{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pypair.association import binary_binary, continuous_continuous, binary_continuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preparing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reading and filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bd_all = pd.read_spss('17_abril.sav')\n",
    "\n",
    "# Filter the dataset to work only with alcohol patients\n",
    "bd = bd_all[bd_all['Alcohol_DxCIE'] == 'Sí']\n",
    "\n",
    "# Filter the dataset to work only with 'Situacion_tratamiento' == 'Abandono' or 'Alta'\n",
    "bd = bd[(bd['Situacion_tratamiento'] == 'Abandono') | (bd['Situacion_tratamiento'] == 'Alta terapéutica')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Defining sets of patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-pandemic\n",
    "conj_pre = bd[bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio y fin prepandemia']\n",
    "# Pre-pandemic abandono\n",
    "pre_abandono = conj_pre[conj_pre['Situacion_tratamiento'] == 'Abandono']\n",
    "# Pre-pandemic alta\n",
    "pre_alta = conj_pre[conj_pre['Situacion_tratamiento'] == 'Alta terapéutica']\n",
    "\n",
    "# Post-pandemic\n",
    "# Merging last two classes to balance sets\n",
    "conj_post = bd[(bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio prepandemia y fin en pandemia') | \n",
    "               (bd['Pandemia_inicio_fin_tratamiento'] == 'inicio y fin en pandemia')]\n",
    "# Post-pandemic abandono\n",
    "post_abandono = conj_post[conj_post['Situacion_tratamiento'] == 'Abandono']\n",
    "# Post-pandemic alta\n",
    "post_alta = conj_post[conj_post['Situacion_tratamiento'] == 'Alta terapéutica']\n",
    "\n",
    "# Concatenate the two data frames and add a new column to distinguish between them. Useful for plots\n",
    "conj_post['Group'] = 'Post'\n",
    "conj_pre['Group'] = 'Pre'\n",
    "combined_pre_post = pd.concat([conj_post, conj_pre])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting the dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"PRE\")\n",
    "print(conj_pre.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"PRE-ABANDONO\")\n",
    "print(pre_abandono.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"PRE-ALTA\")\n",
    "print(pre_alta.info())\n",
    "print (\"-------------------------------\")\n",
    "\n",
    "print(\"\\n\\n\\n\")\n",
    "\n",
    "print (\"POST\")\n",
    "print(conj_post.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"POST-ABANDONO\")\n",
    "print(post_abandono.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"POST-ALTA\")\n",
    "print(post_alta.info())\n",
    "print (\"-------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Replacing unknown values with the mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9.0 represents unknown according to Variables.docx \n",
    "print(bd['Social_inclusion'].unique())\n",
    "mode_soc_inc = bd['Social_inclusion'].mode()[0]\n",
    "# print(mode_soc_inc)\n",
    "bd['Social_inclusion'] = bd['Social_inclusion'].replace('9.0', mode_soc_inc)\n",
    "print(bd['Social_inclusion'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd['Alterations_early_childhood_develop'].unique())\n",
    "mode_alt = bd['Alterations_early_childhood_develop'].mode()[0]\n",
    "bd['Alterations_early_childhood_develop'] = bd['Alterations_early_childhood_develop'].replace('9', mode_alt)\n",
    "print(bd['Alterations_early_childhood_develop'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd['Risk_stigma'].unique())\n",
    "mode_stigma = bd['Risk_stigma'].mode()[0]\n",
    "bd['Risk_stigma'] = bd['Risk_stigma'].replace(99.0, mode_stigma)\n",
    "print(bd['Risk_stigma'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd['NumHijos'].unique())\n",
    "mode_hijos = bd['NumHijos'].mode()[0]\n",
    "bd['NumHijos'] = bd['NumHijos'].replace(99.0, mode_hijos)\n",
    "print(bd['NumHijos'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quantifying Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Total missing values Age: {bd['Age'].isnull().sum()}\")\n",
    "print(f\"Total missing values Años_consumo_droga: {bd['Años_consumo_droga'].isnull().sum()}\")\n",
    "print(f\"Total missing values Risk_stigma: {bd['Risk_stigma'].isnull().sum()}\")\n",
    "print(f\"Total missing values NumHijos: {bd['NumHijos'].isnull().sum()}\")\n",
    "\n",
    "print(\"\\tCONJUNTO PREPANDEMIA\")\n",
    "print(f\"\\t\\tMissing values Age: {conj_pre['Age'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Años_consumo_droga: {conj_pre['Años_consumo_droga'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Risk_stigma: {conj_pre['Risk_stigma'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values NumHijos: {conj_pre['NumHijos'].isnull().sum()}\")\n",
    "\n",
    "print(\"\\tCONJUNTO POSTPANDEMIA\")\n",
    "print(f\"\\t\\tMissing values Age: {conj_post['Age'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Años_consumo_droga: {conj_post['Años_consumo_droga'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Risk_stigma: {conj_post['Risk_stigma'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values NumHijos: {conj_post['NumHijos'].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Classifying variables into numerical and discrete/categorical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_atts = ['Education', 'Social_protection', 'Job_insecurity', 'Housing',\n",
    "        'Alterations_early_childhood_develop', 'Social_inclusion',\n",
    "        'Risk_stigma', 'Sex', 'NumHijos', 'Smoking', 'Biological_vulnerability',\n",
    "        'Opiaceos_DxCIE', 'Cannabis_DXCIE', 'BZD_DxCIE', 'Cocaina_DxCIE',\n",
    "        'Alucinogenos_DXCIE', 'Tabaco_DXCIE', 'FrecuenciaConsumo30Dias',\n",
    "        'OtrosDx_Psiquiatrico', 'Tx_previos', 'Readmisiones_estudios', 'Nreadmision'\n",
    "        ]\n",
    "\n",
    "num_atts = ['Structural_conflic', 'Adherencia_tto_recalc', 'Age', 'Años_consumo_droga', 'Tiempo_tx']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of discrete attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Count plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(disc_atts), 1, figsize=(15, 5*len(disc_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n",
    "\n",
    "for i, disc_att in enumerate(disc_atts):\n",
    "    ax = sns.countplot(x=disc_att, data=combined_pre_post, hue=combined_pre_post[['Situacion_tratamiento', 'Group']].apply(tuple, axis=1),\n",
    "                       hue_order=[('Abandono', 'Pre'),('Alta terapéutica', 'Pre'), ('Abandono', 'Post'), ('Alta terapéutica', 'Post')],\n",
    "                       ax=axs[i])\n",
    "    ax.set_title(disc_att, fontsize=16, fontweight='bold')\n",
    "    ax.get_legend().set_title(\"Groups\")\n",
    "    \n",
    "    # Adding count annotations\n",
    "    for p in ax.patches:\n",
    "        if p.get_label() == '_nolegend_':\n",
    "            ax.annotate(format(p.get_height(), '.0f'), \n",
    "                        (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                        ha = 'center', va = 'center', \n",
    "                        xytext = (0, 9), \n",
    "                        textcoords = 'offset points')\n",
    "\n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/countplots.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Normalized count plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot countplot \n",
    "def plot_count_perc_norm(i: int, group:int, disc_att:str) -> None:\n",
    "    \"\"\"\n",
    "        group: 1 (all), 2 (pre), 3 (post) \n",
    "    \"\"\"\n",
    "\n",
    "    # Define data to work with based on group\n",
    "    if group == 1:\n",
    "        df = bd \n",
    "    elif group == 2:\n",
    "        df = conj_pre\n",
    "    elif group == 3:\n",
    "        df = conj_post\n",
    "\n",
    "    # GOAL: find percentage of each possible category within the total of its situacion_tto subset\n",
    "    # Group data by 'Situacion_tratamiento' and 'Education' and count occurrences\n",
    "    grouped_counts = df.groupby(['Situacion_tratamiento', disc_att]).size().reset_index(name='count')\n",
    "    # Calculate total count for each 'Situacion_tratamiento' group\n",
    "    total_counts = df.groupby('Situacion_tratamiento')[disc_att].count()\n",
    "    # Divide each count by its corresponding total count and calculate percentage\n",
    "    grouped_counts['percentage'] = grouped_counts.apply(lambda row: row['count'] / total_counts[row['Situacion_tratamiento']] * 100, axis=1)\n",
    "    \n",
    "    # Follow the same order in plot as in computations\n",
    "    col_order = grouped_counts[grouped_counts['Situacion_tratamiento'] == 'Abandono'][disc_att].tolist()\n",
    "\n",
    "    # Create countplot and split each bar into two based on the value of sit_tto\n",
    "    ax = sns.countplot(x=disc_att, hue='Situacion_tratamiento', data=df, order=col_order, ax=axs[i, group-2])\n",
    "\n",
    "    # Adjust y-axis to represent percentages out of the total count\n",
    "    ax.set_ylim(0, 100)\n",
    "\n",
    "    percentages = grouped_counts['percentage']\n",
    "    for i, p in enumerate(ax.patches):\n",
    "        # Skip going over the legend values\n",
    "        if p.get_label() == \"_nolegend_\":\n",
    "            # Set height to corresponding percentage and annotate result\n",
    "            height = percentages[i]\n",
    "            p.set_height(height)\n",
    "            ax.annotate(f'{height:.2f}%', (p.get_x() + p.get_width() / 2., height),\n",
    "                        ha='center', va='bottom', fontsize=6, color='black', xytext=(0, 5),\n",
    "                        textcoords='offset points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(disc_atts), 2, figsize=(15, 7*len(disc_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.5)\n",
    "\n",
    "for i, disc_att in enumerate(disc_atts):\n",
    "\n",
    "    # # 1: ALL    \n",
    "    # plot_count_perc_norm(i, 1, disc_att)\n",
    "    # axs[i, 0].set_title(\"\\nALL\")\n",
    "    # axs[i, 0].set_xlabel(disc_att, fontweight='bold')\n",
    "    # axs[i, 0].set_ylabel(\"% of total within its Sit_tto group\")\n",
    "    # axs[i, 0].tick_params(axis='x', rotation=90)\n",
    "    \n",
    "    # 2: PRE\n",
    "    plot_count_perc_norm(i, 2, disc_att)\n",
    "    axs[i, 0].set_title(\"\\nPRE\")\n",
    "    axs[i, 0].set_xlabel(disc_att, fontweight='bold')\n",
    "    axs[i, 0].set_ylabel(\"% of total within its Sit_tto group\")\n",
    "    axs[i, 0].tick_params(axis='x', rotation=90)\n",
    "\n",
    "    # 3: POST\n",
    "    plot_count_perc_norm(i, 3, disc_att)\n",
    "    axs[i, 1].set_title(\"\\nPOST\")\n",
    "    axs[i, 1].set_xlabel(disc_att, fontweight='bold')\n",
    "    axs[i, 1].set_ylabel(\"% of total within its Sit_tto group\")\n",
    "    axs[i, 1].tick_params(axis='x', rotation=90)\n",
    "\n",
    "    \n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/norm_countplots.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of numeric attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd[num_atts].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(num_atts), 1, figsize=(12, 5*len(num_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.5)\n",
    "\n",
    "for i, num_att in enumerate(num_atts):\n",
    "    plt.subplot(len(num_atts), 1, i+1)\n",
    "    sns.boxplot(\n",
    "        data=combined_pre_post,\n",
    "        x = num_att,\n",
    "        y = 'Group',\n",
    "        hue='Situacion_tratamiento',\n",
    "    )\n",
    "\n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/boxplots.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(num_atts), 3, figsize=(15, 6*len(num_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.5)\n",
    "\n",
    "for i, num_att in enumerate(num_atts):\n",
    "\n",
    "    # 1: All alcohol patients\n",
    "    sns.histplot(data=bd,x=num_att,bins=15, hue='Situacion_tratamiento', stat='probability', common_norm=False, kde=True,\n",
    "                 line_kws={'lw': 5}, alpha = 0.4, ax=axs[i, 0])\n",
    "    axs[i, 0].set_title(f\"\\nDistr. of {num_att}  - ALL\")\n",
    "\n",
    "    # 2: PRE\n",
    "    sns.histplot(data=conj_pre,x=num_att,bins=15, hue='Situacion_tratamiento', stat='probability', common_norm=False, kde=True, \n",
    "                 line_kws={'lw': 5}, alpha = 0.4, ax=axs[i, 1])\n",
    "    axs[i, 1].set_title(f\"\\nDistr. of {num_att}  - PRE\")\n",
    "\n",
    "    # Subplot 3: POST\n",
    "    sns.histplot(data=conj_post,x=num_att,bins=15, hue='Situacion_tratamiento', stat='probability', common_norm=False, kde=True, \n",
    "                 line_kws={'lw': 5}, alpha = 0.4, ax=axs[i, 2])\n",
    "    axs[i, 2].set_title(f\"\\nDistr. of {num_att}  - POST\")\n",
    "\n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/histograms.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Turning binary variables into 0/1 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Alterations_early_childhood_develop'\n",
    "alterations_mapping = {\n",
    "    'No alterations (first exposure at 11 or more years)' : 0,\n",
    "    'Alterations (first exposure before 11 years old)': 1,\n",
    "}\n",
    "\n",
    "bd['Alterations_early_childhood_develop_REDEF'] = bd['Alterations_early_childhood_develop'].map(alterations_mapping)\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# Social protection\n",
    "bd['Social_protection_REDEF'] = bd['Social_protection'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Risk_stigma'\n",
    "bd['Risk_stigma_REDEF'] = bd['Risk_stigma'].map({'No':0, 'Yes':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Sex'\n",
    "bd['Sex_REDEF'] = bd['Sex'].map({'Hombre':0, 'Mujer':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Smoking'\n",
    "bd['Smoking_REDEF'] = bd['Smoking'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Biological_vulnerability'\n",
    "bd['Biological_vulnerability_REDEF'] = bd['Biological_vulnerability'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Droga_DxCIE'\n",
    "bd['Opiaceos_DxCIE_REDEF'] = bd['Opiaceos_DxCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Cannabis_DXCIE_REDEF'] = bd['Cannabis_DXCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['BZD_DxCIE_REDEF'] = bd['BZD_DxCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Cocaina_DxCIE_REDEF'] = bd['Cocaina_DxCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Alucinogenos_DXCIE_REDEF'] = bd['Alucinogenos_DXCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Tabaco_DXCIE_REDEF'] = bd['Tabaco_DXCIE'].map({'No': 0, 'Sí': 1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'OtrosDx_Psiquiatrico'\n",
    "bd['OtrosDx_Psiquiatrico_REDEF'] = bd['OtrosDx_Psiquiatrico'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Tx_previos'\n",
    "bd['Tx_previos_REDEF'] = bd['Tx_previos'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Situacion_tratamiento'\n",
    "bd['Situacion_tratamiento_REDEF'] = bd['Situacion_tratamiento'].map({'Abandono':0, 'Alta terapéutica':1})\n",
    "\n",
    "# --------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Defining groups of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_vars = ['Education', 'Social_protection', 'Job_insecurity', 'Housing', 'Alterations_early_childhood_develop', \n",
    "            'Social_inclusion', 'Risk_stigma', 'Structural_conflic']\n",
    "ind_vars = ['Age', 'Sex', 'NumHijos', 'Smoking', 'Biological_vulnerability', 'Opiaceos_DxCIE', \n",
    "            'Cannabis_DXCIE', 'BZD_DxCIE', 'Cocaina_DxCIE', 'Alucinogenos_DXCIE', 'Tabaco_DXCIE', \n",
    "            'FrecuenciaConsumo30Dias', 'Años_consumo_droga','OtrosDx_Psiquiatrico', 'Tx_previos', 'Adherencia_tto_recalc'] \n",
    "target_var = 'Situacion_tratamiento'\n",
    "\n",
    "# Incluir alcohol?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns that are already numeric and we don't need to redefine \n",
    "no_redef_cols = ['Structural_conflic', 'Age', 'NumHijos', 'Años_consumo_droga', 'Adherencia_tto_recalc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res_vars = ['Tiempo_tx', 'Readmisiones_estudios', 'Periodos_COVID', 'Pandemia_inicio_fin_tratamiento', \n",
    "#            'Nreadmision', 'Readmisiones_PRECOVID', 'Readmisiones_COVID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One-hot encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify columns to one hot encode; empty list otherwise\n",
    "one_hot_vars = ['Education', 'Job_insecurity', 'Housing', 'Social_inclusion', 'FrecuenciaConsumo30Dias']\n",
    "\n",
    "one_hots_vars_prefix = {\n",
    "    'Education': 'Ed',\n",
    "    'Job_insecurity': 'JobIn',\n",
    "    'Housing': 'Hous', \n",
    "    'Social_inclusion': 'SocInc',\n",
    "    'FrecuenciaConsumo30Dias': 'Frec30',\n",
    "}\n",
    "\n",
    "one_hot_cols_dic = {}\n",
    "\n",
    "for one_hot_var in one_hot_vars:\n",
    "    # Create one hot encoding version of attribute and concatenate new columns to main df\n",
    "    encoded_var = pd.get_dummies(bd[one_hot_var], prefix=one_hots_vars_prefix[one_hot_var])\n",
    "    bd = pd.concat([bd, encoded_var], axis=1)\n",
    "    one_hot_cols_dic[one_hot_var] = encoded_var.columns.tolist()\n",
    "\n",
    "# print(one_hot_cols_dic['FrecuenciaConsumo30Dias'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Defining final version of columns of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc_vars_enc = []\n",
    "for soc_var in social_vars:\n",
    "    # If no need to redefine, append directly\n",
    "    if soc_var in no_redef_cols:\n",
    "        soc_vars_enc.append(soc_var)\n",
    "    # If need to redefine\n",
    "    else:\n",
    "        # Check if it was one-hot encoded\n",
    "        if soc_var in one_hot_vars:\n",
    "            # Append all one hot columns\n",
    "            soc_vars_enc = soc_vars_enc + one_hot_cols_dic[soc_var]\n",
    "        # If not, use redefined version through mapping\n",
    "        else:\n",
    "            soc_vars_enc.append(soc_var + '_REDEF')\n",
    "\n",
    "ind_vars_enc = []\n",
    "for ind_var in ind_vars:\n",
    "    # If no need to redefine, append directly\n",
    "    if ind_var in no_redef_cols:\n",
    "        ind_vars_enc.append(ind_var)\n",
    "    # If need to redefine\n",
    "    else:\n",
    "        # Check if it was one-hot encoded\n",
    "        if ind_var in one_hot_vars:\n",
    "            # Append all one hot columns\n",
    "            ind_vars_enc = ind_vars_enc + one_hot_cols_dic[ind_var]\n",
    "        # If not, use redefined version through mapping\n",
    "        else:\n",
    "            ind_vars_enc.append(ind_var + '_REDEF')\n",
    "\n",
    "# Final version of columns we need to use for correlation analysis\n",
    "corr_cols = soc_vars_enc + ind_vars_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ed_Not Complete primary school', 'Ed_Primary education', 'Ed_Secondary Education', 'Ed_Secondary more technical education', 'Ed_Tertiary', 'Ed_Unknowledge', 'Social_protection_REDEF', 'JobIn_Non-stable', 'JobIn_Stable', 'JobIn_Unemployed', 'JobIn_unkwnodledge', 'Hous_Institutional', 'Hous_Stable', 'Hous_Unstable', 'Hous_unknowledge', 'Alterations_early_childhood_develop_REDEF', 'SocInc_Live with families or friends', 'SocInc_live alone', 'SocInc_live in institutions', 'Risk_stigma_REDEF', 'Structural_conflic', 'Age', 'Sex_REDEF', 'NumHijos', 'Smoking_REDEF', 'Biological_vulnerability_REDEF', 'Opiaceos_DxCIE_REDEF', 'Cannabis_DXCIE_REDEF', 'BZD_DxCIE_REDEF', 'Cocaina_DxCIE_REDEF', 'Alucinogenos_DXCIE_REDEF', 'Tabaco_DXCIE_REDEF', 'Frec30_1 día/semana', 'Frec30_2-3 días\\u200e/semana', 'Frec30_4-6 días/semana', 'Frec30_Desconocido', 'Frec30_Menos de 1 día\\u200e/semana', 'Frec30_No consumio', 'Frec30_Todos los días', 'Años_consumo_droga', 'OtrosDx_Psiquiatrico_REDEF', 'Tx_previos_REDEF', 'Adherencia_tto_recalc']\n"
     ]
    }
   ],
   "source": [
    "print(corr_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-pandemic\n",
    "conj_pre = bd[bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio y fin prepandemia']\n",
    "# Pre-pandemic abandono\n",
    "pre_abandono = conj_pre[conj_pre['Situacion_tratamiento'] == 'Abandono']\n",
    "# Pre-pandemic alta\n",
    "pre_alta = conj_pre[conj_pre['Situacion_tratamiento'] == 'Alta terapéutica']\n",
    "\n",
    "# Post-pandemic\n",
    "# Merging last two classes to balance sets\n",
    "conj_post = bd[(bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio prepandemia y fin en pandemia') | \n",
    "               (bd['Pandemia_inicio_fin_tratamiento'] == 'inicio y fin en pandemia')]\n",
    "# Post-pandemic abandono\n",
    "post_abandono = conj_post[conj_post['Situacion_tratamiento'] == 'Abandono']\n",
    "# Post-pandemic alta\n",
    "post_alta = conj_post[conj_post['Situacion_tratamiento'] == 'Alta terapéutica']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_vars = [col for col in corr_cols if len(bd[col].unique()) == 2] + ['Situacion_tratamiento_REDEF', 'Risk_stigma_REDEF']\n",
    "cont_vars = ['Structural_conflic', 'Age', 'NumHijos', 'Años_consumo_droga', 'Adherencia_tto_recalc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_corr_matrix(df, cols):\n",
    "    \n",
    "    # Initialize nxn matrix to zeroes\n",
    "    n = len(cols)\n",
    "    corr_matrix = np.zeros((n,n))\n",
    "\n",
    "    for i, var_i in enumerate(cols):\n",
    "        for j, var_j in enumerate(cols):\n",
    "            # Fill lower triangle of matrix\n",
    "            if i > j:\n",
    "                # Binary with binary correlation: tetrachoric\n",
    "                if var_i in binary_vars and var_j in binary_vars:\n",
    "                    corr = binary_binary(df[var_i], df[var_j], measure='tetrachoric')\n",
    "                # Continuous with continuous correlation: \n",
    "                elif var_i in cont_vars and var_j in cont_vars:\n",
    "                    # Returning nan sometimes:\n",
    "                    # corr_tuple = continuous_continuous(df[var_i], df[var_j], measure = 'spearman')\n",
    "                    # corr = corr_tuple[0]\n",
    "                    corr = df[var_i].corr(df[var_j], method='spearman')\n",
    "                # Binary vs Continuous correlation:\n",
    "                else:\n",
    "                    if var_i in binary_vars:\n",
    "                        bin_var = var_i\n",
    "                        cont_var = var_j\n",
    "                    else:\n",
    "                        bin_var = var_j\n",
    "                        cont_var = var_i\n",
    "                    corr = binary_continuous(df[bin_var], df[cont_var], measure='biserial')\n",
    "                # Assign value to matrix\n",
    "                corr_matrix[i][j] = corr \n",
    "                      \n",
    "    return corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(sit_tto: int, group:int) -> None:\n",
    "    \"\"\"\n",
    "        sit_tto: 1 (include it as another var), 2 (only abandono), 3 (only alta)\n",
    "        group: 1 (all alcohol patients), 2 (pre), 3 (post)\n",
    "    \"\"\"\n",
    "\n",
    "    # Define columns based on sit_tto arg\n",
    "    if sit_tto == 1:\n",
    "        # Include target as another variable\n",
    "        cols = [target_var + '_REDEF'] + corr_cols\n",
    "    else:\n",
    "        cols = corr_cols\n",
    "        \n",
    "    # Title plot and select datat based on group and sit_tto\n",
    "    if group == 1:\n",
    "        plot_title = \"Correl Matrix - ALL\"\n",
    "        if sit_tto == 1:\n",
    "            bd_ca = bd[cols]\n",
    "        elif sit_tto == 2:\n",
    "            bd_ca = bd[bd['Situacion_tratamiento'] == 'Abandono'][cols]\n",
    "        elif sit_tto == 3:\n",
    "            bd_ca = bd[bd['Situacion_tratamiento'] == 'Alta terapéutica'][cols]\n",
    "    elif group == 2:\n",
    "        plot_title = \"Correl Matrix - PRE\"\n",
    "        if sit_tto == 1:    \n",
    "            bd_ca = conj_pre[cols]\n",
    "        elif sit_tto == 2:\n",
    "            bd_ca = pre_abandono[cols]\n",
    "        elif sit_tto == 3:\n",
    "            bd_ca = pre_alta[cols]\n",
    "    elif group == 3:\n",
    "        plot_title = \"Correl Matrix - POST\"\n",
    "        if sit_tto == 1:    \n",
    "            bd_ca = conj_post[cols]\n",
    "        elif sit_tto == 2:\n",
    "            bd_ca = post_abandono[cols]\n",
    "        elif sit_tto == 3:\n",
    "            bd_ca = post_alta[cols]\n",
    "            \n",
    "    # Complete title\n",
    "    if sit_tto == 2:\n",
    "        plot_title += \" - ABANDONO\"\n",
    "    elif sit_tto == 3:\n",
    "        plot_title += \" - ALTA\"\n",
    "\n",
    "    corr_matrix = get_corr_matrix(bd_ca, cols)\n",
    "\n",
    "    # Create a mask for the upper triangle\n",
    "    mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
    "\n",
    "    # Create heatmap correlation matrix\n",
    "    dataplot = sns.heatmap(corr_matrix, mask=mask, xticklabels=cols, yticklabels=cols, cmap=\"coolwarm\", vmin=-1, vmax=1, annot=True, fmt=\".2f\", annot_kws={\"size\": 4})\n",
    "\n",
    "    # Group ind vs social vars by color and modify tick label names\n",
    "    for tick_label in dataplot.axes.xaxis.get_ticklabels():\n",
    "        if tick_label.get_text() in ind_vars_enc:\n",
    "            tick_label.set_color('green')\n",
    "        elif tick_label.get_text() in soc_vars_enc:\n",
    "            tick_label.set_color('purple')  \n",
    "    for tick_label in dataplot.axes.yaxis.get_ticklabels():\n",
    "        if tick_label.get_text() in ind_vars_enc:\n",
    "            tick_label.set_color('green')\n",
    "        elif tick_label.get_text() in soc_vars_enc:\n",
    "            tick_label.set_color('purple') \n",
    "\n",
    "    # Increase the size of xtick labels\n",
    "    # dataplot.tick_params(axis='x', labelsize=12)\n",
    "\n",
    "    # Increase the size of ytick labels\n",
    "    # dataplot.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "    # Add legend and place it in lower left \n",
    "    plt.legend(handles=[\n",
    "        plt.Line2D([0], [0], marker='o', color='w', label='Social Factors', markerfacecolor='purple', markersize=10),\n",
    "        plt.Line2D([0], [0], marker='o', color='w', label='Individual Factors', markerfacecolor='green', markersize=10)\n",
    "    ], bbox_to_anchor=(-0.1, -0.1), fontsize = 20)\n",
    "\n",
    "    plt.title(\"\\n\\n\" + plot_title, fontdict={'fontsize': 30, 'fontweight': 'bold'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 5000x5000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 3, figsize=(50, 50))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=2)\n",
    "\n",
    "# Go through possible values for 'Situacion_tratamiento' and 'Group'\n",
    "for sit_tto in range(1,2):\n",
    "    for group in range(1,2):\n",
    "        plt.subplot(3, 3, 3*(sit_tto-1) + group)  # Calculate the subplot position dynamically\n",
    "        plot_heatmap(sit_tto, group)\n",
    "        \n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/heatmaps_one_hot.svg', dpi=550, bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}