rating_training_banks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción de calificaciones usando Random Forest.\n",
    "\n",
    "Problema supervisado. Cada observación corresponde a las variables asociadas a una compañía en una fecha particular. No se controla por fecha o industría.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Cargar librería requeridas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "from rating_package.rating_prediction_functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Cargar archivos requeridos\n",
    "\n",
    "Para cargar los archivo de datos se debe tener en cuenta lo siguiente:\n",
    "\n",
    "* Todas las variables del archivo `research_data` que se desean incorporar al proceso de entrenamiento deben estar creadas en el archivo `features.csv`, y la llave corresponde a la columna `Key`. \n",
    "* Deben existir dos columnas para identificar las observaciones: `[Fecha, Ticker]`.\n",
    "* Debe existir variable `IssuerRating` que corresponde al output.\n",
    "* En caso de crear nuevas variables en el archivo `features.csv`, se deben evitar comas, tildes y otros símbolos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nombres de variables y llaves:\n",
    "feat_key = pd.read_csv('data/features_banks.csv', sep=',', index_col = [\"Feature\"], encoding = \"latin1\")\n",
    "feat_key_pure = pd.read_csv('data/features_pure_banks.csv', sep=',', index_col = [\"Feature\"], encoding = \"latin1\")\n",
    "\n",
    "# Encoder para calificaciones:\n",
    "le = pd.read_csv('data/lab_encoder_banks.csv', sep=',', index_col = 0, encoding = \"latin1\")\n",
    "\n",
    "# Datos de entrenamiento:\n",
    "data = pd.read_csv('data/research_data_banks.csv', sep=',', index_col = [\"Fecha\", 'Ticker'], encoding = \"latin1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Proceso de entrenamiento.\n",
    "#### 3.1. Parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_nan = True # Remover filas con datos faltantes.\n",
    "perc_train_size = 0.8 # Porcentaje de observaciones para entrenamiento.\n",
    "model_file = 'model/rating_random_forest_banks.sav' # Modelo.\n",
    "model_file_pure = 'model/rating_random_forest_pure_banks.sav' # Modelo.\n",
    "sov_encoder_file = 'model/sov_lab_encoder_banks.sav' # Encoder de rating soberano.\n",
    "output_file = 'output/pred_test_banks.csv' # Archivo de salida con prediciones.\n",
    "output_file_pure = 'output/pred_test_banks_pure.csv' # Archivo de salida con prediciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2. Entrenar modelo e imprimir resultados sobre muestra de testing\n",
    "Se publican los siguientes archivos: \n",
    "\n",
    "* El archivo en la ruta `output_file` contiene las observaciones usadas para testing, el rating real y el rating que predice el argoritmo.\n",
    "\n",
    "#### 3.2.1. Entrenamiento de modelo con datos de emergentes con Riesgo Soberano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\daniel.velasquez\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Muestra de entrenamiento: 905\n",
      "Muestra de testing: 227\n",
      "\n",
      "Score sobre muestra de testing:\n",
      "0.933920704845815\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\daniel.velasquez\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ranking:\n",
      "1. Rating Soberano (0.121323)\n",
      "2.  Equity + Allowances Losses / Loans LTM (0.043390)\n",
      "3.  Asset Turnover Anualizado LTM (0.041291)\n",
      "4.  Productive Assets / Int. Bearing Liab. LTM (0.040680)\n",
      "5.  Total Gross Loans / Total Deposits LTM (0.039376)\n",
      "6.  NII / Productive Assets (Trimestre Anualizado) LTM (0.038659)\n",
      "7.  Non-Interest Expenses / Total Income LTM (0.036775)\n",
      "8.  Total Deposits / Total Liabilities LTM (0.036433)\n",
      "9.  Equity + Allowances Losses / Loans (0.035808)\n",
      "10.  Total Gross Loans / Total Deposits (0.035324)\n",
      "11.  NII / Productive Assets (Trimestre Anualizado) (0.035041)\n",
      "12.  Allowances / G. Loans LTM (0.033551)\n",
      "13.  Leverage (ROE / ROA) LTM (0.033310)\n",
      "14.  Asset Turnover Anualizado (0.031198)\n",
      "15.  Non-Interest Income / Total Income LTM (0.031096)\n",
      "16.  Liquid Assets / Total Assets LTM (0.030731)\n",
      "17.  Total Deposits / Total Liabilities (0.030014)\n",
      "18.  Allowances / G. Loans (0.028836)\n",
      "19.  Productive Assets / Int. Bearing Liab. (0.028464)\n",
      "20.  Return on Assets (Trimestre Anualizado) LTM (0.028386)\n",
      "21.  Net Margin del Trimestre LTM (0.028024)\n",
      "22.  Leverage (ROE / ROA) (0.027510)\n",
      "23.  Liquid Assets / Total Assets (0.026507)\n",
      "24.  Prov. Exp. /  Gross Loans (Anualizado) LTM (0.024720)\n",
      "25.  Return on Equity LTM (0.022045)\n",
      "26.  Non-Interest Income / Total Income (0.019546)\n",
      "27.  Non-Interest Expenses / Total Income (0.017674)\n",
      "28.  Prov. Exp. /  Gross Loans (Anualizado) (0.014731)\n",
      "29.  Return on Assets (Trimestre Anualizado) (0.014210)\n",
      "30.  Net Margin del Trimestre (0.013200)\n",
      "31.  Return on Equity (0.012145)\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_training(data, feat_key, le, remove_nan, perc_train_size, output_file, model_file, sov_encoder_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 3.2.2. Entrenamiento de modelo con datos de desarrollados sin Riesgo Soberano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\daniel.velasquez\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Muestra de entrenamiento: 905\n",
      "Muestra de testing: 227\n",
      "\n",
      "Score sobre muestra de testing:\n",
      "0.8766519823788547\n",
      "\n",
      "\n",
      "Ranking:\n",
      "1.  Equity + Allowances Losses / Loans LTM (0.051442)\n",
      "2.  Asset Turnover Anualizado LTM (0.049486)\n",
      "3.  Total Gross Loans / Total Deposits LTM (0.049088)\n",
      "4.  Productive Assets / Int. Bearing Liab. LTM (0.044032)\n",
      "5.  Total Deposits / Total Liabilities LTM (0.042765)\n",
      "6.  Non-Interest Expenses / Total Income LTM (0.042405)\n",
      "7.  Equity + Allowances Losses / Loans (0.040208)\n",
      "8.  NII / Productive Assets (Trimestre Anualizado) LTM (0.039790)\n",
      "9.  NII / Productive Assets (Trimestre Anualizado) (0.039530)\n",
      "10.  Total Gross Loans / Total Deposits (0.038328)\n",
      "11.  Allowances / G. Loans LTM (0.038148)\n",
      "12.  Leverage (ROE / ROA) LTM (0.037637)\n",
      "13.  Allowances / G. Loans (0.036695)\n",
      "14.  Asset Turnover Anualizado (0.036457)\n",
      "15.  Total Deposits / Total Liabilities (0.034611)\n",
      "16.  Liquid Assets / Total Assets LTM (0.034122)\n",
      "17.  Non-Interest Income / Total Income LTM (0.033593)\n",
      "18.  Leverage (ROE / ROA) (0.032707)\n",
      "19.  Productive Assets / Int. Bearing Liab. (0.031577)\n",
      "20.  Return on Assets (Trimestre Anualizado) LTM (0.030658)\n",
      "21.  Net Margin del Trimestre LTM (0.030337)\n",
      "22.  Prov. Exp. /  Gross Loans (Anualizado) LTM (0.027669)\n",
      "23.  Liquid Assets / Total Assets (0.027409)\n",
      "24.  Return on Equity LTM (0.026070)\n",
      "25.  Non-Interest Income / Total Income (0.022658)\n",
      "26.  Non-Interest Expenses / Total Income (0.018673)\n",
      "27.  Return on Assets (Trimestre Anualizado) (0.016400)\n",
      "28.  Net Margin del Trimestre (0.016089)\n",
      "29.  Prov. Exp. /  Gross Loans (Anualizado) (0.016071)\n",
      "30.  Return on Equity (0.015346)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('data/research_data_banks.csv', sep=',', index_col = [\"Fecha\", 'Ticker'], encoding = \"latin1\")\n",
    "model_training(data, feat_key_pure, le, remove_nan, perc_train_size, output_file_pure, model_file_pure, sov_encoder_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a83829e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('data/research_data_banks.csv', sep=',', index_col = [\"Fecha\", 'Ticker'], encoding = \"latin1\")\n",
    "pd.Series(np.array(data['IssuerRating'])).value_counts().plot('bar')\n"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}