rating_prediction_banks_user.ipynb

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   "source": [
    "# Predicción de calificaciones de bancos usando Random Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Cargar librería requeridas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.externals import joblib\n",
    "from rating_package.rating_prediction_functions import *\n",
    "feat_key = pd.read_csv('data/features_banks.csv', sep=',', index_col = [\"Feature\"], encoding = \"latin1\")\n",
    "le = pd.read_csv('data/lab_encoder_banks.csv', sep=',', index_col = 0, encoding = \"latin1\")\n",
    "rf = joblib.load('model/rating_random_forest_banks.sav')\n",
    "rf_pure = joblib.load('model/rating_random_forest_pure_banks.sav')\n",
    "sov_lab_encoder = joblib.load('model/sov_lab_encoder_banks.sav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Cargar archivos y modelos\n",
    "\n",
    "Para cargar los archivos de datos se debe tener en cuenta lo siguiente:\n",
    "\n",
    "* Todas las variables del archivo `rating_pred` que se desean incorporar al proceso de entrenamiento deben estar creadas en el archivo `features.csv`, y la llave corresponde a la columna `Feature`. \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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datos para predicción:\n",
    "data = pd.read_csv('input/rating_bancos_pred.csv', sep=',', index_col = [\"Indicadores\"], encoding = \"latin1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file = 'output/credivalores.csv' # Nombre de archivo donde se publican los resultados. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_prediction(data, rf, rf_pure, feat_key, le, sov_lab_encoder, output_file)"
   ]
  }
 ],
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