <td id=\"T_b98e3_row7_col8\" class=\"data row7 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row8_col0\" class=\"data row8 col0\" >Zimbabwe</td>\n",
" <td id=\"T_b98e3_row8_col1\" class=\"data row8 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row8_col2\" class=\"data row8 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row8_col3\" class=\"data row8 col3\" >Abel Muzorewa</td>\n",
" <td id=\"T_b98e3_row8_col4\" class=\"data row8 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row8_col5\" class=\"data row8 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row8_col6\" class=\"data row8 col6\" >1979</td>\n",
" <td id=\"T_b98e3_row8_col7\" class=\"data row8 col7\" >1</td>\n",
" <td id=\"T_b98e3_row8_col8\" class=\"data row8 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_b98e3_row9_col0\" class=\"data row9 col0\" >Zimbabwe</td>\n",
" <td id=\"T_b98e3_row9_col1\" class=\"data row9 col1\" >Eastern Africa</td>\n",
" <td id=\"T_b98e3_row9_col2\" class=\"data row9 col2\" >Africa</td>\n",
" <td id=\"T_b98e3_row9_col3\" class=\"data row9 col3\" >Robert Mugabe</td>\n",
" <td id=\"T_b98e3_row9_col4\" class=\"data row9 col4\" >Non-democracy</td>\n",
" <td id=\"T_b98e3_row9_col5\" class=\"data row9 col5\" >Civilian Dict</td>\n",
" <td id=\"T_b98e3_row9_col6\" class=\"data row9 col6\" >1980</td>\n",
" <td id=\"T_b98e3_row9_col7\" class=\"data row9 col7\" >29</td>\n",
" <td id=\"T_b98e3_row9_col8\" class=\"data row9 col8\" >0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-o57OZxvuTvu"
},
"source": [
"Let's look at right-censored samples."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:12.185066Z",
"start_time": "2020-01-09T22:37:10.519270Z"
},
"scrolled": false,
"id": "vzaXN9QWuTvv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "714b42be-b626-4eb6-b13f-5f97d26af4c9"
},
"source": [
"format_dict = {'ehead':'{}','duration':'{}', 'observed':'{}'}\n",
"(df.query('ctryname == \"France\"')[['ehead', 'duration', 'observed']].style.format(format_dict)\n",
" .hide(axis=\"index\")\n",
" .highlight_min('observed', color='lightgreen'))"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7bee4cba35d0>"
],
"text/html": [
"<style type=\"text/css\">\n",
"#T_384cd_row31_col2 {\n",
" background-color: lightgreen;\n",
"}\n",
"</style>\n",
"<table id=\"T_384cd\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_384cd_level0_col0\" class=\"col_heading level0 col0\" >ehead</th>\n",
" <th id=\"T_384cd_level0_col1\" class=\"col_heading level0 col1\" >duration</th>\n",
" <th id=\"T_384cd_level0_col2\" class=\"col_heading level0 col2\" >observed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_384cd_row0_col0\" class=\"data row0 col0\" >Leon Blum</td>\n",
" <td id=\"T_384cd_row0_col1\" class=\"data row0 col1\" >1</td>\n",
" <td id=\"T_384cd_row0_col2\" class=\"data row0 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row1_col0\" class=\"data row1 col0\" >Robert Schuman</td>\n",
" <td id=\"T_384cd_row1_col1\" class=\"data row1 col1\" >1</td>\n",
" <td id=\"T_384cd_row1_col2\" class=\"data row1 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row2_col0\" class=\"data row2 col0\" >Henri Queuille</td>\n",
" <td id=\"T_384cd_row2_col1\" class=\"data row2 col1\" >1</td>\n",
" <td id=\"T_384cd_row2_col2\" class=\"data row2 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row3_col0\" class=\"data row3 col0\" >Georges Bidault</td>\n",
" <td id=\"T_384cd_row3_col1\" class=\"data row3 col1\" >1</td>\n",
" <td id=\"T_384cd_row3_col2\" class=\"data row3 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row4_col0\" class=\"data row4 col0\" >Rene Pleven</td>\n",
" <td id=\"T_384cd_row4_col1\" class=\"data row4 col1\" >2</td>\n",
" <td id=\"T_384cd_row4_col2\" class=\"data row4 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row5_col0\" class=\"data row5 col0\" >Antoine Pinay</td>\n",
" <td id=\"T_384cd_row5_col1\" class=\"data row5 col1\" >1</td>\n",
" <td id=\"T_384cd_row5_col2\" class=\"data row5 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row6_col0\" class=\"data row6 col0\" >Joseph Laniel</td>\n",
" <td id=\"T_384cd_row6_col1\" class=\"data row6 col1\" >1</td>\n",
" <td id=\"T_384cd_row6_col2\" class=\"data row6 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row7_col0\" class=\"data row7 col0\" >Pierre Mendes-France</td>\n",
" <td id=\"T_384cd_row7_col1\" class=\"data row7 col1\" >1</td>\n",
" <td id=\"T_384cd_row7_col2\" class=\"data row7 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row8_col0\" class=\"data row8 col0\" >Edgar Faure</td>\n",
" <td id=\"T_384cd_row8_col1\" class=\"data row8 col1\" >1</td>\n",
" <td id=\"T_384cd_row8_col2\" class=\"data row8 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row9_col0\" class=\"data row9 col0\" >Guy Mollet</td>\n",
" <td id=\"T_384cd_row9_col1\" class=\"data row9 col1\" >1</td>\n",
" <td id=\"T_384cd_row9_col2\" class=\"data row9 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row10_col0\" class=\"data row10 col0\" >Felix Gaillard</td>\n",
" <td id=\"T_384cd_row10_col1\" class=\"data row10 col1\" >1</td>\n",
" <td id=\"T_384cd_row10_col2\" class=\"data row10 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row11_col0\" class=\"data row11 col0\" >Charles de Gaulle</td>\n",
" <td id=\"T_384cd_row11_col1\" class=\"data row11 col1\" >1</td>\n",
" <td id=\"T_384cd_row11_col2\" class=\"data row11 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row12_col0\" class=\"data row12 col0\" >Michel Debre</td>\n",
" <td id=\"T_384cd_row12_col1\" class=\"data row12 col1\" >3</td>\n",
" <td id=\"T_384cd_row12_col2\" class=\"data row12 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row13_col0\" class=\"data row13 col0\" >Georges Pompidou</td>\n",
" <td id=\"T_384cd_row13_col1\" class=\"data row13 col1\" >3</td>\n",
" <td id=\"T_384cd_row13_col2\" class=\"data row13 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row14_col0\" class=\"data row14 col0\" >Georges Pompidou</td>\n",
" <td id=\"T_384cd_row14_col1\" class=\"data row14 col1\" >3</td>\n",
" <td id=\"T_384cd_row14_col2\" class=\"data row14 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row15_col0\" class=\"data row15 col0\" >Maurice Couve de Murville</td>\n",
" <td id=\"T_384cd_row15_col1\" class=\"data row15 col1\" >1</td>\n",
" <td id=\"T_384cd_row15_col2\" class=\"data row15 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row16_col0\" class=\"data row16 col0\" >Jacques Chaban-Delmas</td>\n",
" <td id=\"T_384cd_row16_col1\" class=\"data row16 col1\" >3</td>\n",
" <td id=\"T_384cd_row16_col2\" class=\"data row16 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row17_col0\" class=\"data row17 col0\" >Pierre Messmer</td>\n",
" <td id=\"T_384cd_row17_col1\" class=\"data row17 col1\" >2</td>\n",
" <td id=\"T_384cd_row17_col2\" class=\"data row17 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row18_col0\" class=\"data row18 col0\" >Jacques Chirac</td>\n",
" <td id=\"T_384cd_row18_col1\" class=\"data row18 col1\" >2</td>\n",
" <td id=\"T_384cd_row18_col2\" class=\"data row18 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row19_col0\" class=\"data row19 col0\" >Raymond Barre</td>\n",
" <td id=\"T_384cd_row19_col1\" class=\"data row19 col1\" >5</td>\n",
" <td id=\"T_384cd_row19_col2\" class=\"data row19 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row20_col0\" class=\"data row20 col0\" >Pierre Mauroy</td>\n",
" <td id=\"T_384cd_row20_col1\" class=\"data row20 col1\" >3</td>\n",
" <td id=\"T_384cd_row20_col2\" class=\"data row20 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row21_col0\" class=\"data row21 col0\" >Laurent Fabius</td>\n",
" <td id=\"T_384cd_row21_col1\" class=\"data row21 col1\" >2</td>\n",
" <td id=\"T_384cd_row21_col2\" class=\"data row21 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row22_col0\" class=\"data row22 col0\" >Jacques Chirac</td>\n",
" <td id=\"T_384cd_row22_col1\" class=\"data row22 col1\" >2</td>\n",
" <td id=\"T_384cd_row22_col2\" class=\"data row22 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row23_col0\" class=\"data row23 col0\" >Michel Rocard</td>\n",
" <td id=\"T_384cd_row23_col1\" class=\"data row23 col1\" >3</td>\n",
" <td id=\"T_384cd_row23_col2\" class=\"data row23 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row24_col0\" class=\"data row24 col0\" >Edith Cresson</td>\n",
" <td id=\"T_384cd_row24_col1\" class=\"data row24 col1\" >1</td>\n",
" <td id=\"T_384cd_row24_col2\" class=\"data row24 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row25_col0\" class=\"data row25 col0\" >Pierre Beregovoy</td>\n",
" <td id=\"T_384cd_row25_col1\" class=\"data row25 col1\" >1</td>\n",
" <td id=\"T_384cd_row25_col2\" class=\"data row25 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row26_col0\" class=\"data row26 col0\" >Edouard Balladur</td>\n",
" <td id=\"T_384cd_row26_col1\" class=\"data row26 col1\" >2</td>\n",
" <td id=\"T_384cd_row26_col2\" class=\"data row26 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row27_col0\" class=\"data row27 col0\" >Alain Juppe</td>\n",
" <td id=\"T_384cd_row27_col1\" class=\"data row27 col1\" >2</td>\n",
" <td id=\"T_384cd_row27_col2\" class=\"data row27 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row28_col0\" class=\"data row28 col0\" >Lionel Jospin</td>\n",
" <td id=\"T_384cd_row28_col1\" class=\"data row28 col1\" >5</td>\n",
" <td id=\"T_384cd_row28_col2\" class=\"data row28 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row29_col0\" class=\"data row29 col0\" >Jean-Pierre Raffarin</td>\n",
" <td id=\"T_384cd_row29_col1\" class=\"data row29 col1\" >3</td>\n",
" <td id=\"T_384cd_row29_col2\" class=\"data row29 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row30_col0\" class=\"data row30 col0\" >Dominique de Villepin</td>\n",
" <td id=\"T_384cd_row30_col1\" class=\"data row30 col1\" >2</td>\n",
" <td id=\"T_384cd_row30_col2\" class=\"data row30 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_384cd_row31_col0\" class=\"data row31 col0\" >Fran�ois Fillon</td>\n",
" <td id=\"T_384cd_row31_col1\" class=\"data row31 col1\" >2</td>\n",
" <td id=\"T_384cd_row31_col2\" class=\"data row31 col2\" >0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:12.216114Z",
"start_time": "2020-01-09T22:37:12.189453Z"
},
"id": "AVHDxLziuTv2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "447eeda9-dc3f-4141-e685-5d029b1a7eeb"
},
"source": [
"print(f'samples: {len(df)}\\n')\n",
"print(f'right censored samples: {len(df.query(\"observed == 0\"))}')\n",
"print(f'right censored samples (%): {100*len(df.query(\"observed == 0\"))/len(df):.1f}%')"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"samples: 1808\n",
"\n",
"right censored samples: 340\n",
"right censored samples (%): 18.8%\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3v0OGKU_uTv9"
},
"source": [
"# How can we estimate the probability of a government survival?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J0UzmdpeuTv9"
},
"source": [
"**Example:** I want to estimate the survival function of a new machine and I have 100 of these new machines. After the first year:\n",
"\n",
"Samples | I\n",
"--- | ---\n",
"Initial numbers | 100\n",
"Deaths in first year of age | 70\n",
"One-year survivors | `30`\n",
"\n",
"Therefore, a reasonable estimate of the survival probability of 1 year is 0.3.\n",
"\n",
"I have increased my production. So now I have 1000 new machines.\n",
"\n",
"Samples | I | II\n",
"--- | --- | ---\n",
"Initial numbers | 100 | 1000\n",
"Deaths in first year of age | 70 | 750\n",
"One-year survivors | `30` | `250`\n",
"Deaths in second year of age | 15 |\n",
"Two-year survivors | `15` |\n",
"\n",
"What would be a good estimate of the survival probability of 1 year? and 2 years?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RVevvzzCuTv-"
},
"source": [
"The estimate of the probability of survival of 1 year would be\n",
"$\\hat{P}(1)=(30+250)/(100+1000) \\sim 0.255$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BlBS5dveuTwA"
},
"source": [
"$\\hat{P}(2|1) = 15/30 = 0.5$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zVja8-P5uTwA"
},
"source": [
"$\\hat{P}(2)=0.255\\times0.5=0.127$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pG5yhITUuTwB"
},
"source": [
"# Kaplan-Meier estimator"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E6hn2l6PuTwC"
},
"source": [
"**Definition:** Kaplan-Meier estimator of the survival function is given by\n",
"\n",
"$$\n",
"\\hat{S}(t):=\\prod_{i:t_i\\le t}\\left(1-\\frac{d_i}{n_i}\\right)\n",
"$$\n",
"where $t_i$ is a time where at least one event happened, $d_i$ the number of events that happened at time $t_i$, and $n_i$ the individuals known to have survived up to time $t_i$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cojEUEjsuTwD"
},
"source": [
"We use the [Kaplan-Meier estimator](https://en.wikipedia.org/wiki/Kaplan?Meier_estimator) to estimate the probability of a government survival."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:16.155051Z",
"start_time": "2020-01-09T22:37:12.219801Z"
},
"scrolled": false,
"id": "LCGcjoexuTwE",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "740eecff-eafc-46d0-b8a2-bd393c2afe92"
},
"source": [
"from lifelines import KaplanMeierFitter\n",
"kmf = KaplanMeierFitter()\n",
"kmf.fit(df['duration'],df['observed'], label='Estimate for average government')\n",
"\n",
"fig, ax = plt.subplots(figsize=(10,7))\n",
"kmf.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pdrG0N6nuTwL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "55e76d9b-87bb-4ef2-a039-0d72fde3168c"
},
"source": [
"print(f'The median number of years of government survival is {kmf.median_survival_time_}')"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The median number of years of government survival is 4.0\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "NQHPG2x6uTwQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "6bbec1e6-3a8f-4f6d-d7c3-d50638ec00dd"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"for r in df['democracy'].unique():\n",
" ix = df['democracy'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" kmf.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PBKPtOFKuTwV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f0c8e6f3-5b03-48aa-e08f-12edd13cc431"
},
"source": [
"for r in df['democracy'].unique():\n",
" ix = df['democracy'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" print(f'The median number of years for a {r} is {kmf.median_survival_time_}')"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The median number of years for a Non-democracy is 6.0\n",
"The median number of years for a Democracy is 3.0\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZhK-X5-fuTwc"
},
"source": [
"How can we tell if these survival functions are different?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uL59D8GAuTwc"
},
"source": [
"# Log-rank test (not recommended but still very common statistical test)"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "LDq8D5ovuTwe",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fa73657d-32f0-4cd7-9497-7107ab13485f"
},
"source": [
"df['democracy'].unique()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['Non-democracy', 'Democracy'], dtype=object)"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kaRMHNtSuTwi"
},
"source": [
"from lifelines.statistics import logrank_test"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1qrrTDDWuTwn"
},
"source": [
"ix = df['democracy'] == 'Democracy'\n",
"T_democracy, E_democracy = df.loc[ix, 'duration'], df.loc[ix, 'observed']\n",
"T_non_democracy, E_non_democracy = df.loc[~ix, 'duration'], df.loc[~ix, 'observed']"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df['un_continent_name'].unique()"
],
"metadata": {
"id": "70-XsKOt5PFB",
"outputId": "88b9d989-7a93-4c5d-9663-06c98e7feff4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['Asia', 'Europe', 'Africa', 'Americas', 'Oceania'], dtype=object)"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"idx_europe = df['un_continent_name'] == 'Europe'"
],
"metadata": {
"id": "QmZQB2wV5Zke"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"T_europe, E_europe = df.loc[idx_europe, 'duration'], df.loc[idx_europe, 'observed']"
],
"metadata": {
"id": "3zLbY9sg5LiD"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"idx_oceania = df['un_continent_name'] == 'Oceania'"
],
"metadata": {
"id": "8wdHaP5W5kxE"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"T_oceania, E_oceania = df.loc[idx_oceania, 'duration'], df.loc[idx_oceania, 'observed']"
],
"metadata": {
"id": "PGxbg-uj5qW3"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"source": [
"idx_africa = df['un_continent_name'] == 'Africa'"
],
"metadata": {
"id": "nK9smUiI5_sD"
},
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"source": [
"T_africa, E_africa = df.loc[idx_africa, 'duration'], df.loc[idx_africa, 'observed']"
],
"metadata": {
"id": "3CS-RGyt6FFb"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"results = logrank_test(T_europe, T_oceania, event_observed_A=E_europe, event_observed_B=E_oceania)\n",
"results.print_summary()"
],
"metadata": {
"id": "Q8sK9crB5wfd",
"outputId": "5047bc52-866e-419f-b76e-5d5933876452",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
}
},
"execution_count": 24,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.StatisticalResult: logrank_test>\n",
" t_0 = -1\n",
" null_distribution = chi squared\n",
"degrees_of_freedom = 1\n",
" test_name = logrank_test\n",
"\n",
"---\n",
" test_statistic p -log2(p)\n",
" 8.20 <0.005 7.90"
],
"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>t_0</th>\n",
" <td>-1</td>\n",
" </tr>\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>test_name</th>\n",
" <td>logrank_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>8.20</td>\n",
" <td>&lt;0.005</td>\n",
" <td>7.90</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/latex": "\\begin{tabular}{lrrr}\n & test_statistic & p & -log2(p) \\\\\n0 & 8.20 & 0.00 & 7.90 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(results.p_value)"
],
"metadata": {
"id": "sB-MKu-458sP",
"outputId": "28d88dd1-c0a6-4781-f72c-6042ea356bcc",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0.004196935934746862\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"results = logrank_test(T_europe, T_africa, event_observed_A=E_europe, event_observed_B=E_africa)\n",
"results.print_summary()"
],
"metadata": {
"id": "TUELl8Hd6M13",
"outputId": "d35da03f-f7ae-4f67-de24-9d9bae1ddcf7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
}
},
"execution_count": 28,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.StatisticalResult: logrank_test>\n",
" t_0 = -1\n",
" null_distribution = chi squared\n",
"degrees_of_freedom = 1\n",
" test_name = logrank_test\n",
"\n",
"---\n",
" test_statistic p -log2(p)\n",
" 106.35 <0.005 80.42"
],
"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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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>t_0</th>\n",
" <td>-1</td>\n",
" </tr>\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>test_name</th>\n",
" <td>logrank_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>106.35</td>\n",
" <td>&lt;0.005</td>\n",
" <td>80.42</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/latex": "\\begin{tabular}{lrrr}\n & test_statistic & p & -log2(p) \\\\\n0 & 106.35 & 0.00 & 80.42 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(results.p_value)"
],
"metadata": {
"id": "N6NjSC2c6R2X",
"outputId": "7e21c4cf-ba7f-44be-c66d-f26116188128",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"6.1783870393467935e-25\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "Qw20wiiRuTws",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "cc70b21d-13e0-4a87-e619-cd5f74d4549c"
},
"source": [
"results = logrank_test(T_democracy, T_non_democracy, event_observed_A=E_democracy, event_observed_B=E_non_democracy)\n",
"results.print_summary()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.StatisticalResult: logrank_test>\n",
" t_0 = -1\n",
" null_distribution = chi squared\n",
"degrees_of_freedom = 1\n",
" test_name = logrank_test\n",
"\n",
"---\n",
" test_statistic p -log2(p)\n",
" 260.47 <0.005 192.23"
],
"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>t_0</th>\n",
" <td>-1</td>\n",
" </tr>\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>test_name</th>\n",
" <td>logrank_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>260.47</td>\n",
" <td>&lt;0.005</td>\n",
" <td>192.23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/latex": "\\begin{tabular}{lrrr}\n & test_statistic & p & -log2(p) \\\\\n0 & 260.47 & 0.00 & 192.23 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1Oms68oOuTww",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7cbb9c1e-6267-4319-959f-46d2b2fb5222"
},
"source": [
"print(results.p_value)\n",
"print(results.test_statistic)"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.3557143218482446e-58\n",
"260.46953907795944\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qTX47C6-uTw1"
},
"source": [
"# Univariate Cox regression"
]
},
{
"cell_type": "code",
"metadata": {
"id": "meyGKaYcuTw4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"outputId": "2334f687-3e4d-4b78-ea8a-7d5d788450ac"
},
"source": [
"from lifelines import CoxPHFitter\n",
"\n",
"cph = CoxPHFitter()\n",
"df_Uni_Cox = df.copy()\n",
"df_Uni_Cox['indicator'] = df_Uni_Cox['democracy'] == 'Democracy'\n",
"cph.fit(df_Uni_Cox[['indicator', 'duration', 'observed']], 'duration', 'observed')\n",
"cph.print_summary()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
" duration col = 'duration'\n",
" event col = 'observed'\n",
" baseline estimation = breslow\n",
" number of observations = 1808\n",
"number of events observed = 1468\n",
" partial log-likelihood = -9614.27\n",
" time fit was run = 2025-05-28 07:44:55 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",
"indicator 0.96 2.62 0.06 0.84 1.09 2.32 2.96\n",
"\n",
" cmp to z p -log2(p)\n",
"covariate \n",
"indicator 0.00 15.40 <0.005 175.43\n",
"---\n",
"Concordance = 0.59\n",
"Partial AIC = 19230.53\n",
"log-likelihood ratio test = 264.03 on 1 df\n",
"-log2(p) of ll-ratio test = 194.81"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>model</th>\n",
" <td>lifelines.CoxPHFitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>duration col</th>\n",
" <td>'duration'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>event col</th>\n",
" <td>'observed'</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>1808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of events observed</th>\n",
" <td>1468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial log-likelihood</th>\n",
" <td>-9614.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time fit was run</th>\n",
" <td>2025-05-28 07:44:55 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;\">cmp to</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>indicator</th>\n",
" <td>0.96</td>\n",
" <td>2.62</td>\n",
" <td>0.06</td>\n",
" <td>0.84</td>\n",
" <td>1.09</td>\n",
" <td>2.32</td>\n",
" <td>2.96</td>\n",
" <td>0.00</td>\n",
" <td>15.40</td>\n",
" <td>&lt;0.005</td>\n",
" <td>175.43</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><br><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>Concordance</th>\n",
" <td>0.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Partial AIC</th>\n",
" <td>19230.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>log-likelihood ratio test</th>\n",
" <td>264.03 on 1 df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-log2(p) of ll-ratio test</th>\n",
" <td>194.81</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate & & & & & & & & & & & \\\\\nindicator & 0.96 & 2.62 & 0.06 & 0.84 & 1.09 & 2.32 & 2.96 & 0.00 & 15.40 & 0.00 & 175.43 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.131185Z",
"start_time": "2020-01-09T22:37:16.573714Z"
},
"id": "USPOd2fOuTw8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "65ded818-cccb-43ca-e0b2-faf55754798c"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"\n",
"for r in df['regime'].unique():\n",
" ix = df['regime'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" kmf.survival_function_.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.690930Z",
"start_time": "2020-01-09T22:37:17.134877Z"
},
"scrolled": false,
"id": "ydmaYBqnuTxA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "65f1c517-d2ae-40e5-bd11-6ad5edd7574c"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"for r in df['un_continent_name'].unique():\n",
" ix = df['un_continent_name'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" kmf.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.723699Z",
"start_time": "2020-01-09T22:37:17.694880Z"
},
"scrolled": false,
"id": "NXIl1sBfuTxF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"outputId": "1376fd90-d54d-4407-faa8-748f4a22daa2"
},
"source": [
"df.query('ctryname == \"United States of America\"').tail(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ctryname un_region_name un_continent_name \\\n",
"1721 United States of America Northern America Americas \n",
"1722 United States of America Northern America Americas \n",
"1723 United States of America Northern America Americas \n",
"\n",
" ehead democracy regime start_year duration \\\n",
"1721 George Bush Democracy Presidential Dem 1989 4 \n",
"1722 Bill Clinton Democracy Presidential Dem 1993 8 \n",
"1723 George W. Bush Democracy Presidential Dem 2001 8 \n",
"\n",
" observed \n",
"1721 1 \n",
"1722 1 \n",
"1723 0 "
],
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"summary": "{\n \"name\": \"df\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"ctryname\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"United States of America\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"un_region_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Northern America\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"un_continent_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Americas\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ehead\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"George Bush\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"democracy\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Democracy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"regime\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Presidential Dem\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"start_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 1989,\n \"max\": 2001,\n \"num_unique_values\": 3,\n \"samples\": [\n 1989\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 4,\n \"max\": 8,\n \"num_unique_values\": 2,\n \"samples\": [\n 8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"observed\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.748036Z",
"start_time": "2020-01-09T22:37:17.725349Z"
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"source": [
"df.query('ctryname == \"United Kingdom\"').tail(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ctryname un_region_name un_continent_name ehead \\\n",
"1710 United Kingdom Northern Europe Europe John Major \n",
"1711 United Kingdom Northern Europe Europe Tony Blair \n",
"1712 United Kingdom Northern Europe Europe Gordon Brown \n",
"\n",
" democracy regime start_year duration observed \n",
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"1711 Democracy Parliamentary Dem 1997 10 1 \n",
"1712 Democracy Parliamentary Dem 2007 2 0 "
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"summary": "{\n \"name\": \"df\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"ctryname\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"United Kingdom\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"un_region_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Northern Europe\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"un_continent_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Europe\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ehead\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"John Major\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"democracy\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Democracy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"regime\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Parliamentary Dem\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"start_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8,\n \"min\": 1990,\n \"max\": 2007,\n \"num_unique_values\": 3,\n \"samples\": [\n 1990\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 2,\n \"max\": 10,\n \"num_unique_values\": 3,\n \"samples\": [\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"observed\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:18.129226Z",
"start_time": "2020-01-09T22:37:17.749639Z"
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"id": "010659VeuTxM",
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"source": [
"ix_US = df['ctryname'] == 'United States of America'\n",
"ix_UK = df['ctryname'] == 'United Kingdom'\n",
"\n",
"kmf_US = KaplanMeierFitter()\n",
"kmf_US.fit(df['duration'].loc[ix_US], df['observed'].loc[ix_US], label='USA')\n",
"\n",
"kmf_UK = KaplanMeierFitter()\n",
"kmf_UK.fit(df['duration'].loc[ix_UK], df['observed'].loc[ix_UK], label='UK')\n",
"\n",
"plt.figure(figsize=(10,7))\n",
"ax = plt.subplot(111)\n",
"kmf_US.plot(ax=ax)\n",
"kmf_UK.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VaQCN1ChuTxs"
},
"source": [
"# Multivariate Cox regression"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "nCPi7YY1uTxQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1f56cb3c-c03b-41ff-c3d0-7d2e446e492d"
},
"source": [
"df.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['ctryname', 'un_region_name', 'un_continent_name', 'ehead', 'democracy',\n",
" 'regime', 'start_year', 'duration', 'observed'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "xphf-zpauTxU"
},
"source": [
"df = df.drop(columns=['ctryname', 'un_region_name', 'ehead', 'regime', 'start_year'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NOuRvPJXuTxZ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6d0ff229-11c0-4275-b921-a882699fe9d3"
},
"source": [
"df.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['un_continent_name', 'democracy', 'duration', 'observed'], dtype='object')"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tzcg35DtuTxd"
},
"source": [
"# df_hazard = pd.get_dummies(df, drop_first=True, columns=df.columns.drop(['duration', 'observed']))\n",
"df_hazard = pd.get_dummies(df, columns=df.columns.drop(['duration', 'observed']))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "MGxRvM20uTxg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "79468c5b-bdb9-4ca6-d615-01c97d3841a4"
},
"source": [
"df_hazard.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['duration', 'observed', 'un_continent_name_Africa',\n",
" 'un_continent_name_Americas', 'un_continent_name_Asia',\n",
" 'un_continent_name_Europe', 'un_continent_name_Oceania',\n",
" 'democracy_Democracy', 'democracy_Non-democracy'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "i9b7PSd2uTxl"
},
"source": [
"df_hazard = df_hazard.drop(columns=['un_continent_name_Americas', 'democracy_Democracy'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3-O2MTLPuTxo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9166057a-9d66-49cb-ee09-32e3b9493f10"
},
"source": [
"df_hazard.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['duration', 'observed', 'un_continent_name_Africa',\n",
" 'un_continent_name_Asia', 'un_continent_name_Europe',\n",
" 'un_continent_name_Oceania', 'democracy_Non-democracy'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IUKzqcFvuTxs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 679
},
"outputId": "01d2412e-dbab-4851-c419-56931ffd14a9"
},
"source": [
"from lifelines import CoxPHFitter\n",
"\n",
"cph = CoxPHFitter(penalizer=0.1)\n",
"cph.fit(df_hazard, 'duration', 'observed')\n",
"cph.print_summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
" duration col = 'duration'\n",
" event col = 'observed'\n",
" penalizer = 0.1\n",
" l1 ratio = 0.0\n",
" baseline estimation = breslow\n",
" number of observations = 1808\n",
"number of events observed = 1468\n",
" partial log-likelihood = -9613.12\n",
" time fit was run = 2024-12-04 06:24:28 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",
"un_continent_name_Africa -0.20 0.82 0.08 -0.36 -0.04 0.70 0.96\n",
"un_continent_name_Asia -0.06 0.95 0.07 -0.20 0.09 0.82 1.09\n",
"un_continent_name_Europe 0.23 1.26 0.06 0.11 0.35 1.12 1.43\n",
"un_continent_name_Oceania -0.12 0.89 0.11 -0.33 0.10 0.72 1.10\n",
"democracy_Non-democracy -0.72 0.49 0.06 -0.84 -0.60 0.43 0.55\n",
"\n",
" cmp to z p -log2(p)\n",
"covariate \n",
"un_continent_name_Africa 0.00 -2.48 0.01 6.25\n",
"un_continent_name_Asia 0.00 -0.77 0.44 1.19\n",
"un_continent_name_Europe 0.00 3.79 <0.005 12.70\n",
"un_continent_name_Oceania 0.00 -1.07 0.29 1.80\n",
"democracy_Non-democracy 0.00 -11.48 <0.005 98.97\n",
"---\n",
"Concordance = 0.62\n",
"Partial AIC = 19236.25\n",
"log-likelihood ratio test = 266.31 on 5 df\n",
"-log2(p) of ll-ratio test = 181.91"
],
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"<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>'duration'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>event col</th>\n",
" <td>'observed'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>penalizer</th>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>l1 ratio</th>\n",
" <td>0.0</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>1808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of events observed</th>\n",
" <td>1468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial log-likelihood</th>\n",
" <td>-9613.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time fit was run</th>\n",
" <td>2024-12-04 06:24:28 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;\">cmp to</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>un_continent_name_Africa</th>\n",
" <td>-0.20</td>\n",
" <td>0.82</td>\n",
" <td>0.08</td>\n",
" <td>-0.36</td>\n",
" <td>-0.04</td>\n",
" <td>0.70</td>\n",
" <td>0.96</td>\n",
" <td>0.00</td>\n",
" <td>-2.48</td>\n",
" <td>0.01</td>\n",
" <td>6.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>un_continent_name_Asia</th>\n",
" <td>-0.06</td>\n",
" <td>0.95</td>\n",
" <td>0.07</td>\n",
" <td>-0.20</td>\n",
" <td>0.09</td>\n",
" <td>0.82</td>\n",
" <td>1.09</td>\n",
" <td>0.00</td>\n",
" <td>-0.77</td>\n",
" <td>0.44</td>\n",
" <td>1.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>un_continent_name_Europe</th>\n",
" <td>0.23</td>\n",
" <td>1.26</td>\n",
" <td>0.06</td>\n",
" <td>0.11</td>\n",
" <td>0.35</td>\n",
" <td>1.12</td>\n",
" <td>1.43</td>\n",
" <td>0.00</td>\n",
" <td>3.79</td>\n",
" <td>&lt;0.005</td>\n",
" <td>12.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>un_continent_name_Oceania</th>\n",
" <td>-0.12</td>\n",
" <td>0.89</td>\n",
" <td>0.11</td>\n",
" <td>-0.33</td>\n",
" <td>0.10</td>\n",
" <td>0.72</td>\n",
" <td>1.10</td>\n",
" <td>0.00</td>\n",
" <td>-1.07</td>\n",
" <td>0.29</td>\n",
" <td>1.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>democracy_Non-democracy</th>\n",
" <td>-0.72</td>\n",
" <td>0.49</td>\n",
" <td>0.06</td>\n",
" <td>-0.84</td>\n",
" <td>-0.60</td>\n",
" <td>0.43</td>\n",
" <td>0.55</td>\n",
" <td>0.00</td>\n",
" <td>-11.48</td>\n",
" <td>&lt;0.005</td>\n",
" <td>98.97</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><br><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>Concordance</th>\n",
" <td>0.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Partial AIC</th>\n",
" <td>19236.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>log-likelihood ratio test</th>\n",
" <td>266.31 on 5 df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-log2(p) of ll-ratio test</th>\n",
" <td>181.91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate & & & & & & & & & & & \\\\\nun_continent_name_Africa & -0.20 & 0.82 & 0.08 & -0.36 & -0.04 & 0.70 & 0.96 & 0.00 & -2.48 & 0.01 & 6.25 \\\\\nun_continent_name_Asia & -0.06 & 0.95 & 0.07 & -0.20 & 0.09 & 0.82 & 1.09 & 0.00 & -0.77 & 0.44 & 1.19 \\\\\nun_continent_name_Europe & 0.23 & 1.26 & 0.06 & 0.11 & 0.35 & 1.12 & 1.43 & 0.00 & 3.79 & 0.00 & 12.70 \\\\\nun_continent_name_Oceania & -0.12 & 0.89 & 0.11 & -0.33 & 0.10 & 0.72 & 1.10 & 0.00 & -1.07 & 0.29 & 1.80 \\\\\ndemocracy_Non-democracy & -0.72 & 0.49 & 0.06 & -0.84 & -0.60 & 0.43 & 0.55 & 0.00 & -11.48 & 0.00 & 98.97 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iYkJ1coruTxx",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 573
},
"outputId": "52043275-fbb9-4212-e030-6ba39995f831"
},
"source": [
"fig_coef, ax_coef = plt.subplots(figsize=(12,7))\n",
"ax_coef.set_title('Survival Regression: Coefficients and Confident Intervals')\n",
"cph.plot(ax=ax_coef)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
],
"image/png": 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TFdfbu3dvNWrUSIcPH1Z0dLQk6dNPP9WZM2cc5+Klzp8/71QS2u32It7pG09KSgqjW9wgJSVFMTExV1yGY33tinOsAQC4HsLCwtSlSxezYwCAyyyGYRjXupLMzEyFh4fr3XffdRQiJ06cUIUKFfT0009r8ODBqlKlipKTk1WuXDnH6x588EHdc889mjhxohITE9WjRw/t2bNHVatWlST169dPixcv1tGjRxUcHCxJatmypWJjY/XWW29p9+7duvXWW7Vhwwbdd999kqTjx48rJiZGixYt0mOPPabHH39cycnJ+v777wvN3rhxY9ntdv3666+X3ccPP/xQ/fr1czyu8krrfeaZZ7R//36tWrVK0oURSXPmzNGePXtksVguu63ExEQNGjRIp06d0siRI7V8+XL98ccfyszMVOnSpbVu3To1btxY8+bN0/Dhw5WSkqKgoCBJ0qpVq9SuXTsdOnRIUVFRSkhI0Pr165WUlCRvb29JUqdOneTl5aUlS5YUuv0vv/xSbdq00YEDBxzv1+rVq9WqVSvHTarfffddvfzyy9qxY4djf7KyshQaGqoVK1aoefPmjm3v3btXXl4XBqtVq1ZNkZGR+vbbbyVJubm5stlsmj9/vrp06VKsfSpfvrx69Oihl19+udD8FotFgwYN0muvvXbZ49y/f38dOXJEH374oSRdcb3Vq1dX9+7dNWzYMEnSQw89pPDw8AIjoPKNHTtW48aNKzA9PT1dVqv1stnMVKVKFT5su0n+cbzcJWYca/e40rEGAOB6OXfunPbu3asqVarI39/f7DgAPJzdbpfNZivW51C3jCBKSkpSVlaW6tev75gWFham2267TdKFS4Zyc3N16623Or3u/PnzCg8Pd3wfGBjoKIckKSoqSrGxsY5yKH9aamqqJGnHjh0qVaqU03bDw8N12223aceOHZIujPTJL62KUrdu3QLTvv76a02aNEl//vmn7Ha7cnJydO7cOZ05c0aBgYFXXG+fPn1Ur149HTx4UOXLl1diYqISEhKuWA5davjw4Xr77be1YMECx0iXfDt27NCdd97pKFIkqUGDBsrLy9POnTsVFRUl6UKxkV8OSVJ0dLS2bdsmSZo4caImTpzomLd9+3bt2LFDMTExTmXevffe67TtrVu3as+ePQoJCXGafu7cOSUlJTm+r169uqMcki68fzVq1HB87+3trfDwcKf39HL7ZLFYdOjQIT3wwAOXPW533313gWlz5szRggULlJycrLNnzyorK8sxwi01NfWK6+3du7fmzp2rYcOG6ejRo/r888+1du3aIpcfOXKkBg8e7PjebrffNEUAH7TdozgjgzjW7sEoLADAjeLw4cN6+eWXNWHCBFWuXNnsOABQbNflJtWZmZny9vbWL7/84lRUSHIqfy69kbDFYil0Wl5eXrG3HRAQcMVlLi4jJGn//v1q27bt/9fefUdFdeVxAP8OIB1ERGpQIkiNsmgWAgbjrqyCDUKMBlGKYBdXE41GcdV1XRt2TYwYsSyRmLUsJ1FZGwo6IjYUC6JCKIpGQIQVQ3v7R45zMqHOwDDofD/nzB/ed+99v+v8zsD8eO8+TJ06FcuXL4exsTFSU1MRERGBqqoq6OrqNjuvm5sbXF1dsWfPHgwePBg3b97Ejz/+2OK4XzEyMsIXX3yBpUuX1tsDp6Wa+j+cMmWKVOHpt0WhplRUVKBfv36Ij4+vd6xbt25Nnrs172lL3k+g/nuakJCAOXPmYO3atfD09ISBgQHWrFmDtLS0Fs8bEhKC+fPnQywW4/z583j77bfh7e3daH8tLS1oaWm1KF4iIiIiIiIiZWqTTaptbW3RqVMnyZdt4NfNo+/evQvg12JJbW0tnjx5Ajs7O6mXubm53Od1cnJCTU2N1HmLi4uRlZUFZ2dnAECfPn1w8uRJmea9fPky6urqsHbtWrz33nuwt7fHw4cPpfq0ZN7IyEjs2rULcXFx8PHxkfvqkaioKKipqdXbVNnJyQkZGRn43//+J2k7d+4c1NTUJFdvNcfY2Fjq/dDQ0ICTkxPy8/Px6NEjSb8LFy5Ijevbty+ys7Nhampa7z1tzYZ8za3JwMAANjY2Mr+nr25DnDZtGtzc3GBnZyd1pVNL5u3atSsCAgIQFxcnuSWSqDl5eXmSR7D//pWXl6fs8IiIiIiIiAC0UYFIX18fERERmDt3Lk6dOoXMzEyEhYVJbi2yt7dHcHAwQkJCcPDgQeTk5ODixYtYsWKFXFfVvNKrVy/4+/tj4sSJSE1NRUZGBsaNGwcrKyv4+/sD+PU2n/T0dEybNg3Xr1/HnTt38NVXX0n2EmqInZ0dqqursXnzZjx48AB79+7Ftm3bpPq0ZN6xY8eioKAAsbGxmDBhgtzr1NbWxtKlS7Fp0yap9uDgYGhrayM0NBSZmZk4ffo0oqKiMH78eMntZfLw8fGBvb09QkNDkZGRgZSUFCxcuLDeuU1MTODv74+UlBTk5OQgOTkZM2fOREFBgdznbsmalixZgrVr12LTpk3Izs7GlStXsHnz5ibn7dWrFy5duoSkpCTcvXsXixYtQnp6ulSflswbGRmJ3bt34/bt2wgNDZV7nR3ZgwcPeMtTG7G2tkb37t0bPd69e/fX5rbDjo55S0RERETUOm32mPs1a9bA29sbI0aMgI+PD95//32pvX3i4uIQEhKCzz77DA4ODggICEB6enqTX55aIi4uDv369cPw4cPh6ekJQRBw5MgRyW1M9vb2+O9//4uMjAy4u7vD09MT//nPf6Ch0fjdda6urli3bh1WrVqFd955B/Hx8VixYoVUn5bM27lzZ3z00UfQ19dHQEBAq9YZGhpab48NXV1dJCUloaSkBH/84x8xatQoDBo0CFu2bGnVudTU1HDo0CFUVlbC3d0dkZGRWL58eb1znz17Ft27d0dgYCCcnJwQERGBly9ftmoD5pasKTQ0FBs2bMCXX34JFxcXDB8+HNnZ2U3OO3nyZAQGBmLMmDHw8PBAcXExpk2bJtWnJfP6+PjAwsICQ4YMafHteKS6zpw5IylcNPY6c+aMssMkIiKiNqShoQFjY+Mmv28QEXVEbfIUM2rcoEGD4OLiUu/qH3o9VVRUwMrKCnFxcQgMDJRprCy7xxMRERERERG1Vrs/xYzqKy0tRXJyMpKTk/Hll18qOxxqpbq6Ojx9+hRr166FkZERRo4cqeyQiIiIiIiIiNpMm91iRtLc3NwQFhaGVatW1dsw2sXFBfr6+g2+GnoqGClfXl4ezMzM8O2332Lnzp28ZJiIiIiIGpSfn48ZM2YgPz9f2aEQEcmE33IVJDc3t9FjR44cQXV1dYPHWrO5NCmOjY0NeDcmERERETWnpqYGJSUlqKmpUXYoREQyYYFICXr06KHsEIiIiIiIiIiIJHiLGRERERERERGRimOBiIiIiIiIiIhIxbFARERERERE1EYsLCwQHR0NCwsLZYdCRCQT7kFERERERETURrS1teHs7KzsMIiIZMYriIiIiIiIiNpISUkJEhISUFJSouxQiIhkwgIRERERERFRGykrK0NiYiLKysqUHQoRkUxYICIiIiIiIiIiUnEsEBERERERERERqTgWiIiIiIiIiIiIVBwLRERERERERG3EwMAAAwcOhIGBgbJDISKSiUgQBEHZQRCpgufPn6Nz584oKyuDoaGhssMhIiIiIiKiN5ws30N5BREREREREVEbqaqqQkFBAaqqqpQdChGRTFggIiIiIiIiaiOFhYX4/PPPUVhYqOxQiIhkoqHsAIhUxau7OZ8/f67kSIiIiIhIUcrLy1FdXY3y8nL+3kdESvfqc6gluwtxDyKidlJQUABra2tlh0FEREREREQqJj8/H2+99VaTfVggImondXV1ePjwIQwMDCASiZQdTofz/PlzWFtbIz8/n5t4U4sxb0hWzBmSFXOGZMWcIXkwb0hWLc0ZQRBQXl4OS0tLqKk1vcsQbzEjaidqamrNVmwJMDQ05A9FkhnzhmTFnCFZMWdIVswZkgfzhmTVkpzp3Llzi+biJtVERERERERERCqOBSIiIiIiIiIiIhXHAhERdQhaWlpYvHgxtLS0lB0KvUaYNyQr5gzJijlDsmLOkDyYNyQrReQMN6kmIiIiIiIiIlJxvIKIiIiIiIiIiEjFsUBERERERERERKTiWCAiIiIiIiIiIlJxLBAREREREREREak4FoiISGlKSkoQHBwMQ0NDGBkZISIiAhUVFU2OKSoqwvjx42Fubg49PT307dsXBw4caKeISdnkyRkAEIvF+POf/ww9PT0YGhpiwIABqKysbIeIqSOQN28AQBAE+Pn5QSQS4fDhw4oNlDoMWXOmpKQEUVFRcHBwgI6ODrp3746ZM2eirKysHaOm9rR161bY2NhAW1sbHh4euHjxYpP9v//+ezg6OkJbWxu9e/fGkSNH2ilS6khkyZvY2Fh4e3ujS5cu6NKlC3x8fJrNM3rzyPpZ80pCQgJEIhECAgJkOh8LRESkNMHBwbh58yaOHz+OH374AWfPnsWkSZOaHBMSEoKsrCwkJibixo0bCAwMxOjRo3H16tV2ipqUSZ6cEYvF8PX1xeDBg3Hx4kWkp6djxowZUFPjj0BVIU/evLJhwwaIRCIFR0gdjaw58/DhQzx8+BAxMTHIzMzErl27cOzYMURERLRj1NRevvvuO3z66adYvHgxrly5AldXVwwZMgRPnjxpsP/58+cRFBSEiIgIXL16FQEBAQgICEBmZmY7R07KJGveJCcnIygoCKdPn4ZYLIa1tTUGDx6MwsLCdo6clEXWnHklNzcXc+bMgbe3t+wnFYiIlODWrVsCACE9PV3SdvToUUEkEgmFhYWNjtPT0xP27Nkj1WZsbCzExsYqLFbqGOTNGQ8PDyE6Oro9QqQOSN68EQRBuHr1qmBlZSU8evRIACAcOnRIwdFSR9CanPmt/fv3C5qamkJ1dbUiwiQlcnd3F6ZPny75d21trWBpaSmsWLGiwf6jR48Whg0bJtXm4eEhTJ48WaFxUscia978Xk1NjWBgYCDs3r1bUSFSByNPztTU1AheXl7Cjh07hNDQUMHf31+mc/LPp0SkFGKxGEZGRnj33XclbT4+PlBTU0NaWlqj47y8vPDdd9+hpKQEdXV1SEhIwMuXLzFw4MB2iJqUSZ6cefLkCdLS0mBqagovLy+YmZnhgw8+QGpqanuFTUom72fNixcvMHbsWGzduhXm5ubtESp1EPLmzO+VlZXB0NAQGhoaigiTlKSqqgqXL1+Gj4+PpE1NTQ0+Pj4Qi8UNjhGLxVL9AWDIkCGN9qc3jzx583svXrxAdXU1jI2NFRUmdSDy5szf//53mJqayn0FKwtERKQURUVFMDU1lWrT0NCAsbExioqKGh23f/9+VFdXo2vXrtDS0sLkyZNx6NAh2NnZKTpkUjJ5cubBgwcAgCVLlmDixIk4duwY+vbti0GDBiE7O1vhMZPyyftZM3v2bHh5ecHf31/RIVIHI2/O/NbTp0+xbNmyFt/KSK+Pp0+fora2FmZmZlLtZmZmjeZHUVGRTP3pzSNP3vzevHnzYGlpWa/YSG8meXImNTUV33zzDWJjY+U+LwtERNSm5s+fD5FI1OTrzp07cs+/aNEiPHv2DCdOnMClS5fw6aefYvTo0bhx40YbroLakyJzpq6uDgAwefJkhIeHw83NDevXr4eDgwN27tzZlsugdqbIvElMTMSpU6ewYcOGtg2alErRP59eef78OYYNGwZnZ2csWbKk9YETkcpbuXIlEhIScOjQIWhrays7HOqAysvLMX78eMTGxsLExETueXjNKxG1qc8++wxhYWFN9unZsyfMzc3rbbBWU1ODkpKSRm/nuH//PrZs2YLMzEy4uLgAAFxdXZGSkoKtW7di27ZtbbIGal+KzBkLCwsAgLOzs1S7k5MT8vLy5A+alE6ReXPq1Cncv38fRkZGUu0fffQRvL29kZyc3IrISVkUmTOvlJeXw9fXFwYGBjh06BA6derU2rCpgzExMYG6ujoeP34s1f748eNG88Pc3Fym/vTmkSdvXomJicHKlStx4sQJ9OnTR5FhUgcia87cv38fubm5GDFihKTt1R9KNTQ0kJWVBVtb22bPywIREbWpbt26oVu3bs328/T0xLNnz3D58mX069cPwK9fyurq6uDh4dHgmBcvXgBAvadPqaurSz4A6fWjyJyxsbGBpaUlsrKypNrv3r0LPz+/1gdPSqPIvJk/fz4iIyOl2nr37o3169dL/eJFrxdF5gzw65VDQ4YMgZaWFhITE/lX/jeUpqYm+vXrh5MnT0oeH11XV4eTJ09ixowZDY7x9PTEyZMnMWvWLEnb8ePH4enp2Q4RU0cgT94AwOrVq7F8+XIkJSVJ7YtGbz5Zc8bR0bHeHRXR0dEoLy/Hxo0bYW1t3bITy7ujNhFRa/n6+gpubm5CWlqakJqaKvTq1UsICgqSHC8oKBAcHByEtLQ0QRAEoaqqSrCzsxO8vb2FtLQ04d69e0JMTIwgEomEH3/8UVnLoHYka84IgiCsX79eMDQ0FL7//nshOztbiI6OFrS1tYV79+4pYwmkBPLkze+BTzFTKbLmTFlZmeDh4SH07t1buHfvnvDo0SPJq6amRlnLIAVJSEgQtLS0hF27dgm3bt0SJk2aJBgZGQlFRUWCIAjC+PHjhfnz50v6nzt3TtDQ0BBiYmKE27dvC4sXLxY6deok3LhxQ1lLICWQNW9WrlwpaGpqCv/+97+lPlPKy8uVtQRqZ7LmzO/J8xQzFoiISGmKi4uFoKAgQV9fXzA0NBTCw8Olfujl5OQIAITTp09L2u7evSsEBgYKpqamgq6urtCnT596j72nN5c8OSMIgrBixQrhrbfeEnR1dQVPT08hJSWlnSMnZZI3b36LBSLVImvOnD59WgDQ4CsnJ0c5iyCF2rx5s9C9e3dBU1NTcHd3Fy5cuCA59sEHHwihoaFS/ffv3y/Y29sLmpqagouLC/+wpaJkyZsePXo0+JmyePHi9g+clEbWz5rfkqdAJBIEQZDxaiciIiIiIiIiInqD8ClmREREREREREQqjgUiIiIiIiIiIiIVxwIREREREREREZGKY4GIiIiIiIiIiEjFsUBERERERERERKTiWCAiIiIiIiIiIlJxLBAREREREREREak4FoiIiIiIiIiIiFQcC0REREREr7mBAwdi1qxZCpl7wIAB+PbbbxUyd1VVFWxsbHDp0qUW9V+0aBEmTZqkkFiU5b333sOBAweUHQYRERELRERERETUsMTERDx+/BiffPKJpM3GxgYbNmyo13fJkiX4wx/+IPVvkUgEkUgEdXV1WFtbY9KkSSgpKZH00dTUxJw5czBv3rxmYykqKsLGjRuxcOFCSVt5eTlmzZqFHj16QEdHB15eXkhPT5caFxYWJonj1cvX11dy/JdffsH48eNhaGgIe3t7nDhxQmr8mjVrEBUV1Wx8APD8+XMsXLgQjo6O0NbWhrm5OXx8fHDw4EEIggCgfjEvOjoa8+fPR11dXYvOQUREpCgsEBERERFRgzZt2oTw8HCoqcn3K6OLiwsePXqEvLw8xMXF4dixY5g6dapUn+DgYKSmpuLmzZtNzrVjxw54eXmhR48ekrbIyEgcP34ce/fuxY0bNzB48GD4+PigsLBQaqyvry8ePXokee3bt09ybPv27bh8+TLEYjEmTZqEsWPHSoo5OTk5iI2NxfLly5td67Nnz+Dl5YU9e/bgiy++wJUrV3D27FmMGTMGn3/+OcrKyhoc5+fnh/Lychw9erTZcxARESkSC0REREREb5jS0lKEhISgS5cu0NXVhZ+fH7Kzs6X6xMbGwtraGrq6uvjwww+xbt06GBkZSY7//PPPOHXqFEaMGCF3HBoaGjA3N4eVlRV8fHzw8ccf4/jx41J9unTpgv79+yMhIaHJuRISEqRiqaysxIEDB7B69WoMGDAAdnZ2WLJkCezs7PDVV19JjdXS0oK5ubnk1aVLF8mx27dvY+TIkXBxccH06dPx888/4+nTpwCAqVOnYtWqVTA0NGx2rQsWLEBubi7S0tIQGhoKZ2dn2NvbY+LEibh27Rr09fUbHKeuro6hQ4c2u34iIiJFY4GIiIiI6A0TFhaGS5cuITExEWKxGIIgYOjQoaiurgYAnDt3DlOmTMFf//pXXLt2DX/5y1/qXSWTmpoKXV1dODk5tUlMubm5SEpKgqamZr1j7u7uSElJaXRsSUkJbt26hXfffVfSVlNTg9raWmhra0v11dHRQWpqqlRbcnIyTE1N4eDggKlTp6K4uFhyzNXVFampqaisrERSUhIsLCxgYmKC+Ph4aGtr48MPP2x2bXV1dUhISEBwcDAsLS3rHdfX14eGhkaj45tbPxERUXto/CcVEREREb12srOzkZiYiHPnzsHLywsAEB8fD2traxw+fBgff/wxNm/eDD8/P8yZMwcAYG9vj/Pnz+OHH36QzPPTTz/BzMyswdvL5s2bh+joaKm2qqoqODs7S7XduHED+vr6qK2txcuXLwEA69atqzefpaUlfvrpp0bXlJeXB0EQpIovBgYG8PT0xLJly+Dk5AQzMzPs27cPYrEYdnZ2kn6+vr4IDAzE22+/jfv372PBggXw8/ODWCyGuro6JkyYgOvXr8PZ2RkmJibYv38/SktL8be//Q3JycmIjo5GQkICbG1tsXPnTlhZWdWL7+nTpygtLYWjo2Oja2iKpaUl8vPzUVdXJ/ftfERERK3FAhERERHRG+T27dvQ0NCAh4eHpK1r165wcHDA7du3AQBZWVn1roxxd3eXKhBVVlbWuzrnlblz5yIsLEyqbdOmTTh79qxUm4ODAxITE/Hy5Uv861//wrVr1xrc8FlHRwcvXrxodE2VlZUAUC+evXv3YsKECbCysoK6ujr69u2LoKAgXL58WdLntxts9+7dG3369IGtrS2Sk5MxaNAgdOrUCVu3bpWaNzw8HDNnzsTVq1dx+PBhZGRkYPXq1Zg5c2aDTxx7tWeRvHR0dFBXV4dffvkFOjo6rZqLiIhIXvwTBRERERHVY2JigtLS0kaP2dnZSb2MjY3r9dPU1ISdnR3eeecdrFy5Eurq6li6dGm9fiUlJejWrVuTsQCoF4+trS3OnDmDiooK5Ofn4+LFi6iurkbPnj0bnatnz54wMTHBvXv3Gjx++vRp3Lx5EzNmzEBycjKGDh0KPT09jB49GsnJyQ2O6datG4yMjHDnzp1Gz9uUkpIS6OnpsThERERKxQIRERER0RvEyckJNTU1SEtLk7QVFxcjKytLcguYg4NDvcfB//7fbm5uKCoqarRIJI/o6GjExMTg4cOHUu2ZmZlwc3NrdJytrS0MDQ1x69atBo/r6enBwsICpaWlSEpKgr+/f6NzFRQUoLi4GBYWFvWOvXz5EtOnT8fXX38NdXV11NbWSvZtqq6uRm1tbYNzqqmp4ZNPPkF8fHy9tQFARUUFampqGo2pufUTERG1BxaIiIiIiN4gvXr1gr+/PyZOnIjU1FRkZGRg3LhxsLKykhROoqKicOTIEaxbtw7Z2dn4+uuvcfToUYhEIsk8bm5uMDExwblz59osNk9PT/Tp0wf//Oc/pdpTUlIwePDgRsepqanBx8en3ubTSUlJOHbsGHJycnD8+HH86U9/gqOjI8LDwwH8WpiZO3cuLly4gNzcXJw8eRL+/v6ws7PDkCFD6p1n2bJlGDp0qKRY079/fxw8eBDXr1/Hli1b0L9//0ZjXL58OaytreHh4YE9e/bg1q1byM7Oxs6dO+Hm5oaKiopGxza3fiIiovbAAhERERHRGyYuLg79+vXD8OHD4enpCUEQcOTIEXTq1AnAr4WPbdu2Yd26dXB1dcWxY8cwe/ZsqT1+1NXVER4ejvj4+DaNbfbs2dixYwfy8/MBAGKxGGVlZRg1alST4yIjI5GQkIC6ujpJW1lZGaZPnw5HR0eEhITg/fffR1JSkmSd6urquH79OkaOHAl7e3tERESgX79+SElJgZaWltT8mZmZ2L9/v9QtcKNGjcKwYcPg7e2N69evY+PGjY3GZ2xsjAsXLmDcuHH4xz/+ATc3N3h7e2Pfvn1Ys2YNOnfu3OC4wsJCnD9/XlLUIiIiUhaR0Npd9YiIiIjotTdx4kTcuXNH6nHrRUVFcHFxwZUrV9CjRw+FnHfMmDFwdXXFggULmuwnCAI8PDwwe/ZsBAUFKSQWZZg3bx5KS0uxfft2ZYdCREQqjlcQEREREamgmJgYZGRk4N69e9i8eTN2796N0NBQqT7m5ub45ptvkJeXp5AYqqqq0Lt3b8yePbvZviKRCNu3b29yL5/XkampKZYtW6bsMIiIiHgFEREREZEqevVUrvLycvTs2RNRUVGYMmWKssMiIiIiJWGBiIiIiIiIiIhIxfEWMyIiIiIiIiIiFccCERERERERERGRimOBiIiIiIiIiIhIxbFARERERERERESk4lggIiIiIiIiIiJScSwQERERERERERGpOBaIiIiIiIiIiIhUHAtEREREREREREQq7v9QPuYqvhWjbgAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "KfaBnER0VoXT"
},
"execution_count": null,
"outputs": []
}
]
}
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