01_DATASET_CLEANING.ipynb

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   "source": [
    "Este Notebook tiene como objetivo limpiar  y depurar un dataset orientado a finetuning conversacional orientado a generación de código de LLMs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Limpieza del Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Crear la carpeta /datasets si no existe\n",
    "output_dir = './datasets'\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "# Cargar el dataset de Huggingface\n",
    "from datasets import load_dataset\n",
    "\n",
    "# Cargar el dataset de Huggingface\n",
    "dataset = load_dataset(\"code_x_glue_ct_code_to_text\", \"python\")\n",
    "\n",
    "# Función para filtrar las columnas 'code' y 'docstring'\n",
    "def filter_columns(example):\n",
    "    return {'code': example['code'], 'docstring': example['docstring']}\n",
    "\n",
    "# Filtrar los splits de entrenamiento, validación y test\n",
    "train_filtered = dataset['train'].map(filter_columns, remove_columns=dataset['train'].column_names)\n",
    "validation_filtered = dataset['validation'].map(filter_columns, remove_columns=dataset['validation'].column_names)\n",
    "test_filtered = dataset['test'].map(filter_columns, remove_columns=dataset['test'].column_names)\n",
    "\n",
    "# Convertir los datasets filtrados a DataFrames de pandas\n",
    "import pandas as pd\n",
    "\n",
    "df_train_filtered = pd.DataFrame(train_filtered)\n",
    "df_validation_filtered = pd.DataFrame(validation_filtered)\n",
    "df_test_filtered = pd.DataFrame(test_filtered)\n",
    "\n",
    "# Guardar los DataFrames resultantes a archivos JSON en la carpeta /datasets\n",
    "output_dir = './datasets'\n",
    "df_train_filtered.to_json(f'{output_dir}/train_filtered.json', orient='records', lines=True)\n",
    "df_validation_filtered.to_json(f'{output_dir}/validation_filtered.json', orient='records', lines=True)\n",
    "df_test_filtered.to_json(f'{output_dir}/test_filtered.json', orient='records', lines=True)\n",
    "\n",
    "print(\"Datasets filtrados guardados exitosamente en la carpeta /datasets.\")\n",
    "\n"
   ]
  }
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