Gamma regression Py.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thống kê Bayes sử dụng Probflow\n",
    "\n",
    "# Bài 5: So sánh 2 phân bố Gamma\n",
    "\n",
    "### BS. Lê Ngọc Khả Nhi\n",
    "\n",
    "\n",
    "# Giới thiệu\n",
    "\n",
    "Chào các bạn, đây là bài thứ 5 trong loạt bài giới thiệu về probflow, một giao thức dựa trên tensorflow hoặc pytorch, giúp người dùng phổ thông thực hành thống kê Bayes dễ dàng hơn.\n",
    "\n",
    "Lần trước, chúng ta đã từng giải quyết bài toán so sánh trung bình giữa 2 hay nhiều phân nhóm bằng mô hình probflow, các mô hình này dựa trên họ phân phối Gaussian hoặc Student t. Tuy nhiên, trong thực tế hầu hết những đại lượng sinh học thường có phân phối lệch dương chứ không đối xứng; do đó một mô hình tuyến tính sử dụng phân phối Gamma có thể sẽ phù hợp và linh hoạt hơn.\n",
    "\n",
    "Trong bài này chúng ta sẽ thực hiện một suy diễn thống kê bằng mô hình hồi quy Gamma. Mục tiêu của bài toán là so sánh nồng độ bilirubin giữa 2 nhóm bệnh nhân mắc bệnh Viêm gan : nhóm thứ nhất bệnh nhẹ hơn và sống sót, nhóm thứ hai nặng hơn và đã tử vong; dựa theo bộ dữ liệu “hepatitis”. Nhi tải dữ liệu từ database openml.org, sau khi loại bỏ missing values,giản lược chúng ta có 149 cases, gồm 119 trường hợp sống sót và 30 trường hợp tử vong. Biến kết quả là BILIRUBIN, phân nhóm là Class.\n",
    "\n",
    "# Thăm dò dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149 entries, 0 to 154\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   BILIRUBIN  149 non-null    float64\n",
      " 1   Class      149 non-null    object \n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 3.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://www.openml.org/data/get_csv/55/dataset_55_hepatitis.csv\",\n",
    "                 na_values = '?')[['BILIRUBIN','Class']].dropna(how = 'any', axis = 0)\n",
    "\n",
    "df.Class = df.Class.map({'LIVE': 'G1_Survived',\n",
    "                         'DIE': 'G2_Dead'\n",
    "                        })\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">BILIRUBIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>5%</th>\n",
       "      <th>50%</th>\n",
       "      <th>95%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G1_Survived</th>\n",
       "      <td>119.0</td>\n",
       "      <td>1.146218</td>\n",
       "      <td>0.722143</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.46</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2_Dead</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2.543333</td>\n",
       "      <td>1.940082</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.545</td>\n",
       "      <td>1.95</td>\n",
       "      <td>6.34</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BILIRUBIN                                                 \n",
       "                count      mean       std  min     5%   50%   95%  max\n",
       "Class                                                                 \n",
       "G1_Survived     119.0  1.146218  0.722143  0.3  0.600  1.00  2.46  4.6\n",
       "G2_Dead          30.0  2.543333  1.940082  0.4  0.545  1.95  6.34  8.0"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Class').describe(percentiles = [0.05,0.5,0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thống kê mô tả cho thấy giá trị trung bình bilirubin cao hơn ở nhóm 2 (Dead) so với nhóm 1 (Survived).\n",
    "Hình ảnh phân bố ở mỗi phân nhóm có thể được khảo sát trực quan bằng KDE plot như sau:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Bilirubin')"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (8,5)\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "for v in df.Class.unique():\n",
    "    sns.kdeplot(df[df.Class == v]['BILIRUBIN'], label = v, shade = True)\n",
    "plt.xlabel('Bilirubin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Như ta thấy, bilirubin là 1 biến liên tục, không âm và có phân bố bất đối xứng, thậm chí hình dạng phân phối cũng không đồng nhất giữa 2 phân nhóm G1 và G2. Hồi quy Gamma là một giải pháp tốt cho trường hợp này.\n",
    "\n",
    "# Hồi quy Gamma dùng statsmodels\n",
    "\n",
    "Trong ngôn ngữ Python, ta có thể làm hồi quy Gamma bằng packages statsmodels như sau:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\genmod\\generalized_linear_model.py:274: DomainWarning: The identity link function does not respect the domain of the Gamma family.\n",
      "  warnings.warn((\"The %s link function does not respect the domain \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>BILIRUBIN</td>    <th>  No. Observations:  </th>  <td>   149</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   147</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>          <td>Gamma</td>      <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>       <td>identity</td>     <th>  Scale:             </th> <td> 0.43341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -154.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 16 Aug 2020</td> <th>  Deviance:          </th> <td>  46.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>17:07:26</td>     <th>  Pearson chi2:      </th>  <td>  63.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>4</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>    1.1462</td> <td>    0.069</td> <td>   16.570</td> <td> 0.000</td> <td>    1.011</td> <td>    1.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.G2_Dead]</th> <td>    1.3971</td> <td>    0.313</td> <td>    4.458</td> <td> 0.000</td> <td>    0.783</td> <td>    2.011</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              BILIRUBIN   No. Observations:                  149\n",
       "Model:                            GLM   Df Residuals:                      147\n",
       "Model Family:                   Gamma   Df Model:                            1\n",
       "Link Function:               identity   Scale:                         0.43341\n",
       "Method:                          IRLS   Log-Likelihood:                -154.39\n",
       "Date:                Sun, 16 Aug 2020   Deviance:                       46.137\n",
       "Time:                        17:07:26   Pearson chi2:                     63.7\n",
       "No. Iterations:                     4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept            1.1462      0.069     16.570      0.000       1.011       1.282\n",
       "Class[T.G2_Dead]     1.3971      0.313      4.458      0.000       0.783       2.011\n",
       "====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod1 = smf.glm(formula='BILIRUBIN ~ Class', \n",
    "               data=df, \n",
    "               family=sm.families.Gamma(sm.families.links.identity())).fit()\n",
    "\n",
    "mod1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý, mô hình này dùng link function (hàm liên kết) là identidy, do đó nội dung sẽ được diễn giải như sau:\n",
    "    \n",
    "Intercept = 1.1462 chính là trung bình Bilirubin của phân nhóm G1 (Survival),\n",
    "\n",
    "Hệ số hồi quy b cho Class[T.G2_Dead] = 1.3971 chính là giá trị tăng trung bình của bilirubin của phân nhóm G2, so với G1. Từ đây ta có thể tính trung bình của phân nhóm G2 = Intercept + b = 1.3971 + 1.1462 = 2.5433\n",
    "\n",
    "Một mô hình khác sử dụng hàm liên kết logarit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>BILIRUBIN</td>    <th>  No. Observations:  </th>  <td>   149</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   147</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>          <td>Gamma</td>      <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>log</td>       <th>  Scale:             </th> <td> 0.43341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -154.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 16 Aug 2020</td> <th>  Deviance:          </th> <td>  46.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>17:07:28</td>     <th>  Pearson chi2:      </th>  <td>  63.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>7</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>    0.1365</td> <td>    0.060</td> <td>    2.261</td> <td> 0.024</td> <td>    0.018</td> <td>    0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.G2_Dead]</th> <td>    0.7970</td> <td>    0.134</td> <td>    5.926</td> <td> 0.000</td> <td>    0.533</td> <td>    1.061</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              BILIRUBIN   No. Observations:                  149\n",
       "Model:                            GLM   Df Residuals:                      147\n",
       "Model Family:                   Gamma   Df Model:                            1\n",
       "Link Function:                    log   Scale:                         0.43341\n",
       "Method:                          IRLS   Log-Likelihood:                -154.39\n",
       "Date:                Sun, 16 Aug 2020   Deviance:                       46.137\n",
       "Time:                        17:07:28   Pearson chi2:                     63.7\n",
       "No. Iterations:                     7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept            0.1365      0.060      2.261      0.024       0.018       0.255\n",
       "Class[T.G2_Dead]     0.7970      0.134      5.926      0.000       0.533       1.061\n",
       "====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod2 = smf.glm(formula='BILIRUBIN ~ Class', \n",
    "               data=df, \n",
    "               family=sm.families.Gamma(sm.families.links.log())).fit()\n",
    "\n",
    "mod2.summary(alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Đối với mô hình dùng hàm liên kết logarit, nếu ta chuyền đổi Intercept bằng hàm exponential, sẽ có trung bình của nhóm G1:\n",
    "exp(0.1365) = 1.146\n",
    "\n",
    "Nếu ta tính exp(b + Intercept) sẽ có giá trị trung bình của nhóm G2 = 2.543,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1462548777757673"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(0.1365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5433955017352687"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(0.7970 + 0.1365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ta cũng có thể kết hợp statsmodels và một kỹ thuật tái chọn mẫu bootstrap để ước tính khoảng tin cậy của hệ số hồi quy b cho phân nhóm 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df = pd.DataFrame(columns=['Intercept','Group_2','Iter'])\n",
    "\n",
    "for i in range(1000):\n",
    "    temp_df = df.sample(df.shape[0], replace=True)\n",
    "    \n",
    "    mod = smf.glm(formula='BILIRUBIN ~ Class', \n",
    "               data=temp_df, \n",
    "               family=sm.families.Gamma(sm.families.links.identity())).fit()\n",
    "    \n",
    "    temp_df = pd.DataFrame({'Intercept': mod.params[0],\n",
    "                            'Group_2': mod.params[1],\n",
    "                            'Iter': i\n",
    "                            }, index = [i])\n",
    "    \n",
    "    out_df = pd.concat([out_df, temp_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>2.5%</th>\n",
       "      <th>50%</th>\n",
       "      <th>97.5%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.145452</td>\n",
       "      <td>0.066824</td>\n",
       "      <td>0.957018</td>\n",
       "      <td>1.019596</td>\n",
       "      <td>1.144535</td>\n",
       "      <td>1.277236</td>\n",
       "      <td>1.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_2</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.407391</td>\n",
       "      <td>0.362747</td>\n",
       "      <td>0.085610</td>\n",
       "      <td>0.751546</td>\n",
       "      <td>1.392393</td>\n",
       "      <td>2.146028</td>\n",
       "      <td>2.653901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      mean       std       min      2.5%       50%     97.5%  \\\n",
       "Intercept  1000.0  1.145452  0.066824  0.957018  1.019596  1.144535  1.277236   \n",
       "Group_2    1000.0  1.407391  0.362747  0.085610  0.751546  1.392393  2.146028   \n",
       "\n",
       "                max  \n",
       "Intercept  1.392857  \n",
       "Group_2    2.653901  "
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df.describe(percentiles = [0.025,0.5,0.975]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(out_df['Group_2'], shade = True, label = 'bootstrap b_G2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tất cả những kết quả trên đây cho phép suy luận rằng nhóm Tử vong (G2) có bilirubin tăng cao một cách có ý nghĩa thống kê so với nhóm Sống sót (G1), trung bình 1.407 đơn vị (97.5%CI: 0.751 đến 2.146).\n",
    "\n",
    "# Suy diễn Bayes phân phối Gamma dùng probflow\n",
    "\n",
    "## Bước 1: Phác thảo cấu trúc mô hình"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "import probflow as pf\n",
    "import tensorflow as tf\n",
    "\n",
    "from patsy import dmatrices \n",
    "\n",
    "import arviz as az"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Câu hỏi nghiên cứu so sánh của chúng ta có thể được chuyển thành mô hình như sau:\n",
    "\n",
    "Ta giả định biến Bilirubin được mô tả bằng một biến số ngẫu nhiên liên tục theo phân phối Gamma gồm 2 tham số:\n",
    "\n",
    "$$ Bilirubin \\sim Gamma(\\alpha, \\beta)$$\n",
    "\n",
    "Trong đó $\\alpha$ là tham số concentration, $\\beta$ là tham số rate, đều là số dương.\n",
    "\n",
    "Mỗi tham số $\\alpha$ , $\\beta$ lại được ước tính bởi mô hình tuyến tính đa biến:\n",
    "\n",
    "$\\alpha \\sim \\mathbf{X}^\\top \\mathbf{W_a} = x_1 wa_1 + x_2 wa_2 $\n",
    "\n",
    "$\\beta \\sim \\mathbf{X}^\\top \\mathbf{W_b} = x_1 wa_1 + x_2 wb_2 $\n",
    "\n",
    "Với X là design matrix, chứa x1, x2 tương ứng với dummy variable của 2 phân nhóm cần so sánh là G1 và G2\n",
    "\n",
    "Sau khi giải mô hình này, ta sẽ thu được 2 matrices W_a và W_b chứa giá trị của tham số wa1, wa2 và wb1, wb2 cho phép mô tả 2 phân bố Gamma của bilirubin(G1) và bilirubin(G2):\n",
    "\n",
    "$$ Bilirubin(G1) \\sim Gamma(wa_1, wb_1)$$\n",
    "\n",
    "$$ Bilirubin(G2) \\sim Gamma(wa_2, wb_2)$$\n",
    "\n",
    "Chú ý: như vậy mô hình này không có intercept\n",
    "\n",
    "## Bước 2: Chuẩn bị design matrix\n",
    "\n",
    "Nhi dùng hàm dmatrics của patsy để tạo ra 1 design matrix X gồm 2 dummy variables của biến Class, và không chứa intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "form = 'BILIRUBIN ~ Class - 1'\n",
    "\n",
    "Y, X = dmatrices(form,\n",
    "                 data=df,\n",
    "                 return_type='dataframe')\n",
    "\n",
    "X = X.astype('float32')\n",
    "Y = Y.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý: nhớ chuyển data type của X và Y thành float32, nếu không probflow sẽ không hoạt động\n",
    "\n",
    "## Bước 3: Dựng mô hình GLM bằng probflow\n",
    "\n",
    "Tiếp theo, ta tạo ra class mô hình GLM, kế thừa cấu trúc và tính năng của class ContinuousModel của probflow.\n",
    "\n",
    "Mô hình này có 2 nhóm tham số cần xác định:\n",
    "\n",
    "Ma trận W_a gồm 2 tham số wa1, wa2 tương ứng với 2 tham số alpha của 2 phân bố Gamma của phân nhóm G1, G2\n",
    "\n",
    "Ma trận W_b gồm 2 tham số wb1, wb2 tương ứng với 2 tham số beta của 2 phân bố Gamma của phân nhóm G1, G2\n",
    "\n",
    "Hàm likelihood của mô hình cho phép ước lượng biến kết quả Y (bilirubin) theo phân phối Gamma.\n",
    "\n",
    "Chú ý: wa, wb luôn có giá trị dương, do đó dùng class pf.PositiveParameter, dims = số cột trong design matrix X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Gamma_reg(pf.ContinuousModel):\n",
    "\n",
    "    def __init__(self, dims):\n",
    "        self.wa = pf.PositiveParameter([dims, 1], name = 'W_a')\n",
    "        self.wb = pf.PositiveParameter([dims, 1], name = 'W_b')\n",
    "    \n",
    "    def __call__(self, x):\n",
    "        return pf.Gamma(concentration = x @ self.wa(),\n",
    "                        rate = x @ self.wb())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Gamma_reg(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bước 4: Huấn luyện mô hình từ dữ liệu\n",
    "\n",
    "Mô hình probflow thực chất là 1 probabilistic neural network, được train như các mô hình keras trong tensorflow:\n",
    "\n",
    "Nhi train mô hình GLM vừa tạo ra trên tập dữ liệu X,Y, với batch_size = 8, learning rate = 0.001 và 3000 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X,Y,\n",
    "          lr = 0.001,\n",
    "          batch_size=8, \n",
    "          epochs=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W_a': array([[3.1196833],\n",
       "        [1.6165739]], dtype=float32),\n",
       " 'W_b': array([[2.65854  ],\n",
       "        [0.6336386]], dtype=float32)}"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.posterior_mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bước 5 : Suy diễn thống kê\n",
    "\n",
    "Sau khi huấn luyện xong, mô hình đã sẵn sàng cho ta sử dụng, tuy nhiên khác với những mô hình phân bố Gaussian hay Student-t, ta không thể dùng trực tiếp phân bố hậu nghiệm của các tham số W_a, W_b để suy diễn thống kê, mà ta cần đưa các tham số này vào phân bố Gamma rồi lấy mẫu (mô phỏng) rất nhiều (thí dụ 10000 lượt) giá trị của Y(G1) và Y(G2). Phân phối hậu nghiệm của khác biệt giữa 2 phân nhóm sẽ được ước tính bằng hiệu của 2 vectors Y(G2) và Y(G1):  Diff = Y(G2) - Y(G1).\n",
    "\n",
    "Nhi viết 1 hàm để xuất ra object arviz data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [],
   "source": [
    "def posterior_dist(gamma_mod = model,\n",
    "                   alpha_w = 'W_a',\n",
    "                   beta_w = 'W_b',\n",
    "                   grp_names = ['Survived', 'Dead'],\n",
    "                   n_chain = 5,\n",
    "                   n_sample = 10000):\n",
    "    params = model.posterior_mean()\n",
    "    g1 = pf.Gamma(float(params[alpha_w][0]), \n",
    "                  float(params[beta_w][0])).sample(n_sample).numpy()\n",
    "    g2 = pf.Gamma(float(params[alpha_w][1]), \n",
    "                  float(params[beta_w][1])).sample(n_sample).numpy()\n",
    "    diff = g2 - g1\n",
    "    sample_dict = {grp_names[0]: g1.reshape(n_chain, n_sample//n_chain), \n",
    "                   grp_names[1]: g2.reshape(n_chain, n_sample//n_chain), \n",
    "                   'Difference': diff.reshape(n_chain, n_sample//n_chain)}\n",
    "    \n",
    "    return az.from_dict(sample_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [],
   "source": [
    "pw = posterior_dist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phân phối hậu nghiệm của Y(G1), Y(G2) như sau:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (chain: 5, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain       (chain) int32 0 1 2 3 4\n",
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       "Attributes:\n",
       "    created_at:     2020-08-16T19:03:36.930324\n",
       "    arviz_version:  0.9.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45533994-d9aa-4f04-aa2e-b3dad26ac776' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45533994-d9aa-4f04-aa2e-b3dad26ac776' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 5</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d93e4662-7968-4ad0-90f0-5137d3295dba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d93e4662-7968-4ad0-90f0-5137d3295dba' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-11ea9069-0026-470e-8bde-e4071380bf23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11ea9069-0026-470e-8bde-e4071380bf23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dbe3631-126d-4e46-b583-18bef33fe72a' class='xr-var-data-in' type='checkbox'><label for='data-4dbe3631-126d-4e46-b583-18bef33fe72a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-82b46014-ea97-4085-a17b-54002619cbb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82b46014-ea97-4085-a17b-54002619cbb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7846317-0432-48ac-a823-43a82964aa2c' class='xr-var-data-in' type='checkbox'><label for='data-b7846317-0432-48ac-a823-43a82964aa2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f80aeeb-ebb0-4b44-bd63-37b617ccf14f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1f80aeeb-ebb0-4b44-bd63-37b617ccf14f' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Survived</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.89715576 1.5405983 ... 1.0070599</div><input id='attrs-1de0101a-17c5-4506-89d0-89f35119f5b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1de0101a-17c5-4506-89d0-89f35119f5b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e86e6cbc-4c01-4d00-9494-72e8dcc732d9' class='xr-var-data-in' type='checkbox'><label for='data-e86e6cbc-4c01-4d00-9494-72e8dcc732d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.89715576, 1.5405983 , 1.8312517 , ..., 0.47288275, 1.241704  ,\n",
       "        2.4324336 ],\n",
       "       [0.6042198 , 2.636256  , 1.9742624 , ..., 1.4216652 , 0.6445833 ,\n",
       "        0.8891342 ],\n",
       "       [0.32899392, 0.23539542, 1.0079968 , ..., 1.2397093 , 1.7173616 ,\n",
       "        0.8297477 ],\n",
       "       [1.2094989 , 0.9700485 , 0.9481779 , ..., 0.7795777 , 0.45539394,\n",
       "        0.50474286],\n",
       "       [1.2612958 , 0.16865152, 1.1161358 , ..., 1.0873417 , 0.7458836 ,\n",
       "        1.0070599 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dead</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0148896 3.215996 ... 2.8783422</div><input id='attrs-df4f3557-f7f9-4762-ace7-ef494ac4c22e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df4f3557-f7f9-4762-ace7-ef494ac4c22e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63bb4e89-a5d7-472b-9de2-7079ce94088e' class='xr-var-data-in' type='checkbox'><label for='data-63bb4e89-a5d7-472b-9de2-7079ce94088e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.0148896 , 3.215996  , 2.0751035 , ..., 0.76007444, 1.7044725 ,\n",
       "        3.3124504 ],\n",
       "       [6.5791388 , 1.9249914 , 3.8507695 , ..., 2.1854954 , 3.2665203 ,\n",
       "        0.99745405],\n",
       "       [2.6354058 , 0.74248624, 1.5792716 , ..., 0.45177543, 4.374634  ,\n",
       "        0.7503097 ],\n",
       "       [4.4351587 , 1.7272084 , 3.821375  , ..., 3.7339413 , 1.2515662 ,\n",
       "        2.4109633 ],\n",
       "       [1.222417  , 6.800532  , 1.7495437 , ..., 1.5000124 , 4.4151826 ,\n",
       "        2.8783422 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Difference</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.117733836 1.6753978 ... 1.8712822</div><input id='attrs-77974d63-43c1-463e-8f0b-fc4762197833' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77974d63-43c1-463e-8f0b-fc4762197833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94c0b11-06ef-44ae-ab54-f4f4a106fc8c' class='xr-var-data-in' type='checkbox'><label for='data-b94c0b11-06ef-44ae-ab54-f4f4a106fc8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.11773384,  1.6753978 ,  0.24385178, ...,  0.2871917 ,\n",
       "         0.46276855,  0.8800168 ],\n",
       "       [ 5.974919  , -0.7112646 ,  1.8765072 , ...,  0.7638302 ,\n",
       "         2.621937  ,  0.10831982],\n",
       "       [ 2.3064117 ,  0.5070908 ,  0.57127476, ..., -0.7879338 ,\n",
       "         2.6572723 , -0.07943797],\n",
       "       [ 3.2256598 ,  0.7571599 ,  2.873197  , ...,  2.9543636 ,\n",
       "         0.79617226,  1.9062204 ],\n",
       "       [-0.0388788 ,  6.6318803 ,  0.63340783, ...,  0.41267073,\n",
       "         3.6692991 ,  1.8712822 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b816ce0-553d-4842-9739-b3ffa6b6d913' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b816ce0-553d-4842-9739-b3ffa6b6d913' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2020-08-16T19:03:36.930324</dd><dt><span>arviz_version :</span></dt><dd>0.9.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (chain: 5, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain       (chain) int32 0 1 2 3 4\n",
       "  * draw        (draw) int32 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "Data variables:\n",
       "    Survived    (chain, draw) float32 0.89715576 1.5405983 ... 1.0070599\n",
       "    Dead        (chain, draw) float32 1.0148896 3.215996 ... 4.4151826 2.8783422\n",
       "    Difference  (chain, draw) float32 0.117733836 1.6753978 ... 1.8712822\n",
       "Attributes:\n",
       "    created_at:     2020-08-16T19:03:36.930324\n",
       "    arviz_version:  0.9.0"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw.posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Survived'}>,\n",
       "        <AxesSubplot:title={'center':'Survived'}>],\n",
       "       [<AxesSubplot:title={'center':'Dead'}>,\n",
       "        <AxesSubplot:title={'center':'Dead'}>],\n",
       "       [<AxesSubplot:title={'center':'Difference'}>,\n",
       "        <AxesSubplot:title={'center':'Difference'}>]], dtype=object)"
      ]
     },
     "execution_count": 602,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(pw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_2.5%</th>\n",
       "      <th>hdi_97.5%</th>\n",
       "      <th>Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.166</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.127</td>\n",
       "      <td>2.477</td>\n",
       "      <td>1.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead</th>\n",
       "      <td>2.529</td>\n",
       "      <td>1.976</td>\n",
       "      <td>0.015</td>\n",
       "      <td>6.431</td>\n",
       "      <td>2.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Difference</th>\n",
       "      <td>1.363</td>\n",
       "      <td>2.081</td>\n",
       "      <td>-1.985</td>\n",
       "      <td>5.804</td>\n",
       "      <td>0.947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean     sd  hdi_2.5%  hdi_97.5%  Median\n",
       "Survived    1.166  0.663     0.127      2.477   1.045\n",
       "Dead        2.529  1.976     0.015      6.431   2.040\n",
       "Difference  1.363  2.081    -1.985      5.804   0.947"
      ]
     },
     "execution_count": 603,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(pw, \n",
    "           hdi_prob = 0.95,\n",
    "           kind = 'stats',\n",
    "           stat_funcs={'Median': np.median})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'Survived'}>,\n",
       "       <AxesSubplot:title={'center':'Dead'}>,\n",
       "       <AxesSubplot:title={'center':'Difference'}>], dtype=object)"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1987.2x414 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(pw, hdi_prob = 0.95,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x28b4114ea30>"
      ]
     },
     "execution_count": 606,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(pw,\n",
    "               kind='ridgeplot',\n",
    "               colors = 'orange',\n",
    "               ridgeplot_alpha = 0.5,\n",
    "               linewidth=1,\n",
    "               combined=False,\n",
    "               credible_interval = 0.95,\n",
    "               ridgeplot_overlap=0.8,\n",
    "               figsize=(8,7))\n",
    "\n",
    "plt.vlines(x=0, ymin = 0, ymax = 3, color = 'black', linestyles='dashed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dựa vào mô hình probflow, ta có thể nói có sự khác biệt trung bình = 1.363 đơn vị giữa 2 phân nhóm G2, G1, với khoảng mật độ cao nhất (HDI) từ -1.98 đến 5.804; kết quả này khá tương đồng với hệ số hồi quy trong mô hình GLM quy ước là 1.397.\n",
    "\n",
    "Chúc các bạn thực hành vui và hẹn gặp lại..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}