{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bed45d12-7681-4ba4-9c89-48a3515704e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please request data from http://workshop.colips.org/news2018/terms&conditions_msri.html\n"
     ]
    }
   ],
   "source": [
    "print(\"Please request data from http://workshop.colips.org/news2018/terms&conditions_msri.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c7c7c7-b9a6-4ea2-a5ef-edaf982ae0ad",
   "metadata": {},
   "source": [
    "### Required columns\n",
    "- target_hinglish\n",
    "- source_hindi\n",
    "- parallel_english\n",
    "- annotations\n",
    "- raw_input\n",
    "- alternates\n",
    "\n",
    "> For **NEWS2018**, only `target_hinglish` and `source_hindi` are valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "965589a9-c62e-4659-a6bc-6f0a2bad5d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: lxml in /opt/conda/lib/python3.7/site-packages (4.8.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install lxml\n",
    "import pandas as pd\n",
    "\n",
    "train_df = pd.read_xml(\"./NEWS2018_M-EnHi_trn.xml\", names=[\"target_hinglish\", \"source_hindi\"], elems_only=True)\n",
    "test_df = pd.read_xml(\"./NEWS2018_M-EnHi_tst.xml\", names=[\"target_hinglish\", \"source_hindi\"], elems_only=True)\n",
    "eval_df = pd.read_xml(\"./NEWS2018_M-EnHi_dev.xml\", names=[\"target_hinglish\", \"source_hindi\"], elems_only=True)\n",
    "\n",
    "# Add empty columns\n",
    "train_df[\"raw_input\"] = \\\n",
    "  train_df[\"parallel_english\"] = \\\n",
    "  train_df[\"alternates\"] = \\\n",
    "  train_df[\"annotations\"] = None\n",
    "\n",
    "# Add empty columns\n",
    "test_df[\"raw_input\"] = \\\n",
    "  test_df[\"parallel_english\"] = \\\n",
    "  test_df[\"alternates\"] = \\\n",
    "  test_df[\"annotations\"] = None\n",
    "\n",
    "# Add empty columns\n",
    "eval_df[\"raw_input\"] = \\\n",
    "  eval_df[\"parallel_english\"] = \\\n",
    "  eval_df[\"alternates\"] = \\\n",
    "  eval_df[\"annotations\"] = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e804366-34cd-45c7-b3c6-46b7b8c1b420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tables\n",
      "  Using cached tables-3.7.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.9 MB)\n",
      "Requirement already satisfied: numpy>=1.19.0 in /opt/conda/lib/python3.7/site-packages (from tables) (1.19.5)\n",
      "Collecting numexpr>=2.6.2\n",
      "  Using cached numexpr-2.8.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (379 kB)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from tables) (21.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->tables) (3.0.6)\n",
      "Installing collected packages: numexpr, tables\n",
      "Successfully installed numexpr-2.8.1 tables-3.7.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py:2718: PerformanceWarning: \n",
      "your performance may suffer as PyTables will pickle object types that it cannot\n",
      "map directly to c-types [inferred_type->mixed,key->block0_values] [items->Index(['target_hinglish', 'source_hindi', 'raw_input', 'parallel_english',\n",
      "       'alternates', 'annotations'],\n",
      "      dtype='object')]\n",
      "\n",
      "  encoding=encoding,\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py:2718: PerformanceWarning: \n",
      "your performance may suffer as PyTables will pickle object types that it cannot\n",
      "map directly to c-types [inferred_type->mixed,key->block0_values] [items->Index(['target_hinglish', 'raw_input', 'parallel_english', 'alternates',\n",
      "       'annotations'],\n",
      "      dtype='object')]\n",
      "\n",
      "  encoding=encoding,\n"
     ]
    }
   ],
   "source": [
    "!pip install tables\n",
    "\n",
    "# Save to hdfs files\n",
    "train_df.to_hdf(\"./data.h5\", \"train\", complevel=9)\n",
    "test_df.to_hdf(\"./data.h5\", \"test\", complevel=9)\n",
    "eval_df.to_hdf(\"./data.h5\", \"eval\", complevel=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3298f2f3-3e21-478e-b027-947c992f880d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirm that everything worked as expected\n",
    "\n",
    "# Load from hdfs files\n",
    "_train_df = pd.read_hdf(\"./data.h5\", \"train\")\n",
    "_test_df = pd.read_hdf(\"./data.h5\", \"test\")\n",
    "_eval_df = pd.read_hdf(\"./data.h5\", \"eval\")\n",
    "\n",
    "assert (len(_train_df) == len(train_df)) == \\\n",
    "  (len(_eval_df) == len(eval_df)) == \\\n",
    "  (len(_test_df) == len(test_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60461121-bed5-4ba0-ba7d-dd46256c62e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm NEWS2018_M-EnHi_trn.xml\n",
    "!rm NEWS2018_M-EnHi_tst.xml\n",
    "!rm NEWS2018_M-EnHi_dev.xml"
   ]
  }
 ],
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