{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67b48c21-1fc3-4be7-ae4b-e4c2a75aaaf7",
   "metadata": {},
   "source": [
    "# Quickstart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1dbe5778-ec37-4e3c-8f57-085dcf6051c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Mon Sep 05 2022\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.7\n",
      "IPython version      : 8.0.1\n",
      "\n",
      "graphviz: not installed\n",
      "\n",
      "matplotlib : 3.5.1\n",
      "numpy      : 1.21.5\n",
      "statsmodels: 0.13.2\n",
      "pandas     : 1.4.1\n",
      "\n",
      "Watermark: 2.3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from typing import Union, Optional, List, Dict, Tuple, Any\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64b5fc87-1592-44fd-a943-df10042810bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import myforestplot as mfp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f1f90f-416e-478c-a7ce-9063e4bc9c18",
   "metadata": {},
   "source": [
    "## Create pretty statsmodels result dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2981f7f3-d1aa-4aac-8e79-9136deae5f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = pd.read_csv(\"titanic.csv\")\n",
    "titanic = titanic[[\"survived\", \"pclass\", \"sex\", \"age\", \"embark_town\"]]\n",
    "titanic = titanic.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a30bcab-4850-4838-a76a-3dc433259b22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.509889\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "res = smf.logit(\"survived ~ sex + age + embark_town\", data=titanic).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e810fe5-876c-46e6-8a18-abcdcab1c78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>survived</td>     <th>  No. Observations:  </th>  <td>   712</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   707</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 05 Sep 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.2444</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:25:50</td>     <th>  Log-Likelihood:    </th> <td> -363.04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -480.45</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>1.209e-49</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>    2.2046</td> <td>    0.322</td> <td>    6.851</td> <td> 0.000</td> <td>    1.574</td> <td>    2.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex[T.male]</th>                <td>   -2.4760</td> <td>    0.191</td> <td>  -12.976</td> <td> 0.000</td> <td>   -2.850</td> <td>   -2.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>embark_town[T.Queenstown]</th>  <td>   -1.8156</td> <td>    0.535</td> <td>   -3.393</td> <td> 0.001</td> <td>   -2.864</td> <td>   -0.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>embark_town[T.Southampton]</th> <td>   -1.0069</td> <td>    0.237</td> <td>   -4.251</td> <td> 0.000</td> <td>   -1.471</td> <td>   -0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>                        <td>   -0.0081</td> <td>    0.007</td> <td>   -1.233</td> <td> 0.217</td> <td>   -0.021</td> <td>    0.005</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               survived   No. Observations:                  712\n",
       "Model:                          Logit   Df Residuals:                      707\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Mon, 05 Sep 2022   Pseudo R-squ.:                  0.2444\n",
       "Time:                        15:25:50   Log-Likelihood:                -363.04\n",
       "converged:                       True   LL-Null:                       -480.45\n",
       "Covariance Type:            nonrobust   LLR p-value:                 1.209e-49\n",
       "==============================================================================================\n",
       "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------\n",
       "Intercept                      2.2046      0.322      6.851      0.000       1.574       2.835\n",
       "sex[T.male]                   -2.4760      0.191    -12.976      0.000      -2.850      -2.102\n",
       "embark_town[T.Queenstown]     -1.8156      0.535     -3.393      0.001      -2.864      -0.767\n",
       "embark_town[T.Southampton]    -1.0069      0.237     -4.251      0.000      -1.471      -0.543\n",
       "age                           -0.0081      0.007     -1.233      0.217      -0.021       0.005\n",
       "==============================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b86ef38-48f5-46a9-bf06-2d309664e307",
   "metadata": {},
   "source": [
    "After fitting, clean dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92ea0326-57ae-459b-8e8d-899c443f4e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = {\"embark_town\": ['Southampton', 'Cherbourg', 'Queenstown'],\n",
    "               \"sex\": [\"male\",\"female\"],\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1b9768c-b9b2-45dc-b6bb-2560a1c6bb8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sum = mfp.statsmodels_pretty_result_dataframe(titanic, res, categorical, \n",
    "                                                 fml=\".3f\", \n",
    "                                                 order=[\"age\", \"sex\", \"embark_town\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c7e735b-c6bc-4108-af19-616df53c3391",
   "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>category</th>\n",
       "      <th>item</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>risk</th>\n",
       "      <th>nobs</th>\n",
       "      <th>risk_pretty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>age</td>\n",
       "      <td>0.979300</td>\n",
       "      <td>1.004771</td>\n",
       "      <td>0.991954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99 (0.98, 1.00)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.057848</td>\n",
       "      <td>0.122213</td>\n",
       "      <td>0.084082</td>\n",
       "      <td>453.0</td>\n",
       "      <td>0.08 (0.06, 0.12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sex</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259.0</td>\n",
       "      <td>Ref.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>embark_town</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>0.229654</td>\n",
       "      <td>0.581167</td>\n",
       "      <td>0.365332</td>\n",
       "      <td>554.0</td>\n",
       "      <td>0.37 (0.23, 0.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>embark_town</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
       "      <td>Ref.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>embark_town</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>0.057027</td>\n",
       "      <td>0.464428</td>\n",
       "      <td>0.162742</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.16 (0.06, 0.46)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category         item         0         1      risk   nobs  \\\n",
       "3          age          age  0.979300  1.004771  0.991954    NaN   \n",
       "0          sex         male  0.057848  0.122213  0.084082  453.0   \n",
       "5          sex       female       NaN       NaN       NaN  259.0   \n",
       "2  embark_town  Southampton  0.229654  0.581167  0.365332  554.0   \n",
       "4  embark_town    Cherbourg       NaN       NaN       NaN  130.0   \n",
       "1  embark_town   Queenstown  0.057027  0.464428  0.162742   28.0   \n",
       "\n",
       "         risk_pretty  \n",
       "3  0.99 (0.98, 1.00)  \n",
       "0  0.08 (0.06, 0.12)  \n",
       "5               Ref.  \n",
       "2  0.37 (0.23, 0.58)  \n",
       "4               Ref.  \n",
       "1  0.16 (0.06, 0.46)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72e1ffb-7581-4aca-9687-73edbdd3bbdc",
   "metadata": {},
   "source": [
    "The above type of dataframe structure is required. If you prepare dataframe like above, you do not need to use \"mfp.statsmodels_pretty_result_dataframe\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d99ccfe-7e7a-495b-a3ca-a9eef3ec7002",
   "metadata": {},
   "source": [
    "## Create forestplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "213764ad-ff5e-4319-bd12-c80c151a22bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_sum.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de3c113a-cb84-47fc-b97e-d4a39c4298d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"xerr_lower\"] = df[\"risk\"] - df[0]\n",
    "df[\"xerr_upper\"] = df[1] - df[\"risk\"]\n",
    "df[\"nobs\"] = (df[\"nobs\"]\n",
    "              .replace(np.nan, titanic.shape[0])\n",
    "              .astype(int)\n",
    "              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eda4d8d9-79eb-403a-ac87-293bed4b5a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp = mfp.Forestplot(ratio=(8,3), dpi=150, figsize=(7,3), df=df)\n",
    "fp.errorbar(errorbar_kwds=None)\n",
    "fp.ax2.set_xlim([0, 1.5])\n",
    "fp.ax2.set_xticks([0, 0.5, 1, 1.5])\n",
    "fp.ax2.axvline(x=1, ymin=0, ymax=0.95, color=\"black\", alpha=0.5)\n",
    "\n",
    "fp.ax1.set_xlim([0.1, 1])\n",
    "fontsize=8\n",
    "fp.embed_strings(\"category\", 0.1, header=\"Category\", fontsize=fontsize, \n",
    "                 duplicate_hide=True, \n",
    "                 text_kwds=dict(fontweight=\"bold\"),\n",
    "                 header_kwds=dict(fontweight=\"bold\")\n",
    "                 )\n",
    "fp.embed_strings(\"item\", 0.36, header=\"\", fontsize=fontsize, replace={\"age\":\"\"})\n",
    "fp.embed_strings(\"nobs\", 0.60, header=\"N\", fontsize=fontsize)\n",
    "fp.embed_strings(\"risk_pretty\", 0.72, header=\"OR (95% CI)\", fontsize=fontsize)\n",
    "fp.horizontal_variable_separators()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5fdccc-c408-46c7-84a5-9371aa148aa6",
   "metadata": {},
   "source": [
    "Since this figure does not include all the participants rows, let's include that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18ffe830-7efb-433f-bd00-c6e6619589f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp = mfp.Forestplot(ratio=(8,3), dpi=150, figsize=(7,3), df=df)\n",
    "fp.errorbar(errorbar_kwds=None)\n",
    "fp.ax2.set_xlim([0, 1.5])\n",
    "fp.ax2.set_xticks([0, 0.5, 1, 1.5])\n",
    "fp.ax2.axvline(x=1, ymin=0, ymax=0.95, color=\"black\", alpha=0.5)\n",
    "\n",
    "fp.ax1.set_xlim([0.1, 1])\n",
    "fontsize = 8\n",
    "y_header = 1.5\n",
    "fp.embed_strings(\"category\", 0.1, header=\"Category\", fontsize=fontsize, \n",
    "                 duplicate_hide=True, \n",
    "                 text_kwds=dict(fontweight=\"bold\"),\n",
    "                 header_kwds=dict(fontweight=\"bold\"),\n",
    "                 y_header=1.5\n",
    "                 )\n",
    "fp.ax1.text(0.10, 0.8, \"All participants\", fontweight=\"bold\", fontsize=fontsize)\n",
    "fp.embed_strings(\"item\", 0.36, header=\"\", fontsize=fontsize, replace={\"age\":\"\"},\n",
    "                 y_header=1.5)\n",
    "fp.embed_strings(\"nobs\", 0.60, header=\"N\", fontsize=fontsize,\n",
    "                 y_header=1.5)\n",
    "fp.ax1.text(0.60, 0.8, \"712\", fontsize=fontsize)\n",
    "fp.embed_strings(\"risk_pretty\", 0.72, header=\"OR (95% CI)\", fontsize=fontsize,\n",
    "                 y_header=1.5)\n",
    "fp.horizontal_variable_separators()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c469a8e8-e9b9-4598-8d20-ef0d0aaed11a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
